System, computer program, and non-transitory computer-readable medium for navigating a vehicle

Abstract

To provide a system and method for vehicle navigation.SOLUTION: A system for vehicle navigation may include instructions that cause a processor to receive navigational information associated with a vehicle including an indicator of a location of the vehicle, and determine target navigational map segments to retrieve from a map database. The determination of the target navigational map segments may be based on the vehicle location indicator and on map segment connectivity information associated with the stored navigational map segments. The instructions also cause the processor to initiate a download of the target navigational map segments from the map database, and navigate the vehicle along a target trajectory included in one or more of the target navigational map segments downloaded from the map database.SELECTED DRAWING: Figure 38

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Application No. 62 / 994,003, filed March 24, 2020, and U.S. Provisional Application No. 63 / 066,564, filed August 17, 2020. The above applications are incorporated herein by reference in their entireties.

[0002] The present disclosure relates generally to vehicle navigation. [Background technology]

[0003] As technology continues to evolve, the goal of fully autonomous vehicles capable of navigating roads becomes more realistic. An autonomous vehicle may need to take into account various factors and, based on those factors, make appropriate decisions to safely and accurately reach its intended destination. For example, an autonomous vehicle 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., a GPS device, a speed sensor, an accelerometer, a suspension sensor, etc.). At the same time, to navigate to its destination, an autonomous vehicle may need to identify its location within a particular road (e.g., a particular lane within a multi-lane road), navigate alongside other vehicles, avoid obstacles and pedestrians, observe traffic signals and signs, and navigate from one road to another at appropriate intersections or interchanges. Utilizing and interpreting the vast amount of information collected by an autonomous vehicle as the vehicle travels to its destination poses many design challenges. The vast amount of data (e.g., captured image data, map data, GPS data, sensor data, etc.) that an autonomous vehicle may need to analyze, access, and / or store poses challenges that can limit or even adversely affect autonomous navigation in practice. Furthermore, if an autonomous vehicle relies on traditional mapping techniques to navigate, the vast amount of data required to store and update maps poses daunting challenges. Summary of the Invention

[0004] SUMMARY OF THE INVENTION Embodiments according to the present disclosure provide systems and methods for vehicle navigation.

[0005] In one embodiment, a system for navigating a vehicle may include at least one processor having a circuit and a memory. The memory includes instructions that, when executed by the circuit, cause the at least one processor to receive navigation information associated with the vehicle, the navigation information including at least an indication of the vehicle's location. The instructions, when executed by the circuit, may also cause the at least one processor to determine a plurality of target navigation map segments to retrieve from a map database. The map database may include a plurality of stored navigation map segments, each corresponding to an area in the real world. The determination of the plurality of target navigation map segments may be based on map segment connectivity information associated with the plurality of stored navigation map segments based on the indication of the vehicle's location. The instructions, when executed by the circuit, may further cause the at least one processor to initiate downloading of the plurality of target navigation map segments from the map database. The instructions, when executed by the circuit, may also cause the at least one processor to navigate the vehicle along at least one target trajectory included in one or more of the plurality of target navigation map segments downloaded from the map database.

[0006] In one embodiment, a non-transitory computer-readable medium may include instructions that, when executed by at least one processor, cause the at least one processor to perform operations including receiving navigation information associated with a vehicle. The navigation information may include at least an indication of a location of the vehicle. The operations may also include determining a plurality of target navigation map segments to retrieve from a map database. The map database may include a plurality of stored navigation map segments, each corresponding to an area in the real world. The determination of the plurality of target navigation map segments may be based on map segment connectivity information associated with the plurality of stored navigation map segments based on the indication of the vehicle location. The operations may further include initiating downloading of the plurality of target navigation map segments from the map database. The operations may also include navigating the vehicle along at least one target trajectory included in one or more of the plurality of target navigation map segments downloaded from the map database.

[0007] In one embodiment, a non-transitory computer-readable medium may include a digital map for use in navigating a vehicle. The digital map may include a plurality of navigation map segments, each corresponding to an area in the real world. The digital map may also include, for each of the plurality of navigation map segments, at least one map segment connectivity indicator associated with each boundary shared with an adjacent navigation map segment. The at least one map segment connectivity indicator may be stored on the computer-readable medium as a Boolean bit.

[0008] The foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the scope of the claims. [Brief explanation of the drawings]

[0009] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various disclosed embodiments.

[0010] [Figure 1] 1 is a diagrammatic representation of an exemplary system according to the disclosed embodiments.

[0011] [Figure 2A] 1 is a side view representation of an exemplary vehicle including a system according to disclosed embodiments.

[0012] [Figure 2B] 2B is a top view representation of the vehicle and system shown in FIG. 2A according to a disclosed embodiment.

[0013] [Figure 2C] 1 is a top view representation of another embodiment of a vehicle including a system according to the disclosed embodiments.

[0014] [Figure 2D] 1 is a top view representation of yet another embodiment of a vehicle including a system according to the disclosed embodiments.

[0015] [Figure 2E] 1 is a top view representation of yet another embodiment of a vehicle including a system according to the disclosed embodiments.

[0016] [Figure 2F] 1 is a diagrammatic representation of an exemplary vehicle control system according to the disclosed embodiments.

[0017] [Figure 3A] 1 is a diagrammatic representation of the interior of a vehicle including a rearview mirror and a user interface of a vehicle imaging system according to disclosed embodiments.

[0018] [Figure 3B] 1 is a diagram of an example of a camera mount configured to be positioned behind a rearview mirror and opposite a vehicle windshield, according to a disclosed embodiment.

[0019] [Figure 3C]3C is a diagram of the camera mount shown in FIG. 3B from a different perspective, according to a disclosed embodiment.

[0020] [Figure 3D] 1 is a diagram of an example of a camera mount configured to be positioned behind a rearview mirror and opposite a vehicle windshield, according to a disclosed embodiment.

[0021] [Figure 4] FIG. 1 is an exemplary block diagram of a memory configured to store instructions for performing one or more operations in accordance with the disclosed embodiments.

[0022] [Figure 5A] 1 is a flowchart illustrating an exemplary process for generating one or more navigational responses based on monocular image analysis, according to disclosed embodiments.

[0023] [Figure 5B] 1 is a flowchart illustrating an exemplary process for detecting one or more vehicles and / or pedestrians in a set of images, according to disclosed embodiments.

[0024] [Figure 5C] 1 is a flowchart illustrating an exemplary process for detecting road markings and / or lane geometry information in a set of images, according to disclosed embodiments.

[0025] [Figure 5D] 1 is a flowchart illustrating an exemplary process for detecting traffic lights in a set of images, according to disclosed embodiments.

[0026] [Figure 5E] 1 is a flowchart illustrating an example process for generating one or more navigational responses based on a vehicle path, according to disclosed embodiments.

[0027] [Figure 5F]1 is a flowchart illustrating an example process for determining whether a leading vehicle is changing lanes, according to disclosed embodiments.

[0028] [Figure 6] 1 is a flowchart illustrating an exemplary process for generating one or more navigational responses based on stereo image analysis, according to disclosed embodiments.

[0029] [Figure 7] 1 is a flowchart illustrating an exemplary process for generating one or more navigational responses based on an analysis of three sets of images, according to disclosed embodiments.

[0030] [Figure 8] 1 illustrates a sparse map for providing autonomous vehicle navigation according to a disclosed embodiment.

[0031] [Figure 9A] 1 illustrates a polynomial representation of a portion of a road segment according to a disclosed embodiment.

[0032] [Figure 9B] 1 illustrates a curve in three-dimensional space representing a desired trajectory of a vehicle for a particular road segment contained in a sparse map, according to a disclosed embodiment.

[0033] [Figure 10] 1 illustrates examples of landmarks that may be included in a sparse map, consistent with the disclosed embodiments.

[0034] [Figure 11A] 1 illustrates a polynomial representation of a trajectory according to a disclosed embodiment.

[0035] [Figure 11B] 1 illustrates a target trajectory along a multi-lane road according to a disclosed embodiment. [Figure 11C] 1 illustrates a target trajectory along a multi-lane road according to a disclosed embodiment.

[0036] [Figure 11D] 1 illustrates an exemplary road signature profile, according to a disclosed embodiment.

[0037] [Figure 12] FIG. 1 is a schematic diagram of a system using crowdsourced data received from multiple vehicles for autonomous vehicle navigation, according to disclosed embodiments.

[0038] [Figure 13] 1 illustrates an example autonomous vehicle road navigation model represented by a plurality of cubic splines, according to the disclosed embodiments.

[0039] [Figure 14] 1 illustrates a map skeleton generated from combining location information from many runs, according to disclosed embodiments.

[0040] [Figure 15] 10 illustrates an example of longitudinal alignment of two runs with exemplary signs as landmarks, according to disclosed embodiments.

[0041] [Figure 16] 10 illustrates an example of longitudinal alignment of many runs with exemplary signs as landmarks, according to disclosed embodiments.

[0042] [Figure 17] FIG. 1 is a schematic diagram of a system for generating driving data using a camera, a vehicle, and a server, according to disclosed embodiments.

[0043] [Figure 18] FIG. 1 is a schematic diagram of a system for crowdsourcing sparse maps, according to disclosed embodiments.

[0044] [Figure 19]1 is a flowchart illustrating an example process for generating a sparse map for autonomous vehicle navigation along a road segment, according to a disclosed embodiment.

[0045] [Figure 20] FIG. 1 illustrates a block diagram of a server according to the disclosed embodiments.

[0046] [Figure 21] FIG. 1 illustrates a block diagram of a memory in accordance with disclosed embodiments.

[0047] [Figure 22] 1 illustrates a process for clustering vehicle trajectories associated with a vehicle according to a disclosed embodiment.

[0048] [Figure 23] 1 illustrates a navigation system for a vehicle that may be used for autonomous navigation, according to disclosed embodiments.

[0049] [Figure 24A] 1 illustrates exemplary lane markings that may be detected according to disclosed embodiments. [Figure 24B] 1 illustrates exemplary lane markings that may be detected according to disclosed embodiments. [Figure 24C] 1 illustrates exemplary lane markings that may be detected according to disclosed embodiments. [Figure 24D] 1 illustrates exemplary lane markings that may be detected according to disclosed embodiments.

[0050] [Figure 24E] 1 illustrates an example mapped lane marking in accordance with the disclosed embodiments.

[0051] [Figure 24F] 1 illustrates an example anomaly associated with detecting lane markings, according to disclosed embodiments.

[0052] [Figure 25A] 1 illustrates an exemplary image of a vehicle's surroundings for navigation based on mapped lane marks, according to a disclosed embodiment.

[0053] [Figure 25B] 10 illustrates a vehicle lateral localization correction based on mapped lane marks in a road navigation model according to a disclosed embodiment.

[0054] [Figure 26A] 1 is a flowchart illustrating an exemplary process for mapping lane marks for use in autonomous vehicle navigation, according to disclosed embodiments.

[0055] [Figure 26B] 1 is a flowchart illustrating an exemplary process for autonomously navigating a host vehicle along a road segment using mapped lane markings, according to disclosed embodiments.

[0056] [Figure 27] 1 illustrates an exemplary system for providing one or more map segments to one or more vehicles, according to disclosed embodiments.

[0057] [Figure 28A] 1 illustrates an exemplary potential travel envelope for a vehicle, according to disclosed embodiments. [Figure 28B] 1 illustrates an exemplary potential travel envelope for a vehicle, according to disclosed embodiments. [Figure 28C] 1 illustrates an exemplary potential travel envelope for a vehicle, according to disclosed embodiments. [Figure 28D] 1 illustrates an exemplary potential travel envelope for a vehicle, according to disclosed embodiments.

[0058] [Figure 28E]1 illustrates an exemplary map tile associated with a potential travel envelope for a vehicle, consistent with the disclosed embodiments. [Figure 28F] 1 illustrates an exemplary map tile associated with a potential travel envelope for a vehicle, consistent with the disclosed embodiments. [Figure 28G] 1 illustrates an exemplary map tile associated with a potential travel envelope for a vehicle, consistent with the disclosed embodiments. [Figure 28H] 1 illustrates an exemplary map tile associated with a potential travel envelope for a vehicle, consistent with the disclosed embodiments.

[0059] [Figure 29A] 1 illustrates an exemplary map tile, consistent with the disclosed embodiments. [Figure 29B] 1 illustrates an exemplary map tile, consistent with the disclosed embodiments.

[0060] [Figure 30] 1 illustrates an exemplary process for obtaining map tiles, consistent with the disclosed embodiments.

[0061] [Figure 31A] 1 illustrates an exemplary process for decoding map tiles, consistent with disclosed embodiments. [Figure 31B] 1 illustrates an exemplary process for decoding map tiles, consistent with disclosed embodiments. [Figure 31C] 1 illustrates an exemplary process for decoding map tiles, consistent with disclosed embodiments. [Figure 31D] 1 illustrates an exemplary process for decoding map tiles, consistent with disclosed embodiments.

[0062] [Figure 32] 1 is a flowchart illustrating an example process for providing one or more map segments to one or more vehicles, according to disclosed embodiments.

[0063] [Figure 33] 1 illustrates an exemplary system for automatically generating a navigation map associated with one or more road segments, according to the disclosed embodiments.

[0064] [Figure 34A] 1 illustrates an exemplary process for collecting navigation information, consistent with the disclosed embodiments. [Figure 34B] 1 illustrates an exemplary process for collecting navigation information, consistent with the disclosed embodiments. [Figure 34C] 1 illustrates an exemplary process for collecting navigation information, consistent with the disclosed embodiments.

[0065] [Figure 35] 1 is a flowchart illustrating an example process for automatically generating a navigation map associated with one or more road segments, according to the disclosed embodiments.

[0066] [Figure 36] FIG. 1 is a schematic diagram of an exemplary system for providing one or more map segments to one or more vehicles, according to disclosed embodiments.

[0067] [Figure 37] FIG. 2 is a schematic diagram of exemplary map tiles and tile partitions.

[0068] [Figure 38] FIG. 2 is a schematic diagram of exemplary map tiles and tile partitions.

[0069] [Figure 39] 1 illustrates an exemplary vehicle according to disclosed embodiments.

[0070] [Figure 40] 1 is a flowchart illustrating an exemplary process for navigating a vehicle, according to disclosed embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0071] The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar parts. While several exemplary embodiments are described herein, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or changes may be made to the components illustrated in the drawings, and the exemplary methods described herein may be modified by substituting, reordering, deleting, or adding steps of the disclosed methods. Therefore, the following detailed description is not limited to the disclosed embodiments and examples. Instead, the appropriate scope is defined by the appended claims.

[0072] Autonomous Vehicle Overview

[0073] As used throughout this disclosure, the term "autonomous vehicle" refers to a vehicle that can implement 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 need not be fully automatic (e.g., operate completely without a driver or driver input). Rather, an autonomous vehicle includes a vehicle that can operate under driver control during certain periods of time and without driver control during other periods of time. An autonomous vehicle can also include a vehicle that controls only some aspects of vehicle navigation, such as steering (e.g., to maintain the vehicle course between vehicle lane constraints), but leaves other aspects (e.g., braking) to the driver. In some cases, an autonomous vehicle may handle some or all aspects of the vehicle's braking, speed control, and / or steering.

[0074] Because human drivers typically rely on visual cues and observations to control their vehicles, transportation infrastructure is built accordingly, with lane markings, traffic signs, and traffic lights designed to provide all visual information to the driver. In light of these design characteristics of transportation infrastructure, autonomous vehicles may include cameras and processing units that analyze visual information captured from the vehicle's environment. Visual information may include, for example, transportation infrastructure components observable by the driver (e.g., lane markings, traffic signs, traffic lights, etc.) and other obstacles (e.g., other vehicles, pedestrians, debris, etc.). Additionally, autonomous vehicles may use stored information when navigating, such as information that provides a model of the vehicle's environment. For example, the vehicle may 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 while the vehicle is traveling, and the vehicle (and other vehicles) may use the information to locate itself within the model.

[0075] In some embodiments of the present disclosure, an autonomous vehicle may use information obtained while navigating (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) while navigating. In still other embodiments, an autonomous vehicle may use a combination of information obtained while navigating and information obtained from past navigation. The following sections provide an overview of a system according to disclosed embodiments, followed by an overview of a forward-looking imaging system and method according to the system. In the following sections, systems and methods for building, using, and updating sparse maps for autonomous vehicle navigation are disclosed.

[0076] System Overview

[0077] FIG. 1 is a block diagram representation of a system 100 according to an exemplary embodiment of the disclosure. System 100 may include various components depending on the requirements of a particular implementation. 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. Processing unit 110 may include one or more processing devices. In some embodiments, processing unit 110 may include an application processor 180, an image processor 190, or any other suitable processing device. Similarly, image acquisition unit 120 may include any number of image acquisition devices and components depending on the requirements of a particular application. In some embodiments, image acquisition unit 120 may include one or more imaging devices (e.g., cameras), such as imaging device 122, imaging device 124, imaging device 126, etc. System 100 may also include a data interface 128 that communicatively connects processing device 110 to image acquisition device 120. For example, data interface 128 may include one or more any wired and / or wireless links for transmitting image data acquired by image acquisition device 120 to processing unit 110.

[0078] Wireless transceiver 172 may include one or more devices configured to exchange transmissions with one or more networks (e.g., cellular, Internet, etc.) over a wireless interface using radio frequencies, infrared frequencies, magnetic fields, or electric fields. Wireless transceiver 172 may send and / or receive data using any known standard (e.g., Wi-Fi, Bluetooth, Bluetooth Smart, 802.15.4, ZigBee, etc.). Such transmissions may include communications from the host vehicle to one or more remotely located servers. Such transmissions may also include communications (one-way or two-way) between the host vehicle and one or more target vehicles within the host vehicle's environment (e.g., to facilitate coordinating the host vehicle's navigation in light of or with target vehicles within the host vehicle's environment), as well as broadcast transmissions to unspecified recipients in the transmitting vehicle's vicinity.

[0079] Both application processor 180 and image processor 190 may include various types of processing devices. For example, either or both of application processor 180 and image processor 190 may include a microprocessor, a preprocessor (e.g., an image preprocessor), a graphics processing unit (GPU), a central processing unit (CPU), support circuitry, a digital signal processor, an integrated circuit, memory, or any other type of device suitable for running applications and processing and analyzing images. In some embodiments, application processor 180 and / or image processor 190 may include any type of single-core or multi-core processor, mobile device microcontroller, central processing unit, etc. Various processing devices may be used and may include various architectures (e.g., x86 processor, ARM, etc.), including, for example, processors available from manufacturers such as Intel®, AMD®, or GPUs available from manufacturers such as NVIDIA®, ATI®, etc.

[0080] In some embodiments, application processor 180 and / or image processor 190 may include any of the EyeQ series of processor chips available from Mobileye®. These processor designs include multiple processing units, each with its own local memory and instruction set. Such processors may include video inputs that receive image data from multiple image sensors and may also include video output capabilities. In one example, the EyeQ2® uses 90 nm-micron technology operating at 332 MHz. The EyeQ2® architecture consists of two floating-point, hyper-threaded 32-bit RISC CPUs (MIPS32® 34K® cores), five Vision Computation Engines (VCEs), three Vector Microcode Processors (VMP®), a Denali 64-bit mobile DDR controller, a 128-bit internal audio interconnect, dual 16-bit video input and 18-bit video output controllers, a 16-channel DMA, and several peripherals. The MIPS34K CPU manages five VCEs, three VMP™ processors and DMA, a second MIPS34K CPU and multi-channel DMA, and other peripherals. The five VCEs, three VMP™ processors, and the MIPS34K CPU can perform intensive vision calculations required by feature-rich bundled applications. In another example, the disclosed embodiments may use the EyeQ3™, a third-generation processor that is six times more powerful than the EyeQ2™. In another example, the EyeQ4™ and / or EyeQ5™ processors may be used with the disclosed embodiments. Of course, newer or future EyeQ processing devices may also be used with the disclosed embodiments.

[0081] Any of the processing devices disclosed herein can be configured to perform specific functions. Configuring a processing device, such as any of the described EyeQ processors or other controllers or microprocessors, to perform specific functions may include programming computer-executable instructions and providing those instructions to the processing device for execution during operation of the processing device. In some embodiments, configuring a processing device may include directly programming architectural instructions into the processing device. For example, processing devices such as field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), and the like, may be configured using, for example, one or more hardware description languages ​​(HDLs).

[0082] In other embodiments, configuring the processing device may include storing executable instructions on a memory accessible by the processing device during operation. For example, the processing device may access the memory during operation to retrieve and execute the stored instructions. In any event, 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 a host vehicle.

[0083] 1 shows two separate processing devices included in processing unit 110, more or fewer processing devices may be used. For example, in some embodiments, a single processing device may be used to accomplish the tasks of application processor 180 and image processor 190. In other embodiments, these tasks may be performed by three or more processing devices. Furthermore, in some embodiments, system 100 may include one or more of processing units 110 without including other components, such as image acquisition unit 120.

[0084] 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 preprocessor, a central processing unit (CPU), a graphics processing unit (GPU), support circuits, a digital signal processor, an integrated circuit, memory, or any other type of device that processes and analyzes images. The image preprocessor may include a video processor that captures, digitizes, and processes 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 commonly known in the art, including cache, power supplies, clocks, and input / output circuits. The memory may store software that, when executed by the processor, controls the operation of the system. The memory may include databases 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 example, the memory may be separate from the processing unit 110. In another example, the memory may be integrated into the processing unit 110.

[0085] Each memory 140, 150 may contain software instructions that, when executed by a processor (e.g., application processor 180 and / or image processor 190), may control the operation of various aspects of system 100. These memory units may include various databases and image processing software, as well as trained systems such as 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 any other type of storage device. In some embodiments, memory units 140, 150 may be separate from application processor 180 and / or image processor 190. In other embodiments, these memory units may be integrated into application processor 180 and / or image processor 190.

[0086] Position sensor 130 may include any type of device suitable for determining a location associated with at least one component of system 100. In some embodiments, position sensor 130 may include a GPS receiver. Such a receiver may determine the location and velocity of a user by processing signals broadcast by Global Positioning System satellites. Position information from position sensor 130 may be provided to application processor 180 and / or image processor 190.

[0087] In some embodiments, system 100 may include components such as a speed sensor (e.g., a tachometer, speedometer) for measuring the speed of vehicle 200 and / or an accelerometer (either single-axis or multi-axis) for measuring the acceleration of vehicle 200.

[0088] User interface 170 may include any device suitable for providing information or receiving input from one or more users of system 100. In some embodiments, user interface 170 may include user input devices including, for example, a touchscreen, a microphone, a keyboard, a pointer device, a trackwheel, a camera, a knob, buttons, etc. Using such input devices, a user may be able to provide information input or commands to system 100 by typing instructions or information, providing voice commands, selecting on-screen menu options using buttons, pointer or eye tracking, or through any other suitable technique for communicating information to system 100.

[0089] User interface 170 may comprise one or more processing devices configured to provide information to or receive information from a user and process that information, for example, for use by application processor 180. In some embodiments, such processing devices may execute instructions to recognize and track eye movements, receive and interpret voice commands, recognize and interpret touches and / or gestures made on a touchscreen, respond to keyboard entries or menu selections, etc. In some embodiments, user interface 170 may include a display, a speaker, a tactile device, and / or any other device that provides output information to a user.

[0090] Map database 160 may include any type of database that stores map data useful to system 100. In some embodiments, map database 160 may include data related to the locations in a reference coordinate system of various items, including roads, water features, geographic features, businesses, points of interest, restaurants, gas stations, etc. Map database 160 may store not only the locations of such items, but also descriptors related to those items, including, for example, names associated with any of the stored features. In some embodiments, map database 160 may be physically located with other components of system 100. Alternatively or additionally, map database 160, or portions thereof, may be located remotely with respect to other components of system 100 (e.g., processing unit 110). In such embodiments, information from map database 160 may be downloaded to a network via a wired or wireless data connection (e.g., via a cellular network and / or the Internet, etc.). In some cases, map database 160 may store sparse data models including polynomial representations of particular road features (e.g., lane markings) or a desired trajectory of the host vehicle. Systems and methods for generating such maps are discussed below with reference to FIGS.

[0091] Imaging devices 122, 124, and 126 may each include any type of device suitable for capturing at least one image of an environment. Furthermore, any number of imaging devices may be used to obtain images for input to the image processor. Some embodiments may include only a single imaging device, while other embodiments may include two, three, or even four or more imaging devices. Imaging devices 122, 124, and 126 are further described below with reference to Figures 2B-2E.

[0092] System 100 or various components of system 100 may be incorporated into a variety of different platforms. In some embodiments, system 100 may be included in vehicle 200, as shown in FIG. 2A. For example, vehicle 200 may include processing unit 110 and any other components of system 100, as described above with respect to FIG. 1. In some embodiments, vehicle 200 may include only a single imaging device (e.g., a camera), while in other embodiments, such as those discussed in connection with FIGS. 2B-2E, multiple imaging devices may be used. For example, as shown in FIG. 2A, either of imaging devices 122 and 124 of vehicle 200 may be part of an ADAS (Advanced Driver Assistance Systems) imaging set.

