Traffic light correlation mapping based on machine learning

Abstract

The present disclosure provides systems and methods for generating a crowdsourcing map for use in vehicle navigation. In one implementation, a system may include at least one processor configured to: receive driving information collected from a vehicle traversing an intersection; aggregating the received driving information to determine a location of the traffic light and a spline representation for a drivable path; inputting the determined position and the spline representation to a trained model, the trained model configured to generate a traffic light correlation map indicative of traffic light correlation of the intersection to traffic light correlation of drivable path pairs; inputting the observed vehicle behavior to the at least one trained model to generate an updated traffic light correlation map; storing a traffic light correlation indicator of the traffic light and drivable path pair in the crowdsourcing map; and transmitting the crowdsourcing map to a vehicle for navigating the road segments.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims the benefit of priority of U.S. Provisional Application No. 63 / 315,247, filed Mar. 1, 2022. The foregoing application is hereby incorporated by reference in its entirety. BACKGROUND OF THE DISCLOSURE FIELD OF THE DISCLOSURE

[0003] The present disclosure generally relates to autonomous vehicle navigation and, more particularly, to systems and methods for mapping traffic light relevance for vehicle navigation.

[0004] Background Information

[0005] As technology continues to advance, the goal of fully autonomous vehicles that can drive on roadways is within reach. Autonomous vehicles may need to consider multiple factors and make appropriate decisions based on these factors to safely and accurately reach a desired destination. For example, autonomous vehicles may need to process and interpret visual information (e.g., information captured from a camera) and may also use information obtained from other sources (e.g., from a GPS device, speed sensor, accelerometer, suspension sensor, etc.). At the same time, to travel to a destination, an autonomous vehicle may also need to identify its position within a particular roadway (e.g., a particular lane within a multi-lane road), drive side-by-side with other vehicles, avoid obstacles and pedestrians, observe traffic signals and signs, and proceed from one road to another at an appropriate intersection or crossway. Utilizing and interpreting the vast amount of information collected by an autonomous vehicle as the vehicle travels to its destination presents numerous design challenges. The large amount of data that an autonomous vehicle may need to analyze, access, and / or store (e.g., captured image data, map data, GPS data, sensor data, etc.) presents practical limitations or even adversely affects the autonomous navigation challenge. In addition, the large amount of data required to store and update a map if an autonomous vehicle relies on traditional mapping techniques presents a daunting challenge. SUMMARY OF THE DISCLOSURE

[0006] Embodiments consistent with the present disclosure provide systems and methods for autonomous vehicle navigation. The disclosed embodiments may use cameras to provide autonomous vehicle navigation features. For example, consistent with the disclosed embodiments, the disclosed systems may include one, two, or more cameras that monitor the environment of the vehicle. The disclosed systems may provide a navigation response based on, for example, an analysis of images captured by one or more of the cameras.

[0007] In an embodiment, a system for generating a crowdsourced map for use in vehicle navigation may include at least one processor, the at least one processor including circuitry and a memory. The memory may include instructions that, when executed by the circuitry, cause the at least one processor to: receive driving information collected from a plurality of vehicles traversing a road segment that intersects an intersection associated with a plurality of traffic lights; aggregate the received driving information to determine the location of each of the plurality of traffic lights and to determine a spline representation of each of one or more drivable paths associated with the road segment; provide the determined location of each of the plurality of traffic lights and the spline representation of each of the one or more drivable paths as inputs to at least one trained model, where the at least one trained model is configured to generate a traffic light correlation map based on the determined location of each of the plurality of traffic lights and the spline representation of each of the one or more drivable paths, the traffic light correlation map including a traffic light correlation indicator for each pair of a plurality of traffic lights and drivable paths selected from the plurality of traffic lights and the one or more drivable paths; provide the observed vehicle behavior represented by the received driving information as an input to at least one trained model, where the at least one trained model is configured to generate an updated traffic light correlation map based on the traffic light correlation map and the observed vehicle behavior, where generating the updated traffic light correlation map includes modifying at least one traffic light correlation indicator of at least one traffic light and drivable path pair among the plurality of traffic light and drivable path pairs; store the traffic light correlation indicator of each pair of the plurality of traffic light and drivable path pairs in the crowdsourced map based on the updated traffic light correlation map; and transmit the crowdsourced map to at least one vehicle expected to traverse the road segment for navigating the road segment relative to the stored traffic light correlation indicator of each pair of the plurality of traffic light and drivable path pairs.

[0008] In an embodiment, a method for generating a crowdsourced map for use in vehicle navigation may include: receiving driving information collected from a plurality of vehicles traversing a road segment that intersects an intersection associated with a plurality of traffic lights; aggregating the received driving information to determine the location of each of the plurality of traffic lights and to determine a spline representation of each of one or more drivable paths associated with the road segment; providing the determined location of each of the plurality of traffic lights and the spline representation of each of the one or more drivable paths as inputs to at least one trained model, where the at least one trained model is configured to generate a traffic light correlation map based on the determined location of each of the plurality of traffic lights and the spline representation of each of the one or more drivable paths, the traffic light correlation map including a traffic light correlation indicator for each pair of a plurality of traffic lights and drivable paths selected from the plurality of traffic lights and the one or more drivable paths; providing the observed vehicle behavior represented by the received driving information as an input to at least one trained model, where the at least one trained model is configured to generate an updated traffic light correlation map based on the traffic light correlation map and the observed vehicle behavior, where generating the updated traffic light correlation map includes modifying at least one traffic light correlation indicator of at least one traffic light and drivable path pair among the plurality of traffic light and drivable path pairs; storing the traffic light correlation indicator for each pair of the plurality of traffic light and drivable path pairs in the crowdsourced map based on the updated traffic light correlation map; and transmitting the crowdsourced map to at least one vehicle expected to traverse the road segment for navigating the road segment relative to the stored traffic light correlation indicator for each pair of the plurality of traffic light and drivable path pairs.

[0009] Consistent with other disclosed embodiments, a non-transitory computer-readable storage medium may store program instructions that are executed by at least one processing device and perform any of the methods described herein.

[0010] The foregoing general description and the following detailed description are merely exemplary and explanatory and do not limit the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The drawings incorporated in and constituting a part of this disclosure illustrate various disclosed embodiments. In the drawings:

[0012] Figure 1 Is a schematic representation of an exemplary system consistent with the disclosed embodiments.

[0013] Figure 2A Is a schematic side view representation of an exemplary vehicle including a system consistent with the disclosed embodiments.

[0014] Figure 2B Schematic top - view representations of vehicles and systems consistent with the disclosed embodiments Figure 2A as shown in

[0015] Figure 2C Schematic top - view representation of another embodiment of a vehicle including a system consistent with the disclosed embodiments

[0016] Figure 2D Schematic top - view representation of yet another embodiment of a vehicle including a system consistent with the disclosed embodiments

[0017] Figure 2E Schematic top - view representation of yet another embodiment of a vehicle including a system consistent with the disclosed embodiments

[0018] Figure 2F Schematic representation of an exemplary vehicle control system consistent with the disclosed embodiments

[0019] Figure 3A Schematic representation of the interior of a vehicle consistent with the disclosed embodiments including a rear - view mirror and a user interface for a vehicle imaging system

[0020] Figure 3B Illustration of an example of a camera mount configured to be positioned behind a rear - view mirror and against a vehicle windshield consistent with the disclosed embodiments

[0021] Figure 3C from different perspectives Figure 3B as shown in

[0022] Figure 3D Illustration of an example of a camera mount configured to be positioned behind a rear - view mirror and against a vehicle windshield consistent with the disclosed embodiments

[0023] Figure 4 Exemplary block diagram of a memory configured to store instructions for performing one or more operations consistent with the disclosed embodiments

[0024] Figure 5A Flowchart showing an exemplary process for causing one or more navigation responses based on monocular image analysis consistent with the disclosed embodiments

[0025] Figure 5B Flowchart showing an exemplary process for detecting one or more vehicles and / or pedestrians in a set of images consistent with the disclosed embodiments

[0026] Figure 5CFlowchart showing an exemplary process for detecting road markings and / or lane geometry information in a set of images consistent with the disclosed embodiments.

[0027] Figure 5D Flowchart showing an exemplary process for detecting traffic lights in a set of images consistent with the disclosed embodiments.

[0028] Figure 5E Flowchart showing an exemplary process for causing one or more navigation responses based on a vehicle path consistent with the disclosed embodiments.

[0029] Figure 5F Flowchart showing a process for determining whether a vehicle ahead is changing lanes consistent with the disclosed embodiments.

[0030] Figure 6 Flowchart showing an exemplary process for causing one or more navigation responses based on stereo image analysis consistent with the disclosed embodiments.

[0031] Figure 7 Flowchart showing an exemplary process for causing one or more navigation responses based on the analysis of three sets of images consistent with the disclosed embodiments.

[0032] Figure 8 Shows a sparse map for providing autonomous vehicle navigation consistent with the disclosed embodiments.

[0033] Figure 9A Displays a polynomial representation of a portion of a road segment consistent with the disclosed embodiments.

[0034] Figure 9B Displays a curve in three-dimensional space representing a target trajectory of a vehicle for a road segment, the curve being included in a sparse map consistent with the disclosed embodiments.

[0035] Figure 10 Displays example landmarks that may be included in a sparse map consistent with the disclosed embodiments.

[0036] Figure 11A Shows a polynomial representation of a trajectory consistent with the disclosed embodiments.

[0037] Figure 11B and 11C Shows a target trajectory along a multi-lane road consistent with the disclosed embodiments.

[0038] Figure 11D Shows an example road feature profile curve consistent with the disclosed embodiments.

[0039] Figure 12Schematic illustration of a system for autonomous vehicle navigation using crowdsourced data received from multiple vehicles, consistent with the disclosed embodiments.

[0040] Figure 13 An example autonomous vehicle road navigation model represented by multiple 3D splines, consistent with the disclosed embodiments, is shown.

[0041] Figure 14 A map skeleton generated from combined position information from multiple drives, consistent with the disclosed embodiments, is shown.

[0042] Figure 15 An example of the longitudinal alignment of two drives with an example sign as a landmark, consistent with the disclosed embodiments, is shown.

[0043] Figure 16 An example of the longitudinal alignment of many drives with an example sign as a landmark, consistent with the disclosed embodiments, is shown.

[0044] Figure 17 Schematic illustration of a system for generating drive data using cameras, vehicles, and servers, consistent with the disclosed embodiments.

[0045] Figure 18 Schematic illustration of a system for crowdsourcing a sparse map, consistent with the disclosed embodiments.

[0046] Figure 19 Flowchart showing an exemplary process for generating a sparse map for autonomous vehicle navigation along a road segment, consistent with the disclosed embodiments.

[0047] Figure 20 A block diagram of a server, consistent with the disclosed embodiments, is shown.

[0048] Figure 21 A block diagram of a memory, consistent with the disclosed embodiments, is shown.

[0049] Figure 22 A process for clustering vehicle trajectories associated with a vehicle, consistent with the disclosed embodiments, is shown.

[0050] Figure 23 A navigation system for a vehicle, consistent with the disclosed embodiments, which can be used for autonomous navigation, is shown.

[0051] Figure 24A 、 24B 24C and 24D show exemplary lane markings that can be detected, consistent with the disclosed embodiments.

[0052] Figure 24EShows exemplary mapped lane markings consistent with the disclosed embodiments.

[0053] Figure 24F Shows exemplary anomalies associated with detecting lane markings consistent with the disclosed embodiments.

[0054] Figure 25A Shows an exemplary image of the vehicle's surrounding environment for navigation based on the mapped lane markings consistent with the disclosed embodiments.

[0055] Figure 25B Shows the lateral positioning correction of the vehicle based on the mapped lane markings in a road navigation model consistent with the disclosed embodiments.

[0056] Figure 25C and 25D Provides a conceptual representation of a positioning technique for positioning the host vehicle along a target trajectory using mapped features included in a sparse map.

[0057] Figure 26A Is a flowchart showing an exemplary process for mapping lane markings for use in autonomous vehicle navigation consistent with the disclosed embodiments.

[0058] Figure 26B Is a flowchart showing an exemplary process for autonomously navigating the host vehicle along a road segment using the mapped lane markings consistent with the disclosed embodiments.

[0059] Figure 27 Shows an exemplary system for vehicle navigation consistent with the disclosed embodiments.

[0060] Figure 28 Is a schematic illustration of an exemplary vehicle at an intersection consistent with the disclosed embodiments.

[0061] Figure 29A Is a flowchart showing an exemplary process for vehicle navigation consistent with the disclosed embodiments.

[0062] Figure 29B Is a flowchart showing an exemplary process for updating a road navigation model consistent with the disclosed embodiments.

[0063] Figure 29C Is a flowchart showing an exemplary process for vehicle navigation consistent with the disclosed embodiments.

[0064] Figure 30A Is a schematic illustration of a roadway including an intersection consistent with the disclosed embodiments.

[0065] Figure 30BSchematic illustration of a triangulation technique for determining the position of a vehicle relative to a traffic light, consistent with the disclosed embodiments.

[0066] Figure 31A and 31B Exemplary graph for determining time-dependent variables of a vehicle's navigation, consistent with the disclosed embodiments.

[0067] Figure 32 Exemplary process for updating an autonomous vehicle road navigation model, consistent with the disclosed embodiments.

[0068] Figure 33 Exemplary process for selecting and implementing navigation actions, consistent with the disclosed embodiments.

[0069] Figure 34A Example image representing the environment of the host vehicle, consistent with the disclosed embodiments.

[0070] Figure 34B Example representation of a traffic light that can be detected in the image, consistent with the disclosed embodiments.

[0071] Figure 34C Representation of a traffic light that can be captured in a subsequent image, consistent with the disclosed embodiments.

[0072] Figure 35 Example technique for determining a portion of an image associated with a traffic light fixture based on the vehicle's motion history, consistent with the disclosed embodiments.

[0073] Figure 36 Flowchart showing an example process for collecting data for a sparse map, consistent with the disclosed embodiments.

[0074] Figure 37A Example intersection for which traffic light relevance can be determined, consistent with the disclosed embodiments.

[0075] Figure 37B Example grouping of traffic lights, consistent with the disclosed embodiments.

[0076] Figure 37C Example drivable paths that can be associated with an intersection, consistent with the disclosed embodiments.

[0077] Figure 38A Flowchart showing an example process for generating a crowdsourced map for use in vehicle navigation, consistent with the disclosed embodiments.

[0078] Figure 38BFlowchart showing an example process for generating a crowdsourced map for use in vehicle navigation consistent with the disclosed embodiments.

[0079] Figure 39 Flowchart showing an example process for navigating a host vehicle consistent with the disclosed embodiments.

[0080] Figure 40 Example image representing the environment of a host vehicle consistent with the disclosed embodiments.

[0081] Figure 41 Example road segment along which the relevance of traffic signs can be determined consistent with the disclosed embodiments.

[0082] Figure 42 Flowchart showing an example process for generating a crowdsourced map for use in vehicle navigation consistent with the disclosed embodiments.

[0083] Figure 43 Flowchart showing an example process for navigating a host vehicle consistent with the disclosed embodiments.

[0084] Figure 44 Example intersection for which the relevance of traffic lights can be determined consistent with the disclosed embodiments.

[0085] Figure 45 Example process for determining a traffic light relevance map consistent with the disclosed embodiments.

[0086] Figure 46 Example traffic light relevance map that can be generated using a trained model consistent with the disclosed embodiments.

[0087] Figure 47 Example modification of a traffic light relevance map based on observed behavior and traffic light states consistent with the disclosed embodiments.

[0088] Figure 48 Flowchart showing an example process for generating a crowdsourced map for use in vehicle navigation consistent with the disclosed embodiments. DETAILED DESCRIPTION

[0089] The following detailed description refers to the accompanying drawings. Whenever possible, the same reference numbers are used in the drawings and the following description to refer to the same or like parts. Although several illustrative embodiments are described herein, modifications, adaptations, and other implementations are possible. For example, components shown in the drawings may be replaced, added, or modified, and the illustrative methods described herein may be modified by replacing, reordering, removing, or adding steps to the disclosed methods. Accordingly, the following detailed description is not limited to the disclosed embodiments and examples. Instead, the appropriate scope is defined by the appended claims.

[0090] Overview of Autonomous Vehicles

[0091] As used throughout this disclosure, the term "autonomous vehicle" refers to a vehicle capable of achieving at least one navigation change without driver input. A "navigation change" refers to a change in one or more of the vehicle's steering, braking, or acceleration. To be autonomous, a vehicle does not need to be fully automatic (e.g., operate completely without a driver or without driver input). Instead, autonomous vehicles include those vehicles that can operate under driver control during certain time periods and without driver control during other time periods. Autonomous vehicles can also include vehicles that control only some aspects of vehicle navigation, such as steering (e.g., to maintain the vehicle's route between lane constraints), but leave other aspects (e.g., braking) to the driver. In some cases, an autonomous vehicle can handle some or all aspects of the vehicle's braking, speed control, and / or steering.

[0092] Since human drivers typically rely on visual cues and observations to control a vehicle, a corresponding traffic infrastructure has been built, where lane markings, traffic signs, and traffic lights are all designed to provide visual information to drivers. Given these design characteristics of the traffic infrastructure, an autonomous vehicle can include a camera and a processing unit that analyzes visual information captured from the vehicle's environment. Visual information can include, for example, driver-observable components of the traffic infrastructure (e.g., lane markings, traffic signs, traffic lights, etc.) and other obstacles (e.g., other vehicles, pedestrians, debris, etc.). Additionally, an autonomous vehicle can also use stored information, such as information that provides a model of the vehicle's environment during navigation. For example, a vehicle can use GPS data, sensor data (e.g., from accelerometers, speed sensors, suspension sensors, etc.), and / or other map data to provide information related to the vehicle's environment as it travels, and the vehicle (and other vehicles) can use this information to position itself on the model.

[0093] In some embodiments of the present disclosure, an autonomous vehicle may use information obtained during navigation (e.g., from cameras, GPS devices, accelerometers, speed sensors, suspension sensors, etc.). In other embodiments, an autonomous vehicle may use information obtained from past navigation by the vehicle (or other vehicles) during navigation. In still other embodiments, an autonomous vehicle may use a combination of information obtained during navigation and information obtained from past navigation. The following sections provide an overview of a system consistent with the disclosed embodiments, followed by an overview of a forward imaging system and method consistent with the system. The following sections disclose systems and methods for constructing, using, and updating a sparse map for autonomous vehicle navigation.

[0094] System Overview

[0095] Figure 1 FIG. 7 is a block diagram representation of a system 100 consistent with an exemplary disclosed embodiment. Depending on the requirements of a particular implementation, system 100 may include various components. In some embodiments, system 100 may include a processing unit 110, an image acquisition unit 120, a position sensor 130, one or more memory units 140, 150, a map database 160, a user interface 170, and a wireless transceiver 172. The processing unit 110 may include one or more processing devices. In some embodiments, the processing unit 110 may include an application processor 180, an image processor 190, or any other suitable processing device. Similarly, depending on the requirements of a particular application, the image acquisition unit 120 may include any number of image acquisition devices and components. In some embodiments, the image acquisition unit 120 may include one or more image capture devices (e.g., cameras), such as image capture device 122, image capture device 124, and image capture device 126. System 100 may also include a data interface 128 communicatively connecting the processing device 110 to the graphics acquisition device 120. For example, the data interface 128 may include any one or more wired and / or wireless links for transmitting image data acquired by the image accusation device 120 to the processing unit 110.

[0096] The wireless transceiver 172 may include one or more devices configured to exchange transmissions with one or more networks (e.g., cellular networks, the Internet, etc.) via an air interface using radio frequencies, infrared frequencies, magnetic fields, or electric fields. The wireless transceiver 172 may use any known standard (e.g., Wi-Fi, transmit and / or receive data via wireless communication technologies such as Bluetooth Smart, 802.15.4, ZigBee, etc. Such transmissions may include communication from the host vehicle to one or more remotely located servers. Such transmissions may also include communication (one-way or two-way) between the host vehicle and one or more target vehicles in the environment of the host vehicle (e.g., to facilitate or coordinate the navigation of the host vehicle in view of or in conjunction with target vehicles in the environment of the host vehicle), or even broadcast transmissions to unspecified recipients in the vicinity of the transmitting vehicle.

[0097] Both the application processor 180 and the image processor 190 may include various types of processing devices. For example, either or both of the application processor 180 and the image processor 190 may include a microprocessor, a pre-processor (such as an image pre-processor), a graphics processing unit (GPU), a central processing unit (CPU), support circuitry, a digital signal processor, an integrated circuit, a memory, or any other type of device suitable for running application programs and suitable for image processing and analysis. In some embodiments, the application processor 180 and / or the image processor 190 may include any type of single-core or multi-core processor, a mobile device microcontroller, a central processing unit, etc. Various processing devices may be used, including, for example, processors available from manufacturers such as etc., or GPUs available from manufacturers such as etc., and may include various architectures (e.g., x86 processors, etc.).

[0098] In some embodiments, the application processor 180 and / or the image processor 190 may include any one of the EyeQ series processor chips available from These processor designs each include multiple processing units with local memory and instruction sets. Such processors may include video input terminals for receiving image data from multiple image sensors and may also include video output capabilities. In one example, uses 90nm-micron technology operating at 332Mhz. The architecture consists of: two floating-point, hyper-threaded 32-bit RISC CPUs ( cores), five vision computing engines (VCEs), three vector microcode processors Denali 64-bit mobile DDR controller, 128-bit internal Sonics interconnect, 16-bit video input and 18-bit video output dual controllers, 16-channel DMA, and several peripheral devices. The MIPS34K CPU manages five VCEs, three VMPs TM and DMA, a second MIPS34K CPU, and multi-channel DMA, and other peripheral devices. Five VCEs, three The MIPS34K CPU can perform the intensive visual computations required for multifunctional bundled applications. In another example, it can be used in the disclosed embodiments which is a third-generation processor and is six times more powerful than In other examples, it can be used in the disclosed embodiments and / or Of course, any newer or future EyeQ processing device can also be used with the disclosed embodiments.

[0099] Any processing device disclosed herein can be configured to perform a specific function. Configuring a processing device (such as any of the described EyeQ processors or other controllers or microprocessors) to perform a specific function can include programming computer-executable instructions and making these instructions available for the processing device to execute during operation of the processing device. In some embodiments, configuring the processing device can include directly programming the processing device using architectural instructions. For example, a processing device such as a field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), etc. can be configured using, for example, one or more hardware description languages (HDLs).

[0100] In other embodiments, configuring the processing device can include storing the executable instructions on a memory accessible by the processing device during operation. For example, the processing device can access the memory to obtain and execute the stored instructions. In either case, a processing device configured to perform the sensing, image analysis, and / or navigation functions disclosed herein represents a dedicated-hardware-based system that controls multiple hardware-based components of the host vehicle.

[0101] Although Figure 1 FIG. depicts two separate processing devices included in processing unit 110, more or fewer processing devices can be used. For example, in some embodiments, a single processing device can be used to perform the tasks of application processor 180 and image processor 190. In some embodiments, these tasks can be performed by more than two processing devices. Additionally, in some embodiments, system 100 can include one or more processing units 110 without including other components, such as image acquisition unit 120.

[0102] The processing unit 110 may include various types of devices. For example, the processing unit 110 may include various devices such as a controller, an image pre-processor, a central processing unit (CPU), a graphics processing unit (GPU), support circuits, a digital signal processor, an integrated circuit, a memory, or any other type of device for image processing and analysis. The image pre-processor may include a video processor for capturing, digitizing, and processing images from an image sensor. The CPU may include any number of microcontrollers or microprocessors. The GPU may also include any number of microcontrollers or microprocessors. The support circuits may be any number of circuits well known in the art, including a cache, a power supply, a clock, and input / output circuits. The memory may store software that controls the operation of the system when executed by the processor. The memory may include a database and image processing software. The memory may include any number of random access memories, read-only memories, flash memories, disk drives, optical storage devices, tape storage devices, removable storage devices, and other types of storage devices. In one case, the memory may be separate from the processing unit 110. In another case, the memory may be integrated into the processing unit 110.

[0103] Each of the memories 140, 150 may include software instructions that, when executed by a processor (e.g., the application processor 180 and / or the image processor 190), may control the operation of various aspects of the system 100. These memory units may include various databases and image processing software as well as trained systems such as, for example, neural networks or deep neural networks. The memory units may include random access memory (RAM), read-only memory (ROM), flash memory, disk drives, optical storage devices, tape storage devices, removable storage devices, and / or other types of storage devices. In some embodiments, the memory units 140, 150 may be separate from the application processor 180 and / or the image processor 190. In other embodiments, these memory units may be integrated into the application processor 180 and / or the image processor 190.

[0104] The position sensor 130 may include any type of device suitable for determining the position associated with at least one component of the system 100. In some embodiments, the position sensor 130 may include a GPS receiver. Such a receiver may determine the user's position and speed by processing signals broadcast by global positioning system satellites. The position information from the position sensor 130 may be made available to the application processor 180 and / or the image processor 190.

[0105] In some embodiments, the system 100 may include components such as a speed sensor (e.g., a tachometer, a speedometer) for measuring the speed of the vehicle 200 and / or an accelerometer (single-axis or multi-axis) for measuring the acceleration of the vehicle 200.

[0106] The user interface 170 may include any device suitable for providing information to one or more users of the system 100 or for receiving input from one or more users of the system. In some embodiments, the user interface 170 may include user input devices, including for example a touch screen, a microphone, a keyboard, a pointer device, a roller, a camera, a knob, a button, etc. Using such input devices, a user may be able to provide information input or commands to the system 100 by typing instructions or information, providing voice commands, using buttons, pointers, or eye-tracking capabilities to select menu options on a screen, or by any other suitable technique for communicating information to the system 100.

[0107] The user interface 170 may be equipped with one or more processing devices configured to provide information to the user or receive information from the user and process the information for use by, for example, the application processor 180. In some embodiments, such processing devices may execute instructions for: identifying and tracking eye movements, receiving and interpreting voice commands, identifying and interpreting touches and / or gestures made on a touch screen, responding to keyboard input or menu selections, etc. In some embodiments, the user interface 170 may include a display, a speaker, a haptic device, and / or any other device for providing output information to the user.

[0108] The map database 160 may include any type of database for storing map data useful to the system 100. In some embodiments, the map database 160 may include data related to the positions of various items in a reference coordinate system, the various items including roads, water features, geographical features, businesses, points of interest, restaurants, gas stations, etc. The map database 160 may store not only the positions of such items but also descriptors associated with these items, the descriptors including, for example, names associated with any stored features. In some embodiments, the map database 160 may be physically located with other components of the system 100. Alternatively or additionally, the map database 160 or a portion thereof may be remotely located relative to other components of the system 100 (e.g., the processing unit 110). In such embodiments, information from the map database 160 may be downloaded via a wired or wireless data connection to a network (e.g., via a cellular network and / or the Internet, etc.). In some cases, the map database 160 may store a sparse data model that includes a polynomial representation of specific road features (e.g., lane markings) or target trajectories for the host vehicle. Systems and methods for generating such maps are discussed below with reference to Figures 8 - 19 for discussion of systems and methods for generating such maps.

[0109] The image capture devices 122, 124, and 126 can each include any type of device suitable for capturing at least one image from the environment. Additionally, any number of image capture devices can be used to obtain images for input to the image processor. Some embodiments can include only a single image capture device, while other embodiments can include two, three, or even four or more image capture devices. This will be further described below with reference to Figures 2B - 2E for the image capture devices 122, 124, and 126.

[0110] System 100 or its various components can be incorporated into a variety of different platforms. In some embodiments, system 100 can be included on a vehicle 200, as Figure 2A shown. For example, vehicle 200 can be equipped with a processing unit 110 and any other components of system 100, as described above with respect to Figure 1 While in some embodiments, vehicle 200 can be equipped with only a single image capture device (e.g., a camera), in other embodiments, such as those discussed in connection with Figures 2B - 2E multiple image capture devices can be used. For example, as Figure 2A shown, either of the image capture devices 122 and 124 of vehicle 200 can be part of an ADAS (Advanced Driver Assistance System) imaging suite.

[0111] The image capture device included on vehicle 200 as part of the image acquisition unit 120 can be located at any suitable location. In some embodiments, as Figures 2A - 2E and FIGS. 3A through 3C show, the image capture device 122 can be located near the rearview mirror. This location can provide a line of sight similar to that of the driver of vehicle 200, which can help determine what is visible and not visible to the driver. The image capture device 122 can be located at any position near the rearview mirror, but placing the image capture device 122 on the driver's side of the mirror can further help obtain an image representative of the driver's field of view and / or line of sight.

[0112] Other locations of the image capture device for the image acquisition unit 120 may also be used. For example, the image capture device 124 may be located above or within the bumper of the vehicle 200. Such locations may be particularly suitable for image capture devices with a wide field of view. The line of sight of the image capture device located on the bumper may be different from the driver's line of sight, and thus, the bumper image capture device and the driver may not always see the same object. The image capture devices (e.g., image capture devices 122, 124, and 126) may also be located in other positions. For example, the image capture device may be located above or within one or both of the side mirrors of the vehicle 200, on the roof of the vehicle 200, on the hood of the vehicle 200, on the trunk of the vehicle 200, on the side of the vehicle 200, mounted on any window of the vehicle 200, positioned behind or in front of it, and installed in or near the headlight pattern on the front and / or rear of the vehicle 200, etc.