[0093] The imaging device included in vehicle 200 as part of image capture unit 120 may be positioned in any suitable location. In some embodiments, as shown in Figures 2A-2E and 3A-3C, imaging device 122 may be positioned near the rearview mirror. This location may provide a line of sight similar to that of the driver of vehicle 200 and may assist in determining what the driver can and cannot see. While imaging device 122 may be located anywhere near the rearview mirror, placing imaging device 122 on the driver's side of the mirror may further assist in capturing images representative of the driver's field of view and / or line of sight.

[0094] Other locations for the imaging devices of the image acquisition unit 120 can also be used. For example, the imaging device 124 can be located on or within the bumper of the vehicle 200. Such a location can be particularly suitable for an imaging device with a wide field of view. The line of sight of an imaging device located on the bumper can be different from the line of sight of the driver, and thus the bumper imaging device and the driver do not always see the same object. The imaging devices (e.g., imaging devices 122, 124, and 126) can also be located in other locations. For example, the imaging devices can be located in or on 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, in the trunk of the vehicle 200, on the sides of the vehicle 200, mounted on, positioned behind, or positioned in front of any window of the vehicle 200, mounted in or near the front and / or rear lights of the vehicle 200, etc.

[0095] In addition to the imaging device, vehicle 200 may include various other components of system 100. For example, processing unit 110 may be integrated into the vehicle's engine control unit (ECU) or may be included in vehicle 200 separate from the ECU. Vehicle 200 may also be equipped with a location sensor 130, such as a GPS receiver, and vehicle 200 may also include a map database 160 and memory units 140 and 150.

[0096] As described above, wireless transceiver 172 may transmit and / or receive data via one or more networks (e.g., a cellular network, the Internet, etc.). For example, wireless transceiver 172 may upload data collected by system 100 to one or more servers and download data from one or more servers. Via wireless transceiver 172, system 100 may receive updates to data stored in map database 160, memory 140, and / or memory 150, for example, periodically or on demand. Similarly, wireless transceiver 172 may upload any data from system 100 (e.g., images captured by image acquisition unit 120, data received by position sensor 130, other sensors or vehicle control systems, etc.) and / or any data processed by processing unit 110 to one or more servers.

[0097] System 100 may upload data to a server (e.g., the cloud) based on a privacy level setting. For example, system 100 may implement a privacy level setting that regulates or limits the types of data (including metadata) that may uniquely identify the vehicle and / or the driver / owner of the vehicle that are sent to the server. Such settings may be set by a user via wireless transceiver 172, initialized by factory default settings, or set by data received by wireless transceiver 172, for example.

[0098] In some embodiments, system 100 may upload data according to a "high" privacy level, under which settings system 100 may transmit data (e.g., location information related to a route, captured images, etc.) without any details about the specific vehicle and / or driver / owner. For example, when uploading data according to a "high" privacy setting, system 100 may not include the vehicle identification number (VIN) or the name of the vehicle's driver or owner, and instead may transmit data such as captured images and / or limited location information related to a route.

[0099] Other privacy levels are contemplated. For example, system 100 may transmit data to a server according to a “medium” privacy level, which may include additional information not included under a “high” privacy level, such as the make and / or model of the vehicle and / or vehicle type (e.g., passenger car, sport utility vehicle, truck, etc.). In some embodiments, system 100 may upload data according to a “low” privacy level. Under the “low” privacy level setting, system 100 may upload and include sufficient data to uniquely identify a particular vehicle, owner / driver, and / or some or all of the route traveled by the vehicle. Such “low” privacy level data may include, for example, one or more of the VIN, driver / owner name, vehicle's starting point prior to departure, the vehicle's intended destination, vehicle make and / or model, vehicle type, etc.

[0100] Figure 2A is a side view representation of an exemplary vehicle imaging system according to a disclosed embodiment. Figure 2B is a schematic top view of the embodiment shown in Figure 2A. As shown in Figure 2B, the disclosed embodiment may include a vehicle 200 including within its body a system 100 having a first imaging device 122 positioned near a rearview mirror and / or near a driver of the vehicle 200, a second imaging device 124 positioned on or within a bumper region (e.g., one of bumper regions 210) of the vehicle 200, and a processing unit 110.

[0101] As shown in Figure 2C, both imaging devices 122 and 124 may be positioned near the rearview mirror and / or near the driver of vehicle 200. Furthermore, while two imaging devices 122 and 124 are shown in Figures 2B and 2C, it should be understood that other embodiments may include three or more imaging devices. For example, in the embodiment shown in Figures 2D and 2E, a first imaging device 122, a second imaging device 124, and a third imaging device 126 are included in system 100 of vehicle 200.

[0102] 2D , imaging device 122 may be positioned near the rearview mirror and / or near the driver of vehicle 200, and imaging devices 124 and 126 may be positioned on or in a bumper area (e.g., one of bumper areas 210) of vehicle 200. Also, as shown in FIG. 2E , imaging devices 122, 124, and 126 may be positioned near the rearview mirror and / or near the driver's seat of vehicle 200. The disclosed embodiments are not limited to any particular number or configuration of imaging devices, and imaging devices may be positioned in any suitable location in and / or on vehicle 200.

[0103] It should be understood that the disclosed embodiments are not limited to vehicles and may be applicable in other contexts. It should also be understood that the disclosed embodiments are not limited to a particular type of vehicle 200 and may be applicable to all types of vehicles, including cars, trucks, trailers, and other types of vehicles.

[0104] The first imaging device 122 may include any suitable type of imaging device. The imaging device 122 may include an optical axis. In one example, the imaging device 122 may include an Aptina M9V024 WVGA sensor with a global shutter. In other embodiments, the imaging device 122 may provide a resolution of 1280 x 960 pixels and may include a rolling shutter. The imaging device 122 may include various optical elements. In some embodiments, one or more lenses may be included to provide, for example, a desired focal length and field of view for the imaging device. In some embodiments, a 6 mm lens or a 12 mm lens may be associated with the imaging device 122. In some embodiments, the imaging device 122 may be configured to capture images having a desired field of view (FOV) 202, as shown in FIG. 2D . For example, the imaging device 122 may be configured to have a typical FOV, such as within the range of 40 degrees to 56 degrees, including a 46 degree FOV, a 50 degree FOV, a 52 degree FOV, or a degree greater than 52 degrees. Alternatively, imaging device 122 may be configured to have a narrow FOV in the range of 23 to 40 degrees, such as a 28-degree FOV or a 36-degree FOV. Additionally, imaging device 122 may be configured to have a wide FOV in the range of 100 to 180 degrees. In some embodiments, imaging device 122 may include a wide-angle bumper camera or a bumper camera with an FOV of up to 180 degrees. In some embodiments, imaging device 122 may be a 7.2-megapixel imaging device with an aspect ratio of approximately 2:1 (e.g., H×V=3800×1900 pixels) and a horizontal FOV of approximately 100 degrees. Such an imaging device may be used instead of a three-imaging device configuration. Due to significant lens distortion, the vertical FOV of such an imaging device may be much lower than 50 degrees in implementations in which the imaging device uses a radially symmetric lens. For example, such a lens may not be radially symmetric, thereby allowing a vertical FOV greater than 50 degrees with a horizontal FOV of 100 degrees.

[0105] The first imaging device 122 may acquire a plurality of first images of a scene associated with the vehicle 200. The plurality of first images may each be acquired as a series of image scan lines, which may be captured using a rolling shutter. Each scan line may include a plurality of pixels.

[0106] The first imaging device 122 may have a scan rate associated with the acquisition of each of the first series of image scan lines. The scan rate may refer to the rate at which the image sensor can acquire image data associated with each pixel included in a particular scan line.

[0107] Image capture devices 122, 124, and 126 may include any suitable type and number of image sensors, including, for example, CCD sensors or CMOS sensors. In one embodiment, a CMOS image sensor may be utilized with a rolling shutter, whereby each pixel in a row is read one at a time, and the scanning of the rows proceeds row by row until the entire image frame is captured. In some embodiments, the rows may be captured sequentially from top to bottom for the frame.

[0108] In some embodiments, one or more of the imaging devices disclosed herein (e.g., imaging devices 122, 124, and 126) may constitute a high-resolution imager and may have a resolution of greater than 5M pixels, greater than 7M pixels, greater than 10M pixels, or more.

[0109] The use of a rolling shutter can result in pixels in different rows being exposed and imaged at different times, which can cause skew and other image artifacts in the captured image frame. On the other hand, if the imaging device 122 is configured to operate using a global or synchronous shutter, all pixels can be exposed for the same amount of time during a common exposure period. As a result, image data in a frame collected from a system utilizing a global shutter represents a snapshot of the entire FOV (such as FOV 202) at a particular time. Conversely, when a rolling shutter is applied, each row in a frame is exposed and the data is imaged at a different time. Therefore, moving objects can appear distorted in an imaging device with a rolling shutter. This phenomenon is described in more detail below.

[0110] The second imaging device 124 and the third imaging device 126 may be any type of imaging device. Like the first imaging device 122, each of the imaging devices 124 and 126 may include an optical axis. In one embodiment, each of the imaging devices 124 and 126 may include an Aptina M9V024 WVGA sensor with a global shutter. Alternatively, each of the imaging devices 124 and 126 may include a rolling shutter. Like the imaging device 122, the imaging devices 124 and 126 may be configured to include various lenses and optical elements. In some embodiments, the lenses associated with the imaging devices 124 and 126 may provide the same FOV as the imaging device 122 (such as FOV 202) or a narrower FOV (such as FOVs 204 and 206). For example, the imaging devices 124 and 126 may have an FOV of 40 degrees, 30 degrees, 26 degrees, 23 degrees, 20 degrees, or less than 20 degrees.

[0111] Image capture devices 124 and 126 may acquire second and third multiple images of a scene associated with vehicle 200. Each of the second and third multiple images may be acquired as second and third series of image scan lines, which may be captured using a rolling shutter. Each scan line or row may have a plurality of pixels. Image capture devices 124 and 126 may have second and third scan rates associated with acquiring each image scan line included in the second and third series.

[0112] Each imaging device 122, 124, and 126 may be positioned in any suitable location and in any suitable orientation relative to vehicle 200. The relative positioning of imaging devices 122, 124, and 126 may be selected to assist in fusing together information obtained from the imaging devices. For example, in some embodiments, the FOV associated with imaging device 124 (e.g., FOV 204) may partially or completely overlap with the FOV associated with imaging device 122 (e.g., FOV 202) and the FOV associated with imaging device 126 (e.g., FOV 206).

[0113] The imaging devices 122, 124, and 126 may be positioned on the vehicle 200 at any suitable relative height. In one example, there may be a height difference between the imaging devices 122, 124, and 126, which may provide sufficient parallax information to enable stereo analysis. For example, as shown in FIG. 2A, the two imaging devices 122 and 124 are at different heights. There may also be a lateral displacement difference between the imaging devices 122, 124, and 126, which may provide additional parallax information for stereo analysis by the processing unit 110, for example. The lateral displacement difference may be calculated as d x In some embodiments, a forward or rearward displacement (e.g., range displacement) may exist between imaging devices 122, 124, 126. For example, imaging device 122 may be positioned 0.5 to 2 meters or more behind imaging device 124 and / or imaging device 126. This type of displacement may allow one of the imaging devices to cover a potential blind spot of the other imaging device.

[0114] Image capture device 122 may have any suitable resolution capability (e.g., number of pixels associated with the image sensor), and the resolution of the image sensor associated with image capture device 122 may be higher, lower, or the same as the resolution of the image sensors associated with image capture devices 124 and 126. In some embodiments, the image sensors associated with image capture device 122 and / or image capture devices 124 and 126 may have a resolution of 640×480, 1024×768, 1280×960, or any other suitable resolution.

[0115] The frame rate (e.g., the rate at which the imaging device acquires a set of pixel data for one image frame before moving on to capturing pixel data associated with the next image frame) may be controllable. The frame rate associated with imaging device 122 may be higher, lower, or the same as the frame rate associated with imaging devices 124 and 126. The frame rate associated with imaging devices 122, 124, and 126 may depend on various factors that may affect the timing of the frame rate. For example, one or more of imaging devices 122, 124, and 126 may include a selectable pixel delay period imposed before or after acquisition of image data associated with one or more pixels of the image sensors within imaging devices 122, 124, and / or 126. Generally, image data corresponding to each pixel may be acquired according to the device's clock rate (e.g., one pixel per clock cycle). Additionally, in embodiments including a rolling shutter, one or more of imaging devices 122, 124, and 126 may include a selectable horizontal blanking period imposed before or after acquisition of image data associated with a row of pixels of an image sensor in imaging devices 122, 124, and / or 126. Additionally, one or more of imaging devices 122, 124, and / or 126 may include a selectable vertical blanking period imposed before or after acquisition of image data associated with an image frame of imaging devices 122, 124, and 126.

[0116] These timing controls may enable synchronization of frame rates associated with imaging devices 122, 124, and 126, even when the line scan rate of each imaging device is different. Additionally, as discussed in more detail below, among other factors (e.g., image sensor resolution, maximum line scan rate, etc.), these selectable timing controls may enable synchronization of image capture from areas where the FOV of imaging device 122 overlaps with the FOV of one or more of imaging devices 124 and 126, even when the field of view of imaging device 122 differs from the FOV of imaging devices 124 and 126.

[0117] The frame rate timing for imaging devices 122, 124, and 126 may depend on the resolution of the associated image sensors. For example, assuming both devices have similar line scan rates, if one device includes an image sensor with a resolution of 640x480 and the other device includes an image sensor with a resolution of 1280x960, capturing a frame of image data from the sensor with the higher resolution will require a longer time.

[0118] Another factor that may affect the timing of image data acquisition at imaging devices 122, 124, and 126 is the maximum line scan rate. For example, acquisition of a row of image data from the image sensors included in imaging devices 122, 124, and 126 requires some minimum amount of time. Assuming no pixel delay period is added, this minimum amount of time to acquire a row of image data will be related to the maximum line scan rate of a particular device. Devices offering higher maximum line scan rates have the potential to provide higher frame rates than devices with lower maximum line scan rates. In some embodiments, one or both of imaging devices 124 and 126 may have a maximum line scan rate that is higher than the maximum line scan rate associated with imaging device 122. In some embodiments, the maximum line scan rate of imaging devices 124 and / or 126 may be 1.25, 1.5, 1.75, or 2 times or more the maximum line scan rate of imaging device 122.

[0119] In another embodiment, imaging devices 122, 124, and 126 may have the same maximum line scan rate, but imaging 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 both of imaging device 124 and imaging device 126 operate at a line scan rate that is equal to the line scan rate of imaging device 122. In other examples, the system may be configured such that the line scan rate of imaging device 124 and / or imaging device 126 may be 1.25, 1.5, 1.75, or 2 times or more the line scan rate of imaging device 122.

[0120] In some embodiments, imaging devices 122, 124, and 126 may be asymmetric. That is, the imaging devices may include cameras with different fields of view (FOV) and focal lengths. The fields of view of imaging devices 122, 124, and 126 may include, for example, any desired area of ​​the environment of vehicle 200. In some embodiments, one or more of imaging devices 122, 124, and 126 may be configured to acquire image data from an environment in front of vehicle 200, an environment behind vehicle 200, an environment on either side of vehicle 200, or a combination thereof.

[0121] Additionally, the focal length associated with each imaging device 122, 124, and / or 126 may be selectable (e.g., by including an appropriate lens, etc.) so that each device captures images of objects at a desired distance range relative to vehicle 200. For example, in some embodiments, imaging devices 122, 124, and 126 may capture images of close-up objects within a few meters of the vehicle. Imaging devices 122, 124, 126 may also be configured to capture images of objects at greater distances from the vehicle (e.g., 25 m, 50 m, 100 m, 150 m, or more). Furthermore, the focal lengths of imaging devices 122, 124, and 126 may be selected such that one imaging device (e.g., imaging device 122) can capture images of objects relatively close to the vehicle (e.g., within 10 m or within 20 m), while other imaging devices (e.g., imaging devices 124 and 126) can capture images of objects farther away from vehicle 200 (e.g., more than 20 m, more than 50 m, more than 100 m, more than 150 m, etc.).

[0122] According to some embodiments, the FOV of one or more of imaging devices 122, 124, and 126 may have a wide angle. For example, it may be advantageous to have an FOV of 140 degrees, particularly for imaging devices 122, 124, and 126 that may be used to capture images of areas near vehicle 200. For example, imaging device 122 may be used to capture images of areas to the right or left of vehicle 200, and in such embodiments, it may be desirable for imaging device 122 to have a wide FOV (e.g., at least 140 degrees).

[0123] The field of view associated with each of the imaging devices 122, 124, and 126 may depend on the respective focal lengths. For example, as the focal lengths increase, the corresponding field of view decreases.

[0124] Imaging devices 122, 124, and 126 may be configured to have any suitable field of view. In one particular example, imaging device 122 may have a horizontal FOV of 46 degrees, imaging device 124 may have a horizontal FOV of 23 degrees, and imaging device 126 may have a horizontal FOV of 23 to 46 degrees. In another example, imaging device 122 may have a horizontal FOV of 52 degrees, imaging device 124 may have a horizontal FOV of 26 degrees, and imaging device 126 may have a horizontal FOV of 26 to 52 degrees. In some embodiments, the ratio between the FOV of imaging device 122 and the FOV of imaging device 124 and / or imaging device 126 may vary from 1.5 to 2.0. In other embodiments, this ratio may vary from 1.25 to 2.25.

[0125] System 100 may be configured so that the field of view of imaging device 122 at least partially or completely overlaps the field of view of imaging device 124 and / or imaging device 126. In some embodiments, system 100 may be configured so that the fields of view of imaging devices 124 and 126, for example, fall within the field of view of imaging device 122 (e.g., are smaller than the field of view of imaging device 122) and share a common center with the field of view of imaging device 122. In other embodiments, imaging devices 122, 124, and 126 may image adjacent FOVs or may have partially overlapping FOVs. In some embodiments, the fields of view of imaging devices 122, 124, and 126 may be aligned such that the center of imaging device 124 and / or 126, which has a narrower FOV, may be located in the bottom half of the field of view of device 122, which has a wider FOV.

[0126] 2F is a diagrammatic representation of an exemplary vehicle control system according to disclosed embodiments. As shown in FIG. 2F, vehicle 200 may include a throttle system 220, a braking system 230, and a steering system 240. System 100 may 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., one or more any wired and / or wireless links or links that transmit data). For example, based on analysis of images acquired by imaging devices 122, 124, and / or 126, system 100 may 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 it to accelerate, turn, lane shift, etc.). Additionally, system 100 may receive inputs indicative of the operating conditions of vehicle 200 (e.g., speed, whether vehicle 200 is braking and / or turning, etc.) from one or more of throttle system 220, braking system 230, and steering system 24. Further details are provided below in connection with Figures 4-7.

[0127] As shown in FIG. 3A , vehicle 200 may also include a user interface 170 for interacting with a driver or passenger of vehicle 200. For example, user interface 170 in a vehicle application may include a touchscreen 320, knobs 330, buttons 340, and a microphone 350. A driver or passenger of vehicle 200 may also interact with system 100 using a steering wheel (e.g., located on or near a steering column of vehicle 200, including, for example, a turn signal handle) and buttons (e.g., located on the steering wheel of vehicle 200), and the like. In some embodiments, microphone 350 may be positioned adjacent to rearview mirror 310. Similarly, in some embodiments, imaging device 122 may be located near rearview mirror 310. In some embodiments, 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 speaker 360.

[0128] 3B-3D are diagrams of an exemplary camera mount 370 configured to be positioned behind a rearview mirror (e.g., rearview mirror 310) and facing a vehicle windshield, according to disclosed embodiments. As shown in FIG. 3B , camera mount 370 may include imaging devices 122, 124, and 126. Imaging devices 124 and 126 may be positioned behind glare shield 380, which may be in direct contact with the vehicle windshield and may include a film and / or anti-reflective material composition. For example, glare shield 380 may be positioned such that the shield is aligned facing the vehicle windshield with a matching slope. In some embodiments, each of imaging devices 122, 124, and 126 may be positioned behind glare shield 380, for example, as shown in FIG. 3D . The disclosed embodiments are not limited to any particular configuration of imaging devices 122, 124, and 126, camera mount 370, and glare shield 380. FIG. 3C is a front view of the camera mount 370 shown in FIG. 3B.

[0129] As will be appreciated by those skilled in the art having the benefit of this disclosure, many variations and / or modifications may be made to the above-disclosed embodiments. For example, not all components are essential to the operation of system 100. Furthermore, any component may be located in any suitable portion of system 100, and the components may be rearranged in various configurations while still providing the functionality of the disclosed embodiments. Accordingly, the configurations discussed above are examples, and regardless of the configuration described above, system 100 may provide a wide range of functionality for analyzing the surroundings of vehicle 200 and navigating vehicle 200 in response to the analysis.

[0130] As discussed in further detail below, according to various disclosed embodiments, 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 capture unit 120, location sensor 130, and other sensors. Furthermore, system 100 may analyze the collected data to identify whether vehicle 200 should take a particular action and then automatically take the determined action without human intervention. For example, when vehicle 200 navigates 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). Furthermore, system 100 may analyze the collected data and issue warnings and / or alerts to vehicle occupants based on the analysis of the collected data. Further details regarding various embodiments provided by system 100 are provided below.

[0131] Forward-facing multi-imaging system

[0132] As discussed above, system 100 may provide driving assistance features using a multi-camera system. The multi-camera system may use one or more cameras facing forward of the vehicle. In other embodiments, the multi-camera system may include one or more cameras facing the side of the vehicle or the rear of the vehicle. In one embodiment, for example, system 100 may use a two-camera imaging system, in which a first camera and a second camera (e.g., imaging devices 122 and 124) may be positioned at the front and / or side of the vehicle (e.g., vehicle 200). The first camera may have a field of view that is larger than, smaller than, or partially overlaps with the field of view of the second camera. Furthermore, the first camera may be connected to a first image processor to perform monocular image analysis of images provided by the first camera, and the second camera may be connected to a second image processor to perform monocular image analysis of 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 and second cameras and perform stereo analysis. In another embodiment, system 100 may use a three-camera imaging system, where each camera has a different field of view. Thus, such a system may make decisions based on information derived from objects located at various distances both in front of and to the sides of the vehicle. References to monocular image analysis may refer to cases where image analysis is performed based on images captured from a single viewpoint (e.g., a single camera). Stereo image analysis may refer to cases where image analysis is performed based on two or more images captured with one or more image capture parameters changed. For example, captured images suitable for performing stereo analysis may include images captured from two or more different positions, images captured from different fields of view, images captured using different focal lengths, images captured with parallax information, etc.

[0133] For example, in one embodiment, system 100 may implement a three-camera configuration using imaging devices 122, 124, and 126. In such a configuration, imaging device 122 may provide a narrow field of view (e.g., 34 degrees or other value selected from the range of approximately 20 to 45 degrees), imaging device 124 may provide a wide field of view (e.g., 150 degrees or other value selected from the range of approximately 100 to approximately 180 degrees), and imaging device 126 may provide a medium field of view (e.g., 46 degrees or other value selected from the range of approximately 35 to approximately 60 degrees). In some embodiments, imaging device 126 may operate as a main or primary camera. Imaging 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 imaging devices 122, 124, and 126 may be mounted behind a glare shield 380 that is flush with the windshield of vehicle 200. Such a shield may operate to minimize the impact of any reflections from the interior of the vehicle on imaging devices 122, 124, and 126.

[0134] 3B and 3C, the wide field of view camera (e.g., imaging device 124 in the example above) may be mounted lower than the narrow main field of view camera (e.g., imaging devices 122 and 126 in the example above). This configuration may provide a free line of sight from the wide field of view camera. To reduce reflections, the camera may be mounted near the windshield of vehicle 200 and may include a polarizer to attenuate reflected light.

[0135] A 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), each dedicated to processing images captured by one or more of imaging devices 122, 124, and 126.

[0136] In a three-camera system, a first processing device may receive images from both the primary camera and the narrow FOV camera and perform vision processing for the narrow FOV camera to detect, for example, other vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. Additionally, the first processing device may calculate pixel discrepancies between the images from the primary 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 3D information calculated based on information from another camera.

[0137] The second processing device may receive images from the primary camera, perform vision processing, and detect other vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. Furthermore, the second processing device may calculate camera displacement, calculate pixel discrepancies between successive images based on the displacement, and create a 3D reconstruction (e.g., structure-from-motion) of the scene. The second processing device may send the structure-from-motion based on the 3D reconstruction to the first processing device, and combine the structure-from-motion with the stereoscopic 3D image.

[0138] The third processing device may receive images from the 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 and identify moving objects in the images, such as vehicles changing lanes, pedestrians, etc.

[0139] In some embodiments, having image-based information streams captured and processed independently may provide an opportunity for redundancy in the system. Such redundancy may include, for example, using a first imaging device and the images processed from that device to verify and / or capture information obtained by capturing and processing image information from at least a second imaging device.

[0140] In some embodiments, system 100 may use two imaging devices (e.g., imaging devices 122 and 124) in providing navigation assistance to vehicle 200, and may use a third imaging device (e.g., imaging device 126) to provide redundancy and verify the analysis of data received from the other two imaging devices. For example, in such a configuration, imaging devices 122 and 124 may provide images for stereo analysis by system 100 for navigating vehicle 200, and imaging device 126 may provide images for monocular analysis by system 100 to provide redundancy and verification of information derived based on images captured from imaging devices 122 and / or imaging device 124. That is, imaging device 126 (and corresponding processing device) may be viewed as providing a redundant subsystem that provides a check on the analysis derived from imaging devices 122 and 124 (e.g., to provide an automatic emergency braking (AEB) system). Additionally, in some embodiments, redundancy and validation of received data can be supplemented based on information received from one or more sensors (e.g., radar, lidar, acoustic sensors, information received from one or more transceivers outside the vehicle, etc.).

[0141] Those skilled in the art will recognize that the above camera configurations, camera placements, camera numbers, camera locations, etc. are merely exemplary. These components, etc., described for an 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 follow below.

[0142] 4 is an example functional block diagram of memory 140 and / or 150 that may be stored / programmed with instructions to perform one or more operations in accordance with the disclosed embodiments. While reference is made below to memory 140, those skilled in the art will recognize that instructions may be stored in memory 140 and / or 150.

[0143] 4 , memory 140 may store a monocular image analysis module 402, a stereo image analysis module 404, a velocity and acceleration module 406, and a navigation response module 408. The disclosed embodiments are not limited to any particular configuration of memory 140. Furthermore, application processor 180 and / or image processor 190 may execute instructions stored in any 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 collectively. Accordingly, any steps of the following processes may be performed by one or more processing devices.

[0144] In one embodiment, monocular image analysis module 402 may store instructions (e.g., computer vision software) that, when executed by processing unit 110, perform monocular image analysis of a set of images acquired by one of imaging devices 122, 124, and 126. In some embodiments, processing unit 110 may combine information from the set of images with additional sensory information (e.g., information from radar, lidar, etc.) to perform the monocular image analysis. As described below in connection with FIGS. 5A through 5D , monocular image analysis module 402 may include instructions for detecting a set of features in the set of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazards, and any other features associated with the vehicle's environment. Based on the analysis, system 100 may cause (e.g., via processing unit 110) vehicle 200 to make one or more navigational responses, such as a turn, a lane shift, an acceleration change, and the like, as discussed below in connection with navigation response module 408.

[0145] In one embodiment, stereo image analysis module 404 may store instructions (e.g., computer vision software) that, when executed by processing unit 110, perform stereo image analysis of first and second sets of images acquired by a combination of imaging devices selected from imaging devices 122, 124, and 126. In some embodiments, processing unit 110 may combine information from the first and second sets of images with additional sensory information (e.g., information from radar) to perform stereo image analysis. For example, stereo image analysis module 404 may include instructions to perform stereo image analysis based on the first set of images acquired by imaging device 124 and the second set of images acquired by imaging device 126. As described below in connection with FIG. 6 , stereo image analysis module 404 may include instructions to detect sets of features in the first and second sets of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazards, and the like. Based on the analysis, processing unit 110 may cause one or more navigational responses in vehicle 200, such as turns, lane shifts, and acceleration changes, and the like, as discussed below in connection with navigation response module 408. Additionally, in some embodiments, stereo image analysis module 404 may implement techniques related to trained systems (such as neural networks or deep neural networks) or untrained systems, such as systems that may be configured to use computer vision algorithms to detect and / or label objects in an environment where sensory information has been imaged and processed. In one embodiment, stereo image analysis module 404 and / or other image processing modules may be configured to use a combination of trained and untrained systems.

[0146] In one embodiment, speed and acceleration module 406 may store software configured to analyze data received from one or more computational and electromechanical devices within vehicle 200 configured to alter the speed and / or acceleration of vehicle 200. For example, processing unit 110 may execute instructions associated with speed and acceleration module 406 to calculate a target speed of vehicle 200 based on data derived from the execution of monocular image analysis module 402 and / or stereo image analysis module 404. Such data may include, for example, target position, speed, and / or acceleration, the position and / or speed of vehicle 200 relative to nearby vehicles, pedestrians, or road objects, and the position information of vehicle 200 relative to road lane markings, and the like. Additionally, processing unit 110 may calculate a target speed of vehicle 200 based on sensory input (e.g., information from radar) and input from other systems of vehicle 200, such as throttle system 220, braking system 230, and / or steering system 240 of vehicle 200. Based on the calculated target speed, the processing unit 110 may send electronic signals to the throttle system 220, the braking system 230 and / or the steering system 240 of the vehicle 200 to trigger a change in speed and / or acceleration, for example, by physically releasing the brakes or easing the accelerator of the vehicle 200.

[0147] In one embodiment, the navigation response module 408 may store executable software by the processing unit 110 to determine a desired navigation response based on data derived from the execution of 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 for the vehicle 200, and the like. Furthermore, in some embodiments, the navigation response may be based (partially or fully) on map data, a predetermined position of the vehicle 200, and / or the relative speed or relative acceleration between the vehicle 200 and one or more objects detected from 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 configured to determine a desired navigation response based on sensory input (e.g., information from radar) and inputs from other systems of the vehicle 200, such as the throttle system 220, the braking system 230, and the steering system 240 of the vehicle 200. Based on the desired navigation response, processing unit 110 may send electronic signals to throttle system 220, braking system 230, and steering system 240 of vehicle 200 to trigger the desired navigation response, for example, by turning the steering wheel of vehicle 200 to achieve a predetermined angle of rotation. In some embodiments, processing unit 110 may use the output of navigation response module 408 (e.g., the desired navigation response) as input to the execution of velocity and acceleration module 406 to calculate a change in velocity of vehicle 200.

[0148] Additionally, any of the modules disclosed herein (e.g., modules 402, 404, and 406) may implement techniques related to trained systems (such as neural networks or deep neural networks) or untrained systems.

[0149] 5A is a flowchart illustrating an example process 500A for generating one or more navigational responses based on monocular image analysis, according to a disclosed embodiment. At step 510, processing unit 110 may receive a plurality of images via data interface 128 between processing unit 110 and image acquisition unit 120. For example, a camera (such as imaging device 122 having field of view 202) included in image acquisition unit 120 may capture a plurality of images of an area in front of vehicle 200 (or, for example, to the side or rear of the vehicle) and transmit them to processing unit 110 via a data connection (e.g., digital, wired, USB, wireless, Bluetooth, etc.). Processing unit 110 may execute monocular image analysis module 402 to analyze the plurality of images at step 520, as described in further detail below in connection with FIGS. 5B through 5D . By performing the analysis, processing unit 110 may detect a set of features within the set of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps and traffic lights, and the like.

[0150] Processing unit 110 may also execute monocular image analysis module 402 in step 520 to detect various road hazards, such as truck tire parts, fallen road signs, loose cargo and small animals, and the like. Road hazards may vary in structure, shape, size, and color, making such hazards more difficult to detect. In some embodiments, processing unit 110 may execute monocular image analysis module 402 to perform multi-frame analysis on multiple images to detect road hazards. For example, processing unit 110 may estimate camera motion between consecutive image frames and calculate pixel discrepancies between frames to construct a 3D map of the road. Processing unit 110 may then use the 3D map to detect the road surface and hazards present on the road surface.

[0151] In step 530, processing unit 110 may execute navigation response module 408 to cause one or more navigational responses in vehicle 200 based on the analysis performed in step 520 and the techniques described above in connection with FIG. 4 . The navigational responses may include, for example, a turn, a lane shift, an acceleration change, and the like. In some embodiments, processing unit 110 may use data derived from execution of speed and acceleration module 406 to cause one or more navigational responses. Furthermore, multiple navigational responses may be performed simultaneously, sequentially, or any combination thereof. For example, processing unit 110 may cause vehicle 200 to cross a lane and then, for example, accelerate, for example, by sequentially sending control signals to steering system 240 and throttle system 220 of vehicle 200. Alternatively, processing unit 110 may cause vehicle 200 to brake and simultaneously shift lanes, for example, by simultaneously sending control signals to braking system 230 and steering system 240 of vehicle 200.

[0152] FIG. 5B is a flowchart illustrating an example process 500B for detecting one or more vehicles and / or pedestrians in a set of images according to disclosed embodiments. Processing unit 110 may execute monocular image analysis module 402 to perform process 500B. In step 540, processing unit 110 may identify a set of candidate objects representing possible vehicles and / or pedestrians. For example, processing unit 110 may scan one or more images, compare the images to one or more predetermined patterns, and identify locations within each image that may contain a target object (e.g., a vehicle, a pedestrian, or a portion thereof). The predetermined patterns may be specified to achieve a high rate of “false hits” and a low rate of “misses.” For example, processing unit 110 may use a low similarity threshold to the predetermined patterns to identify candidate objects as possible vehicles or pedestrians. By doing so, processing unit 110 may reduce the probability of missing (e.g., not identifying) a candidate object representing a vehicle or pedestrian.

[0153] At step 542, processing unit 110 may filter the set of candidate objects to eliminate certain candidates (e.g., irrelevant or less relevant objects) based on classification criteria. Such criteria may be derived from various characteristics associated with object types stored in a database (e.g., a database stored in memory 140). The characteristics may include the object's shape, dimensions, texture, and location (e.g., relative to vehicle 200), and the like. Thus, processing unit 110 may use one or more sets of criteria to reject false candidates from the set of candidate objects.

[0154] At step 544, processing unit 110 may analyze multiple image frames to identify whether objects in the set of candidate objects represent vehicles and / or pedestrians. For example, processing unit 110 may track detected candidate objects across consecutive frames and accumulate frame-by-frame data associated with the detected objects (e.g., size, position relative to vehicle 200, etc.). Additionally, processing unit 110 may estimate parameters of the detected objects and compare the object's frame-by-frame position data to predicted positions.

[0155] In step 546, processing unit 110 may construct a set of measurements of the detected objects. Such measurements may include, for example, position, velocity, and acceleration values ​​(relative to vehicle 200) associated with the detected objects. In some embodiments, processing unit 110 may construct the measurements based on estimation techniques using a series of time-based observations, such as a Kalman filter or linear quadratic estimation (LQE), and / or based on modeling data available for different object types (e.g., cars, trucks, pedestrians, bicycles, road signs, etc.). A Kalman filter may be based on measurements of the object's scale, where the scale measurement is proportional to the time to impact (e.g., the amount of time it takes vehicle 200 to reach the object). Thus, by performing steps 540 through 546, processing unit 110 may identify vehicles and pedestrians appearing in the set of captured images and derive information associated with the vehicles and pedestrians (e.g., position, velocity, size). Based on the identification and derived information, processing unit 110 may cause one or more navigation responses in vehicle 200, as described above in connection with FIG. 5A .

[0156] In step 548, processing unit 110 may perform optical flow analysis of one or more images to reduce the probability of detecting “false hits” and missing candidate objects representing vehicles or pedestrians. Optical flow analysis may refer to analyzing movement patterns, distinct from road surface movement, for vehicle 200 in one or more images associated with other vehicles and pedestrians, for example. Processing unit 110 may calculate the movement of candidate objects by observing different positions of the objects across multiple image frames captured at different times. Processing unit 110 may use the position and time values ​​as inputs to a mathematical model to calculate the movement of candidate objects. Thus, optical flow analysis may provide another method of detecting vehicles and pedestrians near vehicle 200. Processing unit 110 may perform optical flow analysis in combination with steps 540 through 546 to provide redundancy for detecting vehicles and pedestrians and increase the reliability of system 100.

[0157] 5C is a flowchart illustrating an example process 500C for detecting road marks and / or lane geometry information in a set of images according to the disclosed embodiments. Processing unit 110 may execute monocular image analysis module 402 to perform process 500C. In step 550, processing unit 110 may detect a set of objects by scanning one or more images. To detect lane mark segments, lane geometry information, and other related road marks, processing unit 110 may filter the set of objects to exclude those determined to be irrelevant (e.g., small holes, small rocks, etc.). In step 552, processing unit 110 may group together segments detected in step 550 that belong to the same road or lane mark. Based on the grouping, processing unit 110 may develop a model, such as a mathematical model, to represent the detected segments.

[0158] At step 554, processing unit 110 may construct a set of measurements associated with the detected segment. In some embodiments, processing unit 110 may create a projection of the detected segment from the image plane onto the real-world plane. The projection may be characterized using a third-order polynomial with coefficients corresponding to physical properties of the detected road, such as its position, slope, curvature, and curvature derivative. In generating the projection, processing unit 110 may take into account road surface variations and pitch and roll rates associated with vehicle 200. Additionally, processing unit 110 may model road height by analyzing the position and motion cues present on the road surface. Furthermore, processing unit 110 may estimate pitch and roll rates associated with vehicle 200 by tracking a set of feature points in one or more images.

[0159] In step 556, processing unit 110 may perform a multi-frame analysis, for example, by tracking the detected segment across successive image frames and accumulating frame-by-frame data associated with the detected segment. When processing unit 110 performs a multi-frame analysis, the set of measurements constructed in step 554 may become more reliable and may be associated with an increasingly higher degree of confidence. Thus, by performing steps 550, 552, 554, and 556, processing unit 110 may identify road marks appearing in the set of captured images and derive lane geometry information. Based on the identification and derived information, processing unit 110 may cause one or more navigational responses in vehicle 200, as described above in connection with FIG. 5A .

[0160] In step 558, processing unit 110 may consider additional sources of information to further develop a safety model of vehicle 200 in the vehicle's surroundings. Processing unit 110 may use the safety model to define situations in which system 100 may safely perform autonomous control of vehicle 200. To develop the safety model, in some embodiments, 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 map database 160). By considering additional sources of information, processing unit 110 may provide redundancy in detecting road marks and lane geometry and increase the reliability of system 100.

[0161] FIG. 5D is a flowchart illustrating an example process 500D for detecting traffic lights in a set of images according to disclosed embodiments. Processing unit 110 may execute monocular image analysis module 402 to perform process 500D. In step 560, processing unit 110 may scan the set of images and identify objects that appear in locations within the images that are likely to contain traffic lights. For example, processing unit 110 may filter the identified objects to construct a set of candidate objects that excludes objects that are unlikely to correspond to traffic lights. Filtering may be based on various characteristics associated with traffic lights, such as shape, size, texture, and location (e.g., relative to vehicle 200), and the like. Such characteristics may be based on many examples of traffic lights and traffic control signals and may be stored in a database. In some embodiments, processing unit 110 may perform multi-frame analysis on the set of candidate objects that reflect possible traffic lights. For example, processing unit 110 may track the candidate objects across consecutive image frames, estimate the real-world locations of the candidate objects, and filter out moving objects (which are unlikely to be traffic lights). In some embodiments, processing unit 110 may perform color analysis on the candidate objects to identify the relative positions of detected colors appearing within potential traffic lights.

[0162] In step 562, 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 vehicle 200, (ii) markings (such as arrow markings) detected on the road, and (iii) a description of the intersection extracted from map data (such as data from map database 160). Processing unit 110 may perform the analysis using information derived from execution of monocular analysis module 402. In addition, processing unit 110 may identify correspondences between traffic lights detected in step 560 and lanes appearing near vehicle 200.

[0163] As vehicle 200 approaches the intersection, in step 564, processing unit 110 may update the confidence associated with the analyzed intersection geometry and detected traffic lights. For example, the number of traffic lights estimated to appear at the intersection compared to the number that actually appear at the intersection may affect the confidence. Thus, based on the confidence, processing unit 110 may delegate control to the driver of vehicle 200 to improve the safety situation. By performing steps 560, 562, and 564, processing unit 110 may identify traffic lights appearing in the set of captured images and analyze the intersection geometry information. Based on the identification and analysis, processing unit 110 may cause one or more navigation responses in vehicle 200, as described above in connection with FIG. 5A .

[0164] 5E is a flowchart illustrating an example process 500E for generating one or more navigation responses in vehicle 200 based on a vehicle path, according to a disclosed embodiment. In 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 represented by coordinates (x, y), with distances d between any two points in the set of points. i may be in the range of 1 to 5 meters. In one embodiment, processing unit 110 may construct an initial vehicle path using two polynomials, such as left and right road polynomials. Processing unit 110 may calculate the geometry midpoint between the two polynomials and, if there is a predetermined offset (a zero offset may correspond to driving in the center of the lane), offset each point in the resulting vehicle path by the predetermined offset (e.g., a smart lane offset). 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 and an estimated lane width to offset each point in the vehicle path by half the estimated lane width plus a predetermined offset (e.g., a smart lane offset).

[0165] In step 572, processing unit 110 may update the vehicle path constructed in step 570. Processing unit 110 may update the distance d k is the distance d i A higher resolution may be used to reconstruct the vehicle path constructed in step 570 so that the distance d is shorter than k may be in the range of 0.1 to 0.3 meters. Processing unit 110 may reconstruct the vehicle path using a parabolic spline algorithm, which may result in 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).

[0166] In step 574, processing unit 110 calculates the look-ahead point ((x l ,z l ) in coordinates. Processing unit 110 may extract look-ahead points from cumulative distance vector S, which may be associated with a look-ahead distance and a look-ahead time. The look-ahead distance may have a lower bound ranging from 10 to 20 meters and may be calculated as the product of the speed of vehicle 200 and the look-ahead time. For example, as the speed of vehicle 200 decreases, the look-ahead distance may also decrease (e.g., until the lower bound is reached). 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 associated with producing a navigation response in vehicle 200, such as a heading error tracking control loop. For example, the gain of the heading error tracking control loop may depend on the bandwidth of the yaw rate loop, the steering actuator loop, and vehicle lateral dynamics, and the like. Thus, the higher the gain of the heading error tracking control loop, the shorter the look-ahead time.

[0167] In step 576, processing unit 110 may determine a heading error and yaw rate command based on the look-ahead point identified in step 574. Processing unit 110 may calculate the arctangent of the look-ahead point, e.g., arctan(x l / z l) to identify the heading error. 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 a lower limit. If the look ahead distance is at a lower limit, the high-level control gain may be equal to (2*velocity of vehicle 200 / look ahead distance).

[0168] 5F is a flowchart illustrating an example process 500F for determining whether a leading vehicle is changing lanes, according to the disclosed embodiments. In step 580, processing unit 110 may determine navigation information associated with the leading vehicle (e.g., a vehicle traveling in front of vehicle 200). For example, processing unit 110 may determine the position, velocity (e.g., direction and speed), and / or acceleration of the leading vehicle using the techniques described above in connection with FIGS. 5A and 5B. Processing unit 110 may also be configured to determine one or more road polynomials, lookahead points (associated with vehicle 200), and / or snail trails (e.g., a set of points describing the path taken by the leading vehicle) using the techniques described above in connection with FIG. 5E.

[0169] In step 582, processing unit 110 may analyze the navigation information identified in step 580. In one embodiment, processing unit 110 may calculate the distance (e.g., along the trail) between the snail trail and the road polynomial. If the difference in this distance along the trail exceeds a predetermined threshold (e.g., 0.1 to 0.2 meters for a straight road, 0.3 to 0.4 meters for a gently curving road, or 0.5 to 0.6 meters for a sharply curving road), processing unit 110 may determine that the leading vehicle is likely changing lanes. If multiple vehicles are detected traveling in front of vehicle 200, processing unit 110 may compare the snail trails associated with each vehicle. Based on the comparison, processing unit 110 may determine that a vehicle whose snail trail does not match the snail trails of the other vehicles is likely changing lanes. Processing unit 110 may further compare the curvature of the snail trail (associated with the leading vehicle) to the expected curvature of the road segment along which the leading vehicle is traveling. The expected curvature may be extracted from map data (e.g., data from map database 160), road polynomials, snail trails of other vehicles, prior knowledge about the road, and the like. If the difference between the snail trail curvature and the expected curvature of the road segment exceeds a predetermined threshold, processing unit 110 may determine that the leading vehicle is likely changing lanes.

[0170] In another embodiment, processing unit 110 may compare the instantaneous position of the leading vehicle to a look-ahead point (associated with vehicle 200) over a specific time period (e.g., 0.5 to 1.5 seconds). If the cumulative sum of the difference and change in distance between the instantaneous position of the leading vehicle and the look-ahead point during the specific time period exceeds a predetermined threshold (e.g., 0.3 to 0.4 meters for straight roads, 0.7 to 0.8 meters for gently curving roads, and 1.3 to 1.7 meters for sharply curving roads), processing unit 110 may determine that the leading vehicle is likely changing lanes. In another embodiment, processing unit 110 may analyze the geometry of the snail trail by comparing the lateral movement distance traveled along the trail to the expected curvature of the snail trail. The expected radius of curvature is calculated as:(δ z 2 +δ x 2 ) / 2 / (δ x ) in which σ x represents the lateral movement distance, and σ z represents the longitudinal movement distance. If the difference between the lateral movement distance and the expected curvature exceeds a predetermined threshold (e.g., 500 to 700 meters), processing unit 110 may determine that the leading vehicle is likely changing lanes. In another embodiment, processing unit 110 may analyze the position of the leading vehicle. If the position of the leading vehicle obscures the road polynomial (e.g., the leading vehicle overlies the road polynomial), processing unit 110 may determine that the leading vehicle is likely changing lanes. If the position of the leading vehicle is such that another vehicle is detected ahead of the leading vehicle and the snail trails of the two vehicles are not parallel, processing unit 110 may determine that the (closer) leading vehicle is likely changing lanes.

[0171] In step 584, processing unit 110 may determine whether leading vehicle 200 is changing lanes based on the analysis performed in step 582. For example, processing unit 110 may make a determination based on a weighted average of the individual analyses performed in step 582. Under such a scheme, for example, a determination by processing unit 110 that the leading vehicle is likely to be changing lanes based on a particular type of analysis may be assigned a value of "1" (whereas a "0" represents a determination that the leading vehicle is unlikely to be changing lanes). Different weights may be assigned to different analyses performed in step 582, and the disclosed embodiments are not limited to any particular combination of analyses and weights.

[0172] 6 is a flowchart illustrating an example process 600 for generating one or more navigational responses based on stereo image analysis, according to disclosed embodiments. In step 610, processing unit 110 may receive first and second pluralities of images via data interface 128. For example, cameras included in image acquisition unit 120 (such as imaging devices 122 and 124 having fields of view 202 and 204) may capture first and second pluralities of images of an area ahead of vehicle 200 and transmit them to processing unit 110 via a digital connection (e.g., USB, wireless, Bluetooth, etc.). In some embodiments, processing unit 110 may receive the first and second pluralities of images via two or more data interfaces. The disclosed embodiments are not limited to any particular data interface configuration or protocol.

[0173] In step 620, processing unit 110 may execute stereo image analysis module 404 to perform stereo image analysis of the first and second plurality of images to create a 3D map of the road ahead of the vehicle and detect features in the images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, road hazards, and the like. The stereo image analysis may be performed similarly to the steps described above in connection with FIGS. 5A through 5D . For example, processing unit 110 may execute stereo image analysis module 404 to detect candidate objects (e.g., vehicles, pedestrians, road marks, traffic lights, road hazards, etc.) in the first and second plurality of images, filter out a subset of the candidate objects based on various criteria, perform multi-frame analysis, construct measurements, and identify confidence levels for the remaining candidate objects. In performing the above steps, processing unit 110 may consider information from both the first and second plurality of images, rather than information from only one set of images. For example, processing unit 110 may analyze differences in pixel-level data (or other subsets of data from the two streams of captured images) of a candidate object that appears in both the first and second multiple images. As another example, processing unit 110 may estimate the position and / or velocity (e.g., relative to vehicle 200) of a candidate object by observing that the object appears in one of the multiple images but not the other, or other differences that may exist for the object appearing in the two image streams. For example, the position, velocity, and / or acceleration relative to vehicle 200 may be determined based on the trajectory, position, motion characteristics, etc. of features associated with the object that appear in one or both of the image streams.

[0174] In step 630, processing unit 110 may execute navigation response module 408 to generate one or more navigational responses in vehicle 200 based on the analysis performed in step 620 and the techniques described above in connection with FIG. 4. The navigational responses may include, for example, turns, lane shifts, acceleration changes, speed changes and braking, and the like. In some embodiments, processing unit 110 may generate one or more navigational responses using data derived from execution of speed and acceleration module 406. Furthermore, multiple navigational responses may be performed simultaneously, sequentially, or any combination thereof.

[0175] 7 is a flowchart illustrating an example process 700 for generating one or more navigational responses based on the analysis of three sets of images, according to disclosed embodiments. In step 710, processing unit 110 may receive first, second, and third pluralities of images via data interface 128. For example, cameras included in image acquisition unit 120 (such as imaging devices 122, 124, and 126 having fields of view 202, 204, and 206) may capture first, second, and third pluralities of images of an area in front of and / or to the sides of vehicle 200 and transmit them to processing unit 110 via a digital connection (e.g., USB, wireless, Bluetooth, etc.). In some embodiments, processing unit 110 may receive the first, second, and third pluralities of images via three or more data interfaces. For example, imaging devices 122, 124, and 126 may each have an associated data interface that communicates data to processing unit 110. The disclosed embodiments are not limited to any particular data interface configuration or protocol.