[0113] In addition to the image capture device, the vehicle 200 may also include various other components of the system 100. For example, the processing unit 110 may be included on the vehicle 200, or integrated with or separate from the engine control unit (ECU) of the vehicle. The vehicle 200 may also be equipped with a position sensor 130, such as a GPS receiver, and may also include a map database 160, as well as memory units 140 and 150.

[0114] As previously discussed, the wireless transceiver 172 may receive data via one or more networks (e.g., cellular networks, the Internet, etc.). For example, the wireless transceiver 172 may upload the data collected by the system 100 to one or more servers and download data from one or more servers. Via the wireless transceiver 172, the system 100 may receive, for example, periodic or on-demand updates to the data stored in the map database 160, the memory 140, and / or the memory 150. Similarly, the wireless transceiver 172 may upload any data from the system 100 (e.g., images captured by the image acquisition unit 120, data received by the position sensor 130 or other sensors, vehicle control systems, etc.) and / or any data processed by the processing unit 110 to one or more servers.

[0115] The system 100 may upload data to a server (e.g., to the cloud) based on privacy level settings. For example, the system 100 may implement privacy level settings to regulate or limit the type of data (including metadata) sent to a server that can uniquely identify the vehicle and / or the driver / owner of the vehicle. Such settings may be set by the user via, for example, the wireless transceiver 172, initialized through factory default settings, or initialized by data received by the wireless transceiver 172.

[0116] In some embodiments, system 100 may upload data according to a "high" privacy level, and under a certain setting, system 100 may transmit data (e.g., location information related to a route, captured images, etc.) without any details about a specific vehicle and / or driver / owner. For example, when uploading data according to a "high" privacy setting, system 100 may not include a vehicle identification number (VIN) or the name of the driver or owner of the vehicle, and may instead transmit data such as captured images and / or limited location information related to a route.

[0117] Other privacy levels are envisioned. For example, system 100 may transmit data to a server according to a "medium" privacy level and include additional information not included under the "high" privacy level, such as the make and / or model and / or type of vehicle (e.g., passenger vehicle, sport utility vehicle, truck, etc.). In some embodiments, system 100 may upload data according to a "low" privacy level. Under a "low" privacy level setting, system 100 may upload data and include information sufficient to uniquely identify a specific vehicle, owner / driver, and / or part or all of the route traveled by the vehicle. Such "low" privacy level data may include, for example, one or more of the following: VIN, driver / owner name, original point of the vehicle before departure, intended destination of the vehicle, make and / or model of the vehicle, type of the vehicle, etc.

[0118] Figure 2A Schematic side view representation of an exemplary vehicle imaging system consistent with the disclosed embodiments. Figure 2B is Figure 2A Schematic top view illustration of the shown embodiment. As Figure 2B shown, the disclosed embodiments may include a vehicle 200 that includes system 100 in its body, the system having a first image capture device 122 positioned near the rearview mirror and / or near the driver of vehicle 200, a second image capture device 124 positioned above or within a bumper region of vehicle 200 (e.g., one of bumper regions 210), and a processing unit 110.

[0119] As Figure 2C shown, both image capture devices 122 and 124 may be positioned near the rearview mirror and / or near the driver of vehicle 200. Additionally, although Figure 2B and 2C show two image capture devices 122 and 124, it should be understood that other embodiments may include more than two image capture devices. For example, in Figure 2D and 2EIn the illustrated embodiment, the first image capture device 122, the second image capture device 124, and the third image capture device 126 are included in the system 100 of the vehicle 200.

[0120] As Figure 2D shown, the image capture device 122 can be positioned near the rearview mirror of the vehicle 200 and / or near the driver, and the image capture devices 124 and 126 can be positioned above or within the bumper area of the vehicle 200 (e.g., one of the bumper areas 210). Additionally, as Figure 2E shown, the image capture devices 122, 124, and 126 can be positioned near the rearview mirror of the vehicle 200 and / or near the driver's seat. The disclosed embodiments are not limited to any particular number and configuration of image capture devices, and the image capture devices can be positioned at any suitable location within and / or on the vehicle 200.

[0121] It should be understood that the disclosed embodiments are not limited to vehicles and can be applied to other environments. It should also be understood that the disclosed embodiments are not limited to a particular type of vehicle 200 and can be applicable to all types of vehicles, including cars, trucks, trailers, and other types of vehicles.

[0122] The first image capture device 122 can include any suitable type of image capture device. The image capture device 122 can include an optical axis. In one case, the image capture device 122 can include an Aptina M9V024 WVGA sensor having a global shutter. In other embodiments, the image capture device 122 can provide a resolution of 1280 x 960 pixels and can include a rolling shutter. The image capture device 122 can include various optical elements. In some embodiments, one or more lenses can be included, e.g., to provide a desired focal length and field of view for the image capture device. In some embodiments, the image capture device 122 can be associated with a 6 mm lens or a 12 mm lens. In some embodiments, the image capture device 122 can be configured to capture an image having a desired field of view (FOV) 202, as Figure 2DAs shown. For example, the image capture device 122 can be configured to have a conventional FOV, such as in the range of 40 degrees to 56 degrees, including 46-degree FOV, 50-degree FOV, 52-degree FOV or greater. Alternatively, the image capture device 122 can be configured to have a narrow FOV in the range of 23 to 40 degrees, such as 28-degree FOV or 36-degree FOV. Additionally, the image capture device 122 can be configured to have a wide FOV in the range of 100 to 180 degrees. In some embodiments, the image capture device 122 can include a wide-angle bumper camera or a camera with up to 180-degree FOV. In some embodiments, the image capture device 122 can be a 7.2M pixel image capture device having an approximately 100-degree horizontal FOV and an aspect ratio of approximately 2:1 (e.g., HxV = 3800x1900 pixels). Such an image capture device can be used in place of a three-image capture device configuration. Due to significant lens distortion, the vertical FOV of such an image capture device can be significantly less than 50 degrees in implementations where the image capture device uses a radially symmetric lens. For example, such a lens can be non-radially symmetric, which would allow for a vertical FOV greater than 50 degrees and a 100-degree horizontal FOV.

[0123] The first image capture device 122 can acquire a plurality of first images of a scene associated with the vehicle 200. Each of the plurality of first images can be acquired as a series of image scan lines that can be captured using a rolling shutter. Each scan line can include a plurality of pixels.

[0124] The first image capture device 122 can have a scan rate associated with acquiring each of the first series of image scan lines. The scan rate can refer to the rate at which the image sensor can acquire image data associated with each pixel included in a particular scan line.

[0125] The image capture devices 122, 124, and 126 can contain any suitable type and number of image sensors, including, for example, CCD sensors or CMOS sensors. In one embodiment, a CMOS image sensor can be employed with a rolling shutter such that each pixel in a row is read one at a time and the rows are scanned on a row-by-row basis until the entire image frame has been captured. In some embodiments, the rows can be captured sequentially from top to bottom with respect to the frame.

[0126] In some embodiments, one or more of the image capture devices disclosed herein (e.g., image capture devices 122, 124, and 126) can constitute a high-resolution imager and can have a resolution greater than 5M pixels, 7M pixels, 10M pixels, or greater.

[0127] The use of a rolling shutter can cause pixels in different rows to be exposed and captured at different times, which may result in skewing and other image artifacts in the captured image frame. On the other hand, when the image capture device 122 is configured to operate with a global or synchronous shutter, all pixels can be exposed during a common exposure period and for the same amount of time. Thus, the image data in a frame collected from a system employing a global shutter represents a snapshot of the entire FOV (such as FOV 202) at a particular time. In contrast, in a rolling shutter application, each row in the frame is exposed and data is captured at different times. Thus, moving objects may appear distorted in an image capture device with a rolling shutter. This phenomenon will be described in more detail below.

[0128] The second image capture device 124 and the third image capture device 126 can be any type of image capture device. Similar to the first image capture device 122, each of the image capture devices 124 and 126 can include an optical axis. In one embodiment, each of the image capture devices 124 and 126 can include an Aptina M9V024WVGA sensor having a global shutter. Alternatively, each of the image capture devices 124 and 126 can include a rolling shutter. Similar to the image capture device 122, the image capture devices 124 and 126 can be configured to include various lenses and optical elements. In some embodiments, the lenses associated with the image capture devices 124 and 126 can provide a FOV (such as FOVs 204 and 206) that is the same as or narrower than the FOV (such as FOV 202) associated with the image capture device 122. For example, the image capture devices 124 and 126 can have a FOV of 40 degrees, 30 degrees, 26 degrees, 23 degrees, 20 degrees, or less.

[0129] The image capture devices 124 and 126 can acquire a plurality of second and third images of a scene associated with the vehicle 200. Each of the plurality of second and third images can be acquired as a second and third series of image scan lines that can be captured using a rolling shutter. Each scan line or row can have a plurality of pixels. The image capture devices 124 and 126 can have second and third scan rates associated with acquiring each of the image scan lines included in the second and third series.

[0130] Each of the image capture devices 122, 124, and 126 can be positioned on the vehicle 200 at any suitable location and orientation. The relative positioning of the image capture devices 122, 124, and 126 can be selected to facilitate the fusion of information obtained from the image capture devices. For example, in some embodiments, the FOV associated with the image capture device 124 (such as FOV 204) can partially or fully overlap with the FOV associated with the image capture device 122 (such as FOV 202) and the FOV associated with the image capture device 126 (such as FOV 206).

[0131] The image capture devices 122, 124, and 126 can be located on the vehicle 200 at any suitable relative height. In one case, there can be a height difference between the image capture devices 122, 124, and 126, which can provide sufficient parallax information for performing stereoscopic analysis. For example, as Figure 2A shown, two of the image capture devices 122 and 124 are at different heights. There can also be a lateral displacement difference between the image capture devices 122, 124, and 126, giving additional parallax information, for example, for the processing unit 110 to perform stereoscopic analysis. The difference in lateral displacement can be represented by d x as shown in Figure 2C and 2D shown. In some embodiments, there can be a front-to-back displacement (e.g., a range displacement) between the image capture devices 122, 124, and 126. For example, the image capture device 122 can be located 0.5 to 2 meters or more behind the image capture device 124 and / or the image capture device 126. This type of displacement can enable one of the image capture devices to cover potential blind spots of the other image capture devices.

[0132] The image capture device 122 can have any suitable resolution (e.g., the number of pixels associated with the image sensor), and the resolution of the image sensor associated with the image capture device 122 can be higher, lower, or the same as the resolution of the image sensors associated with the image capture devices 124 and 126. In some embodiments, the image sensors associated with the image capture device 122 and / or the image capture devices 124 and 126 can have a resolution of 640x480, 1024x768, 1280x960, or any other suitable resolution.

[0133] The frame rate (e.g., the rate at which an image capture device acquires a set of pixel data for one image frame before proceeding to capture pixel data associated with the next image frame) can be controllable. The frame rate associated with image capture device 122 can be higher, lower, or the same as the frame rates associated with image capture devices 124 and 126. The frame rates associated with image capture devices 122, 124, and 126 can depend on a variety of factors related to timing that can affect the frame rate. For example, one or more of image capture devices 122, 124, and 126 can include an optional pixel delay period that is applied before or after acquiring image data associated with one or more pixels of an image sensor in image capture device 122, 124, and / or 126. Generally, image data corresponding to each pixel can be acquired according to a clock rate for the device (e.g., one pixel per clock cycle). Additionally, in embodiments that include a rolling shutter, one or more of image capture devices 122, 124, and 126 can include an optional horizontal blanking period that is applied before or after acquiring image data associated with a row of pixels of an image sensor in image capture device 122, 124, and / or 126. Further, one or more of image capture devices 122, 124, and / or 126 can include an optional vertical blanking period that is applied before or after acquiring image data associated with an image frame of image capture devices 122, 124, and 126.

[0134] These timing controls can enable synchronization of the frame rates associated with image capture devices 122, 124, and 126, even when the line scan rates of each image capture device are different. Additionally, as will be discussed in more detail below, these factors such as optional timing controls (e.g., image sensor resolution, maximum line scan rate, etc.) can enable synchronization of image capture from an area where the FOV of image capture device 122 overlaps one or more FOVs of image capture devices 124 and 126, even when the field of view of image capture device 122 is different from the FOVs of image capture devices 124 and 126.

[0135] The frame rate timing in image capture devices 122, 124, and 126 can depend on the resolution of the associated image sensor. For example, assuming similar line scan rates for two devices, if one device includes an image sensor with a resolution of 640x480 and another device includes an image sensor with a resolution of 1280x960, it will take more time to acquire one frame of image data from the sensor with the higher resolution.

[0136] Another factor that may affect the timing of image data acquisition in image capture devices 122, 124, and 126 is the maximum line scan rate. For example, it will take a certain minimum amount of time to acquire one line of image data from the image sensors included in image capture devices 122, 124, and 126. Assuming no pixel delay cycles are added, this minimum amount of time for acquiring one line of image data will be related to the maximum line scan rate for a particular device. A device with a higher maximum line scan rate has the potential to provide a higher frame rate compared to a device with a lower maximum line scan rate. In some embodiments, one or more of image capture devices 124 and 126 may have a maximum line scan rate that is higher than the maximum line scan rate associated with image capture device 122. In some embodiments, the maximum line scan rate of image capture device 124 and / or 126 may be 1.25, 1.5, 1.75, or 2 times or more than the maximum line scan rate of image capture device 122.

[0137] In another embodiment, image capture devices 122, 124, and 126 may have the same maximum line scan rate, but image capture device 122 may operate at a scan rate that is less than or equal to its maximum scan rate. The system may be configured such that one or more of image capture devices 124 and 126 operate at a line scan rate that is equal to the line scan rate of image capture device 122. In other cases, the system may be configured such that the line scan rate of image capture device 124 and / or image capture device 126 may be 1.25, 1.5, 1.75, or 2 times or more than the line scan rate of image capture device 122.

[0138] In some embodiments, image capture devices 122, 124, and 126 may be asymmetric. That is, they may include cameras with different fields of view (FOV) and focal lengths. The fields of view of image capture devices 122, 124, and 126 may include, for example, any desired region with respect to the environment of vehicle 200. In some embodiments, one or more of image capture devices 122, 124, and 126 may be configured to acquire image data from an environment located in front of vehicle 200, behind vehicle 200, to the side of vehicle 200, or a combination thereof.

[0139] In addition, the focal length associated with each of the image capture devices 122, 124, and / or 126 may be selectable (e.g., by including an appropriate lens, etc.) such that each device acquires an image of an object at a desired distance range relative to the vehicle 200. For example, in some embodiments, the image capture devices 122, 124, and 126 may acquire images of nearby objects within a few meters of the vehicle. The image capture devices 122, 124, and 126 may also be configured to acquire images of objects at a greater distance from the vehicle (e.g., 25 m, 50 m, 100 m, 150 m, or more). Additionally, the focal lengths of the image capture devices 122, 124, and 126 may be selected such that one image capture device (e.g., image capture device 122) may acquire an image of an object relatively close to the vehicle (e.g., within 10 m or within 20 m), while other image capture devices (e.g., image capture devices 124 and 126) may acquire images of objects farther from the vehicle 200 (e.g., greater than 20 m, 50 m, 100 m, 150 m, etc.).

[0140] According to some embodiments, the FOV of one or more of the image capture devices 122, 124, and 126 may be wide-angle. For example, having an FOV of 140 degrees may be advantageous, particularly for the image capture devices 122, 124, and 126 that may be used to capture images of areas near the vehicle 200. For example, the image capture device 122 may be used to capture images of areas to the right or left of the vehicle 200, and in such embodiments, it may be desirable for the image capture device 122 to have a wide FOV (e.g., at least 140 degrees).

[0141] The field of view associated with each of the image capture devices 122, 124, and 126 may depend on the respective focal length. For example, as the focal length increases, the corresponding field of view decreases.

[0142] The image capture devices 122, 124, and 126 may be configured to have any suitable field of view. In one particular example, the image capture device 122 may have a horizontal FOV of 46 degrees, the image capture device 124 may have a horizontal FOV of 23 degrees, and the image capture device 126 may have a horizontal FOV between 23 degrees and 46 degrees. In another case, the image capture device 122 may have a horizontal FOV of 52 degrees, the image capture device 124 may have a horizontal FOV of 26 degrees, and the image capture device 126 may have a horizontal FOV between 26 degrees and 52 degrees. In some embodiments, the ratio of the FOV of the image capture device 122 to the FOV of the image capture device 124 and / or the image capture device 126 may vary between 1.5 and 2.0. In other embodiments, this ratio may vary between 1.25 and 2.25.

[0143] System 100 can be configured such that the field of view of image capture device 122 at least partially or fully overlaps with the field of view of image capture device 124 and / or image capture device 126. In some embodiments, system 100 can be configured such that the fields of view of image capture devices 124 and 126, for example, fall within (e.g., are narrower than) the field of view of image capture device 122 and share a common center therewith. In other embodiments, image capture devices 122, 124, and 126 can capture adjacent FOVs or can have partial overlap of their FOVs. In some embodiments, the fields of view of image capture devices 122, 124, and 126 can be aligned such that the centers of image capture devices 124 and / or 126 with narrower FOVs can be located in the lower half of the field of view of the device 122 with a wider FOV.

[0144] Figure 2F A schematic representation of an exemplary vehicle control system consistent with the disclosed embodiments. As Figure 2F indicated, vehicle 200 can include a throttle system 220, a braking system 230, and a steering system 240. System 100 can provide inputs (e.g., control signals) to one or more of throttle system 220, braking system 230, and steering system 240 via one or more data links (e.g., any one or more wired and / or wireless links for transmitting data). For example, based on the analysis of images acquired by image capture devices 122, 124, and / or 126, system 100 can provide control signals to one or more of throttle system 220, braking system 230, and steering system 240 to navigate vehicle 200 (e.g., by causing acceleration, turning, lane changes, etc.). Additionally, system 100 can receive inputs from one or more of throttle system 220, braking system 230, and steering system 24 that indicate the operating conditions of vehicle 200 (e.g., speed, whether vehicle 200 is braking and / or turning, etc.). Further details are provided below in connection with Figures 4 - 7 Provide further details.

[0145] As Figure 3AAs shown, vehicle 200 may also include a user interface 170 for interacting with the driver or passengers of vehicle 200. For example, the user interface 170 in a vehicle application may include a touch screen 320, a knob 330, buttons 340, and a microphone 350. The driver or passengers of vehicle 200 may also use a handle (e.g., located on or near the steering column of vehicle 200, including, for example, a turn signal handle), buttons (e.g., located on the steering wheel of vehicle 200), etc. to interact with system 100. In some embodiments, the microphone 350 may be positioned adjacent to the rearview mirror 310. Similarly, in some embodiments, the image capture device 122 may be located near the rearview mirror 310. In some embodiments, the user interface 170 may also include one or more speakers 360 (e.g., speakers of a vehicle audio system). For example, system 100 may provide various notifications (e.g., alerts) via the speakers 360.

[0146] Figures 3B - 3D Illustration of an exemplary camera mount 370 configured to be positioned behind a rearview mirror (e.g., rearview mirror 310) and against a vehicle windshield, consistent with the disclosed embodiments. As Figure 3B shown, the camera mount 370 may include image capture devices 122, 124, and 126. The image capture devices 124 and 126 may be positioned behind a glare shield 380, which may be flush with the vehicle windshield and include a composition of thin film and / or anti-reflective material. For example, the glare shield 380 may be positioned such that the shield is aligned with the vehicle windshield having a matching slope. In some embodiments, each of the image capture devices 122, 124, and 126 may be positioned behind the glare shield 380, as depicted, for example, in Figure 3D The disclosed embodiments are not limited to any particular configuration of the image capture devices 122, 124, and 126, the camera mount 370, and the glare shield 380. Figure 3C For a view from the front Figure 3B Illustration of the camera mount 370 shown in

[0147] As those skilled in the art who benefit from this disclosure will recognize, many changes and / or modifications may be made to the foregoing disclosed embodiments. For example, not all components are necessary for the operation of system 100. Additionally, any component may be located in any suitable part of system 100, and the component may be rearranged into various configurations while providing the functionality of the disclosed embodiments. Accordingly, the foregoing configurations are exemplary, and regardless of the configurations discussed above, system 100 may provide a wide range of functionality to analyze the surrounding environment of vehicle 200 and navigate vehicle 200 in response to that analysis.

[0148] Consistent with various disclosed embodiments, as discussed in further detail below, system 100 may provide various features related to autonomous driving and / or driver assistance technologies. For example, system 100 may analyze image data, location data (e.g., GPS location information), map data, speed data, and / or data from sensors included in vehicle 200. System 100 may collect data for analysis from, for example, image acquisition unit 120, location sensor 130, and other sensors. Additionally, system 100 may analyze the collected data to determine whether vehicle 200 should take a particular action and then automatically take the determined action without human intervention. For example, when vehicle 200 is navigating without human intervention, system 100 may automatically control the braking, acceleration, and / or steering of vehicle 200 (e.g., by sending control signals to one or more of throttle system 220, braking system 230, and steering system 240). Further, system 100 may analyze the collected data and issue warnings and / or alerts to vehicle occupants based on the analysis of the collected data. Additional details regarding various embodiments provided by system 100 are provided below.

[0149] Forward Multi - Imaging System

[0150] As discussed above, system 100 may provide driving assistance functionality using a multi-camera system. The multi-camera system may use one or more cameras oriented in a forward direction of the vehicle. In other embodiments, the multi-camera system may include one or more cameras oriented to the side or rear of the vehicle. In one embodiment, for example, system 100 may use a dual-camera imaging system, where a first camera and a second camera (e.g., image capture devices 122 and 124) may be positioned at the front and / or side of a vehicle (e.g., vehicle 200). The first camera may have a field of view that is greater than, less than, or partially overlapping with that of the second camera. Additionally, the first camera may be connected to a first image processor for monocular image analysis of the images provided by the first camera, and the second camera may be connected to a second image processor for monocular image analysis of the images provided by the second camera. The outputs (e.g., processed information) of the first and second image processors may be combined. In some embodiments, the second image processor may receive images from both the first camera and the second camera for stereoscopic analysis. In another embodiment, system 100 may use a triple-camera imaging system, where each of the cameras has a different field of view. Thus, such a system may make decisions based on information from objects located at different distances in front of and to the side of the vehicle. The mention of monocular image analysis may refer to a situation where image analysis is performed based on images captured from a single viewpoint (e.g., from a single camera). Stereoscopic image analysis may refer to a situation where image analysis is performed based on two or more images captured with one or more variations in image capture parameters. For example, captured images suitable for performing stereoscopic image analysis may include images captured from two or more different positions, from different fields of view, using different focal lengths, along with disparity information, etc.

[0151] For example, in one embodiment, system 100 may implement a three-camera configuration using image capture devices 122, 124, and 126. In such a configuration, image capture device 122 may provide a narrow field of view (e.g., 34 degrees or other values selected from a range of approximately 20 degrees to approximately 45 degrees, etc.), image capture device 124 may provide a wide field of view (e.g., 150 degrees or other values selected from a range of approximately 100 degrees to approximately 180 degrees), and image capture device 126 may provide a medium field of view (e.g., 46 degrees or other values selected from a range of approximately 35 degrees to approximately 60 degrees). In some embodiments, image capture device 126 may act as the main camera or primary camera. Image capture devices 122, 124, and 126 may be positioned behind rearview mirror 310 and positioned substantially side by side (e.g., 6 cm apart). Additionally, in some embodiments, as discussed above, one or more of image capture devices 122, 124, and 126 may be mounted behind glare shield 380 that is flush with the windshield of vehicle 200. Such a shield may serve to minimize the effect of any reflections from inside the sedan on image capture devices 122, 124, and 126.

[0152] In another embodiment, as discussed above in connection with Figure 3B and 3C the wide-field-of-view camera (e.g., image capture device 124 in the above example) may be mounted lower than the narrow-field-of-view camera and the main-field-of-view camera (e.g., image devices 122 and 126 in the above example). This configuration may provide a clear line of sight from the wide-field-of-view camera. To reduce reflections, the camera may be mounted close to the windshield of vehicle 200 and may include a polarizer on the camera to suppress reflected light.

[0153] The three-camera system may provide certain performance characteristics. For example, some embodiments may include the ability to verify the detection of an object by one camera based on the detection results from another camera. In the three-camera configuration discussed above, processing unit 110 may include, for example, three processing devices (e.g., three EyeQ series processor chips as discussed above), where each processing device is dedicated to processing images captured by one or more of image capture devices 122, 124, and 126.

[0154] In a three-camera system, a first processing device may receive images from a main camera and a narrow field-of-view camera and perform vision processing on the narrow FOV camera to, for example, detect other vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. Additionally, the first processing device may calculate the pixel differences between the images from the main camera and the narrow camera and create a 3D reconstruction of the environment of vehicle 200. The first processing device may then combine the 3D reconstruction with 3D map data or with 3D information calculated based on information from another camera.

[0155] A second processing device may receive images from the main camera and perform vision processing to detect other vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. Additionally, the second processing device may calculate camera displacement and calculate the pixel differences between successive images based on the displacement and create a 3D reconstruction of the scene (e.g., structure from motion). The second processing device may send the 3D reconstruction based on structure from motion to the first processing device for combination with a stereo 3D image.

[0156] A third processing device may receive images from a wide FOV camera and process the images to detect vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. The third processing device may further execute additional processing instructions to analyze the images to identify moving objects in the images, such as vehicles changing lanes, pedestrians, etc.

[0157] In some embodiments, enabling image-based information streams to be captured and processed independently can provide opportunities for providing redundancy in the system. Such redundancy may include, for example, using the first image capture device and the images processed from that device to verify and / or supplement information obtained by capturing and processing image information from at least a second image capture device.

[0158] In some embodiments, system 100 may use two image capture devices (e.g., image capture devices 122 and 124) to provide navigation assistance for vehicle 200 and use a third image capture device (e.g., image capture device 126) to provide redundancy and verify the analysis of data received from the other two image capture devices. For example, in such a configuration, image capture devices 122 and 124 may provide images for system 100 to perform stereo analysis for vehicle 200 navigation, while image capture device 126 may provide images for system 100 to perform monocular analysis to provide redundancy and verification of information obtained based on images captured by image capture device 122 and / or image capture device 124. That is, image capture device 126 (and the corresponding processing device) may be considered to provide a redundant subsystem for providing an inspection of the analysis originating from image capture devices 122 and 124 (e.g., to provide an automatic emergency braking (AEB) system). Additionally, in some embodiments, the redundancy and verification of the received data may be supplemented based on information received from one or more sensors (e.g., radar, lidar, acoustic sensors, information received from one or more transceivers external to the vehicle, etc.).

[0159] Those skilled in the art will recognize that the above camera configurations, camera placements, number of cameras, camera positions, etc. are merely examples. These components and other components described with respect to the overall system may be assembled and used in a variety of different configurations without departing from the scope of the disclosed embodiments. Further details regarding the use of multi-camera systems to provide driver assistance and / or autonomous vehicle functionality are provided below.

[0160] Figure 4 FIG. [X] is an example functional block diagram of memories 140 and / or 150, which may be stored / programmed with instructions for performing one or more operations consistent with the disclosed embodiments. Although memory 140 is referred to below, those skilled in the art will recognize that the instructions may be stored in memory 140 and / or 150.

[0161] As Figure 4 shown, memory 140 may store a monocular image analysis module 402, a stereo image analysis module 404, a speed and acceleration module 406, and a navigation response module 408. The disclosed embodiments are not limited to any particular configuration of memory 140. Additionally, application processor 180 and / or image processor 190 may execute instructions stored in any one of modules 402, 404, 406, and 408 included in memory 140. Those skilled in the art will understand that references to processing unit 110 in the following discussion may refer to application processor 180 and image processor 190 individually or jointly. Thus, the steps of any of the following processes may be performed by one or more processing devices.

[0162] In one embodiment, the monocular image analysis module 402 may store instructions (such as computer vision software) that, when executed by the processing unit 110, perform monocular image analysis on a set of images acquired by one of the image capture devices 122, 124, and 126. In some embodiments, the processing unit 110 may combine information from the set of images with additional sensing information (e.g., information from radar, lidar, etc.) to perform monocular image analysis. As described below in connection with Figures 5A - 5D what is described, the monocular image analysis module 402 may include instructions for detecting a set of features within the set of images (such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazardous objects, and any other features associated with the vehicle's environment). Based on this analysis, the system 100 (e.g., via the processing unit 110) may cause one or more navigation responses in the vehicle 200, such as turning, lane changing, changes in acceleration, etc., as discussed below in connection with the navigation response module 408.

[0163] In one embodiment, the stereo image analysis module 404 may store instructions (such as computer vision software) that, when executed by the processing unit 110, perform stereo image analysis on a first set of images and a second set of images acquired by a combination of image capture devices selected from any of the image capture devices 122, 124, and 126. In some embodiments, the processing unit 110 may combine information from the first set of images and the second set of images with additional sensing information (e.g., information from radar) to perform stereo image analysis. For example, the stereo image analysis module 404 may include instructions for performing stereo image analysis based on the first set of images acquired by the image capture device 124 and the second set of images acquired by the image capture device 126. As described in connection with Figure 6 what is described, the stereo image analysis module 404 may include instructions for detecting a set of features within the first set of images and the second set of images (such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazardous objects, etc.). Based on this analysis, the processing unit 110 may cause one or more navigation responses in the vehicle 200, such as turning, lane changing, changes in acceleration, etc., as discussed below in connection with the navigation response module 408. Additionally, in some embodiments, the stereo image analysis module 404 may implement techniques associated with a trained system (such as a neural network or a deep neural network) or an untrained system (such as a system that may be configured to use computer vision algorithms to detect and / or label objects in an environment from which sensing information is captured and processed). In one embodiment, the stereo image analysis module 404 and / or other image processing modules may be configured to use a combination of trained and untrained systems.

[0164] In one embodiment, the speed and acceleration module 406 may store software configured to analyze data received from one or more computing and electromechanical devices in vehicle 200 configured to cause a change in the speed and / or acceleration of vehicle 200. For example, the processing unit 110 may execute instructions associated with the speed and acceleration module 406 to calculate a target speed for vehicle 200 based on data executed from the monocular image analysis module 402 and / or the stereo image analysis module 404. Such data may include, for example, a target position, speed, and / or acceleration, the position and / or speed of vehicle 200 relative to nearby vehicles, pedestrians, or road objects, position information of vehicle 200 relative to the lane markings of the road, and the like. Additionally, the processing unit 110 may calculate the target speed of vehicle 200 based on sensing inputs (e.g., information from radar) and inputs from other systems of vehicle 200 (such as the throttle system 220, the braking system 230, and / or the steering system 240 of vehicle 200). Based on the calculated target speed, the processing unit 110 may transmit an electronic signal to the throttle system 220, the braking system 230, and / or the steering system 240 of vehicle 200 to trigger a change in speed and / or acceleration by, for example, physically depressing the brakes of vehicle 200 or releasing its accelerator.

[0165] In one embodiment, the navigation response module 408 may store software executable by the processing unit 110 to determine a desired navigation response based on data executed from the monocular image analysis module 402 and / or the stereo image analysis module 404. Such data may include position and speed information associated with nearby vehicles, pedestrians, and road objects, target position information of vehicle 200, and the like. Additionally, in some embodiments, the navigation response may be (partially or fully) based on map data, a pre-determined position of vehicle 200, and / or the relative speed or relative acceleration between vehicle 200 and one or more objects detected by the execution of the monocular image analysis module 402 and / or the stereo image analysis module 404. The navigation response module 408 may also be based on sensing inputs (e.g., information from radar) and inputs from other systems of vehicle 200 (such as the throttle system 220, the braking system 230, and the steering system 240 of vehicle 200). Based on the desired navigation response, the processing unit 110 may transmit an electronic signal to the throttle system 220, the braking system 230, and the steering system 240 of vehicle 200 to trigger the desired navigation response by, for example, rotating the steering wheel of vehicle 200 to achieve a rotation of a pre-determined angle. In some embodiments, the processing unit 110 may use the output of the navigation response module 408 (e.g., the desired navigation response) as an input to execute the speed and acceleration module 406 to calculate a change in the speed of vehicle 200.

[0166] In addition, any module disclosed herein (e.g., modules 402, 404, and 406) may implement techniques associated with a trained system, such as a neural network or a deep neural network, or an untrained system.

[0167] Figure 5A Flowchart showing an exemplary process 500A for causing one or more navigation responses based on monocular image analysis consistent with the disclosed embodiments. At step 510, the processing unit 110 may receive a plurality of images via a data interface 128 between the processing unit 110 and the image acquisition unit 120. For example, a camera included in the image acquisition unit 120 (such as the image capture device 122 having a field of view 202) may capture a plurality of images of an area in front of the vehicle 200 (or, for example, the side or rear of the vehicle) and transmit them to the processing unit 110 via a data connection (e.g., a digital connection, a wired connection, a USB connection, a wireless connection, a Bluetooth connection, etc.). The processing unit 110 may execute the monocular image analysis module 402 in step 520 to analyze the plurality of images, as described in further detail below in conjunction with Figures 5B - 5D As further described in detail. By performing this analysis, the processing unit 110 may detect a set of features within a set of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, etc.

[0168] The processing unit 110 may also execute the monocular image analysis module 402 in step 520 to detect various road hazards, such as, for example, a portion of a truck tire, a fallen road sign, loose cargo, small animals, etc. Road hazards may vary in structure, shape, size, and color, which may make the detection of such hazards more challenging. In some embodiments, the processing unit 110 may execute the monocular image analysis module 402 to perform a multi-frame analysis of the plurality of images to detect road hazards. For example, the processing unit 110 may estimate the camera movement between successive image frames and calculate the pixel differences between the frames to construct a 3D map of the road. The processing unit 110 may then use the 3D map to detect the road surface and the hazards present on the road surface.

[0169] At step 530, the processing unit 110 may execute the navigation response module 408 based on the analysis performed in step 520 and as described above in conjunction with Figure 4The described techniques are used to cause one or more navigation responses in vehicle 200. Navigation responses can include, for example, turning, lane changes, changes in acceleration, and the like. In some embodiments, processing unit 110 can use the data executed from speed and acceleration module 406 to cause one or more navigation responses. Additionally, multiple navigation responses can occur as follows: simultaneously, sequentially, or any combination thereof. For example, processing unit 110 can cause vehicle 200 to move across a lane and then accelerate by, for example, sequentially transmitting control signals to steering system 240 and throttle system 220 of vehicle 200. Alternatively, processing unit 110 can cause vehicle 200 to brake while changing lanes by, for example, simultaneously transmitting control signals to braking system 230 and steering system 240 of vehicle 200.

[0170] Figure 5B A flowchart showing an exemplary process 500B for detecting one or more vehicles and / or pedestrians in a set of images consistent with the disclosed embodiments. Processing unit 110 can execute monocular image analysis module 402 to implement process 500B. In step 540, processing unit 110 can determine a set of candidate objects representing possible vehicles and / or pedestrians. For example, processing unit 110 can scan one or more images, compare the images with one or more pre-determined patterns, and identify possible locations within each image that may contain the object of interest (e.g., a vehicle, a pedestrian, or a part thereof). The pre-determined patterns can be related in such a way as to achieve a high "false hit" rate and a low "miss" rate. For example, processing unit 110 can use a low threshold of similarity to a pre-determined pattern to identify candidate objects as possible vehicles or pedestrians. Doing so can enable processing unit 110 to reduce the probability of missing (e.g., not identifying) candidate objects representing vehicles or pedestrians.

[0171] In step 542, processing unit 110 can filter the set of candidate objects based on classification criteria to exclude specific candidate objects (e.g., irrelevant or less relevant objects). Such criteria can be derived from various properties associated with object types stored in a database (e.g., a database stored in memory 140). The properties can include object shape, size, texture, location (e.g., relative to vehicle 200), and the like. Thus, processing unit 110 can use one or more sets of criteria to reject false candidates from the set of candidate objects.

[0172] In step 544, the processing unit 110 may analyze multiple frames of images to determine whether the objects in the set of candidate objects represent vehicles and / or pedestrians. For example, the processing unit 110 may track the detected candidate objects across successive frames and accumulate frame-by-frame data associated with the detected objects (e.g., size, position relative to the vehicle 200, etc.). Additionally, the processing unit 110 may estimate parameters for the detected objects and compare the frame-by-frame position data of the objects with the predicted positions.

[0173] In step 546, the processing unit 110 may construct a set of measurements for the detected objects. Such measurements may include, for example, position, velocity, and acceleration values (relative to the vehicle 200) associated with the detected objects. In some embodiments, the processing unit 110 may construct the measurements based on estimation techniques that use a series of time-based observations (such as Kalman filtering or linear quadratic estimation (LQE)) and / or based on available modeling data for different object types (e.g., sedan, truck, pedestrian, bicycle, road sign, etc.). Kalman filtering may be based on measurements of the scale of the object, where the scale measurement is proportional to the time to collision (e.g., the amount of time for the vehicle 200 to reach the object). Thus, by performing steps 540 to 546, the processing unit 110 can identify vehicles and pedestrians present in the set of captured images and derive information associated with the vehicles and pedestrians (e.g., position, velocity, size). Based on this identification and the derived information, the processing unit 110 may cause one or more navigation responses in the vehicle 200, as described above in connection with Figure 5A as described.

[0174] In step 548, the processing unit 110 may perform optical flow analysis on one or more images to reduce the probability of detecting "false hits" and missing candidate objects that represent vehicles or pedestrians. Optical flow analysis may refer to, for example, analyzing the motion patterns relative to the vehicle 200 in one or more images that are associated with other vehicles and pedestrians and that are different from the motion of the road surface. The processing unit 110 may calculate the motion of the candidate objects by observing the different positions of the objects across multiple image frames captured at different times. The processing unit 110 may use the position and time values as inputs into a mathematical model to calculate the motion of the candidate objects. Thus, optical flow analysis may provide another method for detecting vehicles and pedestrians near the vehicle 200. The processing unit 110 may perform optical flow analysis in combination with steps 540 to 546 to provide redundancy for detecting vehicles and pedestrians and increase the reliability of the system 100.

[0175] Figure 5CFlowchart of an exemplary process 500C for detecting road markings and / or lane geometry information in a set of images consistent with the disclosed embodiments. The processing unit 110 may execute the monocular image analysis module 402 to implement the process 500C. At step 550, the processing unit 110 may detect a set of objects by scanning one or more images. To detect segments of lane markings, lane geometry information, and other relevant road markings, the processing unit 110 may filter the set of objects to exclude those determined to be irrelevant (e.g., small potholes, pebbles, etc.). At step 552, the processing unit 110 may group together segments detected in step 550 that belong to the same road marking or lane marking. Based on this grouping, the processing unit 110 may develop a model (such as a mathematical model) to represent the detected segments.

[0176] At step 554, the processing unit 110 may construct a set of measurements associated with the detected segments. In some embodiments, the processing unit 110 may create a projection of the detected segments from the image plane to the real-world plane. The projection may be characterized using a 3-degree polynomial that has coefficients corresponding to physical properties such as the position, slope, curvature, and curvature derivative of the detected road. When generating the projection, the processing unit 110 may consider changes in the road surface and the pitch rate and roll rate associated with the vehicle 200. Additionally, the processing unit 110 may model the road elevation by analyzing the position and motion cues present on the road surface. Additionally, the processing unit 110 may estimate the pitch rate and roll rate associated with the vehicle 200 by tracking a set of feature points in one or more images.

[0177] At step 556, the processing unit 110 may perform multi-frame analysis, for example, by tracking the detected segments across successive image frames and accumulating frame-by-frame data associated with the detected segments. As the processing unit 110 performs multi-frame analysis, the set of measurements constructed at step 554 may become more reliable and associated with an increasingly high confidence level. Thus, by performing steps 550, 552, 554, and 556, the processing unit 110 may identify road markings present within a set of captured images and derive lane geometry information. Based on this identification and the derived information, the processing unit 110 may cause one or more navigation responses in the vehicle 200, as described above in connection with Figure 5A as described.

[0178] At step 558, the processing unit 110 may consider additional information sources to further develop a safety model of the vehicle 200 in its surrounding environment. The processing unit 110 may use the safety model to define the environment in which the system 100 may perform autonomous control of the vehicle 200 in a safe manner. To develop the safety model, in some embodiments, the processing unit 110 may consider the positions and movements of other vehicles, detected road edges and barriers, and / or general road shape descriptions extracted from map data (such as data from the map database 160). By considering additional information sources, the processing unit 110 may provide redundancy for detecting road markings and lane geometries and increase the reliability of the system 100.

[0179] Figure 5D Flowchart showing an exemplary process 500D for detecting traffic lights in a set of images consistent with the disclosed embodiments. The processing unit 110 may execute the monocular image analysis module 402 to implement the process 500D. At step 560, the processing unit 110 may scan the set of images and identify objects at locations in the images where there may be traffic signal lights. For example, the processing unit 110 may filter the identified objects to build a set of candidate objects, excluding those objects that are less likely to correspond to traffic lights. The filtering may be done based on various properties associated with traffic lights (such as shape, size, texture, location (e.g., relative to the vehicle 200), etc.). Such properties may be based on multiple examples of traffic lights and traffic control signals and stored in a database. In some embodiments, the processing unit 110 may perform multi-frame analysis on a set of candidate objects that reflect possible traffic lights. For example, the processing unit 110 may track candidate objects across successive image frames, estimate the real-world positions of the candidate objects, and filter out objects that are moving (which are less likely to be traffic lights). In some embodiments, the processing unit 110 may perform color analysis on the candidate objects and identify the relative positions of the detected colors that appear inside the possible traffic lights.

[0180] At step 562, the processing unit 110 may analyze the geometry of the intersection. The analysis may be based on any combination of: (i) the number of lanes detected on either side of the vehicle 200, (ii) markings detected on the road (such as arrow markings), and (iii) a description of the intersection extracted from map data (such as data from the map database 160). The processing unit 110 may use information from the execution of the monocular analysis module 402 to perform the analysis. In addition, the processing unit 110 may determine the correspondence between the traffic lights detected at step 560 and the lanes that appear near the vehicle 200.

[0181] When vehicle 200 approaches an intersection, at step 564, processing unit 110 may update the confidence level associated with the analyzed intersection geometry and detected traffic lights. For example, the estimated traffic volume present at the intersection may affect the confidence level as compared to the actual number present at the intersection. Thus, based on the confidence level, processing unit 110 may delegate control to the driver of vehicle 200 to improve safety conditions. By performing steps 560, 562, and 564, processing unit 110 may identify traffic lights present within a set of captured images and analyze intersection geometry information. Based on this identification and analysis, processing unit 110 may cause one or more navigation responses in vehicle 200, as described above in conjunction with Figure 5A as described.

[0182] Figure 5E FIG. is a flow chart of an exemplary process 500E for causing one or more navigation responses in vehicle 200 based on a vehicle path, consistent with the disclosed embodiments. At step 570, processing unit 110 may construct an initial vehicle path associated with vehicle 200. The vehicle path may be represented using a set of points expressed in coordinates (x, z), and the distance d between two points in the set of points i may fall within the range of 1 to 5 meters. In one embodiment, processing unit 110 may use two polynomial traces (such as a left road polynomial trace and a right road polynomial trace) to construct the initial vehicle path. Processing unit 110 may calculate the geometric midpoint between the two polynomial traces and offset each point included in the resulting vehicle path by a predetermined offset (e.g., a smart lane offset), if any (a zero offset may correspond to traveling in the middle of the lane). The offset may be in a direction perpendicular to the segment between any two points in the vehicle path. In another embodiment, processing unit 110 may use one polynomial trace and an estimated lane width to offset each point of the vehicle path by half of the estimated lane width plus a predetermined offset (e.g., a smart lane offset).

[0183] At step 572, processing unit 110 may update the vehicle path constructed at step 570. Processing unit 110 may reconstruct the vehicle path constructed at step 570 using a higher resolution such that the distance d between two points in the set of points representing the vehicle path k is less than the distance d described above i . For example, the distance d k may fall within the range of 0.1 to 0.3 meters. Processing unit 110 may use a parabolic spline algorithm to reconstruct the vehicle path, which may produce a cumulative distance vector S corresponding to the total length of the vehicle path (i.e., based on the set of points representing the vehicle path).

[0184] At step 574, the processing unit 110 may determine a look-ahead point (expressed in coordinates as (x l , z l )) based on the updated vehicle path constructed at step 572. The processing unit 110 may extract the look-ahead point from the cumulative distance vector S, and the look-ahead point may be associated with a look-ahead distance and a look-ahead time. The look-ahead distance (which may have a lower bound ranging from 10 to 20 meters) may be calculated as the product of the speed of the vehicle 200 and the look-ahead time. For example, as the speed of the vehicle 200 decreases, the look-ahead distance may also decrease (e.g., until it reaches the lower bound). The look-ahead time (which may range from 0.5 to 1.5 seconds) may be inversely proportional to the gain of one or more control loops (such as a heading error tracking control loop) associated with causing a navigation response in the vehicle 200. For example, the gain of the heading error tracking control loop may depend on the bandwidths of a yaw rate loop, a steering actuator loop, a car lateral dynamics, etc. Thus, the higher the gain of the heading error tracking control loop, the shorter the look-ahead time.

[0185] At step 576, the processing unit 110 may determine a heading error and a yaw rate command based on the look-ahead point determined at step 574. The processing unit 110 may determine the heading error by calculating the arctangent of the look-ahead point (e.g., arctangent(x l / z l ). The processing unit 110 may determine the yaw rate command as the product of the heading error and a high-level control gain. The high-level control gain may be equal to: (2 / look-ahead time), if the look-ahead distance is not at the lower bound. Otherwise, the high-level control gain may be equal to: (2 * speed of the vehicle 200 / look-ahead distance).

[0186] Figure 5F A flowchart illustrating an exemplary process 500F for determining whether a vehicle ahead is changing lanes in accordance with the disclosed embodiments. At step 580, the processing unit 110 may determine navigation information associated with a vehicle ahead (e.g., a vehicle traveling ahead of the vehicle 200). For example, the processing unit 110 may use the techniques described above in connection with Figure 5A and 5B to determine the position, speed (e.g., direction and speed), and / or acceleration of the vehicle ahead. The processing unit 110 may also use the techniques described above in connection with Figure 5E to determine one or more road polynomial traces, look-ahead points (associated with the vehicle 200), and / or snail traces (e.g., a set of points describing the path taken by the vehicle ahead).

[0187] At step 582, the processing unit 110 may analyze the navigation information determined at step 580. In one embodiment, the processing unit 110 may calculate the distance between the snail trace and the road polynomial trace (e.g., along the trace). If the variance of this distance along the trace exceeds a predetermined threshold (e.g., 0.1 to 0.2 meters on a straight road, 0.3 to 0.4 meters on a moderately curved road, and 0.5 to 0.6 meters on a road with sharp turns), then the processing unit 110 may determine that the vehicle ahead may be changing lanes. In the case where multiple vehicles are detected traveling ahead of vehicle 200, the processing unit 110 may compare the snail traces associated with each vehicle. Based on this comparison, the processing unit 110 may determine that a vehicle whose snail trace does not match the snail traces of other vehicles is likely changing lanes. The processing unit 110 may additionally compare the curvature of the snail trace (associated with the vehicle ahead) with the expected curvature of the road segment on which the vehicle ahead is traveling. The expected curvature may be extracted from map data (e.g., data from the map database 160), from the road polynomial trace, from the snail traces of other vehicles, from prior knowledge about the road, etc. If the difference between the curvature of the snail trace and the expected curvature of the road segment exceeds a predetermined threshold, then the processing unit 110 may determine that the vehicle ahead may be changing lanes.

[0188] In another embodiment, the processing unit 110 may compare the instantaneous position of the vehicle ahead with the look-ahead point (associated with vehicle 200) within a specific time period (e.g., 0.5 to 1.5 seconds). If the distance between the instantaneous position of the vehicle ahead and the look-ahead point changes during the specific time period, and the cumulative sum of the changes exceeds a predetermined threshold (e.g., 0.3 to 0.4 meters on a straight road, 0.7 to 0.8 meters on a moderately curved road, and 1.3 to 1.7 meters on a road with sharp turns), then the processing unit 110 may determine that the vehicle ahead may be changing lanes. In another embodiment, the processing unit 110 may analyze the geometry of the trace by comparing the lateral distance traveled along the snail trace with the expected curvature of the snail trace. The expected radius of curvature may be determined according to the following calculation: (δ z 2 +δ x 2 ) / 2 / (δ x )), where δ x represents the lateral distance traveled, and δ zIndicates the longitudinal distance traveled. If the difference between the lateral distance traveled and the expected curvature exceeds a predetermined threshold (e.g., 500 to 700 meters), the processing unit 110 may determine that the vehicle ahead may be changing lanes. In another embodiment, the processing unit 110 may analyze the position of the vehicle ahead. If the position of the vehicle ahead occludes the road polynomial trace (e.g., the vehicle ahead covers the top of the road polynomial trace), the processing unit 110 may determine that the vehicle ahead may be changing lanes. In a situation where the position of the vehicle ahead is such that another vehicle is detected in front of the vehicle ahead and the snail trails of the two vehicles are not parallel, the processing unit 110 may determine that the (closer) vehicle ahead may be changing lanes.

[0189] In step 584, the processing unit 110 may determine whether the vehicle ahead 200 is changing lanes based on the analysis performed in step 582. For example, the processing unit 110 may make this determination based on a weighted average of the respective analyses performed in step 582. In such a scenario, for example, a decision that the vehicle ahead may be changing lanes made by the processing unit 110 based on a particular type of analysis may be assigned a value of "1" (and "0" to indicate that it is determined that the vehicle ahead is unlikely to be changing lanes). Different analyses performed in step 582 may be assigned different weights, and the disclosed embodiments are not limited to any particular combination of analyses and weights.

[0190] Figure 6 A flowchart showing an exemplary process 600 for causing one or more navigation responses based on stereo image analysis consistent with the disclosed embodiments. In step 610, the processing unit 110 may receive a first and a second plurality of images via the data interface 128. For example, cameras included in the image acquisition unit 120 (such as image capture devices 122 and 124 having fields of view 202 and 204) may capture a first and a second plurality of images of the area in front of the vehicle 200 and transmit them to the processing unit 110 via a digital connection (e.g., a USB connection, a wireless connection, a Bluetooth connection, etc.). In some embodiments, the processing unit 110 may receive the first and the second plurality of images via two or more data interfaces. The disclosed embodiments are not limited to any particular data interface configuration or protocol.

[0191] In step 620, the processing unit 110 may execute the stereo image analysis module 404 to perform stereo image analysis of the first and the second plurality of images to create a 3D map of the road in front of the vehicle and detect features within the images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, road hazards, etc. The stereo image analysis may be performed in a manner consistent with that described above in connection with Figures 5A - 5DThe described steps are performed in a similar manner. For example, the processing unit 110 may execute the stereoscopic image analysis module 404 to detect candidate objects (e.g., vehicles, pedestrians, road markings, traffic lights, road hazards, etc.) within the first and second pluralities of images, filter out a subset of the candidate objects based on various criteria, and perform multi-frame analysis, construct measurement results, and determine confidence levels for the remaining candidate objects. When performing the above steps, the processing unit 110 may consider information from both the first and second pluralities of images, rather than information from only one set of images. For example, the processing unit 110 may analyze differences in pixel-level data (or other subsets of data from the two streams of captured images) for candidate objects that appear in both the first and second pluralities of images. As another example, the processing unit 110 may estimate the position and / or velocity of a candidate object (e.g., relative to the vehicle 200) by observing that the object appears in one rather than the other of the pluralities of images or relative to other differences (which may exist with respect to an object that appears in both image streams). For example, the position, velocity, and / or acceleration relative to the vehicle 200 may be determined based on the trajectories, positions, movement characteristics, etc. of features associated with an object that appears in one or both of the two image streams.

[0192] In step 630, the processing unit 110 may execute the navigation response module 408 to cause one or more navigation responses in the vehicle 200 based on the analysis performed in step 620 and the techniques described above in connection with Figure 4 The navigation responses may include, for example, turning, lane changes, changes in acceleration, changes in speed, braking, etc. In some embodiments, the processing unit 110 may use data obtained from the execution of the speed and acceleration module 406 to cause one or more navigation responses. Additionally, the plurality of navigation responses may occur: simultaneously, sequentially, or any combination thereof.

[0193] Figure 7FIG. 700 is a flowchart illustrating an exemplary process 700 consistent with the disclosed embodiments for causing one or more navigation responses based on an analysis of three sets of images. At step 710, the processing unit 110 may receive a first, second, and third plurality of images via the data interface 128. For example, cameras included in the image acquisition unit 120 (such as image capture devices 122, 124, and 126 having fields of view 202, 204, and 206) may capture a first, second, and third plurality of images of areas in front of and / or to the sides of the vehicle 200 and transmit them to the processing unit 110 via a digital connection (e.g., a USB connection, a wireless connection, a Bluetooth connection, etc.). In some embodiments, the processing unit 110 may receive the first, second, and third plurality of images via three or more data interfaces. For example, each of the image capture devices 122, 124, 126 may have an associated data interface for transmitting data to the processing unit 110. The disclosed embodiments are not limited to any particular data interface configuration or protocol.

[0194] At step 720, the processing unit 110 may analyze the first, second, and third plurality of images to detect features within the images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, road hazards, etc. The analysis may be performed in a manner similar to the steps described above in connection with Figures 5A - 5D FIGS. 5 and 6. For example, the processing unit 110 may perform monocular image analysis on each of the first, second, and third plurality of images (e.g., via the execution of the monocular image analysis module 402 and based on the steps described above in connection with Figures 5A - 5D FIGS. 5 and 6). Alternatively, the processing unit 110 may perform stereo image analysis on the first and second plurality of images, the second and third plurality of images, and / or the first and third plurality of images (e.g., via the execution of the stereo image analysis module 404 and based on the steps described above in connection with Figure 6 FIGS. 5 and 6). The processed information corresponding to the analysis of the first, second, and / or third plurality of images may be combined. In some embodiments, the processing unit 110 may perform a combination of monocular image analysis and stereo image analysis. For example, the processing unit 110 may perform monocular image analysis on the first plurality of images (e.g., via the execution of the monocular image analysis module 402) and stereo image analysis on the second and third plurality of images (e.g., via the execution of the stereo image analysis module 404). The configuration of the image capture devices 122, 124, and 126 - including their respective positions and fields of view 202, 204, and 206 - may affect the type of analysis performed on the first, second, and third plurality of images. The disclosed embodiments are not limited to a particular configuration of the image capture devices 122, 124, and 126, or the type of analysis performed on the first, second, and third plurality of images.

[0195] In some embodiments, the processing unit 110 may test the system 100 based on the images acquired and analyzed in steps 710 and 720. Such tests may provide an indicator of the overall performance of the system 100 for a particular configuration of the image capture devices 122, 124, and 126. For example, the processing unit 110 may determine the ratios of "false positives" (e.g., situations where the system 100 incorrectly determines the presence of a vehicle or pedestrian) and "misses".

[0196] In step 730, the processing unit 110 may cause one or more navigation responses in the vehicle 200 based on information derived from two of the first, second, and third pluralities of images. The selection of two of the first, second, and third pluralities of images may depend on various factors such as, for example, the number, type, and size of objects detected in each of the pluralities of images. The processing unit 110 may also make this selection based on: image quality and resolution, the effective field of view reflected in the images, the number of frames captured, the extent to which one or more target objects actually appear in the frames (e.g., the percentage of frames in which an object appears, the proportion of objects appearing in each such frame, etc.), and so on.

[0197] In some embodiments, the processing unit 110 may select information derived from two of the first, second, and third pluralities of images by determining the extent to which information from one image source is consistent with information from other image sources. For example, the processing unit 110 may combine the processed information from each of the image capture devices 122, 124, and 126 (whether by monocular analysis, stereoscopic analysis, or any combination of the two) and determine visual indicators that are consistent across the images captured from each of the image capture devices 122, 124, and 126 (e.g., lane markings, detected vehicles and their positions and / or paths, detected traffic lights, etc.). The processing unit 110 may also exclude information that is inconsistent across the captured images (e.g., a vehicle changing lanes, the lane model indicating that a vehicle is too close to the vehicle 200, etc.). Thus, the processing unit 110 may select information derived from two of the first, second, and third pluralities of images based on the determination of consistent and inconsistent information.

[0198] Navigation responses may include, for example, turning, lane changes, changes in acceleration, etc. The processing unit 110 may be based on the analysis performed in step 720 and as described above in connection with Figure 4The described techniques are used to cause one or more navigation responses. The processing unit 110 may also use the executed data from the velocity and acceleration module 406 to cause one or more navigation responses. In some embodiments, the processing unit 110 may cause one or more navigation responses based on the relative position, relative orientation, and / or relative acceleration between the vehicle 200 and an object detected within any one of the first, second, and third plurality of images. The one or more navigation responses may occur: simultaneously, sequentially, or any combination thereof.

[0199] Sparse Road Model for Autonomous Vehicle Navigation

[0200] In some embodiments, the disclosed systems and methods may use a sparse map for autonomous vehicle navigation. In particular, the sparse map may be used for autonomous vehicle navigation along a road segment. For example, the sparse map may provide sufficient information for autonomous vehicle navigation without storing and / or updating large amounts of data. As discussed further below in detail, an autonomous vehicle may use the sparse map to navigate one or more roads based on one or more stored trajectories.