[0176] At step 720, 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, and the like. The analysis may be performed similar to the steps described above in connection with FIGS. 5A through 5D and 6. For example, processing unit 110 may perform monocular image analysis on each of the first, second, and third plurality of images (e.g., via execution of monocular image analysis module 402 and based on the steps described above in connection with FIGS. 5A through 5D). Alternatively, 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 execution of stereo image analysis module 404 and based on the steps described above in connection with FIG. 6). The processed information corresponding to the analysis of the first, second, and / or third plurality of images may be combined. In some embodiments, processing unit 110 may perform a combination of monocular image analysis and stereo image analysis. For example, processing unit 110 may perform monocular image analysis on the first plurality of images (e.g., via execution of monocular image analysis module 402) and stereo image analysis on the second and third plurality of images (e.g., via execution of stereo image analysis module 404). The configuration of imaging 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 imaging devices 122, 124, and 126 or the type of analysis performed on the first, second, and third plurality of images.

[0177] In some embodiments, processing unit 110 may perform tests on system 100 based on the images acquired and analyzed in steps 710 and 720. Such tests may provide an indication of the overall performance of system 100 for a particular configuration of imaging devices 122, 124, and 126. For example, processing unit 110 may identify the rate of "false hits" (e.g., when system 100 incorrectly determines the presence of a vehicle or pedestrian) and "misses."

[0178] In step 730, processing unit 110 may cause one or more navigation responses in 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 multiple images. Processing unit 110 may also make the selection based on the quality and resolution of the images, the effective field of view reflected in the images, the number of frames captured, and the extent to which one or more objects of interest actually appear in the frames (e.g., the percentage of frames in which the object appears, the percentage of times the object appears in each such frame, etc.), and the like.

[0179] In some embodiments, processing unit 110 may select information derived from two of the first, second, and third pluralities of images by identifying the extent to which information derived from one image source is consistent with information derived from the other image sources. For example, processing unit 110 may combine processed information (whether monocular analysis, stereo analysis, or any combination of the two) derived from each of imaging devices 122, 124, and 126 to identify visual indicators (e.g., lane markings, detected vehicles and / or their positions and / or paths, detected traffic lights, etc.) that are consistent across the captured images from each of imaging devices 122, 124, and 126. Processing unit 110 may also filter out information that is inconsistent across the captured images (e.g., a vehicle changing lanes, a lane model showing a vehicle too close to vehicle 200, etc.). Thus, processing unit 110 may select information derived from two of the first, second, and third pluralities of images based on the identification of consistent and inconsistent information.

[0180] The navigational responses may include, for example, turns, lane shifts, and acceleration changes, and the like. Processing unit 110 may generate one or more navigational responses based on the analysis performed in step 720 and the techniques described above in connection with FIG. 4 . Processing unit 110 may also generate one or more navigational responses using data derived from execution of velocity and acceleration module 406. In some embodiments, processing unit 110 may generate one or more navigational responses based on the relative position, relative velocity, and / or relative acceleration between vehicle 200 and objects detected in any of the first, second, and third plurality of images. The multiple navigational responses may be performed simultaneously, sequentially, or any combination thereof.

[0181] Sparse Road Models for Autonomous Vehicle Navigation

[0182] In some embodiments, the disclosed systems and methods may use a sparse map for autonomous vehicle navigation. Specifically, the sparse map may be for autonomous vehicle navigation along road segments. For example, the sparse map may provide sufficient information for navigating an autonomous vehicle without storing and / or updating large amounts of data. As discussed in more detail below, an autonomous vehicle may use the sparse map to navigate one or more roads based on one or more stored trajectories.

[0183] Sparse Maps for Autonomous Vehicle Navigation

[0184] In some embodiments, the disclosed systems and methods may generate sparse maps for autonomous vehicle navigation. For example, the sparse map may provide sufficient information for navigation without requiring excessive data storage or data transfer rates. As discussed in further detail below, a vehicle (which may be an autonomous vehicle) may navigate one or more roads using the sparse map. For example, in some embodiments, the sparse map may include data related to the road and potential landmarks along the road that may be sufficient for vehicle navigation but also present a small data footprint. For example, the sparse data map, described in more detail below, may require significantly less storage space and data transfer bandwidth compared to a digital map that includes detailed map information, such as image data collected along the road.

[0185] For example, rather than storing detailed representations of road segments, a sparse data map may store three-dimensional polynomial representations of preferred vehicle paths along roads. These paths may require little data storage space. Furthermore, in the described sparse data maps, landmarks may be identified and included in the sparse map road model to aid navigation. These landmarks may be spaced at any suitable interval to enable vehicle navigation, although in some cases, it is not necessary to identify and include such landmarks at high density and at short intervals. Rather, in some cases, navigation may be possible 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 discussed in more detail in other sections, sparse maps may be generated based on data collected or measured by vehicles equipped with various sensors and devices, such as imaging devices, global positioning system sensors, and motion sensors, as the vehicles travel along roads. In some cases, sparse maps may be generated based on data collected during multiple trips of one or more vehicles along a particular road. Generating a sparse map using multiple trips of one or more vehicles can be referred to as "crowdsourcing" the sparse map.

[0186] According to disclosed embodiments, an autonomous vehicle system may use a sparse map for navigation. For example, the disclosed systems and methods may deliver a sparse map for generating a road navigation model for an autonomous vehicle and navigate the autonomous vehicle along a road segment using the sparse map and / or the generated road navigation model. A sparse map according to the present disclosure may include one or more three-dimensional contours that may represent predetermined trajectories that the autonomous vehicle may traverse when traveling along the associated road segment.

[0187] Sparse maps according to the present disclosure may also include data representing one or more road features. Such road features may include recognized landmarks, road signature profiles, and any other road-related features useful for navigating a vehicle. Sparse maps according to the present disclosure may enable autonomous navigation of a vehicle based on a relatively small amount of data included in the sparse map. For example, even without including detailed representations of roads, such as data detailing road edges, road curvature, images associated with road segments, or other physical features associated with road segments, disclosed embodiments of sparse maps may require relatively little storage space (and relatively little bandwidth when portions of the sparse map are transferred to the vehicle) but may still adequately provide autonomous vehicle navigation. The small data footprint of the disclosed sparse maps, discussed in more detail below, may be achieved in some embodiments by storing representations of road-related elements that require a small amount of data but still enable autonomous navigation.

[0188] For example, rather than storing detailed representations of various aspects of a road, the disclosed sparse maps may store polynomial representations of one or more trajectories that a vehicle may follow along the road. Thus, using the disclosed sparse maps, rather than storing (or having to transfer) details about the physical properties of the road to enable navigation along the road, a vehicle may be navigated along a particular road segment, in some cases, without having to interpret the physical aspects of the road, but rather by aligning its travel path to a trajectory (e.g., a polynomial spline) along the particular road segment. In this way, a vehicle may be navigated primarily based on the stored trajectory (e.g., a polynomial spline), which may require much less storage space than approaches that include storing road images, road parameters, road layouts, etc.

[0189] In addition to the stored polynomial representation of the trajectory along the road segment, the disclosed sparse maps may also include small data objects that may represent road features. In some embodiments, the small data objects may include digital signatures derived from digital images (or digital signals) acquired by sensors (e.g., cameras or other sensors, such as suspension sensors) mounted on vehicles traveling along the road segment. The digital signatures may be of reduced size compared to the signals acquired by the sensors. In some embodiments, the digital signatures may be created to be compatible with classifier functions configured to detect and identify road features from signals acquired by sensors during subsequent travel, for example. In some embodiments, the digital signatures may be created to have as small a footprint as possible while retaining the ability to correlate or match road features with the stored signatures based on images of road features captured by cameras mounted on vehicles traveling along the same road segment at subsequent times (or digital signals generated by sensors, if the stored signatures are not based on images and / or include other data).

[0190] In some embodiments, the size of the data object may be further related to the uniqueness of the road feature. For example, for a road feature detectable by a vehicle-mounted camera, if the vehicle-mounted camera system is coupled to a classifier that can distinguish image data corresponding to that road feature as being associated with a particular type of road feature, such as a road sign, and if such road sign is locally unique in the region (e.g., there are no identical or type of road signs nearby), it may be sufficient to store data indicating the type of road feature and its location.

[0191] As discussed in more detail below, road features (e.g., landmarks along a road segment) may be stored as small data objects that can represent the road features in a relatively few bytes while providing sufficient information to recognize and use such features for navigation. In one example, road signs may be identified as recognized landmarks upon which vehicle navigation may be based. Representations of road signs may be stored in a sparse map, for example, to include a few bytes of data indicating the type of landmark (e.g., a stop sign) and a few bytes of data indicating the location (e.g., coordinates) of the landmark. Navigating based on such data-aspect representations of landmarks (e.g., using representations sufficient to locate, recognize, and navigate based on the landmarks) may provide the desired level of navigation functionality associated with sparse maps without significantly increasing the data overhead associated with sparse maps. Such lean representations of landmarks (and other road features) may take advantage of sensors and processors included on-board such vehicles that are configured to detect, identify, and / or classify specific road features.

[0192] For example, if a sign, or even a particular type of sign, is locally unique in a given area (e.g., there are no other signs, or there are no other signs of the same type), the sparse map may use data indicating the type of landmark (sign or 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 containing a sign (or particular type of sign), a processor may process the image, detect the sign (if in fact present in the image), classify the image as a sign (or as a particular type of sign), and correlate the location of the image with the location of the sign stored in the sparse map.

[0193] Sparse map generation

[0194] In some embodiments, the sparse map may include at least one line representation of a road surface feature extending along the road segment and a plurality of landmarks associated with the road segment. In certain aspects, the sparse map may be generated through "crowdsourcing," e.g., through image analysis of a plurality of images acquired as one or more vehicles traverse the road segment.

[0195] 8 illustrates a sparse map 800 that one or more vehicles, e.g., vehicle 200 (which may be an autonomous vehicle), may access to provide autonomous vehicle navigation. Sparse map 800 may be stored in a memory, such as memory 140 or 150. Such a memory device may include any type of non-transitory storage device or computer-readable medium. For example, in some embodiments, memory 140 or 150 may include a hard drive, a compact disc, flash memory, a magnetic-based memory device, an optical-based memory device, etc. In some embodiments, sparse map 800 may be stored in a database (e.g., map database 160), which may be stored in memory 140 or 150 or another type of storage device.

[0196] In some embodiments, sparse map 800 may be stored on a storage device or non-transitory computer-readable medium (e.g., a storage device included in a navigation system onboard vehicle 200) onboard vehicle 200. A processor (e.g., processing unit 110) onboard vehicle 200 may access sparse map 800 stored on a storage device or computer-readable medium onboard vehicle 200 to generate navigation instructions for guiding autonomous vehicle 200 as the vehicle traverses road segments.

[0197] However, sparse map 800 need not be stored locally with respect to the vehicle. In some embodiments, sparse map 800 may be stored on a storage device or computer-readable medium provided on a remote server in communication with vehicle 200 or a device associated with vehicle 200. A processor (e.g., processing unit 110) onboard vehicle 200 may receive the data included in sparse map 800 from the remote server and execute the data to guide the autonomous navigation of vehicle 200. In such embodiments, the remote server may store all or only a portion of sparse map 800. Accordingly, a storage device or computer-readable medium onboard vehicle 200 and / or one or more additional vehicles may store the remaining portions of sparse map 800.

[0198] Further, in such embodiments, sparse map 800 may be accessible to multiple vehicles (e.g., tens, hundreds, thousands, or millions of vehicles) traversing various road segments. It should also be noted that sparse map 800 may include multiple sub-maps. For example, in some embodiments, sparse map 800 may include hundreds, thousands, millions, or more sub-maps that may be used in navigating a vehicle. Such sub-maps may be referred to as local maps, and a vehicle traveling along a road may access any number of local maps associated with the location in which the vehicle is traveling. The local map areas of sparse map 800 may be stored with a global navigation satellite system (GNSS) key as an index into a database of sparse map 800. Thus, steering angle calculations for navigating a host vehicle in the present system may be performed without relying on the GNSS position of the host vehicle, road features, or landmarks, although such GNSS information may be used to obtain the associated local map.

[0199] In general, sparse map 800 may be generated based on data collected from one or more vehicles as they travel along a road. For example, sensors (e.g., cameras, speedometers, GPS, accelerometers, etc.) mounted on one or more vehicles may be used to record the trajectories of one or more vehicles traveling along a road, and a polynomial representation of the vehicle's preferred trajectory for subsequent travel along the road may be determined based on the collected trajectories traveled by the one or more vehicles. Similarly, data collected by one or more vehicles may assist in identifying potential landmarks along a particular road. Data collected from traversing vehicles may also be used to identify road profile information such as road width profile, road roughness profile, traffic line spacing profile, road conditions, etc. Using the collected information, sparse map 800 may be generated and distributed (e.g., for local storage or via on-the-fly data transmission) for use in navigating one or more autonomous vehicles. However, in some embodiments, map generation may not end with the initial generation of the map. As discussed in more detail below, the sparse map 800 may be continuously or periodically updated based on data collected from vehicles as they continue to traverse roads included in the sparse map 800.

[0200] The data recorded in the sparse map 800 may include location information based on Global Positioning System (GPS) data. For example, location information may be included in the sparse map 800 for various map elements, including, for example, landmark locations, road profile locations, etc. The locations of map elements included in the sparse map 800 may be obtained using GPS data collected from vehicles traversing roads. For example, a vehicle passing an identified landmark may determine the location of the identified landmark using GPS location information associated with the vehicle and determine the location of the identified landmark relative to the vehicle (e.g., based on image analysis of data collected from one or more cameras mounted on the vehicle). Such location determination of the identified landmark (or any other feature included in the sparse map 800) may be repeated as additional vehicles pass the location of the identified landmark. Some or all of the additional location determinations may be used to refine the location information stored in the sparse map 800 associated with the identified landmark. For example, in some embodiments, multiple location measurements associated with a particular feature stored in the sparse map 800 may be averaged together. However, any other mathematical operation may be used to refine the stored location of the map element based on multiple determined locations of the map element.

[0201] The sparse maps of the disclosed embodiments may enable autonomous navigation of a vehicle using a relatively small amount of stored data. In some embodiments, sparse map 800 may have a data density (e.g., including data representing trajectories, landmarks, and any other stored road features) of less than 2 MB per kilometer of road, less than 1 MB per kilometer of road, less than 500 KB per kilometer of road, or less than 100 KB per kilometer of road. In some embodiments, the data density of sparse map 800 may be less than 10 KB per kilometer of road, or even less than 2 KB per kilometer of road (e.g., 1.6 KB per kilometer), or 10 KB or less per kilometer of road, or 20 KB or less per kilometer of road. In some embodiments, most, if not all, U.S. roads may be navigated autonomously using a sparse map having a total of 4 GB or less of data. These data density values ​​may represent averages across the entire sparse map 800, across local maps within sparse map 800, and / or across specific road segments within sparse map 800.

[0202] As mentioned above, the sparse map 800 may include representations of multiple target trajectories 810 for guiding autonomous driving or navigation along a road segment. Such target trajectories may be stored as cubic splines. The target trajectories stored in the sparse map 800 may be determined, for example, based on two or more reconstructed trajectories of a vehicle's previous trajectories along a particular road segment. A road segment may be associated with a single target trajectory or multiple target trajectories. For example, on a two-lane road, a first target trajectory may be stored to represent an intended driving path along the road in a first direction, and a second target trajectory may be stored to represent an intended driving path along the road in another direction (e.g., opposite the first direction). Additional target trajectories may be stored for a particular road segment. For example, on a multi-lane road, one or more target trajectories may be stored representing the vehicle's intended driving path for one or more lanes associated with the multi-lane road. In some embodiments, each lane of a multi-lane road may be associated with its own target trajectory. In other embodiments, there may be fewer stored target trajectories than there are lanes on a multi-lane road. In such cases, a vehicle navigating a multi-lane road may use any of the stored target trajectories to guide navigation, taking into account the amount of lane offset from the lane for which the target trajectory is stored (e.g., if a vehicle is traveling in the leftmost lane of a three-lane highway and target trajectories are stored only for the center lane of the highway, the vehicle may navigate using the target trajectory for the center lane, taking into account the amount of lane offset between the center lane and the leftmost lane, when generating navigation instructions).

[0203] In some embodiments, the target trajectory may represent an ideal path that the vehicle should take as it travels. The target trajectory may be positioned approximately in the center of the driving lane, for example. In other cases, the target trajectory may be positioned elsewhere relative to the road segment. For example, the target trajectory may approximately coincide with the center of the road, the edge of the road, or the edge of the lane. In such cases, navigation based on the target trajectory may include a determined amount of offset to maintain relative to the position of the target trajectory. Furthermore, in some embodiments, the determined amount of offset to maintain relative to the position of the target trajectory may differ based on the type of vehicle (e.g., a passenger car including two axles may have a different offset along at least a portion of the target trajectory than a truck including three or more axles).

[0204] The sparse map 800 may also include data related to a number of predetermined landmarks 820 associated with particular road segments, local maps, etc. As discussed in more detail below, these landmarks may be used to navigate the autonomous vehicle. For example, in some embodiments, the landmarks may be used to determine the vehicle's current position relative to a stored target trajectory. Using this position information, the autonomous vehicle may be able to adjust its heading to match the direction of the target trajectory at the determined location.

[0205] Multiple landmarks 820 may be identified and stored in sparse map 800 at any suitable interval. In some embodiments, landmarks may be stored at a relatively high density (e.g., every few meters or more). However, in some embodiments, significantly larger landmark spacing values ​​may be used. For example, in sparse map 800, identified (or recognized) landmarks may be spaced 10 meters, 20 meters, 50 meters, 100 meters, 1 kilometer, or 2 kilometers apart. In some cases, identified landmarks may be located at distances even greater than 2 kilometers apart.

[0206] While determining the vehicle's position between landmarks, and thus relative to the target trajectory, the vehicle may navigate based on dead reckoning, in which the vehicle uses sensors to determine egomotion and estimate its position relative to the target trajectory. Errors may accumulate during dead reckoning navigation, causing the accuracy of position determination relative to the target trajectory to gradually decrease over time. The vehicle may use landmarks present in the sparse map 800 (and their known locations) to eliminate dead reckoning-induced errors in position determination. In this manner, identified landmarks included in the sparse map 800 may serve as navigation anchors, from which the vehicle's precise position relative to the target trajectory may be determined. Because some error may be acceptable in position determination, identified landmarks need not always be available to the autonomous vehicle. Rather, as noted above, adequate navigation may be possible based on landmark spacing of 10 meters, 20 meters, 50 meters, 100 meters, 500 meters, 1 kilometer, 2 kilometers, or even more. In some embodiments, a density of one identified landmark per kilometer of road may be sufficient to maintain longitudinal positioning accuracy within 1 meter, and therefore not all potential landmarks that appear along a road segment need to be stored in sparse map 800.

[0207] Additionally, in some embodiments, lane marks may be used to locate the vehicle between landmark intervals. Using lane marks between landmark intervals may minimize buildup during dead reckoning navigation.

[0208] In addition to the target trajectory and identified landmarks, the sparse map 800 may include information related to various other road features. For example, FIG. 9A shows a representation of the curves along a particular road segment that may be stored in the sparse map 800. In some embodiments, a single lane of a road may be modeled by a three-dimensional polynomial description of the left and right sides of the road. Such polynomials representing the left and right sides of a single lane are shown in FIG. 9A . Regardless of the number of lanes a road may have, polynomials may be used to represent the road in a manner similar to that shown in FIG. 9A . For example, the left and right sides of a multi-lane road may be represented by polynomials similar to those shown in FIG. 9A , and intermediate lane markings included in a multi-lane road (e.g., dashed line marks representing lane boundaries, solid yellow lines representing boundaries between lanes traveling in different directions, etc.) may also be represented using polynomials such as those shown in FIG. 9A .

[0209] As shown in FIG. 9A , lane 900 may be represented using a polynomial (e.g., a linear, quadratic, cubic, or any suitable degree polynomial). For purposes of illustration, lane 900 is shown as a two-dimensional lane, and the polynomials are shown as two-dimensional polynomials. As shown in FIG. 9A , lane 900 includes a left side 910 and a right side 920. In some embodiments, multiple polynomials may be used to represent locations on each side of a road or lane boundary. For example, left side 910 and right side 920 may each be represented by multiple polynomials of any suitable length. In some cases, the polynomials may be approximately 100 meters long, although other lengths greater or less than 100 meters may also be used. Furthermore, polynomials may overlap one another to facilitate seamless transitions when navigating based on subsequently encountered polynomials as the host vehicle travels along the road. For example, each of the left side 910 and the right side 920 may be represented by multiple third-order polynomials separated into segments approximately 100 meters in length (an example first predetermined range) and overlapping each other by approximately 50 meters. The polynomials representing the left side 910 and the right side 920 may or may not be in the same order. For example, in some embodiments, some polynomials may be second-order polynomials, some may be third-order polynomials, and some may be fourth-order polynomials.

[0210] In the example shown in FIG. 9A , the left side 910 of lane 900 is represented by two groups of third-order polynomials. The first group includes polynomial segments 911, 912, and 913. The second group includes polynomial segments 914, 915, and 916. The two groups are substantially parallel to each other but follow their respective positions on each side of the road. Polynomial segments 911, 912, 913, 914, 915, and 916 are approximately 100 meters long and overlap adjacent segments in the series by approximately 50 meters. However, as noted above, polynomials of different lengths and overlap amounts may also be used. For example, the polynomials may be 500 meters, 1 km, or longer, and the overlap amount may vary from 0 to 50 meters, 50 meters to 100 meters, or more than 100 meters. 9A is shown as representing polynomials extending in 2D space (e.g., on the surface of a piece of paper), it should be understood that these polynomials may represent curves extending in three dimensions (e.g., including a height component) to represent changes in elevation of the road segment in addition to XY curvature. In the example shown in FIG. 9A, the right side 920 of lane 900 is further represented by a first group having polynomial sections 921, 922, and 923, and a second group having polynomial sections 924, 925, and 926.

[0211] Returning to the target trajectories of the sparse map 800, FIG. 9B shows a cubic polynomial that represents a target trajectory for a vehicle traveling along a particular road segment. The target trajectory represents not only the XY path that the host vehicle should travel along a particular road segment, but also the elevation changes that the host vehicle will experience as it travels along the road segment. Thus, each target trajectory in the sparse map 800 may be represented by one or more cubic polynomials, such as cubic polynomial 950 shown in FIG. 9B. The sparse map 800 may include multiple trajectories (e.g., millions or billions or more to represent vehicle trajectories along various road segments along roads around the world). In some embodiments, each target trajectory may correspond to a spline connecting the cubic polynomial segments.

[0212] With respect to the data footprint of the polynomial curves stored in sparse map 800, in some embodiments, each third-order polynomial is represented by four parameters, each of which may require four bytes of data. A suitable representation may be obtained with a third-order polynomial requiring approximately 192 bytes of data per 100 meters. This may translate to a data usage / transfer requirement of approximately 200 KB per hour for a host vehicle traveling approximately 100 km / hr.

[0213] The sparse map 800 may describe the lane network using a combination of geometry descriptors and metadata. The geometry may be described with polynomials or splines, as described above. The metadata may describe the number of lanes, special characteristics (such as carpool lanes), and possibly other sparse labels. The total footprint of such metrics may be negligible.

[0214] Thus, a sparse map according to embodiments 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. Further, in some embodiments, the road surface feature may include at least one of a road edge or a lane marking. Furthermore, as discussed below with respect to "crowdsourcing," the road surface feature may be identified by image analysis of multiple images acquired as one or more vehicles traverse the road segment.

[0215] As described above, sparse map 800 may include a plurality of predetermined landmarks associated with a road segment. Rather than storing actual images of the landmarks and relying, for example, on image recognition analysis based on captured and stored images, each landmark in sparse map 800 may be represented and recognized using less data than a stored actual image would require. The data representing the landmarks may also include sufficient information to describe or identify the landmarks along the road. Storing data describing the characteristics of the landmarks rather than actual images of the landmarks may reduce the size of sparse map 800.

[0216] FIG. 10 shows examples of types of landmarks that may be represented in sparse map 800. Landmarks may include any visible and identifiable object along a road segment. Landmarks may be selected to be fixed and infrequently changed in terms of location and / or content. Landmarks included in sparse map 800 may be useful in determining the position of vehicle 200 relative to a target trajectory as the vehicle traverses a particular road segment. Examples of landmarks may include traffic signs, directional signs, general signs (e.g., rectangular signs), roadside furniture (e.g., lampposts, reflectors, etc.), and any other suitable category. In some embodiments, lane markings on roads may also be included as landmarks in sparse map 800.

[0217] 10 includes traffic signs, directional signs, roadside furniture, and general signs. Traffic signs may 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), and stop signs (e.g., stop sign 1020). Directional signs may include signs including one or more arrows indicating one or more directions to different locations. For example, directional signs may include a highway sign 1025 with an arrow for directing a vehicle to a different road or location, an exit sign 1030 with an arrow for directing a vehicle to exit a road, etc. Thus, at least one of the plurality of landmarks may include a road sign.

[0218] A general sign may be non-traffic related. For example, a general sign may include a billboard used in advertising or a welcome board adjacent to the boundary between two countries, states, counties, cities, or towns. Figure 10 shows a general sign 1040 ("Joe's Restaurant"). As shown in Figure 10, the general sign 1040 may have a rectangular shape, although the general sign 1040 may have other shapes such as a square, circle, triangle, etc.

[0219] Landmarks may also include roadside furniture. Roadside furniture may be objects that are not signs and may not be traffic or directional related. For example, roadside furniture may include lampposts (e.g., lamppost 1035), power poles, traffic light poles, etc.

[0220] Landmarks may also include beacons, which may be specially designed for use in autonomous vehicle navigation systems. For example, such beacons may include freestanding structures placed at predetermined intervals to assist a host vehicle in navigating. Such beacons may also include visual / graphical information added to existing road signs (e.g., icons, emblems, bar codes, etc.) that can be identified or recognized by vehicles traveling along a road segment. Such beacons may also include electronic components. In such embodiments, electronic beacons (e.g., RFID tags, etc.) may be used to transmit non-visual information to the host vehicle. Such information may include, for example, landmark-specific and / or landmark location information that the host vehicle can use in determining its position along the target trajectory.

[0221] In some embodiments, landmarks included in the sparse map 800 may be represented by data objects of a predetermined size. The data representing the landmarks 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 landmark's physical size (e.g., to support estimation of distance to the landmark based on a known size / scale), distance to the previous landmark, lateral offset, height, type code (e.g., landmark type—type of directional sign, traffic sign, etc.), GPS coordinates (e.g., to support global localization), and any other suitable parameters. Each parameter may be associated with a data size. For example, landmark size may be stored using 8 bytes of data. The distance to the previous landmark, lateral offset, and height may be specified using 12 bytes of data. A type code associated with a landmark such as a directional sign or traffic sign may require approximately 2 bytes of data. For a generic sign, an image signature that allows identification of the generic sign may be stored using 50 bytes of data storage. The GPS location of a landmark may be associated with 16 bytes of data storage. These data sizes for each parameter are merely examples and other data sizes may be used.

[0222] Representing landmarks in the sparse map 800 in this manner may provide a lean solution for efficiently representing landmarks in a database. In some embodiments, signs may be referred to as semantic and non-semantic signs. Semantic signs may include any class of signs with a standardized meaning (e.g., speed limit signs, warning signs, directional signs, etc.). Non-semantic signs may include any signs not associated with a standardized meaning (e.g., general advertising signs, signs identifying businesses, etc.). For example, each semantic sign may be represented by 38 bytes of data (e.g., 8 bytes for size, 12 bytes for distance to previous landmark, lateral offset, height, 2 bytes for type code, and 16 bytes for GPS coordinates). The sparse map 800 may use a tag system to represent landmark types. In some cases, each traffic or directional sign may be associated with a unique tag, which may be stored in the database as part of the landmark ID. For example, the database may include as many as 1,000 different tags to represent various traffic signs and as many as 10,000 different tags to represent directional signs. Of course, any suitable number of tags may be used, and additional tags may be created as needed. A generic sign may, in some embodiments, be represented using less than about 100 bytes (e.g., about 86 bytes, including 8 bytes for size, 12 bytes for distance to previous landmark, lateral offset, and height, 50 bytes for image signature, and 16 bytes for GPS coordinates).

[0223] Thus, for semantic road signs that do not require image signatures, the data density impact on sparse map 800 can be on the order of approximately 760 bytes per kilometer, even with a relatively high landmark density of approximately 1 per 50 meters (e.g., 20 landmarks per kilometer x 38 bytes per landmark = 760 bytes). For generic signs that include an image signature component, the data density impact is still on the order of 1.72 KB per kilometer (e.g., 20 landmarks per kilometer x 86 bytes per landmark = 1,720 bytes). For semantic road signs, this corresponds to a data usage of approximately 76 KB per hour for a vehicle traveling at 100 km / hr. For generic signs, this corresponds to a data usage of approximately 170 KB per hour for a vehicle traveling at 100 km / hr.

[0224] In some embodiments, a generally rectangular object, such as a rectangular sign, may be represented in sparse map 800 with 100 bytes of data or less. A representation of a generally rectangular object (e.g., generic sign 1040) in sparse map 800 may include a condensed image signature (e.g., condensed image signature 1045) associated with the generally rectangular object. This condensed image signature may be used, for example, to aid in the identification of the generic sign, e.g., as a recognized landmark. Such a condensed image signature (e.g., image information derived from actual image data representing the object) may avoid the need to store actual images of the object or the need for comparative image analysis to be performed on the actual images in order to recognize the landmark.

[0225] 10 , sparse map 800 may include or store a condensed image signature 1045 associated with generic sign 1040, rather than an actual image of generic sign 1040. For example, after an imaging device (e.g., imaging device 122, 124, or 126) captures an image of generic sign 1040, a processor (e.g., image processor 190 or any other processor capable of processing images, either on-board or remotely located relative to the host vehicle) may perform image analysis to extract / create condensed image signature 1045 that includes a unique signature or pattern associated with generic sign 1040. In one embodiment, condensed image signature 1045 may include a shape, color pattern, brightness pattern, or any other feature that can be extracted from an image of generic sign 1040 to describe generic sign 1040.

[0226] For example, in FIG. 10 , the circles, triangles, and stars shown in condensed image signature 1045 may represent regions of different colors. The patterns represented by the circles, triangles, and stars may be stored in sparse map 800, e.g., within the 50 bytes designated to contain the image signature. Notably, the circles, triangles, and stars are not meant to necessarily indicate that such shapes are stored as part of the image signature. Rather, these shapes are meant to conceptually represent recognizable regions having distinguishable color differences, text areas, graphic shapes, or other variations of characteristics that may be associated with generic signs. Such condensed image signatures can be used to identify landmarks in the form of generic signs. For example, the condensed image signatures can be used to perform a match analysis based on a comparison of image data captured using, for example, a camera mounted on an autonomous vehicle to the stored condensed image signature.

[0227] Thus, multiple landmarks may be identified by image analysis of multiple images acquired as one or more vehicles traverse a road segment. As described below with respect to "crowdsourcing," in some embodiments, image analysis to identify multiple landmarks may include accepting a potential landmark if a ratio of images in which the landmark appears to images in which the landmark does not appear exceeds a threshold. Further, in some embodiments, image analysis to identify multiple landmarks may include rejecting a potential landmark if a ratio of images in which the landmark does not appear to images in which the landmark appears exceeds a threshold.

[0228] Returning to the target trajectory that the host vehicle may use to navigate a particular road segment, FIG. 11A shows a polynomial representation of a trajectory captured during the process of building or maintaining a sparse map 800. The polynomial representation of the target trajectory included in the sparse map 800 may be determined based on two or more reconstructed trajectories of the vehicle's previous trajectories along the same road segment. In some embodiments, the polynomial representation of the target trajectory included in the sparse map 800 may be an aggregation of two or more reconstructed trajectories of the vehicle's previous trajectories along the same road segment. In some embodiments, the polynomial representation of the target trajectory included in the sparse map 800 may be an average of two or more reconstructed trajectories of the vehicle's previous trajectories along the same road segment. Other mathematical operations may also be used to construct a target trajectory along a road path based on reconstructed trajectories collected from vehicles traversing along the road segment.

[0229] As shown in FIG. 11A , a road segment 1100 may be traveled by multiple vehicles 200 at different times. Each vehicle 200 may collect data related to the path the vehicle took along the road segment. The path traveled by a particular vehicle may be determined based on camera data, accelerometer information, speed sensor information, and / or GPS information, among other potential sources of information. Such data may be used to reconstruct the trajectories of the vehicles traveling along the road segment, and based on these reconstructed trajectories, a target trajectory (or multiple target trajectories) for the particular road segment may be determined. Such target trajectories may represent a preferred path for a host vehicle (e.g., as guided by an autonomous navigation system) as the vehicle travels along the road segment.

[0230] 11A , a first reconstructed trajectory 1101 may be determined based on data received from a first vehicle traversing road segment 1100 during a first time period (e.g., day 1), a second reconstructed trajectory 1102 may be obtained from a second vehicle traversing road segment 1100 during a second time period (e.g., day 2), and a third reconstructed trajectory 1103 may be obtained from a third vehicle traversing road segment 1100 during a third time period (e.g., day 3). Each of trajectories 1101, 1102, and 1103 may be represented by a polynomial, such as a cubic polynomial. Note that in some embodiments, any of the reconstructed trajectories may be provided to and assembled on a vehicle traversing road segment 1100.

[0231] 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, detected road geometry, and / or detected landmarks, among others) to one or more servers. The servers may reconstruct trajectories of vehicles 200 based on the received data. The server may also generate target trajectories based on first, second, and third trajectories 1101, 1102, and 1103 to guide navigation of autonomous vehicles that subsequently travel along the same road segment 1100. While 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 FIG. 11A , the target trajectory is represented by 1110. In some embodiments, the target trajectory 1110 may be generated based on an average of the first, second, and third trajectories 1101, 1102, and 1103. In some embodiments, the target trajectory 1110 included in the sparse map 800 may be an aggregation (e.g., a weighted combination) of two or more reconstructed trajectories.

[0232] 11B and 11C further illustrate the concept of a target trajectory associated with road segments present within a geographic region 1111. As shown in FIG. 11B , a first road segment 1120 within the geographic region 1111 may include a multi-lane road including 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 the first direction. The lanes 1122 and 1124 may be separated by a double yellow line 1123. The geographic region 1111 may also include a branch road segment 1130 that intersects with the road segment 1120. The road segment 1130 may include a two-lane road, with each lane designated for a different direction of travel. The geographic region 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.

[0233] 11C , sparse map 800 may include a local map 1140 that includes a road model to assist in the autonomous navigation of a vehicle within geographic region 1111. For example, local map 1140 may include target trajectories for one or more lanes associated with road segments 1120 and / or 1130 within geographic region 1111. For example, local map 1140 may include target trajectories 1141 and / or 1142 that the autonomous vehicle may access or rely on when traversing lane 1122. Similarly, local map 1140 may include target trajectories 1143 and / or 1144 that the autonomous vehicle may access or rely on when traversing lane 1124. Additionally, local map 1140 may include target trajectories 1145 and / or 1146 that the autonomous vehicle may access or rely on when traversing road segment 1130. Target trajectory 1147 represents a preferred path that the autonomous vehicle should follow when transitioning from lane 1120 (specifically, corresponding to target trajectory 1141 associated with the rightmost lane of lane 1120) to road segment 1130 (specifically, corresponding to target trajectory 1145 associated with a first side of road segment 1130). Similarly, target trajectory 1148 represents a preferred path that the autonomous vehicle should follow when transitioning from road segment 1130 (specifically, corresponding to target trajectory 1146) to a portion of road segment 1124 (specifically, corresponding to target trajectory 1143 associated with the left lane of lane 1124, as shown).

[0234] The sparse map 800 may also include representations of other road-related features associated with the geographic region 1111. For example, the sparse map 800 may also include representations of one or more landmarks identified in the geographic region 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 1154, and a fourth landmark 1156 associated with a hazard sign 1138. Such landmarks may be used, for example, to assist an autonomous vehicle in determining its current position relative to any of the indicated target trajectories, so that the vehicle may adjust its heading to match the direction of the target trajectory at the determined location.

[0235] In some embodiments, the sparse map 800 may also include a road signature profile. Such a road signature profile may be associated with any identifiable / measurable variation in at least one parameter associated with the road. For example, in some cases, such a profile may be associated with changes in road surface information, such as changes in the surface roughness of a particular road segment, changes in road width across a particular road segment, changes in the distance between dashed lines drawn along a particular road segment, changes in the curvature of the road along a particular road segment, etc. FIG. 11D shows an example of a road signature profile 1160. While the profile 1160 may represent any of the parameters described above or other parameters, in one example, the profile 1160 may represent a measure of road surface roughness, for example, obtained by monitoring one or more sensors that provide an output indicative of the amount of suspension displacement as the vehicle travels over a particular road segment.

[0236] Alternatively, or simultaneously, profile 1160 may represent changes in road width determined based on image data acquired via a camera mounted on a vehicle traveling a particular road segment. Such a profile may be useful, for example, to determine a particular position of an autonomous vehicle relative to a particular target trajectory. That is, as the autonomous vehicle traverses a road segment, the autonomous vehicle may measure a profile associated with one or more parameters associated with the road segment. If the measured profile can be correlated / matched with a predefined profile that plots changes in the parameters with respect to position along the road segment, the measured predefined profile may be used (e.g., by overlaying corresponding sections of the measured predefined profile) to determine the current position along the road segment, and thus the current position relative to the target trajectory of the road segment.

[0237] In some embodiments, sparse map 800 may include different trajectories based on different characteristics associated with the user of the autonomous vehicle, environmental conditions, and / or other parameters related to the trip. For example, in some embodiments, different trajectories may be generated based on the preferences and / or profiles of different users. Sparse map 800 including such different trajectories may 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 whether the route includes toll roads. The disclosed system may generate different sparse maps with different trajectories based on the preferences or profiles of such different users. As another example, some users may prefer to travel in faster-moving lanes, while other users may prefer to always maintain a center lane position.

[0238] Different trajectories may be generated and included in the sparse map 800 based on different environmental conditions, such as day and night, snow, rain, fog, etc. An autonomous vehicle traveling in different environmental conditions may provide a sparse map 800 generated based on such different environmental conditions. In some embodiments, a camera provided on the autonomous vehicle may detect environmental conditions and provide such information to a server that generates and provides the sparse map. For example, the server may generate or update an already generated sparse map 800 to include trajectories that may be more suitable or safer for autonomous traveling under the detected environmental conditions. Updating the sparse map 800 based on environmental conditions may be performed dynamically as the autonomous vehicle travels along a road.

[0239] Other different parameters related to travel may also be used as the basis for generating and providing different sparse maps for different autonomous vehicles. For example, when an autonomous vehicle is traveling at high speeds, turns may be difficult. Trajectories associated with particular lanes, rather than roads, may be included in sparse map 800 so that the autonomous vehicle may stay within a particular lane as it follows a particular trajectory. If images captured by a camera mounted on the autonomous vehicle indicate that the vehicle has drifted outside of its lane (e.g., crossed a lane marking), an action may be triggered within the vehicle to return the vehicle to its designated lane according to the particular trajectory.

[0240] Crowdsourcing sparse maps

[0241] In some embodiments, the disclosed systems and methods may generate sparse maps for autonomous vehicle navigation. For example, the disclosed systems and methods may use crowdsourced data to generate sparse maps that one or more autonomous vehicles may use to navigate along a system of roads. As used herein, "crowdsourcing" means receiving data from various vehicles (e.g., autonomous vehicles) traveling a road segment at different times and using such data to generate and / or update a road model. The model may then be transmitted to vehicles that later travel along the road segment or to other vehicles to assist autonomous vehicle navigation. The road model may include multiple target trajectories that represent preferred trajectories for the autonomous vehicle to follow when traversing the road segment. The target trajectories may be the same as reconstructed actual trajectories collected from vehicles traversing the road segment and transmitted from the vehicles to a server. In some embodiments, the target trajectories may differ from actual trajectories previously taken by one or more vehicles when traversing the road segment. The target trajectories may be generated based on the actual trajectories (e.g., by averaging or any other suitable operation).

[0242] The vehicle trajectory data that a vehicle may upload to the server may correspond to the vehicle's actual reconstructed trajectory, or may correspond to a recommended trajectory that may be based on or related to the vehicle's actual reconstructed trajectory, but may differ from the actual reconstructed trajectory. For example, the vehicle may modify the actual reconstructed trajectory and send (e.g., recommend) the modified actual trajectory to the server. The road model may use the recommended modified trajectory as a target trajectory for autonomous navigation of other vehicles.

[0243] In addition to trajectory information, other information for potential use in constructing the sparse data map 800 may include information related to potential landmark candidates. For example, by crowdsourcing information, the disclosed systems and methods may identify potential landmarks in the environment and refine the landmark locations. The landmarks may be used by the autonomous vehicle's navigation system to determine and / or adjust the vehicle's position along the target trajectory.

[0244] A reconstructed trajectory that a vehicle may generate as it travels along a road may be obtained by any suitable method. In some embodiments, the reconstructed trajectory may be developed by piecing together segments of vehicle motion using, for example, egomotion estimation (e.g., 3D translation and 3D rotation of the camera and, therefore, the body of the vehicle). Estimates of rotation and translation may be determined based on analysis of images captured by one or more imaging devices, along with information from other sensors or devices, such as inertial sensors and speed sensors. For example, the inertial sensors may include accelerometers or other suitable sensors configured to measure changes in translation and / or rotation of the vehicle body. The vehicle may include a speed sensor to measure the speed of the vehicle.

[0245] In some embodiments, the egomotion of the camera (and therefore the vehicle body) can be estimated based on optical flow analysis of captured images. Optical flow analysis of a series of images identifies pixel movement from the series of images and determines the vehicle's movement based on the identified movement. The egomotion can be integrated over time along a road segment to reconstruct a trajectory associated with the road segment traversed by the vehicle.

[0246] Data (e.g., reconstructed trajectories) collected by multiple vehicles on multiple trips along a road segment at different times may be used to construct a road model (e.g., including a target trajectory, etc.) included in sparse data map 800. Data collected by multiple vehicles on multiple trips along a road segment at different times may also be averaged to increase the accuracy of the model. In some embodiments, data regarding road geometry and / or landmarks may be received from multiple vehicles traveling through a common road segment at different times. Such data received from different vehicles may be combined to generate and / or update a road model.

[0247] The geometry of the reconstructed trajectory (and also the target trajectory) along the road segment may be represented by a curve in three-dimensional space, which may be a spline connecting three-dimensional polynomials. The reconstructed trajectory curve may be determined from an analysis of a video stream or multiple images captured by a camera mounted on the vehicle. In some embodiments, a location is identified in each frame or image several meters ahead of the vehicle's current position. This location is where the vehicle is expected to travel within a predetermined period of time. This operation may be repeated for each frame, while the vehicle may calculate the egomotion (rotation and translation) of the camera. For each frame or image, a short-range model of the desired path is generated by the vehicle in the reference frame attached to the camera. The short-range models may be stitched together to obtain a three-dimensional model of the road in several coordinate frames, which may be any coordinate frame or a predetermined coordinate frame. The three-dimensional model of the road may then be fitted by a spline, which may include or connect one or more polynomials of appropriate degrees.

[0248] One or more detection modules may be used to conclude a short-distance road model for each frame. For example, a bottom-up lane detection module may be used. The bottom-up lane detection module may be useful when lane markings are painted on the road. This module may locate edges in the image and assemble them together to form lane markings. A second module may be used together with the bottom-up lane detection module. The second module is an end-to-end deep neural network that may be trained to predict an accurate short-distance path from the input image. In either module, the road model is detected in the image coordinate frame and transformed into a three-dimensional space that may be virtually connected to the camera.

[0249] Although the reconstructed trajectory modeling method may result in error accumulation due to long-term egomotion integration, which may include noise components, such errors may be insignificant because the generated model may provide sufficient accuracy for navigation at local scales. Additionally, external information sources, such as satellite imagery or geodetic measurements, may be used to cancel the integrated errors. For example, the disclosed systems and methods may use a GNSS receiver to cancel the accumulated errors. However, GNSS positioning signals are not always available and accurate. The disclosed systems and methods may enable steering applications that are weakly dependent on the availability and accuracy of GNSS positioning. In such systems, the use of GNSS signals may be limited. For example, in some embodiments, the disclosed systems may use GNSS signals only for database indexing purposes.

[0250] In some embodiments, a range scale (e.g., local scale) that may be relevant for an autonomous vehicle navigation and steering application may be as large as 50 meters, as large as 100 meters, as large as 200 meters, as large as 300 meters, etc. Such distances may be used because the geometric road model is primarily used for two purposes: planning the trajectory ahead and locating the vehicle on the road model. In some embodiments, when a control algorithm steers a vehicle according to a target point located 1.3 seconds ahead (or any other time, such as 1.5 seconds, 1.7 seconds, 2 seconds, etc.), the planning task may use the model over a typical range of 40 meters ahead (or any other suitable distance ahead, such as 20 meters, 30 meters, 50 meters, etc.). The localization task uses the road model over a typical range of 60 meters behind the vehicle (or any other suitable distance, such as 50 meters, 100 meters, 150 meters, etc.), following a method called “tail alignment,” which is described in more detail in another section. The disclosed systems and methods can generate a model of the geometry with sufficient accuracy over a specified range, such as 100 meters, so that the planned trajectory does not deviate from the lane center by more than 30 cm, for example.

[0251] As mentioned above, a 3D road model can be constructed by detecting short-distance sections and stitching them together. Stitching can be made possible by calculating a six-stage ego-motion model using video and / or images captured by cameras, data from inertial sensors reflecting vehicle motion, and the host vehicle's speed signal. The cumulative error can be small enough at some local range scales, such as 100 meters or so. At all of these range scales, a particular road segment can be completed in a single drive.

[0252] In some embodiments, multiple runs may be used to average the resulting model to further improve its accuracy. The same vehicle may run the same route multiple times, or multiple vehicles may send collected model data to a central server. In either case, a matching procedure may be performed to identify overlapping models and enable averaging to generate a target trajectory. The constructed model (e.g., including the target trajectory) may be used for steering once convergence criteria are met. Subsequent runs may be used to further improve the model and to accommodate infrastructure changes.

[0253] When multiple vehicles are connected to a central server, sharing of driving experience (such as sensor data) between multiple vehicles becomes possible. Each vehicle client may store a partial copy of a universal road model that may be relevant to its current location. A bidirectional update procedure between the vehicle and the server may be performed by the vehicle and the server. The small footprint concept discussed above allows the disclosed system and method to perform bidirectional updates using very low bandwidth.

[0254] Information related to potential landmarks may also be determined and transferred to a central server. For example, the disclosed systems and methods may determine one or more physical characteristics of a potential landmark based on one or more images including the landmark. The physical characteristics may include the physical size of the landmark (e.g., height, width), the distance of the landmark from the vehicle, the distance of the landmark from a previous landmark, the lateral position of the landmark (e.g., the position of the landmark relative to the driving lane), the GPS coordinates of the landmark, the type of landmark, identification of text on the landmark, etc. For example, the vehicle may analyze one or more images captured by a camera to detect potential landmarks, such as speed limit signs.

[0255] A vehicle may determine a distance from the vehicle to a landmark based on an analysis of one or more images. In some embodiments, the distance may be determined based on an analysis of an image of the landmark using appropriate image analysis methods, such as scaling and / or optical flow. In some embodiments, the disclosed systems and methods may be configured to determine a type or classification of a potential landmark. If a vehicle determines that a particular potential landmark corresponds to a predetermined type or classification stored in the sparse map, it may be sufficient for the vehicle to communicate an indication of the landmark type or classification along with its location to a server. The server may store such an indication. Later, another vehicle may capture an image of the landmark, process the image (e.g., using a classifier), and compare the results of processing the image with the indication of the landmark type stored on the server. Various types of landmarks may exist, and different types of landmarks may be associated with different types of data that are uploaded and stored on the server; different processes onboard the vehicle may detect the landmarks and communicate information about the landmarks to the server; a system onboard the vehicle may receive the landmark data from the server and use the landmark data to identify the landmark in autonomous navigation.

[0256] In some embodiments, multiple autonomous vehicles traveling on a road segment may communicate with a server. The vehicles (or clients) may generate curves that describe their travel in any coordinate frame (e.g., by egomotion integration). The vehicles may detect landmarks and place them in the same frame. The vehicles may upload the curves and landmarks to the server. The server may collect data from the vehicles over multiple travels and generate a unified road model. For example, as discussed below with respect to FIG. 19, the server may use the uploaded curves and landmarks to generate a sparse map with a unified road model.

[0257] The server may also distribute the model to clients (e.g., vehicles). For example, the server may distribute sparse maps to one or more vehicles. The server may continuously or periodically update the model as it receives new data from the vehicles. For example, the server may process the new data to evaluate whether the data contains information that should trigger an update or the creation of new data on the server. The server may distribute updated models or updates to the vehicles to provide autonomous vehicle navigation.

[0258] The server may use one or more criteria to determine whether new data received from a vehicle should trigger a model update or the creation of new data. For example, if the new data indicates that a previously recognized landmark at a particular location is no longer present or has been replaced by another landmark, the server may determine that the new data should trigger a model update. As another example, if the new data indicates that a road segment is closed, and this is corroborated by data received from other vehicles, the server may determine that the new data should trigger a model update.

[0259] The server may distribute the updated model (or updated portion of the model) to one or more vehicles traveling on a road segment with which the update to the model is associated. The server may also distribute the updated model to vehicles about to travel on a road segment with which the update to the model is associated, or to vehicles in a planned movement that includes the road segment. For example, while the autonomous vehicle is traveling along another road segment before reaching the road segment with which the update is associated, the server may distribute the update or updated model to the autonomous vehicle before the vehicle reaches the road segment.

[0260] In some embodiments, a remote server may collect trajectories and landmarks from multiple clients (e.g., vehicles traveling along a common road segment). The server may use landmarks to match curves and create an average road model based on the trajectories collected from multiple vehicles. The server may also calculate the most likely path at each node or junction of the road graph and road segment. For example, the remote server may align the trajectories to generate a crowdsourced sparse map from the collected trajectories.