[0201] Sparse Map for Autonomous Vehicle Navigation

[0202] In some embodiments, the disclosed systems and methods may generate a sparse map for autonomous vehicle navigation. For example, the sparse map may provide sufficient information for navigation without excessive data storage or data transfer rate. As discussed further below in detail, a vehicle (which may be an autonomous vehicle) may use the sparse map to navigate one or more roads. For example, in some embodiments, the sparse map may include data related to a road and may possibly include landmarks along the road, which may be sufficient for vehicle navigation but also exhibit a smaller data footprint. For example, compared to a digital map that includes detailed map information such as image data collected along a road, the sparse data map described in detail below may require significantly less storage space and data transfer bandwidth.

[0203] For example, a sparse data map can store a three-dimensional polynomial representation of a preferred vehicle path along a road rather than storing a detailed representation of road segments. These paths may require very little data storage space. Additionally, in the described sparse data map, landmarks can be identified and included in the sparse map road model to assist with navigation. These landmarks can be located at any spacing suitable for enabling vehicle navigation, but in some cases, it is not necessary to identify these landmarks and include them in the model at high density and short spacing. Instead, in some cases, it may be possible to navigate based on landmarks spaced at least 50 meters, at least 100 meters, at least 500 meters, at least 1 kilometer, or at least 2 kilometers apart. As will be discussed in more detail in other sections, as a vehicle travels along a roadway, a sparse map can be generated based on data collected or measured by a vehicle equipped with various sensors and devices such as image capture devices, global positioning system sensors, motion sensors, etc. In some cases, a sparse map can be generated based on data collected during multiple drives of one or more vehicles along a particular roadway. Using multiple drives of one or more vehicles to generate a sparse map can be referred to as "crowdsourcing" the sparse map.

[0204] Consistent with the disclosed embodiments, an autonomous vehicle system can use a sparse map for navigation. For example, the disclosed systems and methods can distribute a sparse map to generate a road navigation model for an autonomous vehicle and can use the sparse map and / or the generated road navigation model to navigate an autonomous vehicle along a road segment. A sparse map consistent with the present disclosure can include one or more three-dimensional profiles that can represent pre-determined trajectories that an autonomous vehicle can traverse as they move along an associated road segment.

[0205] A sparse map consistent with the present disclosure can also include data representing one or more road features. Such road features can include identified landmarks, road feature profile curves, and any other road-related features useful in navigating a vehicle. A sparse map consistent with the present disclosure can enable autonomous navigation of a vehicle based on a relatively small amount of data included in the sparse map. For example, the disclosed embodiments of a sparse map do not include a detailed representation of a road such as road edges, road curvature, an image associated with a road segment, or data specifying other physical characteristics associated with a road segment, but may require relatively little storage space (and relatively little bandwidth when portions of the sparse map are transferred to a vehicle), but can still adequately provide autonomous vehicle navigation. The smaller data footprint of the disclosed sparse map, discussed further below, can be achieved in some embodiments by storing representations of road-related elements that require little data but still enable autonomous navigation.

[0206] For example, the disclosed sparse map may store a polynomial representation of one or more trajectories that a vehicle may follow along a road, rather than storing a detailed representation of various aspects of the road. Thus, instead of storing (or having to transfer) details about the physical nature of the road to enable navigation along the road, with the disclosed sparse map, a vehicle can be navigated along a particular road segment without having to interpret the physical aspects of the road in some cases, but rather by aligning its travel path having a trajectory (e.g., polynomial spline) along the particular road segment. In this way, the vehicle can be navigated primarily based on the stored trajectory (e.g., polynomial spline), which may require significantly less storage space compared to methods that involve storing images of road lanes, road parameters, road layouts, etc.

[0207] In addition to the stored polynomial representation of the trajectory along the road segment, the disclosed sparse map may also include small data objects that may represent road features. In some embodiments, the small data objects may include digital features derived from a digital image (or digital signal) obtained by a sensor (e.g., a camera or other sensors such as suspension sensors) mounted on a vehicle traveling along the road segment. The digital features may have a reduced size relative to the signals acquired by the sensor. In some embodiments, the digital features may be created to be compatible with a classifier function configured to detect and identify road features from signals obtained by the sensor, e.g., during subsequent driving. In some embodiments, the digital features may be created such that they have the smallest possible footprint while retaining the ability to correlate or match road features with the stored features based on an image of the road features (or digital signals generated by the sensor if the stored features are not image-based and / or include other data) captured by a camera mounted on a vehicle traveling along the same road segment at a subsequent time.

[0208] In some embodiments, the size of the data object may be further associated with the uniqueness of the road feature. For example, for road features detectable by a camera mounted on a vehicle, and when the camera system mounted on the vehicle is coupled to a classifier capable of differentiating image data corresponding to the road feature as being associated with a particular type of road feature (e.g., a road sign), and when such road signs are locally unique in the area (e.g., no identical road signs or road signs of the same type are nearby), this may be sufficient for storing data indicating the type of the road feature and its location.

[0209] As discussed further in detail below, road features (e.g., landmarks along a road segment) can be stored as small data objects that can represent the road features with relatively few bytes while providing sufficient information to identify and use such features for navigation. In one example, a road sign can be identified as an identified landmark and the navigation of a vehicle can be based on the identified landmark. The representation of the road sign can be stored in a sparse map to include, for example, several bytes of data indicating a class of landmarks (e.g., a stop sign) and several bytes of data indicating the location of the landmark (e.g., coordinates). Navigating based on such lightweight data representations of landmarks (e.g., using a representation sufficient for localization, identification, and navigation based on landmarks) can provide the desired level of navigation functionality associated with the sparse map without significantly increasing the data overhead associated with the sparse map. This compact representation of landmarks (and other road features) can make full use of sensors and processors configured to detect, identify, and / or classify specific road features loaded on such vehicles.

[0210] When, for example, a sign or even a particular type of sign is locally unique in a given area (e.g., when there are no other signs and no other signs of the same type), the sparse map can use data indicating a class of landmarks (the sign or the particular type of sign), and during navigation (e.g., autonomous navigation), when a camera mounted on an autonomous vehicle captures an image of an area that includes the sign (or the particular type of sign), the processor can process the image, detect the sign (if it is indeed present in the image), classify the image as the sign (or the particular type of sign), and associate the location of the image with the location of the sign as stored in the sparse map.

[0211] The sparse map can include any suitable representation of objects identified along a road segment. In some cases, the objects can be referred to as semantic objects or non-semantic objects. Semantic objects can include, for example, objects associated with a pre-determined type classification. This type classification can help reduce the amount of data required to describe the semantic objects identified in the environment, which is beneficial for both the acquisition phase (e.g., to reduce the cost associated with the bandwidth usage for transferring driving information from multiple acquisition vehicles to a server) and during the navigation phase (e.g., reducing the map data can speed up the transfer of map tiles from the server to the vehicle in navigation and can also reduce the cost associated with the bandwidth usage for such transfer). The semantic object classification types can be assigned to any type of object or feature expected to be encountered along a roadway.

[0212] Semantic objects can be further divided into two or more logical groupings. For example, in some cases, a grouping of semantic object types can be associated with a predefined dimension. Such semantic objects can include specific speed limit signs, yield signs, merge signs, stop signs, traffic lights, direction arrows on the roadway, manhole covers, or any other type of object that may be associated with a standardized size. One benefit provided by such semantic objects is that it may require little data to represent / fully define the object. For example, if the standardized size of the speed limit size is known, the collection vehicle may only need to identify (through analysis of the captured image) the presence of the speed limit sign (of the identified type) and an indication of the location of the detected speed limit sign (e.g., the 2D location in the captured image of the center of the sign or a specific corner of the sign (or alternatively, the 3D location in real-world coordinates)) to provide sufficient information for map generation on the server side. When transmitting the 2D image location to the server, the location of the detected sign associated with the captured image can also be transmitted so that the server can determine the real-world location of the sign (e.g., using structure from motion techniques with multiple captured images from one or more collection vehicles). Even though this information is limited (only a few bytes may be needed to define each detected object), the server can build a map including fully represented speed limit signs based on the type classification (indicating a speed limit sign) received from one or more collection vehicles and the location information of the detected signs.

[0213] Semantic objects can also include other identified object or feature types that are not associated with specific standardized characteristics. Such objects or features can include potholes, asphalt seams, lamp posts, non-standardized signs, curbs, trees, branches, or any other type of identified object type having one or more variable characteristics (e.g., variable size). In such cases, in addition to transmitting to the server an indication of the detected object or feature type (e.g., pothole, pole, etc.) and the location information of the detected object or feature, the collection vehicle can also transmit an indication of the size of the object or feature. The size can be expressed in 2D image dimensions (e.g., having a bounding box or one or more size values) or real-world dimensions (determined through structure from motion calculations, based on lidar or radar system outputs, based on the output of a trained neural network, etc.).

[0214] Non-semantic objects or features can include any detectable object or feature that falls outside of the identified class or type but can still provide valuable information in map generation. In some cases, such non-semantic features can include detected corners of buildings or corners of detected windows of buildings, unique stones or objects near roadways, concrete splashes in road shoulders, or any other detectable object or feature. After detecting such an object or feature, one or more collection vehicles can transmit the location of one or more points (2D image points or 3D real-world points) associated with the detected object / feature to a map generation server. Additionally, a compressed or simplified image segment (e.g., an image hash) can be generated for the region of the captured image that includes the detected object or feature. This image hash can be calculated based on a pre-determined image processing algorithm and can form an effective feature for the detected non-semantic object or feature. Such features can be useful for navigation relative to a sparse map that includes non-semantic features or objects, as a vehicle traversing the roadway can apply an algorithm similar to the one used to generate the image hash in order to confirm / verify the presence of the mapped non-semantic feature or object in the captured image. Using this technique, non-semantic features can increase the richness of a sparse map (e.g., to enhance their usefulness in navigation) without adding significant data overhead.

[0215] As noted, target trajectories can be stored in the sparse map. These target trajectories (e.g., 3D splines) can represent the preferred or recommended paths for each available lane of a roadway, each valid approach through an intersection, for lane changes and exits, etc. In addition to target trajectories, other road features can be detected, collected, and incorporated into the sparse map in the form of representative splines. Such features can include, for example, road edges, lane markings, curbs, guardrails, or any other object or feature that extends along a roadway or road segment.

[0216] Generating Sparse Map

[0217] In some embodiments, the sparse map can include at least one line representation of road surface features that extend along a road segment and a plurality of landmarks associated with the road segment. In certain aspects, the sparse map can be generated via "crowdsourcing", e.g., by image analysis of a plurality of images acquired as one or more vehicles traverse the road segment.

[0218] Figure 8Illustrated is a sparse map 800 that one or more vehicles (e.g., vehicle 200 which may be an autonomous vehicle) can access to provide autonomous vehicle navigation. The sparse map 800 can be stored in a memory, such as memory 140 or 150. Such memory devices can include any type of non-transitory storage device or computer-readable medium. For example, in some embodiments, memory 140 or 150 can include a hard disk drive, an optical disk, flash memory, a magnetic-based memory device, an optical-based memory device, etc. In some embodiments, the sparse map 800 can be stored in a database (e.g., map database 160), which can be stored in memory 140 or 150 or other types of storage devices.

[0219] In some embodiments, the sparse map 800 can be stored on a storage device or non-transitory computer-readable medium configured to be loaded on the vehicle 200 (e.g., a storage device included in a navigation system loaded on the vehicle 200). A processor (e.g., processing unit 110) disposed on the vehicle 200 can access the sparse map 800 stored in the storage device or computer-readable medium configured to be loaded on the vehicle 200 to generate navigation instructions for guiding the autonomous vehicle 200 when the vehicle traverses a road segment.

[0220] However, the sparse map 800 does not need to be stored locally relative to the vehicle. In some embodiments, the sparse map 800 can be stored on a storage device or computer-readable medium disposed on a remote server in communication with the vehicle 200 or a device associated with the vehicle 200. A processor (e.g., processing unit 110) disposed on the vehicle 200 can receive data included in the sparse map 800 from the remote server and can execute the data for guiding the autonomous driving of the vehicle 200. In such embodiments, the remote server can store all or only a portion of the sparse map 800. Accordingly, a storage device or computer-readable medium configured to be loaded on the vehicle 200 and / or one or more additional vehicles can store the remaining portion of the sparse map 800.

[0221] In addition, in such embodiments, the sparse map 800 may be made accessible by multiple vehicles (e.g., dozens, hundreds, thousands, or millions of vehicles, etc.) traversing each road segment. It should also be noted that the sparse map 800 may include multiple sub-maps. For example, in some embodiments, the sparse map 800 may include hundreds, thousands, millions, or more sub-maps (e.g., map tiles) that can be used for vehicle navigation. Such sub-maps may be referred to as local maps or map tiles, and a vehicle traveling along a roadway may access any number of local maps related to the location where the vehicle is traveling. The local map segments of the sparse map 800 may be stored together with a Global Navigation Satellite System (GNSS) key that serves as an index to the database of the sparse map 800. Thus, while the calculation of the steering angle for navigating the host vehicle in this system may be performed without relying on the GNSS position, road features, or landmarks of the host vehicle, such GNSS information may be used to retrieve the relevant local maps.

[0222] Generally, the sparse map 800 may be generated based on data (e.g., driving information) collected from one or more vehicles as they travel along a roadway. For example, using sensors (e.g., cameras, speedometers, GPS, accelerometers, etc.) on one or more vehicles, the trajectories of the one or more vehicles traveling along the roadway may be recorded, and a polynomial representation of the preferred trajectory for the vehicle to make subsequent trips along the roadway may be determined based on the collected trajectories of the vehicle. Similarly, data collected by one or more vehicles may help identify potential landmarks along a particular roadway. Data collected from traversing vehicles may also be used to identify road profile curve information, such as road width profile curves, road roughness profile curves, traffic line spacing profile curves, road conditions, etc. Using the collected information, the sparse map 800 may be generated and distributed (e.g., for local storage or via real-time data transmission) for use in navigating one or more autonomous vehicles. However, in some embodiments, map generation may not end at the initial generation of the map. As will be discussed in more detail below, the sparse map 800 may be continuously or periodically updated based on data collected from the vehicles as they continue to traverse the roadways included in the sparse map 800.

[0223] The data recorded in the sparse map 800 can include location information based on Global Positioning System (GPS) data. For example, the sparse map 800 can include location information for various map elements (including, for example, landmark locations, road contour locations, etc.). The locations of the map elements included in the sparse map 800 can be obtained using GPS data collected from vehicles traversing roadways. For example, a vehicle passing by an identified landmark can use the GPS location information associated with the vehicle and the determination of the location of the identified landmark relative to the vehicle (e.g., based on image analysis of data collected from one or more cameras on the vehicle) to determine the location of the identified landmark. Such location determinations for the identified landmark (or any other feature included in the sparse map 800) can be repeated when additional vehicles pass by the location of the identified landmark. Some or all of the additional location determinations can be used to refine the location information about the identified landmark stored in the sparse map 800. For example, in some embodiments, multiple location measurements regarding a particular feature stored in the sparse map 800 can be averaged together. However, any other mathematical operation can also be used to refine the stored location of a map element based on multiple determined locations for the map element.

[0224] In a specific example, collection vehicles can traverse a particular road segment. Each collection vehicle captures images of its respective environment. The images can be collected at any suitable frame capture rate (e.g., 9Hz, etc.). An image analysis processor on each collection vehicle analyzes the captured images to detect the presence of semantic and / or non-semantic features / objects. At a high level, the collection vehicle transmits an indication of the detection of semantic and / or non-semantic objects / features and the locations associated with these objects / features to a mapping server. More specifically, a type indicator, a size indicator, etc. can be transmitted together with the location information. The location information can include any suitable information for enabling the mapping server to aggregate the detected objects / features into a sparse map that can be used for navigation. In some cases, the location information can include one or more 2D image locations (e.g., X-Y pixel locations) in the captured image where a semantic or non-semantic feature / object is detected. Such image locations can correspond to the center, corners, etc. of the feature / object. In this scenario, to assist the mapping server in reconstructing driving information and aligning driving information from multiple collection vehicles, each collection vehicle can also provide the server with the location (e.g., GPS location) where each image was captured.

[0225] In other cases, the collection vehicle can provide the server with one or more 3D real-world points associated with the detected object / feature. Such 3D points can be relative to a pre-determined origin (such as the origin of the driving segment) and can be determined by any suitable technique. In some cases, structure-from-motion techniques can be used to determine the 3D real-world position of the detected object / feature. For example, a specific object such as a specific speed limit sign can be detected in two or more captured images. Using information of the collection vehicle between the captured images such as known ego-motion (speed, trajectory, GPS position, etc.) and the observed changes in the speed limit sign in the captured images (changes in X-Y pixel position, size change, etc.), the real-world position of one or more points associated with the speed limit sign can be determined and transmitted to the mapping server. Such methods are optional because they require more computation for parts of the collection vehicle system. The sparse map of the disclosed embodiments can use a relatively small amount of stored data to achieve autonomous navigation of the vehicle. In some embodiments, the sparse map 800 can have the following data densities (e.g., including data representing target trajectories, landmarks, and any other stored road features): less than 2MB per kilometer of road, less than 1MB per kilometer of road, less than 500kB per kilometer of road, or less than 100kB per kilometer of road. In some embodiments, the data density of the sparse map 800 can be less than 10kB per kilometer of road or even less than 2kB per kilometer of road (e.g., 1.6kB per kilometer), or not exceeding 10kB per kilometer of road, or not exceeding 20kB per kilometer of road. In some embodiments, most (if not all) roadways in the United States can use a sparse map with a total of 4GB or less of data for autonomous navigation. These data density values can represent the average of the entire sparse map 800, a local map within the sparse map 800, and / or a specific road segment within the sparse map 800.

[0226] As noted, the sparse map 800 can include a representation of multiple target trajectories 810 for guiding autonomous driving or navigation along a road segment. Such target trajectories can be stored as three-dimensional splines. The target trajectories stored in the sparse map 800 can be determined based on, for example, two or more reconstructed trajectories of a vehicle along a particular road segment. A road segment can be associated with a single target trajectory or multiple target trajectories. For example, on a two-lane road, a first target trajectory can be stored to represent an expected travel path along the road in a first direction, and a second target trajectory can be stored to represent an expected travel path along the road in another direction (e.g., opposite the first direction). Additional target trajectories can be stored for a particular road segment. For example, on a multi-lane road, one or more target trajectories can be stored to represent the expected travel path of a vehicle in one or more lanes associated with the multi-lane road. In some embodiments, each lane of a multi-lane road can be associated with its own target trajectory. In other embodiments, there can be fewer stored target trajectories than there are lanes on the multi-lane road. In such cases, a vehicle traveling on the multi-lane road can use any stored target trajectory to guide the vehicle's navigation by considering the lane offset from the lane in which its target trajectory is stored (e.g., if a vehicle is traveling in the leftmost lane of a three-lane highway and a target trajectory is stored only for the middle lane of the highway, then when generating navigation instructions, the vehicle can use the target trajectory of the middle lane to navigate by accounting for the lane offset between the middle lane and the leftmost lane).

[0227] In some embodiments, the target trajectory can represent the ideal path that the vehicle should take as it travels. The target trajectory can be located, for example, at or near the approximate center of the travel lane. In other cases, the target trajectory can be located at other positions relative to the road segment. For example, the target trajectory can approximately coincide with the center of the road, the edge of the road, or the edge of the lane, etc. In such cases, navigation based on the target trajectory can include a determined offset from the position to be maintained relative to the target trajectory. Additionally, in some embodiments, the determined offset from the position to be maintained relative to the target trajectory can vary based on the vehicle type (e.g., a passenger vehicle having two axles can have a different offset than a truck having more than two axles along at least a portion of the target trajectory).

[0228] The sparse map 800 can also include data related to multiple predefined landmarks 820 associated with a particular road segment, local map, etc. As discussed in more detail below, these landmarks can be used in the navigation of an autonomous vehicle. For example, in some embodiments, a landmark can be used to determine the current position of the vehicle relative to the stored target trajectory. Using this position information, the autonomous vehicle can be able to adjust its heading direction to match the direction of the target trajectory at the determined position.

[0229] Multiple landmarks 820 can be recognized and stored in the sparse map 800 at any suitable spacing. In some embodiments, the landmarks can be stored at a relatively high density (e.g., every few meters or more). However, in some embodiments, significantly larger landmark spacing values can be employed. For example, in the sparse map 800, the recognized (or identified) landmarks can be spaced 10 meters, 20 meters, 50 meters, 100 meters, 1 kilometer, or 2 kilometers apart. In some cases, the recognized landmarks can be located at distances even exceeding 2 kilometers apart.

[0230] Between the landmarks, and thus between the determinations of the vehicle's position relative to the target trajectory, the vehicle can navigate based on dead reckoning, in which the vehicle uses sensors to determine its self-motion and estimate its position relative to the target trajectory. Since errors can accumulate during navigation by dead reckoning, over time, the determination of the position relative to the target trajectory can become increasingly inaccurate. The vehicle can use the landmarks (and their known positions) that appear in the sparse map 800 to remove the errors caused by dead reckoning in the position determination. In this way, the recognized landmarks included in the sparse map 800 can be used as navigation anchor points from which the accurate position of the vehicle relative to the target trajectory can be determined. Since a certain amount of error in position localization can be acceptable, the recognized landmarks do not need to always be available to the autonomous vehicle. Instead, even based on landmark spacings such as 10 meters, 20 meters, 50 meters, 100 meters, 500 meters, 1 kilometer, 2 kilometers, or greater as noted above, suitable navigation can be possible. In some embodiments, a density of 1 recognized landmark per 1 km of road can be sufficient to maintain the longitudinal position determination accuracy within 1 m. Thus, not every potential landmark that appears along a road segment needs to be stored in the sparse map 800.

[0231] In addition, in some embodiments, lane markings can be used to position the vehicle during landmark spacing. By using lane markings during landmark spacing, the error accumulation during navigation by dead reckoning can be minimized.

[0232] In addition to the target trajectory and the recognized landmarks, the sparse map 800 can also include information related to various other road features. For example, Figure 9A shows a representation of a curve along a specific road segment that can be stored in the sparse map 800. In some embodiments, a single lane of a road can be modeled by a three-dimensional polynomial description of the left and right sides of the road. Figure 9A Such polynomial traces representing the left and right sides of a single lane are shown in. Regardless of how many lanes a road may have, the road can be represented in a manner similar to Figure 9Ais represented using polynomial traces in a manner similar to that shown. For example, the left and right sides of a multi-lane road can be represented by polynomial traces similar to the polynomial traces shown in Figure 9A and the center lane markings included on the multi-lane road (e.g., short line markings indicating lane boundaries, solid yellow lines indicating the boundary between lanes traveling in different directions, etc.) can also be represented using polynomial traces such as Figure 9A shown in.

[0233] As Figure 9A shown, lane 900 can be represented using a polynomial trace (e.g., a first-order, second-order, third-order, or any suitable-order polynomial trace). For example, lane 900 is shown as a two-dimensional lane and the polynomial trace is shown as a two-dimensional polynomial trace. As Figure 9A depicted, lane 900 includes a left side 910 and a right side 920. In some embodiments, more than one polynomial trace can be used to represent the position of each side of a road or lane boundary. For example, each of the left side 910 and the right side 920 can be represented by a plurality of polynomial traces of any suitable length. In some cases, the polynomial traces can have a length of approximately 100 m, but other lengths greater than or less than 100 m can also be used. Additionally, the polynomial traces can overlap each other so as to facilitate a seamless transition during navigation based on the polynomial traces subsequently encountered as the host vehicle travels along the roadway. For example, each of the left side 910 and the right side 920 can be represented by a plurality of third-order polynomial traces (an example of a first predetermined range) separated into segments of length approximately 100 meters and overlapping each other by approximately 50 meters. The polynomial traces representing the left side 910 and the right side 920 can or can not have the same order. For example, in some embodiments, some polynomial traces can be second-order polynomial traces, some can be third-order polynomial traces, and some can be fourth-order polynomial traces.

[0234] In Figure 9A the example shown, the left side 910 of lane 900 is represented by two grouped third-order polynomial traces. The first group includes polynomial trace segments 911, 912, and 913. The second group includes polynomial trace segments 914, 915, and 916. Although the two groups are substantially parallel to each other, they follow the position of the corresponding side of the road. The polynomial trace segments 911, 912, 913, 914, 915, and 916 have a length of approximately 100 meters and overlap with adjacent segments in the series by approximately 50 meters. However, as previously noted, polynomial traces of different lengths and different amounts of overlap can also be used. For example, the polynomial traces can have a length of 500 m, 1 km, or longer, and the amount of overlap can vary from 0 to 50 m, 50 m to 100 m, or greater than 100 m. Additionally, although Figure 9Aare shown as representing polynomial traces extending in a 2D space (e.g., on the surface of a piece of paper), but it should be understood that these polynomial traces can represent curves extending in three dimensions (e.g., including a height component) to represent elevation changes of a road segment in addition to the X-Y curvature. In Figure 9A the example shown, the right side 920 of lane 900 is further represented by a first grouping having polynomial trace segments 921, 922, and 923 and a second grouping having polynomial trace segments 924, 925, and 926.

[0235] Returning to the target trajectory of the sparse map 800, Figure 9B a three-dimensional polynomial trace representing the target trajectory of a vehicle traveling along a particular road segment is shown. The target trajectory represents not only the X-Y path that the host vehicle should travel along a particular road segment, but also the elevation change that the host vehicle will experience when traveling along the particular road segment. Thus, each target trajectory in the sparse map 800 can be represented by one or more three-dimensional polynomial traces, such as Figure 9B the three-dimensional polynomial trace 950 shown. The sparse map 800 can include multiple trajectories (e.g., millions or billions or more, to represent the trajectories of vehicles along various road segments (along the carriageway) around the world). In some embodiments, each target trajectory can correspond to a spline connecting three-dimensional polynomial trace segments.

[0236] Regarding the data footprint of the polynomial curves stored in the sparse map 800, in some embodiments, each cubic polynomial trace can be represented by four parameters, each of which requires four bytes of data. A suitable representation can be obtained using cubic polynomial traces that require approximately 192 bytes of data per 100 m. For a host vehicle traveling at approximately 100 km / h, this may mean approximately 200 kB per hour in data usage / transfer requirements.

[0237] The sparse map 800 can use a combination of geometric descriptors and metadata to describe the lane network. The geometry can be described by polynomial traces or splines as described above. The metadata can describe the number of lanes, special features (such as carpool lanes), and possibly other sparse markings. The total footprint of such indicators can be negligible.

[0238] Thus, a sparse map according to an embodiment of the present disclosure may include at least one line representation of a road surface feature extending along a road segment, each line representation representing a path along the road segment that substantially corresponds to the road surface feature. In some embodiments, as discussed above, the at least one line representation of the road surface feature may include a spline, a polynomial representation, or a curve. Additionally, in some embodiments, the road surface feature may include at least one of a road edge or a lane marking. Further, as discussed below with respect to "crowdsourcing", the road surface feature may be identified by image analysis of a plurality of images acquired when one or more vehicles traverse the road segment.

[0239] As previously noted, the sparse map 800 may include a plurality of predetermined landmarks associated with the road segment. Each landmark in the sparse map 800 may be represented and identified using less data than would be required to store the actual image of the landmark, rather than storing the actual image of the landmark and relying on, for example, image recognition analysis based on the captured image and the crystallized stored image. The data representing the landmark may still include sufficient information for describing or identifying the landmark along the road. Storing data describing the characteristics of the landmark, rather than the actual image of the landmark, may reduce the size of the sparse map 800.

[0240] Figure 10 Examples of landmark types that may be represented in the sparse map 800 are shown. A landmark may include any visible and identifiable object along the road segment. Landmarks may be selected such that they are fixed and do not change frequently with respect to their location and / or content. The landmarks included in the sparse map 800 may be useful for determining the position of the vehicle 200 relative to a target trajectory when the vehicle traverses a particular road segment. Examples of landmarks may include traffic signs, directional signs, general signs (e.g., rectangular signs), roadside fixtures (e.g., lamp posts, reflectors, etc.), and any other suitable categories. In some embodiments, lane markings on the road may also be included as landmarks in the sparse map 800.

[0241] Figure 10Examples of landmarks shown in FIG. include traffic signs, direction signs, roadside fixtures, and general signs. Traffic signs can include, for example, speed limit signs (e.g., speed limit sign 1000), yield signs (e.g., yield sign 1005), route number signs (e.g., route number sign 1010), traffic light signs (e.g., traffic light sign 1015), stop signs (e.g., stop sign 1020). Direction signs can include signs that include one or more arrows indicating one or more directions to different locations. For example, direction signs can include highway sign 1025 having arrows for guiding a vehicle to different roads or locations, exit sign 1030 having an arrow for guiding a vehicle off the road, etc. Thus, at least one of the plurality of landmarks can include a road sign.

[0242] General signs can be unrelated to traffic. For example, general signs can include billboards for advertising, or welcome signs adjacent to the boundary between two countries, states, counties, cities, or towns. Figure 10 General sign 1040 (“Joe's Restaurant”) is shown. Although general sign 1040 can have a rectangular shape, as Figure 10 shown, general sign 1040 can have other shapes, such as square, circular, triangular, etc.