[0261] The server may average landmark properties received from multiple vehicles traveling along a common road segment, such as the distance from one landmark to another (e.g., the previous landmark along the road segment) measured by the multiple vehicles, to determine arc-length parameters and support position location and speed calibration along each client vehicle's path. The server may average physical dimensions of landmarks measured by multiple vehicles traveling along a common road segment and recognizing the same landmark. The averaged physical dimensions may be used to support distance estimation, such as the distance from the vehicle to the landmark. The server may average lateral positions of landmarks (e.g., the position of the landmark from the lane in which the vehicle is traveling) measured by multiple vehicles traveling along a common road segment and recognizing the same landmark. The averaged lateral positions may be used to support lane assignment. The server may average GPS coordinates of landmarks measured by multiple vehicles traveling along the same road segment and recognizing the same landmark. The averaged GPS coordinates of landmarks may be used to support global localization or positioning of landmarks within a road model.

[0262] In some embodiments, the server may identify model changes, such as construction, detours, new signs, sign removal, etc., based on data received from the vehicles. The server may update the model continuously, periodically, or instantaneously as it receives new data from the vehicles. The server may distribute model updates or updated models to the vehicles to provide autonomous navigation. For example, as discussed further below, the server may use crowdsourced data to filter out "ghost" landmarks detected by the vehicles.

[0263] In some embodiments, the server may analyze driver intervention during autonomous driving. The server may analyze data received from the vehicle at the time and location at which the intervention occurs and / or data received prior to the time at which the intervention occurs. The server may identify specific portions of data that caused or are closely related to the intervention, such as data indicating a temporary lane closure setting or data indicating a pedestrian on 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.

[0264] FIG. 12 is a schematic diagram of a system for generating sparse maps using crowdsourcing (and distributing and navigating using crowdsourced sparse maps). FIG. 12 shows a road segment 1200 including one or more lanes. Multiple vehicles 1205, 1210, 1215, 1220, and 1225 may be traveling on the road segment 1200 at the same time or at different times (although FIG. 12 shows them appearing on the road segment 1200 at the same time). At least one of the vehicles 1205, 1210, 1215, 1220, and 1225 may be an autonomous vehicle. For simplicity of this example, we will assume that all of the vehicles 1205, 1210, 1215, 1220, and 1225 are autonomous vehicles.

[0265] Each vehicle may be similar to a vehicle disclosed in other embodiments (e.g., vehicle 200) and may include components or devices included in or associated with a vehicle disclosed in other embodiments. Each vehicle may be equipped with an imaging device or camera (e.g., imaging device 122 or camera 122). Each vehicle may communicate with a remote server 1230 through one or more networks (e.g., via a cellular network and / or the Internet, etc.) via wireless communication path 1235, as shown by the dotted line. Each vehicle may send data to and receive data from server 1230. For example, server 1230 may collect data from multiple vehicles traveling road segment 1200 at different times and process the collected data to generate an autonomous vehicle road navigation model or model updates. Server 1230 may transmit the autonomous vehicle road navigation model or model updates to vehicles that sent data to server 1230. Server 1230 may transmit the autonomous vehicle road navigation model or model updates to other vehicles that later travel road segment 1200.

[0266] As the vehicles 1205, 1210, 1215, 1220, and 1225 travel along the road segment 1200, navigation information collected (e.g., detected, sensed, or measured) by the vehicles 1205, 1210, 1215, 1220, and 1225 may be transmitted to the server 1230. In some embodiments, the navigation information may be associated with a common road segment 1200. The navigation information may include trajectories associated with each of the vehicles 1205, 1210, 1215, 1220, and 1225 as each vehicle travels along the road segment 1200. In some embodiments, the trajectories may be reconstructed based on data sensed by various sensors and devices provided on the vehicles 1205. For example, the trajectories may be reconstructed based on at least one of accelerometer data, speed data, landmark data, road geometry or profile data, vehicle position data, and egomotion data. In some embodiments, the trajectory may be reconstructed based on data from inertial sensors, such as accelerometers, and the velocity of the vehicle 1205 as sensed by a speed sensor. Additionally, in some embodiments, the trajectory may be determined based on sensed egomotion of the cameras (e.g., by a processor on board each of the vehicles 1205, 1210, 1215, 1220, and 1225), which may indicate three-dimensional translation and / or three-dimensional rotation (or rotational movement). The egomotion of the cameras (and thus the vehicle body) may be determined from an analysis of one or more images captured by the cameras.

[0267] In some embodiments, the trajectory of vehicle 1205 may be determined by a processor onboard vehicle 1205 and transmitted to server 1230. In other embodiments, server 1230 may receive data sensed by various sensors and devices onboard vehicle 1205 and determine the trajectory based on the data received from vehicle 1205.

[0268] In some embodiments, navigation information transmitted from vehicles 1205, 1210, 1215, 1220, and 1225 to server 1230 may include data regarding the road surface, road geometry, or road profile. The geometry of road segment 1200 may include lane structure and / or landmarks. The lane structure may include the total number of lanes on road segment 1200, the type of lane (e.g., unidirectional lane, bidirectional lane, travel lane, passing lane, etc.), markings on the lane, lane width, etc. In some embodiments, navigation information may include lane assignments, such as which lane of multiple lanes the vehicle is traveling in. For example, a numeric value "3" may be associated with a lane assignment to indicate that the vehicle is traveling in the third lane from the left or right. As another example, a text value "center lane" may be associated with a lane assignment to indicate that the vehicle is traveling in the center lane.

[0269] Server 1230 may store the navigation information on a non-transitory computer-readable medium, such as a hard drive, compact disc, tape, memory, etc. Server 1230 may generate (e.g., via a processor included in server 1230) at least a portion of an autonomous vehicle road navigation model of the common road segment 1200 based on navigation information received from multiple vehicles 1205, 1210, 1215, 1220, and 1225 and store the model as part of a sparse map. Server 1230 may determine a trajectory 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 the lanes of the road segment at different times. Server 1230 may generate the autonomous vehicle road navigation model or a portion of the model (e.g., an updated portion) based on the multiple trajectories determined based on the crowdsourced navigation data. Server 1230 may transmit the model or updated portions of the model to one or more of autonomous vehicles 1205, 1210, 1215, 1220, and 1225 traveling on road segment 1200 or any other autonomous vehicles that later travel the road segment to update an existing autonomous vehicle road navigation model provided in the vehicle's navigation system. The autonomous vehicle road navigation model may be used by the autonomous vehicles as they autonomously navigate along common road segment 1200.

[0270] As described above, the autonomous vehicle road navigation model may be included in a sparse map (e.g., sparse map 800 shown in FIG. 8 ). Sparse map 800 may include a sparse record of data related to road geometry and / or landmarks along the road, and may provide sufficient information to guide the autonomous navigation of the autonomous vehicle, but without requiring excessive additional data storage. In some embodiments, the autonomous vehicle road navigation model may be stored separately from sparse map 800, and may use map data from sparse map 800 when the model is implemented for navigation. In some embodiments, the autonomous vehicle road navigation model may use the map data included in sparse map 800 to determine a target trajectory along road segment 1200 to guide the autonomous navigation of autonomous vehicles 1205, 1210, 1215, 1220, and 1225, or other vehicles that subsequently travel along road segment 1200. For example, when the autonomous vehicle road navigation model is executed by a processor included in the navigation system of vehicle 1205, the model may cause the processor to compare a trajectory determined based on navigation information received from vehicle 1205 with a predetermined trajectory included in sparse map 800 to verify and / or correct the current course of travel of vehicle 1205.

[0271] In an autonomous vehicle road navigation model, the geometry of road features or target trajectories may be encoded by curves in three-dimensional space. In one embodiment, the curves may be cubic splines including one or more connected cubic polynomials. As one skilled in the art will appreciate, splines may be numerical functions piecewise defined by a series of polynomials for fitting data. Splines for fitting the three-dimensional road geometry data may include linear splines (first order), quadratic splines (second order), cubic splines (third order), or any other splines (other orders), or combinations thereof. Splines may include one or more cubic polynomials of different orders that connect (e.g., fit) data points of the three-dimensional road geometry data. In some embodiments, the autonomous vehicle road navigation model may include cubic splines corresponding to a common road segment (e.g., road segment 1200) or a target trajectory along a lane of road segment 1200.

[0272] As described above, the autonomous vehicle road navigation model included in the sparse map may include other information, such as the identification of at least one landmark along 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 images of the landmarks. A processor (e.g., processors 180, 190, or processing unit 110) mounted on vehicle 1205 may process the images of the landmarks to extract the landmark identification information. Landmark-specific information may be stored in sparse map 800 rather than actual images of the landmarks. The landmark-specific information may require much less storage space than the actual images. Other sensors or systems (e.g., a GPS system) may also provide specific identification information of landmarks (e.g., the location of the landmarks). The landmarks may include at least one of a traffic sign, an arrow mark, a lane mark, a dashed lane mark, a traffic light, a stop line, a directional sign (e.g., a highway exit sign with an arrow indicating a direction, a highway sign with an arrow pointing in a different direction or location), a landmark beacon, or a light pole. 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 installed in a vehicle, so that when the vehicle passes by the device, the beacon received by the vehicle and the location of the device (e.g., determined from the device's GPS location) can be used as a landmark included in the autonomous vehicle road navigation model and / or sparse map 800.

[0273] The identification of the at least one landmark may include a location of the at least one landmark. The location of the landmark may be determined based on position measurements performed using sensor systems (e.g., global positioning systems, inertial-based positioning systems, landmark beacons, etc.) associated with the multiple vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, the location of the landmark may be determined by averaging position measurements detected, collected, or received by sensor systems on different vehicles 1205, 1210, 1215, 1220, and 1225 over multiple trips. For example, the vehicles 1205, 1210, 1215, 1220, and 1225 may send position measurement data to the server 1230, which may average the position measurements and use the average position measurement as the location of the landmark. The location of the landmark may be continually refined with measurements received from the vehicles on subsequent trips.

[0274] The identification of the landmark may include the size of the landmark. A processor provided in the vehicle (e.g., 1205) may estimate the physical size of the landmark based on an analysis of the image. The server 1230 may receive multiple estimates of the physical size of the same landmark from different vehicles over different trips. The server 1230 may average the different estimates to arrive at the physical size of the landmark and store the landmark size in the road model. The physical size estimate 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 vehicle's current speed and a scale of magnification based on the position of the landmark as it appears in the image relative to the camera's magnification focus. For example, the distance to the landmark may be estimated as Z = V * dt * R / D, where V is the vehicle speed, R is the distance in the image from the landmark to the magnification focus at time t1, D is the change in the distance of the landmark in the image from t1 to t2, and dt represents (t2 - t1). For example, the distance to a landmark may be estimated as Z=V*dt*R / D, where V is the vehicle speed, R is the distance in the image between the landmark and the magnification focus, dt is the time interval, and D is the image displacement of the landmark along the epipolar line. The above equation and other equivalent equations may be used to estimate the distance to a landmark, such as Z=V*ω / Δω, where V is the vehicle speed, ω is the image length (e.g., object width), and Δω is the change in that image length per unit time.

[0275] If the physical size of the landmark is known, the distance to the landmark can also be determined based on the following formula: 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 the landmark passes through in the image. From the above formula, the change in distance Z is ΔZ=f*W*Δω / ω 2 +f*ΔW / ω, where ΔW decays to zero through averaging and Δω is the number of pixels representing the accuracy of the bounding box in the image. An estimate of the physical size of the landmark can be calculated on the server side by averaging multiple observations. The resulting error in distance estimation can be very small. There are two sources of error that can occur when using the above formula: ΔW and Δω. The contribution to the distance error is ΔZ=f*W*Δω / ω 2is given by f*ΔW / ω, where ΔW decays to zero through averaging, and therefore ΔZ is determined by Δω (e.g., the inaccuracy of the image's bounding box).

[0276] For landmarks of unknown dimensions, the distance to the landmark can be estimated by tracking feature points on the landmark between successive frames. For example, a particular feature displayed 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 can be extracted from the distance distribution. For example, the most frequent distance appearing in the distance distribution can be used as the distance estimate. As another example, the mean of the distance distribution can be used as the distance estimate.

[0277] FIG. 13 shows an example autonomous vehicle road navigation model represented by multiple 3-dimensional splines 1301, 1302, and 1303. The curves 1301, 1302, and 1303 shown in FIG. 13 are for illustrative purposes only. Each spline may include one or more 3-dimensional polynomials connecting multiple data points 1310. Each polynomial may be a first-order polynomial, a second-order polynomial, a third-order polynomial, or any suitable combination of polynomials having different orders. Each data point 1310 may be associated with navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, each data point 1310 may be associated with data related to landmarks (e.g., size, location, and landmark identification information) and / or road signature profiles (e.g., road geometry, road roughness profile, road curvature profile, road width profile). In some embodiments, some data points 1310 may be associated with data related to landmarks, and other data points may be associated with data related to road signature profiles.

[0278] FIG. 14 shows raw position data 1410 (e.g., GPS data) received from five separate runs. One run may be distinct from another if separate vehicles crossed at the same time, if the same vehicle crossed at different times, or if separate vehicles crossed at different times. To account for errors in the position data 1410 and different positions of vehicles in the same lane (e.g., one vehicle may be traveling further to the left of the lane than another vehicle), the remote server 1230 may use one or more statistical techniques to generate a map skeleton 1420 and determine whether changes in the raw position data 1410 represent actual deviations or statistical errors. Each route in the skeleton 1420 may be linked back to the raw data 1410 that formed that route. For example, the route between A and B in the skeleton 1420 is linked to raw data 1410 from runs 2, 3, 4, and 5, but not from run 1. While the skeleton 1420 may not be detailed enough to be used for navigating a vehicle (e.g., because it combines travel from multiple lanes on the same road, unlike the splines described above), it may provide useful topological information and may be used to define intersections.

[0279] FIG. 15 illustrates an example in which additional detail may be generated for a sparse map within a section of a map skeleton (e.g., section A to B in skeleton 1420). As shown in FIG. 15, data (e.g., egomotion data, road mark data, and the like) may be shown as a function of position S (or S1 or S2) along the run. Server 1230 may identify landmarks in the sparse map by identifying unique matches between landmarks 1501, 1503, and 1505 of run 1510 and landmarks 1507 and 1509 of run 1520. Such a matching algorithm may lead to the identification of landmarks 1511, 1513, and 1515. However, one skilled in the art will recognize that other matching algorithms may be used. For example, probability optimization may be used instead of or in combination with unique matching. Server 1230 may align runs longitudinally to align matched landmarks. For example, the server 1230 may select one run (e.g., run 1520) as a reference run and then shift and / or elastically stretch the other run (e.g., run 1510) for alignment.

[0280] FIG. 16 shows an example of registered landmark data for use in a sparse map. In the example of FIG. 16, landmark 1610 includes a road sign. The example of FIG. 16 also shows data from multiple runs 1601, 1603, 1605, 1607, 1609, 1611, and 1613. In the example of FIG. 16, the data from run 1613 consists of "ghost" landmarks; none of runs 1601, 1603, 1605, 1607, 1609, and 1611 include identification of landmarks in the vicinity of the identified landmarks in run 1613, and therefore server 1230 may identify the landmark as a "ghost." Thus, server 1230 may accept a potential landmark if the ratio of images in which the landmark appears to images in which the landmark does not appear exceeds a threshold, and / or may reject a potential landmark if the ratio of images in which the landmark does not appear to images in which the landmark appears exceeds a threshold.

[0281] FIG. 17 illustrates a system 1700 for generating trip data that may be used to crowdsource a sparse map. As shown in FIG. 17 , the system 1700 may include a camera 1701 and a location device 1703 (e.g., a GPS locator). The camera 1701 and the location device 1703 may be mounted on a vehicle (e.g., one of vehicles 1205, 1210, 1215, 1220, and 1225). The camera 1701 may generate multiple types of data, such as egomotion data, traffic sign data, road data, or the like. The camera data and location data may be segmented into trip segments 1705. For example, each trip segment 1705 may have camera data and location data for trips of less than 1 km.

[0282] In some embodiments, system 1700 may remove redundancy in travel segment 1705. For example, if a landmark appears in multiple images from camera 1701, system 1700 may remove the redundant data so that travel segment 1705 includes only the location of the landmark and one copy of any metadata associated with the landmark. As a further example, if a lane marking appears in multiple images from camera 1701, system 1700 may remove the redundant data so that travel segment 1705 includes only the location of the lane marking and one copy of any metadata associated with the lane marking.

[0283] System 1700 also includes a server (e.g., server 1230), which may receive trip segments 1705 from the vehicles and recombine the trip segments 1705 into a single trip 1707. Such an arrangement may reduce bandwidth requirements when transferring data between the vehicles and the server, and may also allow the server to store data related to an entire trip.

[0284] FIG. 18 illustrates the system 1700 of FIG. 17 further configured for crowdsourcing a sparse map. As in FIG. 17, the system 1700 includes a vehicle 1810 that captures trip data using, for example, a camera (e.g., generating egomotion data, traffic sign data, road data, or the like) and a location device (e.g., a GPS locator). As in FIG. 17, the vehicle 1810 segments the collected data into trip segments (shown in FIG. 18 as "DS1 1," "DS2 1," and "DSN 1"). The server 1230 then receives the trip segments and reconstructs a trip (shown in FIG. 18 as "Journey 1") from the received segments.

[0285] As further shown in FIG. 18 , system 1700 also receives data from additional vehicles. For example, vehicle 1820 also captures trip data using, for example, a camera (which generates, e.g., egomotion data, traffic sign data, road data, or the like) and a location device (e.g., a GPS locator). Similar to vehicle 1810, vehicle 1820 segments the collected data into trip segments (shown in FIG. 18 as “DS1 2,” “DS2 2,” and “DSN 2”). Server 1230 then receives the trip segments and reconstructs a trip (shown in FIG. 18 as “Journey 2”) from the received segments. Any number of additional vehicles may be used. For example, FIG. 18 also includes “Car N” that captures trip data, segments it into trip segments (shown in FIG. 18 as “DS1 N,” “DS2 N,” and “DSN N”), and sends it to server 1230 for reconstruction into a trip (shown in FIG. 18 as “Journey N”).

[0286] As shown in FIG. 18, the server 1230 may build a sparse map (shown as "Map") using reconstructed trips (e.g., "Run 1," "Run 2," and "Run N") collected from multiple vehicles (e.g., "Car 1" (also referred to as vehicle 1810), "Car 2" (also referred to as vehicle 1820), and "Car N").

[0287] 19 is a flowchart illustrating an example process 1900 for generating a sparse map for autonomous vehicle navigation along a road segment. Process 1900 may be performed by one or more processing devices included in server 1230.

[0288] Process 1900 may include receiving a plurality of images acquired as one or more vehicles traverse the road segment (step 1905). Server 1230 may receive the images from cameras included in one or more of vehicles 1205, 1210, 1215, 1220, and 1225. For example, camera 122 may capture one or more images of the environment surrounding vehicle 1205 as vehicle 1205 travels along road segment 1200. In some embodiments, server 1230 may also receive pruned image data, in which redundancies are removed by a processor on vehicle 1205, as discussed above with respect to FIG. 17 .

[0289] Process 1900 may further include identifying at least one line representation of a road surface feature extending along the road segment based on the multiple images (step 1910). Each line representation may represent a path along the road segment that substantially 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 trajectory of travel along road segment 1200 associated with the road edges or lane markings. In some embodiments, the trajectory (or line representation) may include a spline, a polynomial representation, or a curve. Server 1230 may determine a trajectory of travel of vehicle 1205 based on the egomotion (e.g., three-dimensional translational and / or three-dimensional rotational) of the camera received in step 1905.

[0290] Process 1900 may also include identifying multiple landmarks associated with the road segment based on the multiple images (step 1910). For example, server 1230 may analyze environmental images received from camera 122 to identify one or more landmarks, such as road signs, along road segment 1200. Server 1230 may identify landmarks using analysis of multiple images acquired as one or more vehicles traverse the road segment. To enable crowdsourcing, the analysis may include rules for accepting and rejecting potential landmarks associated with the road segment. For example, the analysis may include accepting a potential landmark if 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 if the ratio of images in which the landmark does not appear to images in which the landmark appears exceeds a threshold.

[0291] 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, by server 1230, clustering vehicle trajectories associated with multiple vehicles traveling on the road segment and determining a target trajectory based on the clustered vehicle trajectories, as discussed in further detail below. Clustering the vehicle trajectories may include clustering, by server 1230, multiple trajectories associated with vehicles traveling on the road segment into multiple clusters based on at least one of the absolute headings of the vehicles or the lane assignments of the vehicles. Generating the target trajectory may include averaging, by server 1230, the clustered trajectories. As a 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.

[0292] The disclosed systems and methods may include other features. For example, the disclosed systems may use local coordinates rather than global coordinates. For autonomous driving, some systems may display data in world coordinates, e.g., using longitude and latitude coordinates on the Earth's surface. To use a map for steering, the host vehicle may determine its position and orientation relative to the map. It seems natural to use an onboard GPS device to position the vehicle on the map and to find the rotational transformation between the body's reference frame and the world's reference frame (e.g., north, east, and down). Once the body's reference frame is aligned with the map's reference frame, the desired route can be represented in the body's reference frame and steering commands can be calculated or generated.

[0293] The disclosed systems and methods may enable autonomous vehicle navigation (e.g., steering control) with low-footprint models that can be collected by the autonomous vehicle itself without the aid of expensive surveying equipment. To support autonomous navigation (e.g., steering applications), the road model may include a sparse map having the road's geometry, its lane structure, and landmarks that can be used to determine the vehicle's location or position along the trajectory included in the model. As discussed above, generation of the sparse map may be performed by a remote server that communicates with and receives data from vehicles traveling on the road. The data may include sensed data, a reconstructed trajectory based on the sensed data, and / or a recommended trajectory that may represent a modified reconstructed trajectory. As discussed below, the server may transmit the model to the vehicle or other vehicles that subsequently travel on the road to assist autonomous navigation.

[0294] 20 shows a block diagram of the server 1230. The 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, the communication unit 2005 may include at least one network interface. The server 1230 may communicate with the vehicles 1205, 1210, 1215, 1220, and 1225 through the communication unit 2005. For example, the server 1230 may receive navigation information transmitted from the vehicles 1205, 1210, 1215, 1220, and 1225 through the communication unit 2005. The server 1230 may distribute an autonomous vehicle road navigation model to one or more autonomous vehicles through the communication unit 2005.

[0295] Server 1230 may include at least one non-transitory storage medium 2010, such as a hard drive, compact disc, 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 autonomous vehicle road navigation models that server 1230 generates 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 FIG. 8).

[0296] In addition to, or instead of, storage device 2010, server 1230 may include memory 2015. Memory 2015 may be similar to or different from memory 140 or 150. Memory 2015 may be 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 for sparse map 800), an autonomous vehicle road navigation model, and / or navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225.

[0297] Server 1230 may include at least one processing device 2020 configured to execute computer codes or instructions stored in memory 2015 to perform various functions. For example, 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. Processing device 2020 may control 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 vehicles that subsequently travel road segment 1200). Processing device 2020 may be similar to or different from processors 180, 190, or processing unit 110.

[0298] 21 shows a block diagram of a memory 2015 that 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 shown in FIG. 21 , 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 either of the modules 2105 and 2110 included in the memory 2015.

[0299] 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 of a common road segment (e.g., road segment 1200) based on navigation information received from the 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 common road segment 1200 into different clusters. The processor 2020 may determine a target trajectory along the common road segment 1200 based on the clustered vehicle trajectories for each of the different clusters. Such an operation may include finding an average or mean 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 common road segment 1200.

[0300] The road model and / or sparse map may store trajectories associated with road segments. These trajectories may be referred to as target trajectories and are provided to the autonomous vehicle for autonomous navigation. The target trajectories may be received from multiple vehicles or may be generated based on actual trajectories or recommended trajectories (actual trajectories with some modifications) received from multiple vehicles. The target trajectories included in the road model or sparse map may be continuously updated (e.g., averaged) with new trajectories received from other vehicles.

[0301] Vehicles traveling along a road segment may collect data through various sensors. The data may include landmarks, road signature profiles, vehicle movements (e.g., accelerometer data, speed data), and vehicle position (e.g., GPS data), and may reconstruct the actual trajectory itself or transmit the data to a server, which may reconstruct the vehicle's actual trajectory. In some embodiments, vehicles may transmit data related to the trajectory (e.g., curves in any reference frame), landmark data, and lane assignments along the traveled path to server 1230. Different vehicles traveling along the same road segment over multiple trips may have different trajectories. Server 1230 may identify the route or trajectory associated with each lane from the trajectories received from the vehicles through a clustering process.

[0302] 22 shows a process of clustering vehicle trajectories associated with vehicles 1205, 1210, 1215, 1220, and 1225 to determine a target trajectory for a common road segment (e.g., road segment 1200). The target trajectory or target trajectories determined from the clustering process may be included in 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 the autonomous vehicle road navigation model, the server 1230 may cluster the vehicle trajectory 1600 into multiple clusters 2205, 2210, 2215, 2220, 2225, and 2230, as shown in FIG. 22.

[0303] Clustering may be performed using various criteria. In some embodiments, all trips within a cluster may be similar in terms of absolute heading along the road segment 1200. The absolute heading may be obtained from GPS signals received by the vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, the absolute heading may be obtained using dead reckoning. As one skilled in the art will appreciate, dead reckoning may be used to determine the current position, and therefore heading, of the vehicles 1205, 1210, 1215, 1220, and 1225 using previously determined positions, estimated speeds, etc. Trajectories clustered by absolute heading may be useful for identifying routes along a road.

[0304] In some embodiments, all trips within a cluster may be similar with respect to lane assignment along the trip of road segment 1200 (e.g., same lane before and after an intersection). Trajectories clustered by lane assignment may be useful for identifying lanes along a road. In some embodiments, both criteria (e.g., absolute heading and lane assignment) may be used for clustering.

[0305] For each cluster 2205, 2210, 2215, 2220, 2225, and 2230, the trajectories may be averaged to obtain a target trajectory associated with a particular cluster. For example, trajectories from multiple runs associated with the same lane cluster may be averaged. The average trajectory may be a target trajectory associated with a particular lane. To average a cluster of trajectories, server 1230 may select a reference frame for any trajectory C. For all other trajectories (C, ..., C), server 1230 may find a rigid transformation that maps C to C, where i = 1, 2, ..., n, where n is a positive integer corresponding to the total number of trajectories included in the cluster. Server 1230 may calculate the average curve or trajectory in the C reference frame.

[0306] In some embodiments, the landmarks may define arc lengths that are consistent between different runs, and the arc lengths may be used to align the track with the lanes. In some embodiments, lane marks before and after intersections may be used to align the track with the lanes.

[0307] To assemble lanes from the trajectory, server 1230 may select a reference frame for any lane. Server 1230 may map overlapping lanes to the selected reference frame. Server 1230 may continue mapping until all lanes are in the same reference frame. Lanes that are adjacent to each other may be aligned as if they were the same lane and may later be shifted laterally.

[0308] Landmarks recognized along a road segment may be mapped to a common reference frame, first at the lane level and then at the intersection level. For example, the same landmark may be recognized multiple times by multiple vehicles on multiple runs. Data about the same landmark received on different runs may be slightly different. Such data may be averaged and mapped to the same reference frame, such as the C0 reference frame. Additionally or alternatively, the variance of data for the same landmark received on multiple runs may be calculated.

[0309] In some embodiments, each lane of road segment 120 may be associated with a target trajectory and specific landmarks. The target trajectory or multiple such target trajectories may be included in an autonomous vehicle road navigation model that may be used later by other autonomous vehicles traveling along the same road segment 1200. Landmarks identified by vehicles 1205, 1210, 1215, 1220, and 1225 while they travel along road segment 1200 may be recorded in association with the target trajectory. The target trajectory and landmark data may be continuously or periodically updated with new data received from other vehicles on subsequent trips.

[0310] For localization of an autonomous vehicle, the disclosed system and method may use an extended Kalman filter. The vehicle's position may be determined based on three-dimensional position data and / or three-dimensional orientation data, and prediction of the vehicle's future position beyond its current position through integration of egomotion. The vehicle's position may be corrected or adjusted based on image observations of landmarks. For example, if the vehicle detects a landmark in an image captured by a camera, the landmark may be compared to known landmarks stored in the road model or sparse map 800. The known landmark may have a known position (e.g., GPS data) along a target trajectory stored in the road model and / or sparse map 800. Based on the current speed and the image of the landmark, the distance from the vehicle to the landmark may be estimated. The vehicle's position along the target trajectory may be adjusted based on the distance to the landmark and the known position of the landmark (stored in the road model or sparse map 800). The landmark position / location data (e.g., meaning an average value from multiple trips) stored in the road model and / or sparse map 800 may be assumed to be accurate.

[0311] In some embodiments, the disclosed system may form a closed-loop subsystem in which an estimate of the vehicle's six degrees of freedom (e.g., three-dimensional position data and three-dimensional orientation data) position can be used to navigate (e.g., steer the wheels) the autonomous vehicle to reach a desired point (e.g., 1.3 seconds ahead of a stored point). Data measured from the steering and actual navigation can then be used to estimate the six degrees of freedom position.

[0312] In some embodiments, poles along roads, such as lampposts and power or cable poles, may be used as landmarks for vehicle location. Other landmarks, such as traffic signs, traffic lights, arrows on the road, stop lines, and static features or signatures of objects along road segments, may also be used as landmarks for vehicle location. When poles are used for location, the x observation of the pole (i.e., the viewing angle from the vehicle) may be used rather than the y observation (i.e., the distance to the pole), since the bottom of the pole may be occluded and not on the road plane.

[0313] FIG. 23 illustrates a navigation system for a vehicle that may be used for autonomous navigation using a crowdsourced sparse map. For purposes of illustration, the vehicle is referred to as vehicle 1205. The vehicle illustrated in FIG. 23 may be any other vehicle disclosed herein, including, for example, vehicles 1210, 1215, 1220, and 1225, as well as vehicle 200 illustrated in other embodiments. As illustrated in FIG. 12, vehicle 1205 may be in communication with server 1230. Vehicle 1205 may include imaging device 122 (e.g., camera 122). Vehicle 1205 may include navigation system 2300 configured to provide navigation guidance for vehicle 1205 traveling along a road (e.g., road segment 1200). Vehicle 1205 may also include other sensors, such as speed sensor 2320 and accelerometer 2325. Speed ​​sensor 2320 may be configured to detect the speed of vehicle 1205. Accelerometer 2325 may be configured to detect acceleration or deceleration of vehicle 1205. 23 may be an autonomous vehicle, and the navigation system 2300 may be used to provide navigation guidance for the autonomous journey. Alternatively, the vehicle 1205 may also be a non-autonomous human-controlled vehicle, and the navigation system 2300 may still be used to provide navigation guidance.

[0314] The navigation system 2300 may include a communication unit 2305 configured to communicate with the server 1230 over the communication path 1235. The navigation system 2300 may also 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 the GPS signals, map data from the sparse map 800 (which may be stored in a storage device onboard the vehicle 1205 and / or received from the server 1230), road geometry sensed by the road profile sensor 2330, images captured by the 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 height, road curvature, etc. For example, the road profile sensor 2330 may include a device that measures suspension movement of the vehicle 2305 to derive a road roughness profile. In some embodiments, the road profile sensor 2330 may include a radar sensor to measure the distance from the vehicle 1205 to the side of the road (e.g., a barrier on the side of the road), thereby measuring the width of the road. In some embodiments, the road profile sensor 2330 may include a device configured to measure the elevation above and below the road. In some embodiments, the road profile sensor 2330 may include a device configured to measure road curvature. For example, a camera (e.g., camera 122 or another camera) may be used to capture images of the road that show the road curvature. The vehicle 1205 may use such images to detect the road curvature.

[0315] The at least one processor 2315 may be programmed to receive, from the camera 122, at least one environmental image associated with the vehicle 1205. 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 vehicle 1205 traveling along the road segment 1200. The at least one processor 2315 may determine the trajectory based on movement of the camera 122 (and thus the vehicle), such as three-dimensional translational movement 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 analysis of multiple images acquired by the camera 122. In some embodiments, the navigation information may include lane assignment information (e.g., whether the vehicle 1205 is traveling in that lane along the road segment 1200). Navigation information transmitted from vehicle 1205 to server 1230 may be used by server 1230 to generate and / or update an autonomous vehicle road navigation model, which may be transmitted from server 1230 to vehicle 1205 to provide autonomous navigation guidance to vehicle 1205.

[0316] The at least one processor 2315 may also be programmed to transmit navigation information from the vehicle 1205 to the server 1230. In some embodiments, the navigation information may be transmitted to the server 1230 along with road information. The road position information may include at least one of a GPS signal received by the GPS unit 2310, landmark information, road geometry, lane information, etc. The at least one processor 2315 may receive an autonomous vehicle road navigation model or a portion of the model from the server 1230. The autonomous vehicle road navigation model received from the server 1230 may include at least one update based on the navigation information transmitted from the vehicle 1205 to the server 1230. The portion of the model transmitted from the server 1230 to the vehicle 1205 may include an updated portion of the model. The at least one processor 2315 may cause at least one navigation maneuver by the vehicle 1205 (e.g., steering, such as making a turn, braking, accelerating, or passing another vehicle) based on the received autonomous vehicle road navigation model or the updated portion of the model.

[0317] The at least one processor 2315 may be configured to communicate with various sensors and components included in the vehicle 1205, including the communication unit 1705, the GPS unit 2315, the camera 122, the speed sensor 2320, the accelerometer 2325, and the road profile sensor 2330. The at least one processor 2315 may collect information or data from the various sensors and components and transmit the information or data to the server 1230 through the communication unit 2305. Alternatively or additionally, the various sensors or components of the vehicle 1205 may also communicate with the server 1230 and transmit data or information collected by the sensors or components to the server 1230.

[0318] In some embodiments, the vehicles 1205, 1210, 1215, 1220, and 1225 may communicate with each other and share navigation information with each other, such that at least one of the vehicles 1205, 1210, 1215, 1220, and 1225 may generate an autonomous vehicle road navigation model using crowdsourcing, for example, based on information shared by other vehicles. In some embodiments, the vehicles 1205, 1210, 1215, 1220, and 1225 may share navigation information with each other, and each vehicle may update its own autonomous vehicle road navigation model, which is provided to the vehicle. In some embodiments, at least one of the vehicles 1205, 1210, 1215, 1220, and 1225 (e.g., vehicle 1205) may function 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 the server 1230. For example, the at least one processor 2315 of the hub vehicle may communicate with and receive navigation information from other vehicles. The at least one processor 2315 of the hub vehicle may generate an autonomous vehicle road navigation model or updates to the model based on the shared information received from the other vehicles. The at least one processor 2315 of the hub vehicle may transmit the autonomous vehicle road navigation model or updates to the model to the other vehicles to provide autonomous navigation guidance.

[0319] Mapping of lane marks and navigation based on the mapped lane marks

[0320] As mentioned above, the autonomous vehicle road navigation model and / or sparse map 800 may include a plurality of mapped lane marks associated with road segments. As discussed in more detail below, these mapped lane marks may be used when the autonomous vehicle navigates. For example, in some embodiments, the mapped lane marks may be used to determine a lateral position and / or orientation relative to a planned trajectory. Using this position information, the autonomous vehicle may be able to adjust its heading to match the direction of the target trajectory at the determined location.

[0321] Vehicle 200 may be configured to detect lane markings on a given road segment. A road segment may include any markings on a road to guide vehicular traffic on the road. For example, lane markings may be solid or dashed lines indicating the ends of travel lanes. Lane markings may also include double lines, such as double solid lines, double dashed lines, or a combination of solid and dashed lines, indicating whether passing is permitted in the adjacent lane. Lane markings may also include highway on- and off-ramp markings, for example, indicating deceleration lanes on exit ramps, or dotted lines indicating that a lane is turn-only or has ended. Markings may also indicate work zones, temporary lane shifts, travel paths through intersections, medians, reserved lanes (e.g., bicycle lanes, HOV lanes, etc.), or other miscellaneous markings (e.g., crosswalks, speed humps, railroad crossings, stop lines, etc.).

[0322] Vehicle 200 may capture images of surrounding lane markings using cameras, such as imaging devices 122 and 124 included in image acquisition unit 120. Vehicle 200 may analyze the images to detect point locations associated with the lane marks based on features identified in one or more of the captured images. These point locations may be uploaded to a server to represent the lane marks in sparse map 800. Depending on the camera's position and field of view, lane marks on both sides of the vehicle may be detected simultaneously from a single image. In other embodiments, different cameras may be used to capture images on multiple sides of the vehicle. Rather than uploading actual images of the lane markings, the marks may be stored in sparse map 800 as a spline or series of points, thus reducing the size of sparse map 800 and / or the data that must be remotely uploaded by the vehicle.

[0323] FIGS. 24A through 24D illustrate exemplary point locations that may be detected by vehicle 200 to represent particular lane marks. Similar to the landmarks described above, vehicle 200 may use various image recognition algorithms or software to identify point locations within captured images. For example, vehicle 200 may recognize a series of endpoints, corners, or various other point locations associated with a particular lane mark. FIG. 24A illustrates a continuous lane mark 2410 that may be detected by vehicle 200. Lane mark 2410 may represent the outer edge of a road, represented by a continuous white line. As shown in FIG. 24A , vehicle 200 may be configured to detect multiple edge location points 2411 along the lane mark. Location points 2411 may be collected to represent the lane mark at any interval sufficient to create lane marks mapped within a sparse map. For example, lane marks may be represented by one point every meter of the detected edge, one point every five meters of the detected edge, or any other suitable interval. In some embodiments, the intervals may be determined by other factors rather than a set interval, such as, for example, based on the point where vehicle 200 has the highest confidence ranking of the detected point locations. While FIG. 24A shows edge location points on the inside edge of lane marking 2410, points may be collected on the outside edge of the line or along both ends. Furthermore, while a single line is shown in FIG. 24A, similar edge points may be detected for double solid lines. For example, point 2411 may be detected along one or both ends of the solid line.

[0324] Vehicle 200 may also represent different lane marks depending on the type or shape of the lane mark. FIG. 24B illustrates an exemplary dashed-line lane marking 2420 that may be detected by vehicle 200. Rather than identifying endpoints as in FIG. 24A, the vehicle may detect a series of corner points 2421 representing the corners of the lane dashed line to define the complete boundary of the dashed line. While FIG. 24B illustrates each corner of a given dashed-line mark being located, vehicle 200 may detect or upload a subset of the points shown in the figure. For example, vehicle 200 may detect the leading edge or leading corner of a given dashed-line mark, or may detect the two corner points closest to the interior of the lane. Furthermore, not all dashed-line marks may be imaged; for example, vehicle 200 may image and / or record points representing a sample of dashed-line marks (e.g., every other, every third, every fifth, etc.) or points representing dashed-line marks at predefined intervals (e.g., every meter, every five meters, every ten meters, etc.). Corner points may also be detected for similar lane markings, such as markings indicating that a lane is for an exit ramp, markings indicating that a particular lane is about to end, or various other lane markings that may have detectable corner points. Corner points may also be detected for lane markings that consist of double dashed lines or a combination of solid and dashed lines.

[0325] In some embodiments, the points uploaded to the server to generate the mapped lane markings may represent points other than the detected endpoints or corner points. FIG. 24C shows a series of points that may represent the centerline of a given lane marking. For example, continuous lane 2410 may be represented by centerline point 2441 along centerline 2440 of the lane marking. In some embodiments, vehicle 200 may be configured to detect these center points using various image recognition techniques, such as convolutional neural networks (CNNs), scale-invariant feature transforms (SIFTs), histogram of oriented gradients (HOG) features, or other techniques. Alternatively, vehicle 200 may detect other points, such as endpoints 2411 shown in FIG. 24A , and may calculate centerline point 2441, for example, by detecting points along each edge and determining the midpoint between the endpoints. Similarly, dashed line lane mark 2420 may be represented by centerline point 2451 along centerline 2450 of the lane marking. Centerline points may be located at the ends of dashed lines, as shown in FIG. 24C, or at various other locations along the centerline. For example, each dashed line may be represented by a single point at the geometric center of the dashed line. Points may also be spaced at predetermined intervals along the centerline (e.g., every 1 meter, every 5 meters, every 10 meters, etc.). Centerline points 2451 may be detected directly by vehicle 200 or may be calculated based on other detected reference points, such as corner points 2421, as shown in FIG. 24B. Centerlines may also be used to represent other lane marking types, such as double lines, using techniques similar to those described above.

[0326] In some embodiments, vehicle 200 may identify points representing other features, such as vertices between two intersecting lane marks. FIG. 24D shows an example point representing an intersection between two lane marks 2460 and 2465. Vehicle 200 may calculate vertex 2466, which represents the intersection between the two lane marks. For example, one of lane marks 2460 or 2465 may represent a train intersection area or other intersection area within a road segment. While lane marks 2460 and 2465 are shown intersecting perpendicularly to one another, various other configurations may be detected. For example, lane marks 2460 and 2465 may intersect at other angles, or one or both of the lane marks may terminate at vertex 2466. Similar techniques may also be applied to intersections between dashed lines or other lane marking types. In addition to vertex 2466, various other points 2467 may also be detected, providing further information about the orientation of lane marks 2460 and 2465.

[0327] Vehicle 200 may associate real-world coordinates with each detected point of the lane markings. For example, a location identifier including the coordinates of each point may be generated and uploaded to a server for mapping the lane markings. The location identifier may further include other identifying information about the point, including whether the point represents a corner point, an end point, a center point, etc. Accordingly, vehicle 200 may be configured to determine the real-world location of each point based on an analysis of the image. For example, vehicle 200 may detect other features in the image, such as the various landmarks described above, to identify the real-world locations of the lane markings. This may include determining the location of the lane markings in the image relative to the detected landmarks, or determining the location of the vehicle based on the detected landmarks and then determining the distance from the vehicle (or the vehicle's target trajectory) to the lane markings. If landmarks are not available, the location of the lane marking points may be determined relative to the vehicle's position determined based on dead reckoning. The real-world coordinates included in the location identifier may be expressed as absolute coordinates (e.g., latitude / longitude coordinates) or may be related to other features, such as based on a longitudinal position along the target trajectory and a lateral distance from the target trajectory. The location identifiers may then be uploaded to a server to generate lane markings that are mapped in a navigation model (such as sparse map 800). In some embodiments, the server may construct splines that represent the lane markings of the road segment. Alternatively, vehicle 200 may generate the splines, upload them to the server, and record them in the navigation model.

[0328] 24E shows an example of a navigation model or sparse map of the corresponding road segment including mapped lane markings. The sparse map may include a target trajectory 2475 for the vehicle to follow along the road segment. As described above, the target trajectory 2475 may represent an ideal path the vehicle would take when traveling the corresponding road segment, or may be located elsewhere on the road (e.g., on the road's centerline, etc.). The target trajectory 2475 may be calculated in various ways as described above, for example, based on an aggregation (e.g., weighted combination) of two or more reconstructed trajectories of vehicles traversing the same road segment.

[0329] In some embodiments, target trajectories may be generated equally for all vehicle types and all road, vehicle, and / or environmental conditions. However, in other embodiments, various other factors or variables may also be considered in generating the target trajectories. Different target trajectories may be generated for different types of vehicles (e.g., passenger cars, light trucks, and full-trailer trucks). For example, a target trajectory with a relatively tighter turning radius may be generated for a small passenger car than for a large semi-trailer truck. 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 conditions or estimated tire conditions, braking conditions or estimated braking conditions, amount of fuel remaining, etc.), or environmental factors (e.g., time of day, visibility, weather, etc.). The target trajectory may also depend on one or more aspects or characteristics of a particular road segment (e.g., speed limit, frequency and size of turns, gradient, etc.). In some embodiments, various user settings may also be used to determine a target trajectory, such as a set driving mode (e.g., desired aggressive driving, economy mode, etc.).

[0330] The sparse map may also include mapped lane marks 2470 and 2480, which represent lane markings along the road segment. The mapped lane marks may be represented by multiple location identifiers 2471 and 2481. As described above, the location identifiers may include locations in real-world coordinates of points associated with the detected lane marks. Like the target trajectory of the model, the lane marks may also include elevation data and may be represented as curves in three-dimensional space. For example, the curves may be splines connecting three-dimensional polynomials of an appropriate degree, or the curves may be calculated based on the location identifiers. The mapped lane marks may also include other information or metadata about the lane marks, such as an identifier for the type of lane mark (e.g., between two lanes with the same direction of travel, between two lanes with opposite directions of travel, edge of the road, etc.) and / or other characteristics of the lane mark (e.g., solid line, dashed line, single line, double line, yellow line, white line, etc.). In some embodiments, the mapped lane marks may be continuously updated within the model, for example, using crowdsourcing techniques. The same vehicle may upload location identifiers during multiple occasions traveling the same road segment, or data may be selected from multiple vehicles (e.g., 1205, 1210, 1215, 1220, and 1225) traveling the road segment at different times. The sparse map 800 may then be updated or refined based on subsequent location identifiers received from the vehicles and stored in the system. As the mapped lane marks are updated and refined, the updated road navigation model and / or sparse map may be distributed to multiple autonomous vehicles.

[0331] Generating lane marks mapped within a sparse map may also include detecting and / or mitigating errors based on anomalies in the image or the actual lane marks themselves. FIG. 24F shows an example anomaly 2495 associated with the detection of a lane mark 2490. The anomaly 2495 may appear in an image captured by vehicle 200, for example, from an object blocking the camera's view of the lane mark, dirt on the lens, etc. In some cases, the anomaly may be due to the lane mark itself, which may be damaged, worn, or partially covered, for example, by dirt, debris, water, snow, or other substances on the road. The anomaly 2495 may result in an erroneous point 2491 being detected by vehicle 200. Sparse map 800 may provide correctly mapped lane marks and filter out the error. In some embodiments, vehicle 200 may detect the error point 2491, for example, by detecting the anomaly 2495 in the image or by identifying the error based on lane mark points detected before and after the anomaly. Based on the detection of the anomaly, the vehicle may exclude point 2491 or adjust it to match other detected points. In other embodiments, the error may be corrected after the point is uploaded by determining that the point is outside of an expected threshold, for example, based on other points uploaded during the same trip or based on an aggregation of data from previous trips along the same road segment.

[0332] The lane marks mapped in the navigation model and / or sparse map may also be used for navigation by an autonomous vehicle traversing the corresponding road. For example, a vehicle navigating along a target trajectory may periodically use mapped lane marks in the sparse map to align itself to the target trajectory. As described above, between landmarks, the vehicle may navigate based on dead reckoning, in which the vehicle uses sensors to determine egomotion and estimate its position relative to the target trajectory. Errors may accumulate over time, gradually reducing the accuracy of the vehicle's position determination relative to the target trajectory. Thus, the vehicle may use lane marks occurring in the sparse map 800 (and their known positions) to reduce dead reckoning-induced errors in its position determination. In this way, identified lane marks included in the sparse map 800 may serve as navigation anchors, from which the vehicle's precise position relative to the target trajectory may be determined.

[0333] 25A shows an example image 2500 of a vehicle's surroundings that may be used for navigation based on mapped lane markings. Image 2500 may be captured by vehicle 200, for example, via imaging devices 122 and 124 included in image acquisition unit 120. Image 2500 may include an image of at least one lane marking 2510, as shown in FIG. 25A. Image 2500 may also include one or more landmarks 2521, such as road signs used for navigation as described above. Some elements shown in FIG. 25A, such as elements 2511, 2530, and 2520, that are not displayed in captured image 2500 but are detected and / or determined by vehicle 200, are also shown for reference.

[0334] Using the various techniques described above with respect to Figures 24A through 24D and 24F, the vehicle may analyze the image 2500 to identify lane markings 2510. Various points 2511 corresponding to features of the lane markings in the image may be detected. For example, the points 2511 may correspond to the edges of a lane marking, the corners of a lane marking, the midpoint of a lane marking, the vertex between two intersecting lane markings, or various other features or locations. The points 2511 may be detected to correspond to the locations of points stored in a navigation model received from a server. For example, if a sparse map is received that includes points representing the centerlines of mapped lane marks, the points 2511 may also be detected based on the centerlines of the lane markings 2510.

[0335] The vehicle may also be configured to determine its longitudinal position, represented by element 2520, located along the target trajectory. The longitudinal position 2520 may be determined from the image 2500, for example, by detecting landmarks 2521 in the image 2500 and comparing the measured position to known landmark locations stored in the road model or sparse map 800. The vehicle's position along the target trajectory may then be determined based on the distance to the landmarks and the known locations of the landmarks. The longitudinal position 2520 may also be determined from images other than those used to determine the positions of the lane marks. For example, the longitudinal position 2520 may be determined by detecting landmarks in images from other cameras in the image acquisition unit 120 taken at or near the same time as the image 2500. In some cases, the vehicle may not be near any landmarks or other reference points to determine the longitudinal position 2520. In such cases, the vehicle may navigate based on dead reckoning and thus use sensors to determine egomotion and estimate the longitudinal position 2520 relative to the target trajectory. The vehicle may also be configured to determine a distance 2530 representing the actual distance between the vehicle observed in the captured image and the lane marking 2510. The angle of the camera, the speed of the vehicle, the width of the vehicle, or various other factors may be taken into account when determining the distance 2530.

[0336] FIG. 25B illustrates lateral localization correction of a vehicle based on mapped lane marks in a road navigation model. As described above, vehicle 200 may determine distance 2530 between vehicle 200 and lane mark 2510 using one or more images captured by vehicle 200. Vehicle 200 may also access a road navigation model, such as sparse map 800, which may include mapped lane marks 2550 and target trajectory 2555. Mapped lane marks 2550 may be modeled using the techniques described above, for example, using crowdsourced location identifiers captured by multiple vehicles. Target trajectory 2555 may also be generated using various techniques described above. Vehicle 200 may also be configured to determine or estimate longitudinal position 2520 along target trajectory 2555, as described above with respect to FIG. 25A. Vehicle 200 may then determine an expected distance 2540 based on the lateral distance between target trajectory 2555 and the mapped lane marking 2550 that corresponds to longitudinal position 2520. The lateral localization of vehicle 200 may be corrected or adjusted by comparing actual distance 2530 measured using captured imagery with expected distance 2540 from the model.