[0243] Landmarks can also include roadside fixtures. Roadside fixtures can be objects that are not signs and can be unrelated to traffic or direction. For example, roadside fixtures can include lamp posts (e.g., lamp post 1035), power line posts, traffic light posts, etc.

[0244] Landmarks can also include beacons that can be specifically designed for use in an autonomous vehicle navigation system. For example, such beacons can include free-standing structures placed at predetermined intervals to assist in navigating the host vehicle. Such beacons can also include visual / graphic information (e.g., icons, symbols, barcodes, etc.) added to existing road signs that can be recognized or identified by a vehicle traveling along a road segment. Such beacons can also include electronic components. In such embodiments, electronic beacons (e.g., RFID tags, etc.) can be used to transmit non-visual information to the host vehicle. Such information can include, for example, landmark identification and / or landmark location information that the host vehicle can use to determine its position along a target trajectory.

[0245] In some embodiments, landmarks included in the sparse map 800 may be represented by data objects of a predetermined size. The data representing a landmark may include any suitable parameters for identifying a particular landmark. For example, in some embodiments, landmarks stored in the sparse map 800 may include parameters such as the physical size of the landmark (e.g., to support estimating the distance to the landmark based on a known size / scale), the distance to the previous landmark, the lateral offset, the height, a type code (e.g., landmark type - what type of directional sign, traffic sign, etc.), GPS coordinates (e.g., to support global positioning), and any other suitable parameters. Each parameter may be associated with a data size. For example, the landmark size may be stored using 8 bytes of data. The distance to the previous landmark, the lateral offset, and the height may be specified using 12 bytes of data. The type code associated with a landmark such as a directional sign or traffic sign may require approximately 2 bytes of data. For a general sign, the image features that implement the recognition of the general sign may be stored using 50 bytes of data storage. The landmark GPS position may be associated with 16 bytes of data storage. These data sizes for each parameter are only examples, and other data sizes may also be used. Representing landmarks in the sparse map 800 in this way may provide a streamlined scheme for efficiently representing landmarks in the database. In some embodiments, an object may be referred to as a standard semantic object or a non-standard semantic object. A standard semantic object may include any class of objects having a set of characteristics that are standardized (e.g., speed limit signs, warning signs, directional signs, traffic lights, etc. having known dimensions or other characteristics). A non-standard semantic object may include any object not associated with a standardized set of characteristics (e.g., general advertising signs that may have variable dimensions, signs identifying business premises, potholes, trees, etc.). Each non-standard semantic object may be represented using 38 bytes of data (e.g., 8 bytes for size; 12 bytes for the distance to the previous landmark, the lateral offset, and the height; and 2 bytes for the type code; and 16 bytes for the position coordinates). A standard semantic object may be represented using even less data, as the mapping server may not require size information to fully represent the object in the sparse map.

[0246] The sparse map 800 can use a tagging system to represent landmark types. In some cases, each traffic sign or directional sign can be associated with its own tag, which can be stored in a database as part of the landmark identification. For example, the database can include approximately 1000 different tags to represent various traffic signs and approximately 10,000 different tags to represent directional signs. Of course, any suitable number of tags can be used, and additional tags can be created as needed. In some embodiments, a general sign can be represented using less than about 100 bytes (e.g., about 86 bytes, including: 8 bytes for size; 12 bytes for distance from the previous landmark, lateral offset, and height; 50 bytes for image features; and 16 bytes for GPS coordinates).

[0247] Thus, for semantic road signs that do not require image features, even at a relatively high landmark density of about 1 per 50 m, the data density impact on the sparse map 800 may be approximately 760 bytes per kilometer (e.g., 20 landmarks per km × 38 bytes per landmark = 760 bytes). Even for general signs that include image feature components, the data density impact is about 1.72 kB per km (e.g., 20 landmarks per km × 86 bytes per landmark = 1,720 bytes). For semantic road signs, this corresponds to a data usage of about 76 kB per hour for a vehicle traveling at 100 km / h. For general signs, this corresponds to a data usage of about 170 kB per hour for a vehicle traveling at 100 km / h. It should be noted that in some environments (e.g., urban environments), there may be a much higher density of detected objects available for inclusion in the sparse map (possibly more than one per meter). In some embodiments, a generally rectangular object, such as a rectangular sign, can be represented in the sparse map 800 by no more than 100 bytes of data. The representation of a generally rectangular object (e.g., general sign 1040) in the sparse map 800 can include a compressed image feature or image hash associated with the generally rectangular object (e.g., compressed image feature 1045). This compressed image feature / image hash can be determined using any suitable image hashing algorithm and can be used, for example, to assist in identifying a general sign, such as an identified landmark. Such compressed image features (e.g., image information derived from the actual image data representing the object) can obviate the need to store the actual image of the object or perform comparative image analysis of the actual image for landmark identification.

[0248] Reference Figure 10, the sparse map 800 may include or store compressed image features 1045 associated with the general sign 1040, rather than the actual image of the general sign 1040. For example, after an image capture device (e.g., image capture devices 122, 124, or 126) captures an image of the general sign 1040, a processor (e.g., image processor 190 or any other processor that can process images located on or remotely relative to the host vehicle) may perform image analysis to extract / create compressed image features 1045 that include unique features or patterns associated with the general sign 1040. In one embodiment, the compressed image features 1045 may include shapes, color patterns, brightness patterns, or any other features that can be extracted from an image of the general sign 1040 to describe the general sign 1040.

[0249] For example, in Figure 10 , the circles, triangles, and stars shown in the compressed image features 1045 may represent regions of different colors. The patterns represented by the circles, triangles, and stars may be stored in the sparse map 800, e.g., within 50 bytes designated to include image features. It is noted that the circles, triangles, and stars are not necessarily intended to indicate that such shapes are stored as part of the image features. Instead, these shapes are intended to conceptually represent distinguishable regions with discernible color differences, text regions, graphic shapes, or other variations of characteristics that may be associated with the general sign. Such compressed image features can be used to identify landmarks in the form of general signs. For example, the compressed image features can be used to perform a same - different analysis based on a comparison of the stored compressed image features with image data captured using, e.g., a camera mounted on an autonomous vehicle.

[0250] Thus, multiple landmarks can be identified through image analysis of multiple images obtained when one or more vehicles traverse a road segment. As described below regarding "crowdsourcing", in some embodiments, the image analysis used to identify multiple landmarks may include accepting potential landmarks when the ratio of images in which a landmark appears to images in which it does not appear exceeds a threshold. Additionally, in some embodiments, the image analysis used to identify multiple landmarks may include rejecting potential landmarks when the ratio of images in which a landmark does not appear to images in which it appears exceeds a threshold.

[0251] Returning to the target trajectory that the host vehicle can use to navigate a particular road segment, Figure 11AShows polynomial representation trajectories captured during the construction or maintenance of the sparse map 800. The polynomial representation of the target trajectory included in the sparse map 800 can be determined based on two or more reconstructed trajectories of the vehicle along the same road segment during previous traversals. In some embodiments, the polynomial representation of the target trajectory included in the sparse map 800 can be an aggregation of two or more reconstructed trajectories of the vehicle along the same road segment during previous traversals. In some embodiments, the polynomial representation of the target trajectory included in the sparse map 800 can be an average of two or more reconstructed trajectories of the vehicle along the same road segment during previous traversals. Other mathematical operations can also be used to construct the target trajectory along the road path based on the reconstructed trajectories collected from vehicles traversing the road segment.

[0252] As Figure 11A shown, the road segment 1100 can be traversed by multiple vehicles 200 at different times. Each vehicle 200 can collect data related to the path taken by the vehicle along the road segment. The path traveled by a particular vehicle can be determined based on potential sources such as camera data, accelerometer information, speed sensor information, and / or GPS information. Such data can be used to reconstruct the trajectories of the vehicles traveling along the road segment, and based on these reconstructed trajectories, the target trajectory (or target trajectories) can be determined for a particular road segment. Such target trajectories can represent the preferred path of the vehicle when the host vehicle (e.g., guided by an autonomous navigation system) travels along the road segment.

[0253] In Figure 11A the example shown, the first reconstructed trajectory 1101 can be determined based on data received from a first vehicle that traversed the road segment 1100 during a first time period (e.g., day 1), the second reconstructed trajectory 1102 can be obtained from a second vehicle that traversed the road segment 1100 during a second time period (e.g., day 2), and the third reconstructed trajectory 1103 can be obtained from a third vehicle that traversed the road segment 1100 during a third time period (e.g., day 3). Each of the trajectories 1101, 1102, and 1103 can be represented by a polynomial trace, such as a three-dimensional polynomial trace. It should be noted that in some embodiments, any of the reconstructed trajectories can be assembled and loaded on a vehicle traversing the road segment 1100.

[0254] Additionally or alternatively, such reconstructed trajectories may be determined on the server side based on information received from vehicles traversing road segment 1100. For example, in some embodiments, vehicles 200 may transmit data related to their movement along road segment 1100 (e.g., steering angle, heading, time, position, speed, sensed road geometry, and / or sensed landmarks, etc.) to one or more servers. The server may reconstruct the trajectories of vehicles 200 based on the received data. The server may also generate target trajectories for guiding the navigation of autonomous vehicles that will travel along the same road segment 1100 at a later time, based on first trajectory 1101, second trajectory 1102, and third trajectory 1103. Although a target trajectory may be associated with a single previous traversal of a road segment, in some embodiments, each target trajectory included in sparse map 800 may be determined based on two or more reconstructed trajectories of vehicles traversing the same road segment. In Figure 11A , the target trajectory is represented by 1110. In some embodiments, target trajectory 1110 may be generated based on the average of first trajectory 1101, second trajectory 1102, and third trajectory 1103. In some embodiments, target trajectory 1110 included in sparse map 800 may be an aggregation (e.g., weighted combination) of two or more reconstructed trajectories.

[0255] At a mapping server, the server may receive actual trajectories for a particular road segment from multiple acquisition vehicles traversing the road segment. To generate target trajectories for each valid path along the road segment (e.g., each lane, each driving direction, each path through an intersection, etc.), the received actual trajectories may be aligned. The alignment process may include using detected objects / features identified along the road segment and the acquired positions of these detected objects / features to relate the actual, acquired trajectories to each other. Once aligned, an average or "best fit" target trajectory for each available lane may be determined based on the aggregated, related / aligned actual trajectories, etc.

[0256] Figure 11B and 11C further illustrates the concept of a target trajectory associated with a road segment existing within geographical region 1111. As Figure 11BAs shown, the first road segment 1120 within the geographical area 1111 may include a multi-lane road, which includes two lanes 1122 designated for vehicle travel in a first direction and two additional lanes 1124 designated for vehicle travel in a second direction opposite to the first direction. The lanes 1122 and the lanes 1124 may be separated by a double yellow line 1123. The geographical area 1111 may also include a branch road segment 1130 that intersects the road segment 1120. The road segment 1130 may include a two-lane road, with each lane designated for a different travel direction. The geographical area 1111 may also include other road features, such as a stop line 1132, a stop sign 1134, a speed limit sign 1136, and a hazard sign 1138.

[0257] As Figure 11C shown, the sparse map 800 may include a local map 1140, which includes a road model for assisting in the autonomous navigation of a vehicle within the geographical area 1111. For example, the local map 1140 may include target trajectories of one or more lanes associated with the road segments 1120 and / or 1130 within the geographical area 1111. For example, the local map 1140 may include target trajectories 1141 and / or 1142 that an autonomous vehicle may utilize or rely on when crossing the lane 1122. Similarly, the local map 1140 may include target trajectories 1143 and / or 1144 that an autonomous vehicle may utilize or rely on when crossing the lane 1124. In addition, the local map 1140 may include target trajectories 1145 and / or 1146 that an autonomous vehicle may utilize or rely on when crossing the road segment 1130. The target trajectory 1147 represents a preferred path that the autonomous vehicle should follow when transitioning from the lane 1120 (and specifically, with respect to the target trajectory 1141 associated with the rightmost lane in the lane 1120) to the road segment 1130 (and specifically, with respect to the target trajectory 1145 associated with the first side of the road segment 1130). Similarly, the target trajectory 1148 represents a preferred path that the autonomous vehicle should follow when transitioning from the road segment 1130 (and specifically, with respect to the target trajectory 1146) to a portion of the road segment 1124 (and specifically, as shown, with respect to the target trajectory 1143 associated with the left lane in the lane 1124).

[0258] The sparse map 800 may also include a representation of other road-related features associated with the geographical area 1111. For example, the sparse map 800 may also include a representation of one or more landmarks identified in the geographical area 1111. Such landmarks may include a first landmark 1150 associated with a stop line 1132, a second landmark 1152 associated with a stop sign 1134, a third landmark 1154 associated with a speed limit sign, and a fourth landmark 1156 associated with a hazard sign 1138. Such landmarks can be used, for example, to assist an autonomous vehicle in determining its current position relative to any shown target trajectory, such that the vehicle can adjust its heading to match the direction of the target trajectory at the determined position.

[0259] In some embodiments, the sparse map 800 may also include road feature profile curves. Such road feature profile curves may be associated with any distinguishable / measurable change in at least one parameter associated with the road. For example, in some cases, such profile curves may be associated with changes in road surface information, such as changes in the surface roughness of a particular road segment, changes in the road width on a particular road segment, changes in the distance between short dashed lines drawn along a particular road segment, changes in the road curvature along a particular road segment, etc. Figure 11D An example of a road feature profile curve 1160 is shown. Although the profile curve 1160 may represent any one of the parameters mentioned above or other parameters, in one example, the profile curve 1160 may represent a measure of road surface roughness, as obtained, for example, by monitoring one or more sensors that provide an output indicative of the amount of suspension displacement of a vehicle as it travels along a particular road segment.

[0260] Alternatively or concurrently, the profile curve 1160 may represent a change in road width, as determined based on image data obtained via a camera mounted on a vehicle traveling along a particular road segment. Such profile curves can be useful, for example, in determining the specific position of an autonomous vehicle relative to a particular target trajectory. That is, when an autonomous vehicle traverses a road segment, it can measure the profile curve associated with one or more parameters associated with the road segment. If the measured profile curve can be correlated / matched with a pre-determined profile curve that depicts the parameter change relative to the position along the road segment, the measured and pre-determined profile curves can be used (e.g., by overlaying corresponding sections of the measured and pre-determined profile curves) to determine the current position along the road segment and, thus, the current position relative to the target trajectory for the road segment.

[0261] In some embodiments, based on different characteristics associated with a user of an autonomous vehicle, environmental conditions, and / or other driving-related parameters, the sparse map 800 can include different trajectories. For example, in some embodiments, different trajectories can be generated based on different user preferences and / or profiles. The sparse map 800 including such different trajectories can be provided to different autonomous vehicles of different users. For example, some users may prefer to avoid toll roads, while other users may prefer to take the shortest or fastest route regardless of the presence of toll roads on the route. The disclosed system can generate different sparse maps with different trajectories based on such different user preferences or profiles. As another example, some users may prefer to drive in the fast-moving lanes, while other users may prefer to always maintain a position in the center lane.

[0262] Based on different environmental conditions, such as day and night, snow, rain, fog, etc., different trajectories can be generated and included in the sparse map 800. An autonomous vehicle driving in different environmental conditions can be provided with the sparse map 800 generated based on such different environmental conditions. In some embodiments, a camera disposed on the autonomous vehicle can detect the environmental conditions and can provide such information back to the server that generates and provides the sparse map. For example, the server can generate or update the already generated sparse map 800 to include trajectories that may be more suitable or safer for autonomous driving under the detected environmental conditions. The update of the sparse map 800 based on environmental conditions can be performed dynamically as the autonomous vehicle travels along the road.

[0263] Other different driving-related parameters can also be used as a basis for generating different sparse maps and providing them to different autonomous vehicles. For example, when an autonomous vehicle is traveling at a high speed, turns may be sharper. Trajectories associated with a specific lane rather than the road can be included in the sparse map 800 such that the autonomous vehicle can stay within a specific lane as the vehicle follows a specific trajectory. When an image captured by a camera mounted on the autonomous vehicle indicates that the vehicle has drifted out of the lane (e.g., crossed the lane markings), an action can be triggered inside the vehicle to make the vehicle return to the designated lane according to a specific trajectory.

[0264] Crowdsourcing Sparse Map

[0265] The disclosed sparse maps can be efficiently (and passively) generated by the power of crowdsourcing. For example, any private or commercial vehicle equipped with a camera (e.g., a simple low-resolution camera typically included as OEM equipment on today's vehicles) and a suitable image analysis processor can be used as a collection vehicle. No special equipment (e.g., high-definition imaging and / or positioning systems) is required. Due to the disclosed crowdsourcing technology, the generated sparse maps can be very accurate and can include extremely refined location information (achieving a navigation error limit of 10 cm or less) as input to the map generation process without any specialized imaging or sensing equipment. Crowdsourcing also enables much faster (and cheaper) updates to the generated maps, as new driving information is continuously available to the mapping server system for any road section traversed by a private or commercial vehicle minimally equipped to also serve as a collection vehicle. There is no need for a designated vehicle equipped with high-definition imaging and mapping sensors. Thus, the costs associated with constructing such specialized vehicles can be avoided. Additionally, updates to the currently disclosed sparse maps can be made much faster than systems that rely on dedicated, specialized mapping vehicles (which are typically limited to a group of specialized vehicles due to their cost and special equipment, and the number of such a group of specialized vehicles is far lower than the number of private or commercial vehicles already available for the disclosed collection techniques).

[0266] The disclosed sparse maps generated by crowdsourcing can be very accurate because they can be generated based on many inputs from multiple (tens, hundreds, millions, etc.) collection vehicles that have collected driving information along a particular road segment. For example, each collection vehicle driving along a particular road segment can record its actual trajectory and can determine location information relative to detected objects / features along the road segment. This information is transmitted from the multiple collection vehicles to the server. The actual trajectories are aggregated to generate a refined target trajectory for each valid driving path along the road segment. Additionally, the location information collected from multiple collection vehicles for each detected object / feature (semantic or non-semantic) along the road segment can also be aggregated. Thus, the mapped location of each detected object / feature can constitute an average of hundreds, thousands, or millions of individually determined locations for each detected object / feature. Such techniques can produce extremely precise mapped locations for detected objects / features.

[0267] In some embodiments, the disclosed systems and methods can generate a sparse map for autonomous vehicle navigation. For example, the disclosed systems and methods can use crowdsourced data to generate a sparse map that one or more autonomous vehicles can use to navigate along a road system. As used herein, "crowdsourcing" means receiving data from various vehicles (e.g., autonomous vehicles) traveling on road segments at different times, and such data is used to generate and / or update a road model, including sparse map tiles. The model or any of its sparse map tiles can then be transmitted to that vehicle or other vehicles traveling along the road segment at a later time to assist autonomous vehicle navigation. The road model can include a plurality of target trajectories representing preferred trajectories that autonomous vehicles should follow as they cross a road segment. The target trajectories can be the same as the reconstructed actual trajectories collected from vehicles crossing the road segment, which can be transmitted from the vehicle to the server. In some embodiments, the target trajectories can be different from the actual trajectories taken by one or more vehicles previously when crossing the road segment. The target trajectories can be generated based on the actual trajectories (e.g., by averaging or any other suitable operation).

[0268] The vehicle trajectory data that a vehicle can upload to the server can correspond to the vehicle's actual reconstructed trajectory or can correspond to a recommended trajectory, which can be based on or related to the vehicle's actual reconstructed trajectory but can be different from the actual reconstructed trajectory. For example, a vehicle can modify its actual, reconstructed trajectory and submit (e.g., recommend) the modified actual trajectory to the server. The road model can use the recommended, modified trajectory as a target trajectory for the autonomous navigation of other vehicles.

[0269] In addition to trajectory information, other information for potential use in constructing the sparse data map 800 can include information related to potential landmark candidates. For example, through the crowdsourcing of information, the disclosed systems and methods can identify potential landmarks in the environment and refine the landmark locations. The landmarks can be used by the navigation system of an autonomous vehicle to determine and / or adjust the vehicle's position along a target trajectory.

[0270] The reconstructed trajectory that a vehicle can generate as the vehicle travels along a road can be obtained by any suitable method. In some embodiments, the reconstructed trajectory can be developed by stitching together segments of the vehicle's motion (e.g., the three-dimensional translation and three-dimensional rotation of the camera and thus the vehicle body) using, for example, ego-motion estimation. The rotation and translation estimation can be determined based on an analysis of images captured by one or more image capture devices and information from other sensors or devices such as inertial sensors and speed sensors. For example, the inertial sensors can include accelerometers or other suitable sensors configured to measure changes in the translation and / or rotation of the vehicle body. The vehicle can include a speed sensor that measures the speed of the vehicle.

[0271] In some embodiments, the ego-motion of the camera (and thus the vehicle body) can be estimated based on an analysis of the optical flow of the captured images. The analysis of the optical flow of a series of images identifies the movement of pixels from the series of images and determines the motion of the vehicle based on the identified movement. The ego-motion can be integrated over time and along a road segment to reconstruct the trajectory associated with the road segment that the vehicle has traversed.

[0272] Data (e.g., reconstructed trajectories) collected by multiple vehicles during multiple drives along a road segment at different times can be used to construct a road model (e.g., including target trajectories, etc.) included in a sparse data map 800. The data collected by multiple vehicles during multiple drives along a road segment at different times can also be averaged to improve the accuracy of the model. In some embodiments, data regarding road geometry and / or landmarks can be received from multiple vehicles traveling through a common road segment at different times. Such data received from different vehicles can be combined to generate and / or update a road model.

[0273] The geometry of the reconstructed trajectory (and target trajectory) along a road segment can be represented by a curve in three-dimensional space, which can be a spline connecting three-dimensional polynomial traces. The reconstructed trajectory curve can be determined based on an analysis of a video stream or multiple images captured by a camera mounted on the vehicle. In some embodiments, a position a few meters in front of the current position of the vehicle is identified in each frame or image. This position is the position that the vehicle is expected to travel to within a predetermined time period. This operation can be repeated frame by frame, and at the same time, the vehicle can calculate the ego-motion (rotation and translation) of the camera. In each frame or image, a short-range model of the desired path is generated by the vehicle in a reference frame attached to the camera. The short-range models can be stitched together to obtain a three-dimensional model of the road in a certain coordinate frame, which can be an arbitrary or predetermined coordinate frame. The three-dimensional model of the road can then be fitted by a spline, which can include or connect one or more polynomial traces of a suitable order.

[0274] To achieve a short-range road model for each frame, one or more detection modules can be used. For example, a bottom-up lane detection module can be used. The bottom-up lane detection module may be useful when lane markings are painted on the road. This module can look for edges in the image and assemble them together to form lane markings. A second module can be used in conjunction with the bottom-up lane detection module. The second module is an end-to-end deep neural network, and the end-to-end deep neural network can be trained to predict the correct short-range path based on the input image. In both modules, the road model can be detected in the image coordinate frame and transformed into a three-dimensional space that can be virtually attached to the camera.

[0275] Although the reconstructed trajectory modeling method may introduce error accumulation due to the integration of self-motion over a long period of time (which may include noise components), such errors may be insignificant because the generated model can provide sufficient accuracy for navigation at a local scale. In addition, the integration error can be eliminated by using external information sources such as satellite images or geodetic results. For example, the disclosed systems and methods can use a GNSS receiver to eliminate the accumulated error. However, GNSS positioning signals may not always be available and accurate. The disclosed systems and methods can implement steering applications that are weakly dependent on the availability and accuracy of GNSS positioning. In such systems, the use of GNSS signals may be restricted. For example, in some embodiments, the disclosed system can use the GNSS signal only for database indexing purposes.

[0276] In some embodiments, the range scale (e.g., local scale) associated with the autonomous vehicle navigation steering application can be about 50 meters, 100 meters, 200 meters, 300 meters, etc. Such distances can be used because the geometric road model is mainly used for two purposes: planning the trajectory ahead and positioning the vehicle on the road model. In some embodiments, when the control algorithm maneuvers the vehicle based on a target point located 1.3 seconds (or any other time, such as 1.5 seconds, 1.7 seconds, 2 seconds, etc.) ahead, the planning task can use the model within a typical range of 40 meters ahead (or any other suitable distance ahead, such as 20 meters, 30 meters, 50 meters). According to a method called "tail alignment" described in more detail in another section, the positioning task uses the road model within a typical range of 60 meters behind the sedan (or any other suitable distance, such as 50 meters, 100 meters, 150 meters, etc.). The disclosed systems and methods can generate a geometric model with sufficient accuracy within a specific range (such as 100 meters) such that the planned trajectory does not deviate from the center of the lane by more than, for example, 30 cm.

[0277] As described above, a three-dimensional road model can be constructed by detecting short segments and stitching them together. The stitching can be achieved by calculating a six-degree-of-freedom ego-motion model using video and / or images captured by a camera, data reflecting the motion of the vehicle from an inertial sensor, and a host vehicle set signal. Within a certain local range scale (such as approximately 100 meters), the accumulated error may be small enough. All of this can be done in a single drive within a particular road segment.

[0278] In some embodiments, multiple drives can be used to average the resulting model and further improve its accuracy. The same car can travel the same route multiple times, or multiple cars can send the model data they collect to a central server. In any case, a matching procedure can be performed to identify overlapping models and achieve averaging in order to generate a target trajectory. Once the convergence criteria are met, the constructed model (e.g., including the target trajectory) can be used for maneuvers. Subsequent drives can be used for further model improvement and adaptation to infrastructure changes.

[0279] If multiple cars are connected to a central server, it becomes feasible to share the driving experience (e.g., sensed data) among them. Each vehicle client can store a partial copy of the common road model that can be related to the current location of the vehicle client. A two-way update procedure between the vehicle and the server can be performed by the vehicle and the server. The small footprint concept discussed above enables the disclosed systems and methods to use very little bandwidth for two-way updates.

[0280] Information related to potential landmarks can also be determined and forwarded to the central server. For example, the disclosed systems and methods can determine one or more physical properties of a landmark based on one or more images including the potential landmark. The physical properties can include the physical size of the landmark (e.g., height, width), the distance from the vehicle to the landmark, the distance between the landmark and the previous landmark, the lateral position of the landmark (e.g., the position of the landmark relative to the travel lane), the GPS coordinates of the landmark, the landmark type, the text identification on the landmark, etc. For example, a vehicle can analyze one or more images captured by a camera to detect potential landmarks such as speed limit signs.

[0281] A vehicle can determine the distance from the vehicle to a landmark or a location associated with the landmark (e.g., any semantic or non-semantic object or feature along a road segment) based on the analysis of one or more images. In some embodiments, the distance can be determined based on the analysis of an image of the landmark using suitable image analysis methods, such as a scale method and / or an optical flow method. As previously noted, the location of the object / feature can include the 2D image location of one or more points associated with the object / feature (e.g., the X-Y pixel location in one or more captured images), or can include the 3D real-world location of one or more points (e.g., determined by structure-from-motion / optical flow techniques, lidar or radar system information, etc.). In some embodiments, the disclosed systems and methods can be configured to determine the type or classification of potential landmarks. In the case where a vehicle determines that a certain potential landmark corresponds to a pre-determined type or classification stored in the sparse map, this can be sufficient for the vehicle to transmit an indication of the type or classification of the landmark and the location of the landmark to the server. The server can store such indications. At a later time, during navigation, a vehicle in navigation can capture an image including a representation of the landmark, process the image (e.g., using a classifier), and compare the resulting landmark in order to confirm the detection of the mapped landmark and use the mapped landmark to localize the vehicle in navigation relative to the sparse map.

[0282] In some embodiments, multiple autonomous vehicles traveling on a road segment can communicate with a server. A vehicle (or client) can generate a curve describing its drive (e.g., by integrating self-motion) in any coordinate frame. The vehicle can detect landmarks and localize them in the same frame. The vehicle can upload the curve and the landmarks to the server. The server can collect data from the vehicles over multiple drives and generate a unified road model. As discussed below with respect to Figure 19 what is discussed, the server can use the uploaded curves and landmarks to generate a sparse map with a unified road model.

[0283] The server can also distribute the model to clients (e.g., vehicles). For example, the server can distribute the sparse map to one or more vehicles. The server can continuously or periodically update the model when new data is received from the vehicles. For example, the server can process the new data to evaluate whether the data includes information that should trigger an update or creation of new data on the server. The server can distribute the updated model or the update to the vehicles to provide autonomous vehicle navigation.

[0284] The server can use one or more criteria to determine whether new data received from a vehicle should trigger an update to the model or the creation of new data. For example, when the new data indicates that a previously identified landmark at a specific location no longer exists or has been replaced by another landmark, the server can determine that the new data should trigger an update to the model. As another example, when the new data indicates that a road segment has been closed and this has been confirmed by data received from other vehicles, the server can determine that the new data should trigger an update to the model.

[0285] The server can distribute the updated model (or the updated part of the model) to one or more vehicles traveling on the road segment associated with the update to the model. The server can also distribute the updated model to vehicles that are about to travel on the road segment or whose planned itinerary includes the road segment associated with the update to the model. For example, when an autonomous vehicle is traveling along another road segment before reaching the road segment associated with the update, the server can distribute the update or the updated model to the autonomous vehicle before it reaches the road segment.

[0286] In some embodiments, a remote server can collect trajectories and landmarks from multiple clients (e.g., vehicles traveling along a common road segment). The server can use the landmarks to match curves and create an average road model based on the trajectories collected from multiple vehicles. The server can also calculate a road map and the most likely paths for each node or junction of the road segment. For example, the remote server can align the trajectories to generate a crowdsourced sparse map from the collected trajectories.

[0287] The server can average the landmark properties received from multiple vehicles traveling along a common road segment (such as the distance between one landmark and another landmark (e.g., the previous landmark along the road segment) measured by multiple vehicles) to determine the arc length parameter and support positioning along the path and speed calibration for each client vehicle. The server can average the physical dimensions of the landmark measured by multiple vehicles traveling along a common road segment and identifying the same landmark. The averaged physical dimensions can be used to support distance estimation, such as the distance from the vehicle to the landmark. The server can average the lateral position of the landmark (e.g., the position from the lane in which the vehicle is traveling to the landmark) measured by multiple vehicles traveling along a common road segment and identifying the same landmark. The averaged lateral agent can be used to support lane assignment. The server can average the GPS coordinates of the landmark measured by multiple vehicles traveling along the same road segment and identifying the same landmark. The averaged GPS landmark of the landmark can be used to support the global localization or positioning of the landmark in the road model.

[0288] In some embodiments, the server may identify model changes based on data received from the vehicle, such as construction, detours, new signs, removal of signs, etc. The server may update the model continuously or periodically or immediately when new data is received from the vehicle. The server may distribute the updates to the model or the updated model to the vehicle to provide autonomous navigation. For example, as further discussed below, the server may use crowdsourced data to filter out "phantom" landmarks detected by the vehicle.

[0289] In some embodiments, the server may analyze driver interventions during autonomous driving. The server may analyze data received from the vehicle at the time and location of the intervention and / or data received prior to the time of the intervention. The server may identify specific portions of the data that caused or are closely related to the intervention, such as data indicating a temporary lane closure setting, data indicating a pedestrian in the road. The server may update the model based on the identified data. For example, the server may modify one or more trajectories stored in the model.

[0290] Figure 12 Schematic illustration of a system for using crowdsourcing to generate a sparse map (and for distributing and navigating using a crowdsourced sparse map). Figure 12 A road segment 1200 including one or more lanes is shown. Multiple vehicles 1205, 1210, 1215, 1220, and 1225 may travel on the road segment 1200 at the same time or at different times (although shown as being on the road segment 1200 at the same time in Figure 12 ). At least one of the vehicles 1205, 1210, 1215, 1220, and 1225 may be an autonomous vehicle. For the simplicity of this example, it is assumed that all vehicles 1205, 1210, 1215, 1220, and 1225 are autonomous vehicles.

[0291] Each vehicle may be similar to the vehicles disclosed in other embodiments (e.g., vehicle 200), and may include components or devices that are included in or associated with the vehicles disclosed in other embodiments. Each vehicle may be equipped with an image capture device or camera (e.g., image capture device 122 or camera 122). Each vehicle may communicate with a remote server 1230 via one or more networks (e.g., via a cellular network and / or the Internet, etc.) over a wireless communication path 1235 indicated by short dashed lines. Each vehicle may transmit data to and receive data from server 1230. For example, server 1230 may collect data from multiple vehicles traveling on road segment 1200 at different times, and may process the collected data to generate an autonomous vehicle road navigation model or an update to the model. Server 1230 may transmit the autonomous vehicle road navigation model or an update to the model to the vehicles that transmitted data to server 1230. Server 1230 may transmit the autonomous vehicle road navigation model or an update to the model to other vehicles that the model is applicable to traveling on road segment 1200 at a later time.

[0292] When vehicles 1205, 1210, 1215, 1220, and 1225 are traveling on road segment 1200, the navigation information collected (e.g., detected, sensed, or measured) by vehicles 1205, 1210, 1215, 1220, and 1225 may be transmitted to server 1230. In some embodiments, the navigation information may be associated with the common road segment 1200. The navigation information may include the trajectories associated with each vehicle as each vehicle 1205, 1210, 1215, 1220, and 1225 travels on road segment 1200. In some embodiments, the trajectories may be reconstructed based on data sensed by various sensors and devices disposed on vehicle 1205. For example, the trajectories may be reconstructed based on at least one of the following: accelerometer data, speed data, landmark data, road geometry or profile data, vehicle positioning data, and ego-motion data. In some embodiments, the trajectories may be reconstructed based on data from inertial sensors (such as accelerometers) and the speed sensed by a speed sensor of vehicle 1205. Additionally, in some embodiments, the trajectories may be determined (e.g., by a processor loaded in each of vehicles 1205, 1210, 1215, 1220, and 1225) based on the sensed ego-motion of the camera, which may indicate three-dimensional translation and / or three-dimensional rotation (or rotational motion). The ego-motion of the camera (and thus the vehicle body) may be determined by the analysis of one or more images captured by the camera.

[0293] In some embodiments, the trajectory of vehicle 1205 can be determined by a processor configured to be loaded on vehicle 1205 and transmitted to server 1230. In other embodiments, server 1230 can receive data sensed by various sensors and devices disposed in vehicle 1205 and determine the trajectory based on the data received from vehicle 1205.

[0294] In some embodiments, the navigation information transmitted from vehicles 1205, 1210, 1215, 1220, and 1225 to server 1230 can include data regarding the road surface, road geometry, or road profile. The geometry of road segment 1200 can include lane structure and / or landmarks. The lane structure can include the total number of lanes of road segment 1200, lane types (e.g., one-way lanes, two-way lanes, driving lanes, passing lanes, etc.), lane markings, the width of the lanes, etc. In some embodiments, the navigation information can include lane assignment, e.g., in which lane of a plurality of lanes a vehicle is traveling. For example, the lane assignment can be associated with the numerical value "3", which indicates that the vehicle is traveling in the third lane from the left or right. As another example, the lane assignment can be associated with the text value "center lane", which indicates that the vehicle is traveling in the center lane.

[0295] Server 1230 can store the navigation information on a non-transitory computer-readable medium, such as a hard drive, optical disc, magnetic tape, memory, etc. Server 1230 can (e.g., via a processor included in server 1230) generate at least a portion of an autonomous vehicle road navigation model for common road segment 1200 based on the navigation information received from multiple vehicles 1205, 1210, 1215, 1220, and 1225 and can store the model as part of a sparse map. Server 1230 can determine the trajectories associated with each lane based on crowdsourced data (e.g., navigation information) received from multiple vehicles (e.g., 1205, 1210, 1215, 1220, and 1225) traveling in the lanes of the road segment at different times. Server 1230 can generate an autonomous vehicle road navigation model or a portion (e.g., an updated portion) of the model based on the multiple trajectories determined based on the crowdsourced navigation data. Server 1230 can transmit the model or the updated portion of the model to one or more of autonomous vehicles 1205, 1210, 1215, 1220, and 1225 traveling in road segment 1200 or any other autonomous vehicle traveling in the road segment at a later time to update an existing autonomous vehicle road navigation model set in the navigation system of the vehicle. The autonomous vehicle road navigation model can be used by an autonomous vehicle during autonomous navigation along common road segment 1200.

[0296] As described above, an autonomous vehicle road navigation model can be included in a sparse map (e.g., Figure 8 the sparse map 800 depicted in). The sparse map 800 can include sparse records of data related to road geometry and / or landmarks along the road, which can provide sufficient information to guide the autonomous navigation of an autonomous vehicle without excessive data storage. In some embodiments, the autonomous vehicle road navigation model can be stored separately from the sparse map 800 and can use map data from the sparse map 800 when the model is executed for navigation. In some embodiments, the autonomous vehicle road navigation model can use map data included in the sparse map 800 to determine a target trajectory along a road segment 1200 to guide the autonomous navigation of autonomous vehicles 1205, 1210, 1215, 1220, and 1225 or other vehicles traveling along the road segment 1200 at a later time. For example, when the autonomous vehicle road navigation model is executed by a processor included in the navigation system of vehicle 1205, the model can cause the processor to compare trajectories (determined based on navigation information received from vehicle 1205 having a pre-determined trajectory included in the sparse map 800) to verify and / or correct the current travel route of vehicle 1205.

[0297] In an autonomous vehicle road navigation model, the geometry of a road feature or target trajectory can be encoded by a curve in three-dimensional space. In one embodiment, the curve can be a three-dimensional spline that includes one or more connected three-dimensional polynomial traces. As will be understood by those skilled in the art, a spline can be a numerical function defined piecewise by a series of polynomial traces used to fit data. The spline used to fit the three-dimensional geometric data of a road can include a linear spline (first order), a quadratic spline (second order), a cubic spline (third order), or any other spline (other order), or a combination thereof. The spline can include one or more three-dimensional polynomial traces of different orders that connect (e.g., fit) data points of the three-dimensional geometric data of the road. In some embodiments, the autonomous vehicle road navigation model can include a three-dimensional spline corresponding to a target trajectory along a common road segment (e.g., road segment 1200) or a lane of road segment 1200.

[0298] As described above, an autonomous vehicle road navigation model included in a sparse map may include other information, such as an identification of at least one landmark along a road segment 1200. The landmark may be visible within the field of view of a camera (e.g., camera 122) mounted on each of vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, camera 122 may capture an image of the landmark. A processor (e.g., processor 180, 190, or processing unit 110) disposed on vehicle 1205 may process the image of the landmark to extract identification information for the landmark. The landmark identification information rather than the actual image of the landmark may be stored in sparse map 800. The landmark identification information may require much less storage space compared to the actual image. Other sensors or systems (e.g., a GPS system) may also provide specific identification information (e.g., the location of the landmark) of the landmark. The landmark may include at least one of the following: a traffic sign, an arrow marking, a lane marking, a short-line lane marking, a traffic light, a stop line, a direction sign (e.g., a highway exit sign with an arrow indicating a direction, a highway sign with arrows pointing to different directions or locations), a landmark beacon, or a lamp post. A landmark beacon refers to a device (e.g., an RFID device) installed along a road segment that transmits or reflects a signal to a receiver mounted on a vehicle such that when the vehicle passes by the device, the beacon received by the vehicle and the location of the device (e.g., determined based on the GPS location of the device) may be used as a landmark to be included in the autonomous vehicle road navigation model and / or sparse map 800.

[0299] The identification of at least one landmark may include the location of the at least one landmark. The location of the landmark may be determined based on position measurements made using sensor systems (e.g., a global positioning system, an inertia-based positioning system, a landmark beacon, etc.) associated with multiple vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, the location of the landmark may be determined by averaging position measurement results detected, collected, or received by sensor systems on different vehicles 1205, 1210, 1215, 1220, and 1225 during multiple drives. For example, vehicles 1205, 1210, 1215, 1220, and 1225 may transmit position measurement data to server 1230, which may average the position measurement results and use the averaged position measurement results as the location of the position landmark. The location of the landmark may be continuously refined by measurement results received from vehicles during subsequent drives.

[0300] The identification of a landmark may include the size of the landmark. A processor disposed on a vehicle (e.g., 1205) may estimate the physical size of a landmark based on the analysis of an image. The server 1230 may receive multiple estimation results of the physical size of the same landmark from different vehicles over multiple drives. The server 1230 may average the different estimation results to obtain the physical size of the landmark and store the landmark size in the road model. The physical size estimation results may be used to further determine or estimate the distance from the vehicle to the landmark. The distance to the landmark may be estimated based on the current speed of the vehicle and the scaling ratio (which is based on the position of the landmark appearing in the image relative to the extended focus of the camera). For example, the distance to the landmark may be estimated by Z = V * dt * R / D, where V is the vehicle speed, R is the distance from the landmark at time t1 to the extended focus in the image, and D is the change in the distance of the landmark in the image from t1 to t2. dt represents (t2 - t1). For example, the distance to the landmark may be estimated by Z = V * dt * R / D, where V is the vehicle speed, R is the distance between the landmark and the extended focus in the image, dt is the time interval, and D is the image displacement of the landmark along the epipolar line. Other equations equivalent to the above equation, such as Z = V * ω / Δω, may be used to estimate the distance to the landmark. Here, V is the vehicle speed, ω is the image length (such as the object width), and Δω is the change in the image length per unit time.

[0301] When the physical size of the landmark is known, the distance to the landmark may also be determined based on the following equation: Z = f * W / ω, where f is the focal length, W is the size of the landmark (e.g., height or width), and ω is the number of pixels when the landmark leaves the image. According to the above equation, the change in the distance Z may be calculated using ΔZ = f * W * Δω / ω 2 + f * ΔW / ω, where by averaging, ΔW decays to zero, and where Δω is the number of pixels representing the bounding box accuracy in the image. The value of the estimated physical size of the landmark may be calculated by averaging multiple observations on the server side. The resulting distance estimation error may be very small. Two error sources may occur when using the above formula, namely ΔW and Δω. Their contributions to the distance error are given by ΔZ = f * W * Δω / ω 2 + f * ΔW / ω. However, by averaging, ΔW decays to zero; thus, ΔZ is determined by Δω (e.g., the inaccuracy of the bounding box in the image).

[0302] For a landmark of unknown size, the distance to the landmark can be estimated by tracking feature points on the landmark between successive frames. For example, specific features that appear on a speed limit sign can be tracked between two or more image frames. Based on these tracked features, a distance distribution for each feature point can be generated. A distance estimate result can be extracted from the distance distribution. For example, the most frequent distance that appears in the distance distribution can be used as the distance estimate result. As another example, the average value of the distance distribution can be used as the distance estimate result.

[0303] Figure 13 An example autonomous vehicle road navigation model represented by a plurality of three-dimensional splines 1301, 1302, and 1303 is shown. Figure 13 The curves 1301, 1302, and 1303 shown therein are for illustrative purposes only. Each spline can include one or more three-dimensional polynomial traces connecting a plurality of data points 1310. Each polynomial trace can be a first-order polynomial trace, a second-order polynomial trace, a third-order polynomial trace, or any suitable combination of polynomial traces of different orders. Each data point 1310 can be associated with navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, each data point 1310 can be associated with data related to: landmarks (e.g., the size, location, and identification information of the landmark) and / or road feature profile curves (e.g., road geometry, road roughness profile curve, road curvature profile curve, road width profile curve). In some embodiments, some data points 1310 can be associated with data related to landmarks, and other data points can be associated with data related to road feature profile curves.

[0304] Figure 14Shows the raw location data 1410 (e.g., GPS data) received from five separate drives. A drive can be separated from another drive if it is traversed by a separate vehicle at the same time, by the same vehicle at different times, or by separate vehicles at separate times. To account for errors in the location data 1410 and for the different positions of vehicles within the same lane (e.g., one vehicle may drive closer to the left side of the lane than another vehicle), the server 1230 can use the following to generate the map skeleton 1420: one or more statistical techniques for determining whether changes in the raw location data 1410 represent actual differences or statistical errors. Each path within the skeleton 1420 can be linked back to the raw data 1410 that formed that path. For example, the path between A and B within the skeleton 1420 is linked to the raw data 1410 from drives 2, 3, 4, and 5 but not from drive 1. The skeleton 1420 may not be detailed enough to be used for vehicle navigation (e.g., because, unlike the splines described above, it combines drives from multiple lanes on the same road), but can provide useful topological information and can be used to define intersections.

[0305] Figure 15 Shows an example by which additional details can be generated for a sparse map within a segment of the map skeleton (e.g., segment A to B within the skeleton 1420). As Figure 15 described, data (e.g., ego-motion data, road marking data, etc.) can be shown as a function of position S (or S1 or S2) along the drive. The server 1230 can generate landmarks for the sparse map by identifying unique matches between the landmarks 1501, 1503, and 1505 of drive 1510 and the landmarks 1507 and 1509 of drive 1520. Such matching algorithms can result in the identification of landmarks 1511, 1513, and 1515. However, those skilled in the art will recognize that other matching algorithms can be used. For example, probabilistic optimization can be used instead of or in combination with the unique match. The server 1230 can longitudinally align the drives to align the matched landmarks. For example, the server 1230 can select one drive (e.g., drive 1520) as a reference drive and then offset and / or elastically stretch the other drives (e.g., drive 1510) for alignment.

[0306] Figure 16 Shows an example of aligned landmark data for a sparse map. In the Figure 16 example, the landmark 1610 includes a road sign. Figure 16 The example of also depicts data from multiple drives 1601, 1603, 1605, 1607, 1609, 1611, and 1613. In the Figure 16In the example of [[ID=]], the data from drive 1613 consists of "phantom" landmarks, and server 1230 can identify it as such because none of drives 1601, 1603, 1605, 1607, 1609, and 1611 include an identification of a landmark near the identified landmark in drive 1613. Thus, server 1230 can accept a potential landmark when the ratio of images in which a landmark appears to images in which a landmark does not appear exceeds a threshold and / or can reject a potential landmark when the ratio of images in which a landmark does not appear to images in which a landmark appears exceeds a threshold.

[0307] Figure 17 FIG. depicts a system 1700 for generating driving data, which can be used for crowdsourcing a sparse map. As Figure 17 depicted, system 1700 can include a camera 1701 and a positioning device 1703 (e.g., a GPS locator). Camera 1701 and positioning device 1703 can be mounted on a vehicle (e.g., one of vehicles 1205, 1210, 1215, 1220, and 1225). Camera 1701 can generate multiple types of multiple data, e.g., ego-motion data, traffic sign data, road data, etc. The camera data and the location data can be segmented into driving segments 1705. For example, driving segments 1705 can each have camera data and location data from a drive of less than 1 km.

[0308] In some embodiments, system 1700 can remove redundancy in driving segments 1705. For example, if a landmark appears in multiple images from camera 1701, system 1700 can strip out the redundant data such that driving segment 1705 contains only one copy of the location of the landmark and any metadata associated with that landmark. As a further example, if lane markings appear in multiple images from camera 1701, system 1700 can strip out the redundant data such that driving segment 1705 contains only one copy of the location of the lane markings and the metadata associated with that lane marking.

[0309] System 1700 also includes a server (e.g., server 1230). Server 1230 can receive driving segments 1705 from the vehicle and recombine the driving segments 1705 into a single drive 1707. Such an arrangement can allow for reduced bandwidth requirements when transferring data between the vehicle and the server, while also allowing the server to store data related to an entire drive.

[0310] Figure 18 FIG. depicts Figure 17 a system 1700 that is further configured for crowdsourcing a sparse map. As in Figure 17In [reference], system 1700 includes vehicle 1810, which uses, for example, cameras (which generate, for example, ego - motion data, traffic - sign data, road data, etc.) and positioning devices (e.g., GPS locators) to capture driving data. As in Figure 17 [reference], vehicle 1810 segments the collected data into driving segments (depicted as "DS1 1", "DS2 1", "DSN 1" in Figure 18 [reference]). Server 1230 then receives the driving segments and reconstructs the driving from the received segments (depicted as "Driving 1" in Figure 18 [reference]).

[0311] As Figure 18 further depicted, system 1700 also receives data from additional vehicles. For example, vehicle 1820 also uses, for example, cameras (which generate, for example, ego - motion data, traffic - sign data, road data, etc.) and positioning devices (e.g., GPS locators). Similar to vehicle 1810, vehicle 1820 segments the collected data into driving segments (depicted as "DS1 2", "DS2 2", "DSN 2" in Figure 18 [reference]). Then server 1230 receives the driving segments and reconstructs the driving from the received segments (depicted as "Driving 2" in Figure 18 [reference]). Any number of additional vehicles can be used. Figure 18 Also included is "Sedan N", which captures driving data, segments the data into driving segments (depicted as "DS1 N", "DS2 N", "DSN N" in Figure 18 [reference]), and sends them to server 1230 to be reconstructed as driving (depicted as "Driving N" in Figure 18 [reference]).

[0312] As Figure 18 depicted, server 1230 can use the reconstructed drivings (e.g., "Driving 1", "Driving 2", and "Driving N") collected from multiple vehicles (e.g., "Sedan 1" (also labeled as vehicle 1810), "Sedan 2" (also labeled as vehicle 1820), and "Sedan N") to construct a sparse map (depicted as "Map").

[0313] Figure 19 FIG. [reference] is a flowchart showing an example process 1900 for generating a sparse map for autonomous vehicle navigation along a road segment. Process 1900 can be performed by one or more processing devices included in server 1230.

[0314] Process 1900 may include receiving a plurality of images acquired when one or more vehicles cross a road segment (step 1905). Server 1230 may receive images from cameras included within one or more of vehicles 1205, 1210, 1215, 1220, and 1225. For example, as vehicle 1205 travels along road segment 1200, camera 122 may capture one or more images of the environment around vehicle 1205. In some embodiments, server 1230 may also receive decimated image data that has had redundancy removed by a processor on vehicle 1205, as discussed above with respect to Figure 17 discussed.

[0315] Process 1900 may further include identifying at least one line representation of a road surface feature extending along the road segment based on the plurality of images (step 1910). Each line representation may represent a path along the road segment that generally corresponds to the road surface feature. For example, server 1230 may analyze the environmental images received from camera 122 to identify road edges or lane markings and determine a travel trajectory associated with the road edge or lane marking along road segment 1200. In some embodiments, the trajectory (or line representation) may include a spline, polynomial representation, or curve. Server 1230 may determine the travel trajectory of vehicle 1205 based on camera ego-motion (e.g., three-dimensional translation and / or three-dimensional rotational motion) received in step 1905.

[0316] Process 1900 may also include identifying a plurality of landmarks associated with the road segment based on the plurality of images (step 1910). For example, server 1230 may analyze the environmental images received from camera 122 to identify one or more landmarks, such as road signs along road segment 1200. Server 1230 may use the analysis of the plurality of images acquired when one or more vehicles cross the road segment to identify landmarks. To enable crowdsourcing, the analysis may include rules regarding accepting and rejecting potential landmarks associated with the road segment. For example, the analysis may include accepting a potential landmark when the ratio of images in which the landmark appears to images in which the landmark does not appear exceeds a threshold and / or rejecting a potential landmark when the ratio of images in which the landmark does not appear to images in which the landmark appears exceeds a threshold.

[0317] Process 1900 may include other operations or steps performed by server 1230. For example, the navigation information may include a target trajectory for a vehicle to travel along a road segment, and process 1900 may include aggregating, by server 1230, vehicle trajectories associated with multiple vehicles traveling on the road segment and determining the target trajectory based on the clustered vehicle trajectories, as discussed in further detail below. Clustering the vehicle trajectories may include, by server 1230, clustering multiple trajectories associated with vehicles traveling on the road segment into multiple clusters based on at least one of: the absolute heading of the vehicle or the lane assignment of the vehicle. Generating the target trajectory may include, by server 1230, averaging the clustered trajectories. By way of further example, process 1900 may include aligning the data received in step 1905. As described above, other processes or steps performed by server 1230 may also be included in process 1900.

[0318] The disclosed systems and methods may include other features. For example, the disclosed systems may use local coordinates instead of global coordinates. For autonomous driving, some systems may present data in world coordinates. For example, longitude coordinates and latitude coordinates on the Earth's surface may be used. To use a map for steering, the host vehicle may determine its position and orientation relative to the map. It may seem natural to use an on-vehicle GPS device to locate the vehicle on the map and find the rotation transformation (e.g., north, east, and down) between the body reference frame and the world reference frame. Once the body reference frame is aligned with the map reference frame, the desired route may be expressed in the body reference frame and a steering command may be calculated or generated.

[0319] The disclosed systems and methods may utilize a low-footprint model to implement autonomous vehicle navigation (e.g., steering control), which may be collected by the autonomous vehicle itself without the assistance of expensive surveying equipment. To support autonomous navigation (e.g., steering applications), the road model may include a sparse map that has the geometry of the road, its lane structure, and landmarks that may be used to determine the location or position of the vehicle along a trajectory included in the model. As discussed above, the generation of the sparse map may be performed by a remote server that communicates with vehicles traveling on the road and receives data from the vehicles. The data may include sensed data, trajectories reconstructed based on the sensed data, and / or recommended trajectories that may represent modified reconstructed trajectories. As discussed below, the server may transmit the model back to the vehicle or other vehicles subsequently traveling on the road to assist with autonomous navigation.

[0320] Figure 20Shows a block diagram of server 1230. Server 1230 may include a communication unit 2005, which may include both hardware components (e.g., communication control circuits, switches, and antennas) and software components (e.g., communication protocols, computer code). For example, communication unit 2005 may include at least one network interface. Server 1230 may communicate with vehicles 1205, 1210, 1215, 1220, and 1225 via communication unit 2005. For example, server 1230 may receive navigation information transmitted from vehicles 1205, 1210, 1215, 1220, and 1225 via communication unit 2005. Server 1230 may distribute an autonomous vehicle road navigation model to one or more autonomous vehicles via communication unit 2005.

[0321] Server 1230 may include at least one non-transitory storage medium 2010, such as a hard disk drive, optical disk, magnetic tape, etc. Storage device 1410 may be configured to store data, such as navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225 and / or an autonomous vehicle road navigation model generated by server 1230 based on the navigation information. Storage device 2010 may be configured to store any other information, such as a sparse map (e.g., sparse map 800 discussed above with respect to Figure 8 discussed sparse map 800).

[0322] As a supplement or alternative to storage device 2010, server 1230 may include a memory 2015. Memory 2015 may be similar to or different from memories 140 or 150. Memory 2015 may be a non-transitory memory, such as flash memory, random access memory, etc. Memory 2015 may be configured to store data, such as computer code or instructions executable by a processor (e.g., processor 2020), map data (e.g., data of sparse map 800), an autonomous vehicle road navigation model, and / or navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225.

[0323] Server 1230 may include at least one processing device 2020 configured to execute computer code or instructions stored in a memory 2015 to perform various functions. For example, the processing device 2020 may analyze navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225 and generate an autonomous vehicle road navigation model based on the analysis. The processing device 2020 may control the communication unit 1405 to distribute the autonomous vehicle road navigation model to one or more autonomous vehicles (e.g., one or more of vehicles 1205, 1210, 1215, 1220, and 1225 or any vehicle traveling on the road segment 1200 at a later time). The processing device 2020 may be similar to or different from the processors 180, 190, or the processing unit 110.

[0324] Figure 21 A block diagram of the memory 2015 is shown, which may store computer code or instructions for performing one or more operations for generating a road navigation model for use in autonomous vehicle navigation. As Figure 21 shown, the memory 2015 may store one or more modules for performing operations for processing vehicle navigation information. For example, the memory 2015 may include a model generation module 2105 and a model distribution module 2110. The processor 2020 may execute instructions stored in any of the modules 2105 and 2110 (included in the memory 2015).

[0325] The model generation module 2105 may store instructions that, when executed by the processor 2020, may generate at least a portion of an autonomous vehicle road navigation model for a shared road segment (e.g., road segment 1200) based on navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225. For example, in generating the autonomous vehicle road navigation model, the processor 2020 may cluster vehicle trajectories along the shared road segment 1200 into different clusters. The processor 2020 may determine a target trajectory along the shared road segment 1200 based on the clustered vehicle trajectories for each of the different clusters. Such operations may include finding the mean or average trajectory of the clustered vehicle trajectories in each cluster (e.g., by averaging data representing the clustered vehicle trajectories). In some embodiments, the target trajectory may be associated with a single lane of the shared road segment 1200.

[0326] A road model and / or a sparse map may store trajectories associated with road segments. These trajectories may be referred to as target trajectories, which are provided to an autonomous vehicle for autonomous navigation. The target trajectories may be received from multiple vehicles, or may be generated based on actual trajectories received from multiple vehicles or recommended trajectories (actual trajectories with some modifications). The target trajectories included in the road model or sparse map may be continuously updated (e.g., averaged) using new trajectories received from other vehicles.

[0327] A vehicle traveling on a road segment may collect data via various sensors. The data may include landmarks, road feature profile curves, vehicle motion (e.g., accelerometer data, speed data), vehicle position (e.g., GPS data), and may reconstruct the actual trajectory itself or transmit the data to a server, which reconstructs the actual trajectory for the vehicle. In some embodiments, the vehicle may transmit data related to the trajectory (e.g., a curve in any reference frame), landmark data, and lane assignments along the travel path to server 1230. Various vehicles traveling along the same road segment under multiple drives may have different trajectories. Server 1230 may identify the routes or trajectories associated with each lane from the trajectories received from the vehicles through a clustering process.

[0328] Figure 22 A process of clustering vehicle trajectories associated with vehicles 1205, 1210, 1215, 1220, and 1225 to determine target trajectories for a shared road segment (e.g., road segment 1200) is shown. The target trajectory or target trajectories determined by the clustering process may be included in an autonomous vehicle road navigation model or sparse map 800. In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 traveling along road segment 1200 may transmit multiple trajectories 2200 to server 1230. In some embodiments, server 1230 may generate trajectories based on landmark, road geometry, and vehicle motion information received from vehicles 1205, 1210, 1215, 1220, and 1225. To generate an autonomous vehicle road navigation model, server 1230 may cluster vehicle trajectories 1600 into multiple clusters 2205, 2210, 2215, 2220, 2225, and 2230, as Figure 22 shown.

[0329] Clustering can be performed using various criteria. In some embodiments, all of the drives in a cluster can be similar in terms of the absolute heading along road segment 1200. The absolute heading can be obtained from GPS signals received by vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, the absolute heading can be obtained using dead reckoning. As will be understood by those skilled in the art, dead reckoning can be used to determine the current position and thereby the heading of vehicles 1205, 1210, 1215, 1220, and 1225 by using previously determined positions, estimated speeds, etc. Trajectories clustered by absolute heading can be used to identify routes along the roadway.

[0330] In some embodiments, all of the drives in a cluster can be similar in terms of the lane assignment of the drives along road segment 1200 (e.g., in the same lane before and after an intersection). Trajectories clustered by lane assignment can be used to identify lanes along the roadway. In some embodiments, both criteria (e.g., absolute heading and lane assignment) can be used for clustering.

[0331] In each of clusters 2205, 2210, 2215, 2220, 2225, and 2230, the trajectories can be averaged to obtain a target trajectory associated with a particular cluster. For example, the trajectories from multiple drives associated with the same lane cluster can be averaged. The averaged trajectory can be the target trajectory associated with a particular lane. To average the trajectory clusters, server 1230 can select a reference frame of any trajectory C0. For all other trajectories (C1, …, Cn), server 1230 can find a rigid transformation that maps Ci to C0, where i = 1, 2, …, n, where n is a positive integer corresponding to the total number of trajectories included in the cluster. Server 1230 can calculate the mean curve or trajectory in the C0 reference frame.

[0332] In some embodiments, landmarks can define an arc length that matches between different drives, and this arc length can be used to align the trajectories with the lanes. In some embodiments, the lane markings before and after an intersection can be used to align the trajectories with the lanes.

[0333] To assemble lanes from the trajectories, server 1230 can select a reference frame of any lane. Server 1230 can map the partially overlapping lanes to the selected reference frame. Server 1230 can continue the mapping until all lanes are in the same reference frame. Lanes that are adjacent to each other can be aligned as if they were the same lane, and then they can be laterally offset.

[0334] Landmarks identified along a road segment can be mapped to a common reference frame first at the lane level and then at the intersection level. For example, the same landmark can be identified multiple times by multiple vehicles in multiple drives. The data received for the same landmark in different drives may vary slightly. Such data can be averaged and mapped to the same reference frame, such as the C0 reference frame. Additionally or alternatively, the variance of the data for the same landmark received in multiple drives can be calculated.

[0335] In some embodiments, each lane of road segment 120 can be associated with a target trajectory and a specific landmark. The target trajectory or multiple such target trajectories can be included in an autonomous vehicle road navigation model that can be used later by other autonomous vehicles traveling along the same road segment 1200. The landmarks identified by vehicles 1205, 1210, 1215, 1220, and 1225 as the vehicle travels along road segment 1200 can be recorded in association with the target trajectory. The data of the target trajectory and the landmarks can be continuously or periodically updated using new data received from other vehicles in subsequent drives.

[0336] To locate an autonomous vehicle, the disclosed systems and methods can use an extended Kalman filter. The position of the vehicle can be determined by integrating self-motion based on three-dimensional position data and / or three-dimensional orientation data, a prediction of a future position in front of the vehicle's current position. The vehicle's positioning can be corrected or adjusted by an image observation of a landmark. For example, when the vehicle detects a landmark within an image captured by a camera, the landmark can be compared to a known landmark stored in the road model or the sparse map 800. The known landmark can have a known position (e.g., GPS data) along the target trajectory stored in the road model and / or the sparse map 800. Based on the current speed and the image of the landmark, the distance from the vehicle to the landmark can be estimated. The position of the vehicle along the target trajectory can be adjusted based on the distance to the landmark and the known position of the landmark (stored in the road model or the sparse map 800). It can be assumed that the position / location data (e.g., the mean from multiple drives) of the landmark stored in the road model and / or the sparse map 800 is accurate.

[0337] In some embodiments, the disclosed system can form a closed-loop subsystem where the estimation of the vehicle's six-degree-of-freedom position (e.g., three-dimensional position data plus three-dimensional orientation data) can be used to navigate the autonomous vehicle (e.g., turn its steering wheel) to reach a desired point (e.g., 1.3 seconds in advance in storage). Subsequently, the data from the steering and the actual navigation measurements can be used to estimate the six-degree-of-freedom position.

[0338] In some embodiments, poles along a road, such as lamp posts and power poles or cable poles, can be used as landmarks for positioning a vehicle. Other landmarks, such as traffic signs, traffic lights, arrows on the road, stop lines, and static features or characteristics of objects along a road segment, can also be used as landmarks for positioning a vehicle. When using a pole for positioning, the x observation of the pole (i.e., the perspective from the vehicle) can be used instead of the y observation (i.e., the distance to the pole), because the bottom of the pole may be occluded and sometimes they are not in the road plane.

[0339] Figure 23 A navigation system for a vehicle is shown, which can be used for autonomous navigation using a crowdsourced sparse map. For example, the vehicle is referred to as vehicle 1205. Figure 23 The vehicle shown can be any other vehicle disclosed herein, including, for example, vehicles 1210, 1215, 1220, and 1225, as well as vehicle 200 shown in other embodiments. As Figure 12 shown, vehicle 1205 can communicate with server 1230. Vehicle 1205 can include an image capture device 122 (e.g., camera 122). Vehicle 1205 can include a navigation system 2300 configured to provide navigation guidance for vehicle 1205 to travel on a road (e.g., road segment 1200). Vehicle 1205 can also include other sensors, such as a speed sensor 2320 and an accelerometer 2325. The speed sensor 2320 can be configured to detect the speed of vehicle 1205. The accelerometer 2325 can be configured to detect the acceleration or deceleration of vehicle 1205. Figure 23 The vehicle 1205 shown can be an autonomous vehicle, and the navigation system 2300 can be used to provide navigation guidance for autonomous driving. Alternatively, vehicle 1205 can also be a non-autonomous human-controlled vehicle, and the navigation system 2300 can still be used to provide navigation guidance.

[0340] The navigation system 2300 may include a communication unit 2305 configured to communicate with a server 1230 via a communication path 1235. The navigation system 2300 may further include a GPS unit 2310 configured to receive and process GPS signals. The navigation system 2300 may further include at least one processor 2315 configured to process data such as GPS signals, map data from a sparse map 800 (which may be stored on a storage device configured to be loaded on the vehicle 1205 and / or received from the server 1230), road geometry sensed by a road profile sensor 2330, images captured by a camera 122, and / or an autonomous vehicle road navigation model received from the server 1230. The road profile sensor 2330 may include different types of devices for measuring different types of road profiles, such as road surface roughness, road width, road elevation, road curvature, etc. For example, the road profile sensor 2330 may include a device that measures the movement of the suspension of the vehicle 2305 to derive a road roughness profile curve. In some embodiments, the road profile sensor 2330 may include a radar sensor to measure the distance from the vehicle 1205 to the roadside (e.g., a barrier on the roadside), thereby measuring the width of the road. In some embodiments, the road profile sensor 2330 may include a device configured to measure the up and down elevation of the road. In some embodiments, the road profile sensor 2330 may include a device configured to measure the road curvature. For example, a camera (e.g., the camera 122 or another camera) may be used to capture an image of the road showing the road curvature. The vehicle 1205 may use such an image to detect the road curvature.

[0341] The at least one processor 2315 may be programmed to receive at least one environmental image associated with the vehicle 1205 from the camera 122. The at least one processor 2315 may analyze the at least one environmental image to determine navigation information associated with the vehicle 1205. The navigation information may include a trajectory associated with the travel of the vehicle 1205 along a road segment 1200. The at least one processor 2315 may determine the trajectory based on the movement of the camera 122 (and thus the vehicle), such as three-dimensional translational and three-dimensional rotational movement. In some embodiments, the at least one processor 2315 may determine the translational and rotational movement of the camera 122 based on an analysis of a plurality of images acquired by the camera 122. In some embodiments, the navigation information may include lane assignment information (e.g., in which lane the vehicle 1205 is traveling along the road segment 1200). The navigation information transmitted from the vehicle 1205 to the server 1230 may be used by the server 1230 to generate and / or update an autonomous vehicle road navigation model, which may be transmitted back from the server 1230 to the vehicle 1205 to provide autonomous navigation guidance to the vehicle 1205.

[0342] At least one processor 2315 may also be programmed to transmit navigation information from vehicle 1205 to server 1230. In some embodiments, the navigation information may be transmitted to server 1230 together with road information. The road location information may include at least one of the following: GPS signals received by GPS unit 2310, landmark information, road geometry, lane information, and the like. At least one processor 2315 may receive an autonomous vehicle road navigation model or a portion of the model from server 1230. The autonomous vehicle road navigation model received from server 1230 may include at least one update based on the navigation information transmitted from vehicle 1205 to server 1230. The portion of the model transmitted from server 1230 to vehicle 1205 may include the updated portion of the model. At least one processor 2315 may cause at least one navigation maneuver (e.g., steering such as making a turn, braking, accelerating, passing another vehicle, etc.) by vehicle 1205 based on the received autonomous vehicle road navigation model or the updated portion of the model.

[0343] At least one processor 2315 may be configured to communicate with various sensors and components included in vehicle 1205, including communication unit 1705, GPS unit 2315, camera 122, speed sensor 2320, accelerometer 2325, and road profile sensor 2330. At least one processor 2315 may collect information or data from the various sensors and components and transmit the information or data to server 1230 via communication unit 2305. Alternatively or additionally, the various sensors or components of vehicle 1205 may also communicate with server 1230 and transmit data or information collected by the sensors or components to server 1230.

[0344] In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 may communicate with each other and may share navigation information with each other such that at least one of vehicles 1205, 1210, 1215, 1220, and 1225 may use crowdsourcing, e.g., based on information shared by other vehicles, to generate an autonomous vehicle road navigation model. In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 may share navigation information with each other and each vehicle may update its own settings in the autonomous vehicle road navigation model in the vehicle. In some embodiments, at least one of vehicles 1205, 1210, 1215, 1220, and 1225 (e.g., vehicle 1205) may act as a hub vehicle. At least one processor 2315 of the hub vehicle (e.g., vehicle 1205) may perform some or all of the functions performed by server 1230. For example, at least one processor 2315 of the hub vehicle may communicate with other vehicles and receive navigation information from other vehicles. One processor 2315 of the hub vehicle may generate an autonomous vehicle road navigation model or an update to the model based on the shared information received from other vehicles. At least one processor 2315 of the hub vehicle may transmit the autonomous vehicle road navigation model or an update to the model to other vehicles to provide autonomous navigation guidance.

[0345] Navigation Based on Sparse Map

[0346] As previously discussed, an autonomous vehicle road navigation model including sparse map 800 may include multiple mapped lane markings and multiple mapped objects / features associated with road segments. As discussed in more detail below, these mapped lane markings, objects, and features may be used when an autonomous vehicle is navigating. For example, in some embodiments, the mapped objects and features may be used to position the host vehicle relative to the map (e.g., relative to the mapped target trajectory). The mapped lane markings may be used (e.g., as a check) to determine the lateral position and / or orientation relative to a planned or target trajectory. Using this position information, the autonomous vehicle may be able to adjust its heading direction to match the direction of the target trajectory at the determined position.

[0347] Vehicle 200 can be configured to detect lane markings in a given road segment. A road segment can include any markings on a road that guide vehicle traffic on a roadway. For example, lane markings can be solid lines or short dashed lines that demarcate the edges of a travel lane. Lane markings can also include double lines that indicate, for example, whether passing is permitted in an adjacent lane, such as double solid lines, double short dashed lines, or a combination of solid lines and short dashed lines. Lane markings can also include highway entrance and exit markings that indicate, for example, a deceleration lane for an exit ramp, or dotted lines that indicate that a lane is for turning only or that a lane is about to end. Markings can further indicate a work zone, a temporary lane change, a travel path through an intersection, a median, a dedicated lane (e.g., a bicycle lane, an HOV lane, etc.), or other miscellaneous markings (e.g., a crosswalk, a speed bump, a railroad crossing, a stop line, etc.).

[0348] Vehicle 200 can use cameras, such as image capture devices 122 and 124 included in image acquisition unit 120, to capture images of surrounding lane markings. Vehicle 200 can analyze the images to detect point locations associated with lane markings based on features identified within one or more of the captured images. These point locations can be uploaded to a server to represent lane markings in sparse map 800. Depending on the location and field of view of the cameras, lane markings can be detected for both sides of the vehicle from a single image simultaneously. In other embodiments, different cameras can be used to capture images on multiple sides of the vehicle. Instead of uploading the actual image of the lane marking, the lane marking can be stored in sparse map 800 as a spline or a series of points, thereby reducing the size of sparse map 800 and / or the data that must be uploaded remotely by the vehicle.

[0349] Figures 24A - 24D Exemplary point locations that can be detected by vehicle 200 to represent a specific lane marking are shown. Similar to the landmarks described above, vehicle 200 can use various image recognition algorithms or software to identify point locations within the captured images. For example, vehicle 200 can identify a series of edge points, corner points, or various other point locations associated with a specific lane marking. Figure 24A A continuous lane marking 2410 that can be detected by vehicle 200 is shown. Lane marking 2410 can represent the outer edge of a roadway, represented by a continuous white line. As Figure 24AAs shown, vehicle 200 can be configured to detect a plurality of edge position points 2411 along a lane marking. The position points 2411 can be collected at any interval sufficient to represent the lane marking in a sparse map. For example, the lane marking can be represented by one point per meter of detected edge, one point per five meters of detected edge, or at other suitable spacings. In some embodiments, the spacing can be determined by other factors rather than at a set interval, such as, for example, points for which vehicle 200 has the highest confidence ranking of the position of the detected points. Although Figure 24A edge position points on the inner edge of lane marking 2410 are shown, points can be collected on the outer edge of the line or along both edges. Additionally, Figure 24A a single line is shown, but similar edge points can be detected for double continuous lines. For example, points 2411 can be detected along the edge of one or both of the continuous lines.

[0350] Depending on the type or shape of the lane marking, vehicle 200 can also represent the lane marking differently. Figure 24B An exemplary short line lane marking 2420 that can be detected by vehicle 200 is shown. As in Figure 24A the vehicle can detect a series of corner points 2421 representing the corners of the lane short lines to define the complete boundary of the short lines. Although Figure 24B each corner of a given short line marking is shown positioned, vehicle 200 can detect or upload a subset of the points shown in the figure. For example, vehicle 200 can detect the leading edge or front corner of a given short line marking, or can detect the two corner points closest to the inside of the lane. Additionally, not every short line marking can be captured. For example, vehicle 200 can capture and / or record points of short line markings representing samples (e.g., every other, every third, every fifth, etc.) or at a predefined spacing (e.g., every meter, every five meters, every 10 meters, etc.) of the short line markings. Corner points can also be detected for similar lane markings, such as markings indicating an exit ramp for a lane, a marking that a particular lane is ending, or various other lane markings that may have detectable corner points. Corner points can also be detected for lane markings consisting of a combination of double short dashed lines or continuous lines and short dashed lines.

[0351] In some embodiments, the points uploaded to the server to generate the mapped lane marking can represent other points in addition to the detected edge points or corner points. Figure 24CShows a series of points that can represent the centerline of a given lane marking. For example, a continuous lane 2410 can be represented by centerline points 2441 along the centerline 2440 of the lane marking. In some embodiments, the vehicle 200 can be configured to detect these center points using various image recognition techniques such as convolutional neural networks (CNNs), scale-invariant feature transform (SIFT), histogram of oriented gradients (HOG) features, or other techniques. Alternatively, the vehicle 200 can detect other points, such as Figure 24A the edge points 2411 shown, and can calculate the centerline points 2441, for example, by detecting points along each edge and determining the midpoint between the edge points. Similarly, a short lane marking 2420 can be represented by centerline points 2451 along the centerline 2450 of the lane marking. The centerline points can be located at the edges of the short lines, as Figure 24C shown, or at various other positions along the centerline. For example, each short line can be represented by a single point at the geometric center of the short line. The points can also be spaced apart at predetermined intervals (e.g., every one meter, every 5 meters, every 10 meters, etc.) along the centerline. The centerline points 2451 can be detected directly by the vehicle 200 or can be calculated based on other detected reference points such as corner points 2421, as Figure 24B shown. Using techniques similar to those described above, the centerline can also be used to represent other lane marking types, such as double lines.

[0352] In some embodiments, the vehicle 200 can identify points representing other features, such as the vertex between two intersecting lane markings. Figure 24D Shows an exemplary point representing the intersection between two lane markings 2460 and 2465. The vehicle 200 can calculate the vertex 2466 representing the intersection between the two lane markings. For example, one of the lane markings 2460 or 2465 can represent a railroad crossing area or other crossing area in a road segment. Although the lane markings 2460 and 2465 are shown as intersecting perpendicularly to each other, various other configurations can be detected. For example, the lane markings 2460 and 2465 can intersect at other angles, or one or both of the lane markings can terminate at the vertex 2466. Similar techniques can also be applied to intersections between short lines or other lane marking types. In addition to the vertex 2466, various other points 2467 can be detected, providing further information about the orientation of the lane markings 2460 and 2465.

[0353] Vehicle 200 can associate real-world coordinates with each detected point of the lane markings. For example, a location identifier can be generated that includes the coordinates of each point for uploading to a server for mapping the lane markings. The location identifier can further include other identification information about the point, including whether the point represents a corner point, an edge point, a center point, etc. Vehicle 200 can thus be configured to determine the real-world position of each point based on an analysis of the image. For example, vehicle 200 can detect other features in the image, such as the various landmarks described above, to localize the real-world position of the lane markings. This can involve determining the position of the lane markings in the image relative to the detected landmarks or determining the position of the vehicle based on the detected landmarks, and subsequently determining the distance from the vehicle (or the vehicle's target trajectory) to the lane markings. When landmarks are not available, the position of the lane marking points can be determined relative to the dead-reckoned position of the vehicle. The real-world coordinates included in the location identifier can be represented as absolute coordinates (e.g., latitude / longitude coordinates), or can be relative to other features, such as based on the longitudinal position along the target trajectory and the lateral distance from the target trajectory. The location identifier can then be uploaded to the server to generate the mapped lane markings in a navigation model, such as sparse map 800. In some embodiments, the server can construct a spline representing the lane markings of a road segment. Alternatively, vehicle 200 can generate a spline and upload it to the server for recording in the navigation model.

[0354] Figure 24E An exemplary navigation model or sparse map is shown for a corresponding road segment that includes the mapped lane markings. The sparse map can include a target trajectory 2475 for the vehicle to travel along the road segment. As described above, the target trajectory 2475 can represent an ideal path for the vehicle to take as it travels along the corresponding road segment, or can be located at other positions on the road (e.g., the centerline of the road, etc.). The target trajectory 2475 can be calculated by the various methods described above, e.g., based on the aggregation (e.g., weighted combination) of two or more reconstructed trajectories of vehicles traversing the same road segment.

[0355] In some embodiments, the target trajectory may be generated equally for all vehicle types and for all road, vehicle, and / or environmental conditions. However, in other embodiments, various other factors or variables may also be considered in generating the target trajectory. Different target trajectories may be generated for different types of vehicles (e.g., private cars, light trucks, and full trailers). For example, a target trajectory with a relatively smaller turning radius compared to a large semi-trailer truck may be generated for a small private car. In some embodiments, road, vehicle, and environmental conditions may also be considered. For example, different target trajectories may be generated for: different road conditions (e.g., wet, snowy, icy, dry, etc.), vehicle conditions (e.g., tire condition or estimated tire condition, braking condition or estimated braking condition, remaining fuel amount, etc.), or environmental factors (e.g., time of day, visibility, weather, etc.). The target trajectory may also depend on one or more aspects or features of a particular road segment (e.g., speed limit, frequency and size of turns, slope, etc.). In some embodiments, various user settings such as the set driving mode (e.g., desired driving aggressiveness, economy mode, etc.) may also be used to determine the target trajectory.

[0356] The sparse map may also include mapped lane markings 2470 and 2480 representing lane markings along a road segment. The mapped lane markings may be represented by a plurality of location identifiers 2471 and 2481. As described above, the location identifiers may include the location in the real-world coordinates of the points associated with the detected lane markings. Similar to the target trajectory in the model, the lane markings may also include elevation data and may be represented as a curve in three-dimensional space. For example, the curve may be a spline connecting three-dimensional polynomial traces of a suitable order, and the curve may be calculated based on the location identifiers. The mapped lane markings may also include other information or metadata about the lane markings, such as an identifier of the lane marking type (e.g., between two lanes with the same direction of travel, between two lanes with opposite directions of travel, the edge of the roadway, etc.) and / or other characteristics of the lane markings (e.g., continuous, dashed, single line, double line, yellow, white, etc.). In some embodiments, the mapped lane markings may be continuously updated in the model, for example, using crowdsourcing techniques. The same vehicle may upload location identifiers during multiple instances of traveling on the same road segment, or data may be selected from multiple vehicles (such as 1205, 1210, 1215, 1220, and 1225) traveling on the road segment at different times. The sparse map 800 may then be updated or refined based on subsequent location identifiers received from vehicles and stored in the system. As the mapped lane markings are updated and refined, the updated road navigation model and / or sparse map may be distributed to multiple autonomous vehicles.

[0357] Generating mapped lane markings in a sparse map may also include detecting and / or reducing errors based on anomalies in the image or the actual lane markings themselves. Figure 24F An exemplary anomaly 2495 associated with detecting lane marking 2490 is shown. Anomaly 2495 may appear in an image captured by vehicle 200, for example, from an object obstructing the camera's view of the lane marking, debris on the lens, etc. In some cases, the anomaly may be due to the lane marking itself, which may be damaged or worn, or partially covered by, for example, dirt, debris, water, snow, or other substances on the road. Anomaly 2495 may cause an incorrect point 2491 to be detected by vehicle 200. Sparse map 800 may provide the correct mapped lane markings and exclude the errors. In some embodiments, vehicle 200 may detect incorrect point 2491, for example, by detecting anomaly 2495 in the image, or by identifying the error based on detected lane marking points before and after the anomaly. Based on the detected anomaly, the vehicle may ignore point 2491 or may adjust it to be in line with other detected points. In other embodiments, the error may be corrected after the point has been uploaded, for example, by determining that the point is beyond an expected threshold based on other points uploaded during the same trip or based on the aggregation of data from previous trips along the same road segment.

[0358] The mapped lane markings in the navigation model and / or sparse map may also be used for navigation by an autonomous vehicle traversing the corresponding roadway. For example, a vehicle navigating along a target trajectory may periodically use the mapped lane markings in the sparse map to align itself with the target trajectory. As mentioned above, between landmarks, the vehicle may navigate based on dead reckoning, where the vehicle uses sensors to determine its self-motion and estimate its position relative to the target trajectory. Errors may accumulate over time, and the determination of the vehicle's position relative to the target trajectory may become increasingly inaccurate. Therefore, the vehicle may use the lane markings (and their known positions) that appear in sparse map 800 to reduce the errors caused by dead reckoning in position determination. Thus, the identified lane markings included in sparse map 800 may be used as navigation anchor points by which the accurate position of the vehicle relative to the target trajectory can be determined.

[0359] Figure 25A An exemplary image 2500 of the vehicle's surrounding environment is shown, which may be used for navigation based on the mapped lane markings. Image 2500 may be captured by vehicle 200, for example, by image capture devices 122 and 124 included in image acquisition unit 120. Image 2500 may include an image of at least one lane marking 2510, as Figure 25AAs shown. The image 2500 may also include one or more landmarks 2521 for navigation as described above, such as road signs. Figure 25A Some elements (such as elements 2511, 2530, 2520) that are shown but do not appear in the captured image 2500 but are detected and / or determined by the vehicle 200 are also shown for reference.

[0360] Using the various techniques described above with respect to Figures 24A - 24D and 24F, the vehicle can analyze the image 2500 to identify the lane markings 2510. Respective points 2511 corresponding to the features of the lane markings in the image can be detected. The points 2511 can correspond, for example, to the edges of the lane markings, the corners of the lane markings, the midpoints of the lane markings, the vertices between two intersecting lane markings, or various other features or positions. The points 2511 can be detected to correspond to the positions of points stored in the navigation model received from the server. For example, if a sparse map containing points representing the centerlines of the mapped lane markings is received, the points 2511 can also be detected based on the centerlines of the lane markings 2510.

[0361] The vehicle can also determine the longitudinal position represented by the element 2520 and located along the target trajectory. The longitudinal position 2520 can be determined from the image 2500, for example, by detecting the landmark 2521 within the image 2500 and comparing the measured position with the known landmark positions stored in the road model or the sparse map 800. The position of the vehicle along the target trajectory can then be determined based on the distance to the landmark and the known position of the landmark. The longitudinal position 2520 can also be determined from an image other than the image used to determine the position of the lane markings. For example, the longitudinal position 2520 can be determined by detecting landmarks in an image captured simultaneously or almost simultaneously with the image 2500 from other cameras within the image acquisition unit 120. In some cases, the vehicle may not be close to any landmark or other reference point for determining the longitudinal position 2520. In such cases, the vehicle can navigate based on dead reckoning and thus can use sensors to determine its self-motion and estimate the longitudinal position 2520 relative to the target trajectory. The vehicle can also determine the distance 2530, which represents the actual distance between the vehicle observed in the captured image and the lane markings 2510. Camera angle, vehicle speed, vehicle width, or various other factors can be taken into account when determining the distance 2530.

[0362] Figure 25BShows the lateral positioning correction of a vehicle based on mapped lane markings in a road navigation model. As described above, vehicle 200 can use one or more images captured by vehicle 200 to determine the distance 2530 between vehicle 200 and lane marking 2510. Vehicle 200 can also use a road navigation model, such as sparse map 800, which can include mapped lane markings 2550 and target trajectory 2555. The mapped lane markings 2550 can be modeled using the techniques described above (e.g., using crowdsourced location identifiers captured by multiple vehicles). The target trajectory 2555 can also be generated using the various techniques described previously. Vehicle 200 can also determine or estimate the longitudinal position 2520 along the target trajectory 2555, as described above with respect to Figure 25A as described. Vehicle 200 can then determine the expected distance 2540 based on the lateral distance between the target trajectory 2555 and the mapped lane markings 2550 corresponding to the longitudinal position 2520. The lateral positioning of vehicle 200 can be corrected or adjusted by comparing the actual distance 2530 measured using the captured images with the expected distance 2540 from the model.

[0363] Figure 25C and 25D Provides an illustration associated with another example for positioning a host vehicle based on mapped landmarks / objects / features in a sparse map during navigation. Figure 25C Conceptually represents a series of images captured from a vehicle traveling along road segment 2560. In this example, road segment 2560 includes a straight section of a two-lane divided highway bounded by road edges 2561 and 2562 and a center lane marking 2563. As shown, the host vehicle is traveling along lane 2564 associated with the mapped target trajectory 2565. Thus, ideally (and in the absence of influencing factors such as the presence of target vehicles or objects in the lane), the host vehicle should closely follow the mapped target trajectory 2565 as it travels along lane 2564 of road segment 2560. In practice, the host vehicle may experience drift as it travels along the mapped target trajectory 2565. For effective and safe travel, this drift should be maintained within an acceptable limit (e.g., a lateral displacement of + / - 10 cm from the target trajectory 2565 or any other suitable threshold). To periodically account for the drift and make any required route corrections to ensure that the host vehicle follows the target trajectory 2565, the disclosed navigation system can be capable of using one or more mapped features / objects included in the sparse map to position the host vehicle along the target trajectory 2565 (e.g., determine the lateral and longitudinal positions of the host vehicle relative to the target trajectory 2565).

[0364] As a simple example,Figure 25C The speed limit sign 2566 is shown as it may appear in five different sequentially captured images as the host vehicle travels along the road segment 2560. For example, at a first time t0, the sign 2566 may appear near the horizon in the captured image. As the host vehicle approaches the sign 2566, at times t1, t2, t3, and t4 in the sequentially captured images, the sign 2566 will appear at different 2D X-Y pixel locations in the captured image. For example, in the captured image space, the sign 2566 will move down and to the right along a curve 2567 (e.g., a curve extending through the center of the sign in each of the five captured image frames). As the host vehicle approaches the sign 2566, the sign will also appear to increase in size (i.e., the sign will occupy a greater number of pixels in subsequent captured images).

[0365] These changes in the image space representation of an object such as the sign 2566 can be utilized to determine the located position of the host vehicle along a target trajectory. For example, as described in the present disclosure, any detectable object or feature, such as a semantic feature like the sign 2566 or a detectable non-semantic feature, can be recognized by one or more collection vehicles that have previously traversed the road segment (e.g., road segment 2560). A mapping server can collect the captured driving information from multiple vehicles, aggregate and correlate the information, and generate a sparse map that includes, for example, a target trajectory 2565 for a lane 2564 of the road segment 2560. The sparse map can also store the location (as well as type information, etc.) of the sign 2566. During navigation (e.g., before entering the road segment 2560), a map tile including the sparse map for the road segment 2560 can be supplied to the host vehicle. To navigate in the lane 2564 of the road segment 2560, the host vehicle can follow the mapped target trajectory 2565.

[0366] The mapped representation of the sign 2566 can be used by the host vehicle to position itself relative to the target trajectory. For example, a camera on the host vehicle will capture an image 2570 of the environment of the host vehicle, and the captured image 2570 can include an image representation of the sign 2566 having a particular size and a particular X-Y image location, as Figure 25DAs shown. This size and X-Y image position can be used to determine the position of the host vehicle relative to the target trajectory 2565. For example, based on a sparse map including a representation of the landmark 2566, the navigation processor of the host vehicle can determine that the representation of the landmark 2566 should appear in the captured image in response to the host vehicle traveling along the target trajectory 2565, such that the center of the landmark 2566 will move along line 2567 (in image space). If the captured image (such as image 2570) shows a center (or other reference point) that is displaced from line 2567 (e.g., the expected image space trajectory), then the host vehicle navigation system can determine that at the time of the captured image, the center is not on the target trajectory 2565. Based on this image, however, the navigation processor can determine an appropriate navigation correction to return the host vehicle to the target trajectory 2565. For example, if the analysis shows that the image position of the landmark 2566 is displaced a distance 2572 to the left of the expected image space position on line 2567 in the image, then the navigation processor can cause the heading of the host vehicle to change (e.g., change the steering angle of the wheels) to move the host vehicle left a distance 2573. In this way, each captured image can be used as part of a feedback loop process such that the difference between the observed image position of the landmark 2566 and the expected image trajectory 2567 can be minimized to ensure that the host vehicle continues to follow the target trajectory 2565 with little deviation. Of course, the more mapped objects available, the more frequently the described positioning techniques can be employed, which can reduce or eliminate deviations from the target trajectory 2565 caused by drift.

[0367] The process described above can be used to detect the lateral orientation or displacement of the host vehicle relative to the target trajectory. The positioning of the host vehicle relative to the target trajectory 2565 can also include determining the longitudinal position of the target vehicle along the target trajectory. For example, the captured image 2570 includes a representation of the landmark 2566 having a specific image size (e.g., a 2D X-Y pixel area). This size can be compared to the expected image size of the mapped landmark 2566 as it travels through image space along line 2567 (e.g., as the size of the landmark progressively increases, as Figure 25C shown). Based on the image size of the landmark 2566 in the image 2570 and based on the expected size progression in image space relative to the mapped target trajectory 2565, the host vehicle can determine its longitudinal position (at the time the image 2570 was captured) relative to the target trajectory 2565. As described above, this longitudinal position plus any lateral displacement relative to the target trajectory 2565 enables the complete positioning of the host vehicle relative to the target trajectory 2565 when the host vehicle is traveling along the road 2560.

[0368] Figure 25C and 25DOnly one example of the disclosed positioning technique using a single mapped object and a single target trajectory is provided. In other examples, there may be more target trajectories (e.g., one target trajectory for each viable lane of a multi-lane highway, urban street, complex intersection, etc.), and there may be more mapped objects available for positioning. For example, a sparse map representing an urban environment may include many objects per meter that are available for positioning.

[0369] Figure 26A A flowchart showing an exemplary process 2600A for mapping lane markings for use in autonomous vehicle navigation consistent with the disclosed embodiments. At step 2610, process 2600A may include receiving two or more location identifiers associated with detected lane markings. For example, step 2610 may be performed by server 1230 or one or more processors associated with the server. The location identifiers may include the location in real-world coordinates of points associated with the detected lane markings, as described above with respect to Figure 24E that which is described. In some embodiments, the location identifiers may also contain other data, such as additional information about the road segment or lane markings. Additional data, such as accelerometer data, speed data, landmark data, road geometry or profile data, vehicle positioning data, ego-motion data, or various other forms of data described above, may also be received during step 2610. The location identifiers may be generated by a vehicle (such as vehicles 1205, 1210, 1215, 1220, and 1225) based on images captured by that vehicle. For example, the identifier may be determined based on: obtaining at least one image representing the environment of the host vehicle from a camera associated with the host vehicle, analyzing the at least one image to detect lane markings in the environment of the host vehicle, and analyzing the at least one image to determine the position of the detected lane markings relative to the location associated with the host vehicle. As described above, lane markings may include various different marking types, and the location identifiers may correspond to various points relative to the lane markings. For example, when the detected lane marking is part of a short dashed line marking a lane boundary, the point may correspond to the detected corner point of the lane marking. When the detected lane marking is part of a continuous line marking a lane boundary, the point may correspond to the detected edge of the lane marking having various spacings as described above. In some embodiments, the point may correspond to the centerline of the detected lane marking, as Figure 24C shown, or may correspond to the vertex between two intersecting lane markings and at least one two other points associated with the intersecting lane markings, as Figure 24D shown.

[0370] In step 2612, process 2600A may include associating detected lane markings with corresponding road segments. For example, server 1230 may analyze real-world coordinates or other information received during step 2610 and compare the coordinates or other information with location information stored in the autonomous vehicle road navigation model. Server 1230 may determine the road segment in the model that corresponds to the real-world road segment of the detected lane markings.

[0371] In step 2614, process 2600A may include updating the autonomous vehicle road navigation model relative to the corresponding road segment based on two or more location identifiers associated with the detected lane markings. For example, the autonomous road navigation model may be the sparse map 800, and server 1230 may update the sparse map to include or adjust the mapped lane markings in the model. Server 1230 may update the model based on the various methods or processes described above with respect to Figure 24E In some embodiments, updating the autonomous vehicle road navigation model may include storing one or more location indicators at the real-world coordinates of the detected lane markings. The autonomous vehicle road navigation model may also include at least one target trajectory for the vehicle to follow along the corresponding road segment, as Figure 24E shown.

[0372] In step 2616, process 2600A may include distributing the updated autonomous vehicle road navigation model to multiple autonomous vehicles. For example, server 1230 may distribute the updated autonomous vehicle road navigation model to vehicles 1205, 1210, 1215, 1220, and 1225 that may use the model for navigation. The autonomous vehicle road navigation model may be distributed via a wireless communication path 1235 via one or more networks (e.g., via a cellular network and / or the Internet, etc.), as Figure 12 shown.

[0373] In some embodiments, lane markings may be mapped using data received from multiple vehicles, such as through crowdsourcing techniques, as described above with respect to Figure 24EAs described. For example, process 2600A may include receiving a first communication from a first host vehicle, including a position identifier associated with a detected lane marking, and receiving a second communication from a second host vehicle, including an additional position identifier associated with the detected lane marking. For example, the second communication may be received from a following vehicle traveling on the same road segment, or from the same vehicle during a subsequent journey along the same road segment. Process 2600A may further include refining the determination of at least one position associated with the detected lane marking based on the position identifier received in the first communication and based on the additional position identifier received in the second communication. This may include using the average of multiple position identifiers and / or filtering out "ghost" identifiers that may not reflect the real-world position of the lane marking.

[0374] Figure 26B A flowchart showing an exemplary process 2600B for autonomously navigating a host vehicle along a road segment using mapped lane markings. Process 2600B may be performed, for example, by the processing unit 110 of the autonomous vehicle 200. At step 2620, process 2600B may include receiving an autonomous vehicle road navigation model from a server-based system. In some embodiments, the autonomous vehicle road navigation model may include a target trajectory of the host vehicle along the road segment and position identifiers associated with one or more lane markings associated with the road segment. For example, vehicle 200 may receive the sparse map 800 or another road navigation model developed using process 2600A. In some embodiments, the target trajectory may be represented as a three-dimensional spline, for example, as Figure 9B shown. As described above with respect to Figures 24A - 24F the position identifier may include the position in the real-world coordinates of a point associated with the lane marking (e.g., the corner point of a dashed lane marking, the edge point of a continuous lane marking, the vertex between two intersecting lane markings, and other points associated with intersecting lane markings, the centerline associated with the lane marking, etc.).

[0375] At step 2621, process 2600B may include receiving at least one image representing the environment of the vehicle. The image may be received from an image capture device of the vehicle, such as image capture devices 122 and 124 included in the image acquisition unit 120. The image may include an image of one or more lane markings, similar to the image 2500 described above.

[0376] At step 2622, process 2600B may include determining the longitudinal position of the host vehicle along the target trajectory. As described with respect to Figure 25A this may be based on other information (e.g., landmarks, etc.) in the captured image or by dead reckoning of the vehicle between detected landmarks.

[0377] In step 2623, process 2600B may include determining an expected lateral distance to a lane marking based on the determined longitudinal position of the host vehicle along a target trajectory and based on two or more position identifiers associated with at least one lane marking. For example, vehicle 200 may use sparse map 800 to determine the expected lateral distance to a lane marking. As Figure 25B shown, the longitudinal position 2520 along the target trajectory 2555 may be determined in step 2622. Using the alternative map 800, vehicle 200 may determine the expected distance 2540 to the mapped lane marking 2550 corresponding to the longitudinal position 2520.

[0378] In step 2624, process 2600B may include analyzing at least one image to identify at least one lane marking. Vehicle 200 may, for example, use various image recognition techniques or algorithms to identify lane markings within the image, as described above. For example, lane marking 2510 may be detected by image analysis of image 2500, as Figure 25A shown.

[0379] In step 2625, process 2600B may include determining an actual lateral distance to at least one lane marking based on the analysis of at least one image. For example, the vehicle may determine distance 2530, as Figure 25A shown, which represents the actual distance between the vehicle and lane marking 2510. Camera angle, vehicle speed, vehicle width, the position of the camera relative to the vehicle, or various other factors may be taken into account when determining distance 2530.

[0380] In step 2626, process 2600B may include determining an autonomous steering action for the host vehicle based on the difference between the expected lateral distance to at least one lane marking and the determined actual lateral distance to at least one lane marking. For example, as described above with respect to Figure 25B vehicle 200 may compare the actual distance 2530 with the expected distance 2540. The difference between the actual distance and the expected distance may indicate the error (and its magnitude) between the actual position of the vehicle and the target trajectory to be followed by the vehicle. Thus, the vehicle may determine an autonomous steering action or other autonomous actions based on the difference. For example, if the actual distance 2530 is less than the expected distance 2540, as Figure 25B shown, then the vehicle may determine an autonomous steering action to guide the vehicle to the left away from lane marking 2510. Thus, the position of the vehicle relative to the target trajectory may be corrected. Process 2600B may be used, for example, to improve the navigation of the vehicle between landmarks.

[0381] Processes 2600A and 2600B only provide examples of techniques that can be used to navigate a host vehicle using the disclosed sparse map. In other examples, processes consistent with those described with respect to Figure 25C and 25D may also be employed.

[0382] Virtual Stop - Line Mapping and Navigation

[0383] As described elsewhere in this disclosure, a vehicle or a driver may navigate a vehicle based on the environment. For example, an autonomous vehicle may navigate based on the markings of a stop line on a road segment and stop at an intersection. However, sometimes, the road segment on which the vehicle is driving may not include markings (or substandard markings due to poor maintenance) indicating the position to stop at an intersection, and the vehicle may not be able to navigate correctly at the intersection. As another example, due to various factors, such as the geometry of the road or intersection or poor visibility conditions (e.g., line of sight blocked by another vehicle, certain weather conditions), etc., an intersection may not be easily detected by the driver or the vehicle. In such cases, it may be desirable to determine a virtual stop line (e.g., an unmarked position) at which the vehicle may stop to navigate through the intersection (by, for example, slowing down or stopping at the intersection). The systems and methods disclosed herein may allow for determining a virtual stop line based on images captured by multiple devices associated with multiple vehicles. The systems and methods may also update a road navigation model based on one or more virtual stop lines and distribute the updated road navigation model to vehicles. The systems and methods may further allow a vehicle to perform one or more navigation actions (e.g., slow down, stop, etc.) based on the virtual stop lines included in the road navigation model.

[0384] Figure 27 An exemplary system 2700 for vehicle navigation consistent with the disclosed embodiments is shown. As Figure 27As shown, the system 2700 may include a server 2701, one or more vehicles 2702 (e.g., vehicles 2702A, 2702B, 2702C, …, 2702N), and one or more vehicle devices 2703 associated with the vehicles (e.g., vehicle devices 2703A, 2703B, 2703C, …, 2703N), a database 2704, and a network 2705. The server 2701 may be configured to update a road navigation model based on driving information received from one or more vehicles (and / or one or more vehicle devices associated with the vehicles). For example, the vehicle 2702 and / or the vehicle device 2703 may be configured to collect driving information and transmit the driving information to the server 2701 to update the road navigation model. The database 2704 may be configured to store information for components of the system 2700 (e.g., the server 2701, the vehicle 2702, and / or the vehicle device 2703). The network 2705 may be configured to facilitate communication between components of the system 2700.

[0385] The server 2701 may be configured to receive driving information from each of a plurality of vehicles. The driving information may include a stop position of a particular vehicle among the plurality of vehicles relative to an intersection stop during driving along a road segment. The server 2701 may also be configured to aggregate the stop positions in the driving information received from the plurality of vehicles and determine a stop line position relative to the intersection based on the aggregated stop positions. The server 2701 may be further configured to update the road navigation model to include the stop line position. In some embodiments, the server 2701 may also be configured to distribute the updated road navigation model to one or more vehicles. For example, the server 2701 may be a cloud server performing the functions disclosed herein. The term “cloud server” refers to a computer platform that provides services via a network such as the Internet. In this example configuration, the server 2701 may use virtual machines that may not correspond to individual hardware. For example, the computing and / or storage capabilities may be achieved by dispatching appropriate portions of the desired computing / storage power from a scalable repository such as a data center or a distributed computing environment. In one example, the server 2701 may implement the methods described herein using custom hardwired logic, one or more application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs), firmware, and / or program logic, which in combination with a computer system cause the server 2701 to be a special purpose machine.

[0386] Vehicle 2702 and / or vehicle device 2703 may be configured to collect driving information and transmit the driving information to server 2701 to update the road navigation model. For example, vehicle 2702A and / or vehicle device 2703A may be configured to receive one or more images captured from the environment of vehicle 2702A. Vehicle 2702A and / or vehicle device 2703A may also be configured to analyze the one or more images to detect an indicator of an intersection. Vehicle 2702A and / or vehicle device 2703A may further be configured to determine a stop position of vehicle 2702A relative to the detected intersection based on an output received from at least one sensor of vehicle 2702A. Vehicle 2702A and / or vehicle device 2703A may also be configured to analyze the one or more images to determine an indicator of whether one or more other vehicles are in front of vehicle 2702A. Vehicle 2702A and / or vehicle device 2703A may further be configured to send the stop position of vehicle 2702A and an indicator of whether one or more other vehicles are in front of vehicle 2702A to server 2701 for updating the road navigation model.

[0387] In some embodiments, vehicle 2702 and / or vehicle device 2703 may be configured to receive an updated road navigation model and cause vehicle 2702 to perform at least one navigation action based on the updated road navigation model. For example, vehicle 2702B and / or vehicle device 2703B may be configured to receive one or more images captured from the environment of vehicle 2702B from a camera of vehicle 2702B. Vehicle 2702B and / or vehicle device 2703B may also be configured to detect an indicator of an intersection in the environment of vehicle 2702B. Vehicle 2702B and / or vehicle device 2703B may further be configured to receive map information including a stop line position relative to the intersection from server 2701. Vehicle 2702B and / or vehicle device 2703B may also be configured to plan a routing path and / or navigate vehicle 2702B according to the map information. For example, vehicle 2702B and / or vehicle device 2703B may be configured to consider the stop line position when planning a route to a destination (e.g., adding a stop time to the estimated time of arrival if passing through the intersection, selecting a different route without passing through the intersection, etc.). As another example, vehicle 2702B and / or vehicle device 2703B may be configured to consider the stop line position as part of a long-term plan well in advance of the approaching stop line position. For example, vehicle 2702B and / or vehicle device 2703B may be configured to decelerate vehicle 2702 when the vehicle reaches within a predetermined distance of the stop line position. Alternatively or additionally, vehicle 2702B and / or vehicle device 2703B may be configured to brake and stop vehicle 2702B before reaching the stop line position.

[0388] In some embodiments, vehicle 2702 may include means having a configuration and / or performing functions similar to those of system 100 described above. Alternatively or additionally, vehicle means 2703 may have a configuration and / or perform functions similar to those of system 100 described above.

[0389] Database 2704 may include a map database configured to store map data for components of system 2700 (e.g., server 2701, vehicle 2702, and / or vehicle means 2703). In some embodiments, server 2701, vehicle 2702, and / or vehicle means 2703 may be configured to access database 2704, obtain data stored from database 2704, and / or upload data to the database via network 2705. For example, server 2701 may transmit data related to one or more road navigation models to database 2704 for storage. Vehicle 2702 and / or vehicle means 2703 may download road navigation models from database 2704. In some embodiments, database 2704 may include data related to the positions of various items in a reference coordinate frame, the items including roads, water features, geographical features, businesses, points of interest, restaurants, gas stations, etc., or combinations thereof. In some embodiments, database 2704 may include a database similar to map database 160 described elsewhere in this disclosure.

[0390] Network 2705 may be any type of network (including infrastructure) that provides communication, exchanges information, and / or facilitates information exchange between components of system 2700. For example, network 2705 may include or be part of the Internet, a local area network, a wireless network (e.g., a Wi-Fi / 802.11 network), or other suitable connection. In other embodiments, one or more components of system 2700 may communicate directly via a dedicated communication link, such as a telephone network, an extranet, an intranet, the Internet, satellite communication, offline communication, wireless communication, transponder communication, a local area network (LAN), a wide area network (WAN), a virtual private network (VPN), etc.

[0391] Figure 28 is a schematic illustration of an exemplary vehicle at an intersection consistent with the disclosed embodiments. As Figure 28As shown, vehicle 2801 can drive in lane 2811, and vehicles 2802 and 2803 can drive in lane 2812. Vehicles 2801, 2802, and / or 2803 can include one or more cameras configured to capture one or more images of the environment and can include one or more devices (e.g., vehicle device 2703) configured to detect an indicator of intersection 2821 based on an analysis of the one or more images. Indicators of an intersection can include one or more road markings, one or more traffic lights, one or more stop signs, one or more crosswalks, one or more vehicles crossing in front of the host vehicle, one or more vehicles stopped at a location near the host vehicle (e.g., within a predetermined distance threshold from the host vehicle), etc., or a combination thereof. For example, vehicle 2801 can be configured to analyze one or more images and detect a traffic light in at least one of the one or more images in the forward direction. As another example, vehicle 2802 (similar to vehicle 2702) can analyze one or more images of the environment of vehicle 2802 and detect vehicle 2804 crossing in front of vehicle 2802 (in this example, moving from right to left) based on the image analysis. As another example, vehicle 2803 can analyze one or more images of the environment of vehicle 2803 and detect a road sign indicating an intersection. For example, vehicle 2803 can analyze one or more images and detect a stop sign, and determine whether the stop sign indicates an intersection based on the facing direction of the stop sign. As another example, vehicle 2803 can analyze one or more images and detect a crosswalk, and determine whether the crosswalk indicates an intersection based on the orientation of the crosswalk relative to vehicle 2803 (e.g., a crosswalk spanning the lane in front of vehicle 2803 can indicate that an intersection is nearby).

[0392] Vehicle 2801, vehicle 2802, and / or vehicle 2803 may also be configured to determine a stop position of the host vehicle relative to a detected intersection based on an output received fr...

Claims

1. A system for generating a crowdsourced map for use in vehicle navigation, the system comprising: At least one processor, comprising circuitry and a memory, wherein the memory includes instructions that, when executed by the circuitry, cause the at least one processor to: Receive driving information collected from a plurality of vehicles traversing a road segment that intersects an intersection associated with a plurality of traffic lights; Aggregate the received driving information to determine the position of each of the plurality of traffic lights and to determine a spline representation of each of one or more drivable paths associated with the road segment; Provide, as an input, the determined position of each of the plurality of traffic lights and the spline representation of each of the one or more drivable paths to at least one trained model, wherein The at least one trained model is configured to generate a traffic light correlation map based on the determined position of each of the plurality of traffic lights and the spline representation of each of the one or more drivable paths, the traffic light correlation map including a traffic light correlation indicator for each pair of a plurality of traffic lights and drivable paths selected from the plurality of traffic lights and the one or more drivable paths; Provide, as an input, the observed vehicle behavior represented by the received driving information to the at least one trained model, wherein the at least one trained model is configured to generate an updated traffic light correlation map based on the traffic light correlation map and the observed vehicle behavior, wherein generating the updated traffic light correlation map includes modifying at least one traffic light correlation indicator of at least one traffic light and drivable path pair among the plurality of traffic light and drivable path pairs; Store, in the crowdsourced map, the traffic light correlation indicator of each pair of the plurality of traffic light and drivable path pairs based on the updated traffic light correlation map; And Transmit the crowdsourced map to at least one vehicle expected to traverse the road segment for navigating the road segment relative to the stored traffic light correlation indicator of each pair of the plurality of traffic light and drivable path pairs.

2. The system according to claim 1, wherein the memory further comprises instructions that, when executed by the circuitry, cause the at least one processor to provide, as input, the status information of the plurality of traffic lights represented by the received driving information to the at least one trained model, and wherein the at least one trained model is configured to generate the updated traffic light correlation map based on the status information of the plurality of traffic lights.

3. The system according to claim 1, wherein the at least one trained model comprises at least a first trained model and a second trained model, and wherein: The determined position of each of the plurality of traffic lights and the spline representation of each of the one or more drivable paths are provided as an input to the first trained model; and The observed vehicle behavior is provided as an input to the second trained model.

4. The system according to claim 1, wherein the determined position of each of the plurality of traffic lights, the spline representation of each of the one or more drivable paths, and the observed vehicle behavior are provided as a single input to the at least one trained model.

5. The system according to claim 1, wherein aggregating the received driving information comprises aligning the driving information.

6. The system according to claim 5, wherein the received driving information comprises at least first driving information collected by a first vehicle and second driving information collected by a second vehicle, and wherein aligning the driving information comprises: Divide the first driving information into at least a first part and a second part and divide the second navigation information into at least a first part and a second part; And Align the first part of the first driving information with the first part of the second driving information and align the second part of the first driving information with the second part of the second driving information.

7. The system according to claim 1, wherein the at least one trained model comprises a convolutional neural network.

8. The system according to claim 1, wherein the observed vehicle behavior includes at least one of the plurality of vehicles crossing the intersection along a drivable path associated with the at least one traffic light and drivable path pair during a detected state of the traffic light associated with the at least one traffic light and drivable path pair.

9. The system according to claim 8, wherein the detected state is green, and modifying the traffic light correlation indicator includes confirming the correlation of the at least one traffic light and drivable path pair.

10. The system according to claim 8, wherein the detected state is red, and modifying the traffic light correlation indicator includes negating the correlation of the at least one traffic light and drivable path pair.

11. The system according to claim 1, wherein the observed vehicle behavior includes deceleration by at least one of the plurality of vehicles during a detected state of the traffic light associated with the at least one traffic light and drivable path pair.

12. The system according to claim 11, wherein the detected state is red, and modifying the traffic light correlation indicator includes confirming the correlation of the at least one traffic light and drivable path pair.

13. The system according to claim 11, wherein the detected state is green, and modifying the traffic light correlation indicator includes negating the correlation of the at least one traffic light and drivable path pair.

14. The system according to claim 11, wherein the observed vehicle behavior is based on the deceleration occurring within a predetermined distance of the traffic light.

15. The system according to claim 1, wherein the observed vehicle behavior includes acceleration by at least one of the plurality of vehicles during a detected state of the traffic light associated with the at least one traffic light and drivable path pair.

16. The system according to claim 15, wherein the detected state is red, and modifying the traffic light correlation indicator includes negating the correlation of the at least one traffic light and drivable path pair.

17. The system according to claim 15, wherein the detected state is green, and modifying the traffic light correlation indicator includes confirming the correlation of the at least one traffic light and drivable path pair.

18. The system according to claim 15, wherein the observed vehicle behavior is based on the acceleration occurring within a predetermined distance of the traffic light.

19. The system according to claim 1, wherein the traffic light correlation indicator is further modified based on the observed behavior of at least one additional object represented in the received driving information.

20. The system according to claim 19, wherein the at least one additional object includes a pedestrian crossing a drivable path associated with the at least one traffic light and drivable path pair during a detected state of the traffic light associated with the at least one traffic light and drivable path pair.

21. The system according to claim 20, wherein the detected state is red, and modifying the traffic light correlation indicator includes confirming the correlation of the at least one traffic light and drivable path pair.

22. The system according to claim 20, wherein the detected state is green, and modifying the traffic light correlation indicator includes negating the correlation of the at least one traffic light and drivable path pair.

23. The system according to claim 1, wherein modifying the traffic light correlation indicator includes the foregoing confirmation or negation of the correlation of the at least one traffic light and drivable path pair based on the characteristics of the drivable path associated with the at least one traffic light and drivable path pair.

24. The system according to claim 23, wherein the characteristic of the drivable path includes the curvature of the drivable path, and the observed vehicle behavior includes deceleration by at least one of the plurality of vehicles determined to be attributable to the curvature.

25. The system according to claim 1, wherein modifying the traffic light correlation indicator includes the foregoing confirmation or negation of the correlation of the at least one traffic light and drivable path pair based on the presence of at least one object.

26. The system according to claim 25, wherein the observed vehicle behavior includes deceleration by at least one of the plurality of vehicles determined to be attributable to the at least one object.

27. The system according to claim 1, wherein the plurality of traffic light and drivable path pairs includes all paired combinations between the plurality of traffic lights and the one or more drivable paths.

28. The system according to claim 1, wherein the memory further includes instructions that, when executed by the circuitry, cause the at least one processor to determine the plurality of traffic light and drivable path pairs based on the position of each of the plurality of traffic lights and the spline representation of the one or more drivable paths.

29. The system according to claim 1, wherein the traffic light correlation indicator of the at least one traffic light and drivable path pair includes a confidence level.

30. The system according to claim 29, wherein modifying the traffic light correlation indicator of the at least one traffic light and drivable path pair comprises modifying the confidence level based on the observed vehicle behavior.

31. A method for generating a crowdsourced map for use in vehicle navigation, the method comprising: Receive driving information collected from a plurality of vehicles traversing a road segment that intersects an intersection associated with a plurality of traffic lights; Aggregate the received driving information to determine the position of each of the plurality of traffic lights and to determine a spline representation of each of the one or more drivable paths associated with the road segment; Provide the determined position of each of the plurality of traffic lights and the spline representation of each of the one or more drivable paths as inputs to at least one trained model, wherein the at least one trained model is configured to generate a traffic light correlation map based on the determined position of each of the plurality of traffic lights and the spline representation of each of the one or more drivable paths, the traffic light correlation map including traffic light correlation indicators for each pair of a plurality of traffic lights and drivable paths selected from the plurality of traffic lights and the one or more drivable paths; Provide the observed vehicle behavior represented by the received driving information as an input to the at least one trained model, wherein the at least one trained model is configured to generate an updated traffic light correlation map based on the traffic light correlation map and the observed vehicle behavior, wherein generating the updated traffic light correlation map includes modifying at least one traffic light correlation indicator of at least one traffic light and drivable path pair among the plurality of traffic light and drivable path pairs; Store the traffic light correlation indicators for each pair of the plurality of traffic lights and drivable paths in a crowdsourced map based on the updated traffic light correlation map; And Transmit the crowdsourced map to at least one vehicle expected to cross the road segment for navigating the road segment relative to the stored traffic light correlation indicators for each pair of the plurality of traffic lights and drivable paths.

32. A non-transitory computer-readable medium containing instructions that, when executed by at least one processor, cause the at least one processor to perform operations including: Receiving driving information collected from a plurality of vehicles traversing a road segment that intersects an intersection associated with a plurality of traffic lights; Aggregate the received driving information to determine the position of each of the plurality of traffic lights and to determine the spline representation of each of the one or more drivable paths associated with the road segment; Provide the determined position of each of the plurality of traffic lights and the spline representation of each of the one or more drivable paths as inputs to at least one trained model, wherein the at least one trained model is configured to generate a traffic light correlation map based on the determined position of each of the plurality of traffic lights and the spline representation of each of the one or more drivable paths, the traffic light correlation map including traffic light correlation indicators for each pair of a plurality of traffic lights and drivable paths selected from the plurality of traffic lights and the one or more drivable paths; Provide the observed vehicle behavior represented by the received driving information as an input to the at least one trained model, wherein the at least one trained model is configured to generate an updated traffic light correlation map based on the traffic light correlation map and the observed vehicle behavior, wherein generating the updated traffic light correlation map includes modifying at least one traffic light correlation indicator of at least one traffic light and drivable path pair among the plurality of traffic light and drivable path pairs; Store the traffic light correlation indicators for each pair of the plurality of traffic lights and drivable paths in a crowdsourced map based on the updated traffic light correlation map; and transmitting the crowdsourced map to at least one vehicle expected to traverse the road segment for navigating the road segment relative to the stored traffic light correlation indicators for each pair of the plurality of traffic light and drivable path pairs.

Images