[0337] FIG. 26A is a flowchart illustrating an example process 2600A for mapping lane markings for use in autonomous vehicle navigation, according to disclosed embodiments. In step 2610, process 2600A may include receiving two or more location identifiers associated with the 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 locations in real-world coordinates of points associated with the detected lane markings, as described above with respect to FIG. 24E. In some embodiments, the location identifiers may also include 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 position data, egomotion data, or various other forms of data described above, may also be received during step 2610. The location identifiers may be generated by vehicles, such as vehicles 1205, 1210, 1215, 1220, and 1225, based on images captured by the vehicles. For example, the identifier may be determined based on obtaining at least one image representing the host vehicle's environment from a camera associated with the host vehicle, analyzing the at least one image to detect lane markings in the host vehicle's environment, and analyzing the at least one image to determine the location of the detected lane markings relative to a location associated with the host vehicle. As described above, the lane markings may include a variety of different mark types, and the location identifier may correspond to various points associated with the lane markings. For example, if the detected lane markings are part of a dashed line marking the lane boundary, the point may correspond to a detected corner of the lane marking. If the detected lane markings are part of a solid line marking the lane boundary, the point may correspond to a detected edge of the lane marking at various intervals, as described above. In some embodiments, the point may correspond to the centerline of the detected lane markings, as shown in FIG. 24C, or to at least one of two other points associated with the lane markings that intersect with the vertex between two intersecting lane markings, as shown in FIG. 24D.

[0338] In step 2612, process 2600A may include associating the detected lane markings with corresponding road segments. For example, server 1230 may analyze the 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 on which the lane marking was detected.

[0339] In step 2614, process 2600A may include updating an autonomous vehicle road navigation model associated with the corresponding road segment based on two or more position identifiers associated with the detected lane marks. For example, the autonomous vehicle road navigation model may be a sparse map 800, and server 1230 may update the sparse map to include or adjust the lane marks mapped to the model. Server 1230 may update the model based on various methods or processes described above with respect to FIG. 24E. In some embodiments, updating the autonomous vehicle road navigation model may include storing one or more indicators of the positions in real-world coordinates of the detected lane marks. 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 shown in FIG. 24E.

[0340] 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 over wireless communication path 1235 via one or more networks (e.g., via a cellular network and / or the Internet, etc.), as shown in FIG.

[0341] In some embodiments, lane marks may be mapped using data received from multiple vehicles, such as via crowdsourcing techniques, as described above with respect to FIG. 24E. For example, process 2600A may include receiving a first communication from a first host vehicle including a location identifier associated with the detected lane mark, and receiving a second communication from a second host vehicle including an additional location identifier associated with the detected lane mark. For example, the second communication may be received from a subsequent vehicle traveling the same road segment, i.e., from the same vehicle traveling subsequently along the same road segment. Process 2600A may further include refining the determination of at least one location associated with the detected lane mark based on the location identifier received in the first communication and based on the additional location identifier received in the second communication. This may include using an average of multiple location identifiers and / or filtering out “ghost” identifiers that may not reflect the real-world location of the lane mark.

[0342] FIG. 26B is a flowchart illustrating an example process 2600B for autonomously navigating a host vehicle along a road segment using mapped lane marks. Process 2600B may be performed, for example, by processing unit 110 of autonomous vehicle 200. In 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 marks associated with the road segment. For example, vehicle 200 may receive sparse map 800 or another road navigation model developed using process 2600A. In some embodiments, the target trajectory may be represented as a cubic spline, for example, as shown in FIG. 9B. As described above with respect to Figures 24A through 24F, the location identifier may include the location in real-world coordinates of a point associated with a lane mark (e.g., a corner point of a dashed lane mark, an end point of a solid lane mark, a vertex between two intersecting lane marks and other points associated with the intersecting lane marks, a centerline associated with a lane mark, etc.).

[0343] In step 2621, process 2600B may include receiving at least one image representing the vehicle's environment. The image may be received from an imaging device on the vehicle, such as via imaging devices 122 and 124 included in image acquisition unit 120. The image may include images of one or more lane markings, similar to image 2500 described above.

[0344] In step 2622, process 2600B may include determining the longitudinal position of the host vehicle along the target trajectory. As discussed above with respect to Figure 25A, this may be based on other information in the captured image (e.g., landmarks, etc.) or by dead-reckoning of the vehicle between detected landmarks.

[0345] In step 2623, process 2600B may include determining an expected lateral distance to a lane mark based on the determined longitudinal position of the host vehicle along the target trajectory and based on two or more position identifiers associated with at least one lane mark. For example, vehicle 200 may use sparse map 800 to determine the expected lateral distance to a lane mark. As shown in FIG. 25B , a longitudinal position 2520 along the target trajectory 2555 may be determined in step 2622. Using sparse map 800, vehicle 200 may determine an expected distance 2540 to a mapped lane mark 2550 that corresponds to longitudinal position 2520.

[0346] In step 2624, process 2600B may include analyzing at least one image to identify at least one lane marking. Vehicle 200 may identify lane markings in the image using various image recognition techniques or algorithms, for example, as described above. For example, lane marking 2510 may be detected via image analysis of image 2500, as shown in FIG. 25A.

[0347] In step 2625, process 2600B may include determining an actual lateral distance to at least one lane marking based on analysis of the at least one image. For example, the vehicle may determine distance 2530, which represents the actual distance between the vehicle and lane marking 2510, as shown in FIG. 25A. The angle of the camera, the speed of the vehicle, the width of the vehicle, the position of the camera relative to the vehicle, or various other factors may be considered in determining distance 2530.

[0348] In step 2626, process 2600B may include determining an autonomous steering action of the host vehicle based on a difference between the expected lateral distance to the at least one lane mark and the determined actual lateral distance to the at least one lane mark. For example, as described above with respect to FIG. 25B , vehicle 200 may compare actual distance 2530 with expected distance 2540. The difference between the actual distance and the expected distance may indicate an error (and its magnitude) between the vehicle's actual position and a target trajectory the vehicle is following. Thus, the vehicle may determine an autonomous steering action or other autonomous action based on the difference. For example, as shown in FIG. 25B , if actual distance 2530 is less than expected distance 2540, the vehicle may determine an autonomous steering action to steer the vehicle left, away from lane marking 2510. The vehicle's position relative to the target trajectory may be corrected accordingly. Process 2600B may be used, for example, to improve the vehicle's navigation between landmarks.

[0349] Map management using electronic vision

[0350] Although processing power and storage capacity are increasing and costs are decreasing, it may still be desirable to use them more efficiently. The systems and methods disclosed herein may enable a vehicle to dynamically receive and load map data related to its travel route, rather than loading a large set of map data that the vehicle may not use while traveling. In doing so, the systems and methods may reduce the vehicle's hardware requirements by receiving and processing map data that the vehicle may need. Furthermore, the systems and methods may also reduce transmission costs for data exchanged between the vehicle and, for example, a central server that deploys map data. Furthermore, the disclosed systems and methods may enable a vehicle to receive up-to-date map data that the vehicle may need more frequently. For example, the systems and methods may determine a potential travel area (or potential travel envelope) for a vehicle based on navigation information such as the vehicle's position, speed, and driving direction. The systems and methods may also be configured to determine one or more road segments associated with the potential travel area from the vehicle and transmit map data associated with the road segments to the vehicle. The vehicle (and / or driver) may navigate according to the received map data.

[0351] 27 illustrates an example system 2700 for providing one or more map segments to one or more vehicles, according to disclosed embodiments. As shown in FIG. 27 , the system 2700 may include a server 2701, one or more vehicles 2702, one or more vehicle devices 2703 associated with the vehicles, a database 2704, and a network 2705. The server 2701 may be configured to provide one or more map segments to the one or more vehicles based on navigation information received from the one or more vehicles (and / or one or more vehicle devices associated with the vehicles). For example, the vehicles 2702 and / or the vehicle devices 2703 may be configured to collect navigation information and transmit the navigation information to the server 2701. The server 2701 may transmit to the vehicles 2702 and / or the vehicle devices 2703 one or more map segments including map information for a geographic region based on the received navigation information. The database 2704 may be configured to store information about the 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 the components of the system 2700.

[0352] Server 2701 may be configured to receive navigation information from vehicle 2702 (and / or vehicle device 2703). In some embodiments, the navigation information may include the position of vehicle 2702, the speed of vehicle 2702, and the direction of travel of vehicle 2702. Server 2701 may also be configured to analyze the received navigation information and determine a potential travel envelope for vehicle 2702. The potential travel envelope of a vehicle may be an area surrounding the vehicle. For example, the potential travel envelope of a vehicle may include an area covering a first predetermined distance from the vehicle in the direction of driving of the vehicle, a second predetermined distance from the vehicle in a direction opposite to the direction of driving of the vehicle, a third predetermined distance from the vehicle to the left of the vehicle, and a fourth predetermined distance from the vehicle to the right of the vehicle. In some embodiments, the first predetermined distance from the vehicle in the direction of driving of the vehicle may include a predetermined distance ahead of the vehicle, which may constitute the electronic field of view of the vehicle. In some embodiments, the vehicle's potential travel envelope may include one or more distances (1, 2, 3, ..., n) from the vehicle of one or more (or all) possible driving directions for the vehicle relative to the vehicle's current location. For example, on a road where the vehicle may potentially make a U-turn, the vehicle's potential travel envelope may include at least a predetermined distance in the forward direction, as well as a predetermined distance from the vehicle in the opposite direction, since the vehicle may perform a U-turn and navigate (generally) in the opposite direction from its current direction of travel. As another example, if driving in the opposite direction is impossible at the current location (e.g., due to a physical barrier) and a U-turn is not possible some distance beyond the current location, the potential travel envelope may not include the distance in the opposite direction. Similar to an actual field of view in the real world, the electronic field of view may be associated with the vehicle's potential travel distance within a specific time window based on the host vehicle's current speed and current heading. The server 2701 may further be configured to send to the vehicle one or more map sections containing map information of a geographic area that at least partially overlaps with the vehicle's 2702 potential travel envelope.

[0353] In some embodiments, server 2701 may be a cloud server that performs the functions disclosed herein. The term "cloud server" refers to a computer platform that provides services over a network such as the Internet. In this configuration example, server 2701 may use virtual machines that may not correspond to individual hardware. For example, computing and / or storage capacity may be implemented by allocating appropriate portions of desired computing / storage capacity from a scalable repository such as a data center or distributed computing environment. In one example, server 2701 may implement the methods described herein using customized hardwired logic, one or more application-specific integrated circuits (ASICs) or field-programmable gate arrays (FPGAs), firmware, and / or program logic that, in combination with a computer system, makes server 2701 a dedicated machine.

[0354] The vehicle 2702 and / or the vehicle device 2703 may be configured to collect navigation information and transmit the navigation information to the server 2701. For example, the vehicle 2702 and / or the vehicle device 2703 may be configured to receive data from one or more sensors and determine navigation information, such as the vehicle's position, speed, and / or driving direction, based on the received data. In some embodiments, the navigation information may include sensor data received from one or more sensors associated with the vehicle 3302 (e.g., from a GPS device, a speed sensor, an accelerometer, a suspension sensor, a camera, a LIDAR device, a visual detection and ranging (VIDAR) device, or the like, or a combination thereof). The vehicle 2702 and / or the vehicle device 2703 may also be configured to transmit the navigation information to the server 2701, for example, via the network 2705. Alternatively or additionally, the vehicle 2702 and / or the vehicle device 2703 may be configured to transmit sensor data to the server 2701. Vehicle 2702 and / or vehicle device 2703 may also be configured to receive map information from server 2701, for example, via network 2705. The map information may include data related to the locations in a reference coordinate system of various items, including, for example, a sparse data model including polynomial representations of roads, water features, geographic features, businesses, points of interest, restaurants, gas stations, specific road features (e.g., lane markings), a target trajectory of the host vehicle, or the like, or a combination thereof. In some embodiments, vehicle 2702 and / or vehicle device 2703 may be configured to plan a routing path and / or navigate vehicle 2702 according to the map information. For example, vehicle 2702 and / or vehicle device 2703 may be configured to determine a route to a destination based on the map information. Alternatively or additionally, vehicle 2702 and / or vehicle device 2703 may be configured to perform at least one navigation operation (e.g., making a turn, stopping at a location, etc.) based on the received map information.In some embodiments, vehicle 2702 may include devices having a similar configuration and / or performing similar functions as system 100 described above. Alternatively or additionally, vehicle devices 2703 may have a similar configuration and / or perform similar functions as system 100 described above.

[0355] 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 device 2703). In some embodiments, server 2701, vehicle 2702, and / or vehicle device 2703 may be configured to access database 2704, retrieve stored data from database 2704 via network 2705, and / or upload data to database 2704. For example, server 2701 may send data related to map information to database 2704 for storage. Vehicle 2702 and / or vehicle device 2703 may download map information and / or data from database 2704. In some embodiments, database 2704 may include data related to the locations in a reference coordinate system of various items, including roads, water features, geographic features, businesses, points of interest, restaurants, gas stations, or the like, or a combination thereof. In some embodiments, database 2704 may include a database similar to map database 160 described elsewhere in this disclosure.

[0356] Network 2705 may be any type of network (including infrastructure) that provides communication, exchanges information, and / or facilitates the exchange of information between components of system 2700. For example, network 2705 may include or be a part of the Internet, a local area network, a wireless network (e.g., a Wi-Fi / 302.11 network), or other suitable connection. In other embodiments, one or more components of system 2700 may communicate directly through dedicated communication links, such as, for example, a telephone network, an extranet, an intranet, the Internet, satellite communications, offline communications, radio communications, transponder communications, a local area network (LAN), a wide area network (WAN), a virtual private network (VPN), or the like.

[0357] As described elsewhere in this disclosure, vehicle 2702 may transmit navigation information to server 2701 via network 2705. Server 2701 may analyze the navigation information received from vehicle 2702 and determine a potential travel envelope for vehicle 2702 based on the analysis of the navigation information. The potential travel envelope may encompass the location of vehicle 2702. In some embodiments, the potential travel envelope may include a boundary. The boundary of a potential travel envelope may have a shape including, for example, a triangular shape, a square shape, a parallelogram shape, a rectangular shape, a square (or substantially square) shape, a trapezoidal shape, a diamond shape, a hexagonal shape, an octagonal shape, a circular (or substantially circular) shape, an elliptical shape, an oval shape, an irregular shape, or the like, or a combination thereof. Figures 28A through 28D show example potential travel envelopes for a vehicle within area 2800, according to disclosed embodiments. As shown in FIG. 28A, server 2701 may determine a potential travel envelope having boundary 2811 that may include the trapezoidal shape of vehicle 2702. As another example, as shown in FIG. 28B, server 2701 may determine a potential travel envelope having boundary 2812 that may include the oval shape of vehicle 2702. As another example, as shown in FIG. 28C, server 2701 may determine a potential travel envelope having boundary 2813 that may include the triangular shape of vehicle 2702. As another example, as shown in FIG. 28D, server 2701 may determine a potential travel envelope having boundary 2814 that may include the rectangular shape of vehicle 2702. Alternatively or additionally, the shape of a potential travel envelope may have boundaries determined by one or more potential routes the vehicle may travel starting from the vehicle's location (e.g., current location). Those skilled in the art will understand that the shape of a potential travel envelope is not limited to the example shapes described in this disclosure. Other shapes are possible. For example, potential movement envelopes may include irregular shapes (e.g., determined based on one or more boundaries of jurisdictions such as countries, states, counties, cities, and / or roads) and / or portions of any of the shapes described herein.

[0358] As described elsewhere in this disclosure, server 2701 may also be configured to transmit to vehicle 2702 one or more map segments including map information for a geographic region that at least partially overlaps with the vehicle's potential travel envelope. In some embodiments, the one or more map segments transmitted to vehicle 2702 may include one or more tiles representing an area of ​​predetermined dimensions. The size and / or shape of the tiles may vary. In some embodiments, the dimensions of the tiles within an area may range from 0.25 square kilometers to 1 square kilometers, 1 square kilometers to 10 square kilometers, and may be limited to subranges of 10 square kilometers to 25 square kilometers, 25 square kilometers to 50 square kilometers, and 50 square kilometers to 100 square kilometers. In some embodiments, the predetermined dimensions of the tiles may be less than or equal to 10 square kilometers. Alternatively, the predetermined dimensions of the tiles may be less than or equal to 1 square kilometer. Alternatively, the predetermined dimension of a tile may be less than or equal to 10 square kilometers. Alternatively or additionally, the size of the tile may vary based on the type of area in which the tile is located. For example, the size of a tile for a rural or sparsely roaded area may be larger than the size of a tile for an urban or heavily roaded area. In some embodiments, the size of the tile may be determined based on the density of information around the vehicle's current location (or where the data is obtained), possible routes for the vehicle, the number of possible routes in the area, the type of route in the area (e.g., highway, urban, rural, unpaved, etc.) and general navigation patterns and / or trends in the relevant area. For example, typically, if a majority of vehicles remain on highways in a particular area or region, map information for a short distance along a side road may be obtained. Typically, if a vehicle actually navigates a side road with less traffic, more map information for the side road (e.g., a longer distance along the side road) may be obtained and / or transmitted to the vehicle. In some embodiments, the tiles may have a rectangular shape, a square shape, a hexagonal shape, or the like, or a combination thereof.Those skilled in the art will appreciate that the shapes of the tiles are not limited to those described in this disclosure. For example, the tiles may include irregular shapes (e.g., determined according to at least the boundaries of a jurisdiction (state, county, city, or town) or other area (e.g., street, highway)). Alternatively or additionally, the tiles may include a portion of any shape disclosed herein.

[0359] 28E through 28H show exemplary map tiles of a vehicle's potential movement envelope, according to disclosed embodiments. As shown in FIGS. 28E through 28H, server 2701 may divide area 2800 (or a smaller or larger area) into multiple tiles 2811. Server 2701 may also be configured to determine one or more tiles that at least partially overlap with vehicle's 2702's potential movement envelope. For example, as shown in FIG. 28E, server 2701 may determine area 2831 having tiles that intersect with or are within boundary 2821 of vehicle's 2702's potential movement envelope. As another example, as shown in FIG. 28F, server 2701 may determine area 2832 having tiles that intersect with or are within boundary 2822 of vehicle's 2702's potential movement envelope. 28G, server 2701 may determine area 2833 having tiles that intersect with or lie within boundary 2823 of the potential movement envelope of vehicle 2702. As another example, as shown in FIG. 28H, server 2701 may determine area 2834 having tiles that intersect with or lie within boundary 2824 of the potential movement envelope of vehicle 2702. In some embodiments, server 2701 may transmit map information and / or data related to one or more road segments within the determined area to vehicle 2702.

[0360] 29A and 29B show exemplary map tiles according to disclosed embodiments. As shown in FIG. 29A, an area (or map) may be divided into multiple tiles at different levels. For example, in some embodiments, an area may be divided into multiple tiles at level 1, each of which may be divided into multiple tiles at level 2. Each of the tiles at level 2 may be divided into multiple tiles at level 3, and so on. FIG. 29B shows multiple tiles within a region. Alternatively or additionally, a region or country may be divided into multiple tiles based on jurisdictions (e.g., states, counties, cities, towns) and / or other areas (e.g., streets, highways). In some embodiments, the dimensions of the tiles may vary. For example, as shown in FIGS. 29A and 29B, an area (or map) may be divided into different levels, and tiles at a particular level may have specific dimensions.

[0361] In some embodiments, tiles may be represented in data blobs that may include a metadata block (e.g., 64 bytes), a signature block (e.g., 256 bytes), and encoded map data blocks (e.g., of various sizes in MapBox format).

[0362] In some embodiments, server 2701 may obtain data relating to one or more tiles within the area and transmit the data to vehicle 2702, for example, via network 2705.

[0363] Alternatively or additionally, vehicle 2702 may retrieve data related to one or more tiles from a storage device. For example, vehicle 2702 may receive one or more road segments from server 2701, as described elsewhere in this disclosure. Vehicle 2702 may also store the received one or more road segments in local storage and load one or more tiles included in the one or more road segments into memory for processing. Alternatively, rather than receiving one or more road segments from server 2701, vehicle 2702 may include local storage configured to store one or more road segments and retrieve data related to the one or more road segments from the local storage.

[0364] In some embodiments, the vehicle 2702 may obtain data (e.g., map information) related to one or more tiles based on the vehicle's location. For example, the vehicle 2702 may determine its current location (as described elsewhere in this disclosure) and determine a first tile in which the current location is located. The vehicle 2702 may also obtain the first tile and one or more (or all) tiles adjacent to the first tile. Alternatively or additionally, the vehicle 2702 may obtain one or more (or all) tiles within a predetermined distance from the first tile. Alternatively or additionally, the vehicle 2702 may obtain one or more (or all) tiles within a predetermined degree of separation from the first tile (e.g., one or more (or all) tiles within a second degree of separation, i.e., one or more (or all) tiles adjacent to the first tile or adjacent to a tile adjacent to the first tile). As the vehicle 2702 moves to the second tile, the vehicle 2702 may acquire the second tile. The vehicle 2702 may also acquire one or more (or all) tiles adjacent to the first tile and the second tile. Alternatively or additionally, the vehicle 2702 may acquire one or more (or all) tiles within a predetermined distance from the second tile. Alternatively or additionally, the vehicle 2702 may acquire one or more (or all) tiles within a predetermined degree of separation from the second tile (e.g., one or more (or all) tiles within a second degree of separation, i.e., one or more (or all) tiles adjacent to the second tile or adjacent to a tile adjacent to the second tile). In some embodiments, the vehicle 2702 may also delete (or overwrite) previously acquired tiles that are not adjacent to the first tile and / or the second tile. Alternatively or additionally, the vehicle 2702 may delete (or overwrite) one or more previously acquired tiles that are not within a predetermined distance from the second tile. Alternatively or additionally, the vehicle 2702 may delete (or overwrite) one or more (or all) previously acquired tiles that are not within a predetermined separation from the second tile.

[0365] FIG. 30 shows an example process for acquiring one or more tiles. As shown in FIG. 30, vehicle 2702 (and / or server 2701) may be configured to determine that vehicle 2702's location is in tile 5 at time 1. Vehicle 2702 may also be configured to acquire (or load) adjacent tiles 1 through 4 and 6 through 9. At time 2, vehicle 2702 (and / or server 2701) may be configured to determine that vehicle 2702's location will move from tile 5 to tile 3. Vehicle 2702 may be configured to acquire (or load) new tiles 10 through 14 that are adjacent to tile 3. Vehicle 2702 may also be configured to retain tiles 2, 3, 5, and 6 and delete tiles 1, 4, and 7 through 9. Thus, vehicle 2702 may acquire (or load) a subset of tiles at a time (e.g., 9 tiles) to reduce memory usage and / or computational load. In some embodiments, the vehicle 2702 may be configured to decode the tiles before loading the data into memory.

[0366] Alternatively or additionally, vehicle 2702 may determine a portion of a tile whose location falls within the tile and load tiles adjacent to that portion. For example, as shown in FIG. 31A , vehicle 2702 may determine that its location is within a subtile (i.e., the subtile with the dot pattern of tile 5) and load map data for tiles adjacent to the subtile (i.e., tiles 4, 7, and 8) into memory for processing. For another example, as shown in FIG. 31B , vehicle 2702 may determine that its location is within the top-left subtile of tile 5. Vehicle 2702 may also load tiles adjacent to the top-left subtile of tile 5 (i.e., tiles 1, 2, and 4). Thus, vehicle 2702 may retrieve (or load) a subset of tiles (e.g., four tiles) at a time to reduce memory usage and / or computational load. As another example, as shown in Figure 31C, vehicle 2702 may determine that its location is within the top right subtile and load map data for tiles adjacent to the subtile (i.e., tiles 2, 3, and 6) into memory for processing. As another example, as shown in Figure 31D, vehicle 2702 may determine that its location is within the bottom right subtile and load map data for tiles adjacent to the subtile (i.e., tiles 6, 8, and 9) into memory for processing. In some embodiments, vehicle 2702 may be configured to decode tiles before loading the data into memory.

[0367] 32 is a flowchart illustrating an example process for providing one or more map segments to one or more vehicles according to the disclosed embodiments. One or more steps of process 3200 may be performed by a vehicle (e.g., vehicle 2702), a device associated with a host vehicle (e.g., vehicle device 2703), and / or a server (e.g., server 2701). The description of process 3200 provided below uses server 2701 as an example, but those skilled in the art will understand that one or more steps of process 3200 may be performed by a vehicle (e.g., vehicle 2702) and a vehicle device (e.g., vehicle device 2703). For example, vehicle 2702 may determine a potential travel envelope based on navigation information. In addition to, or instead of, receiving map data from server 2701, vehicle 2702 may retrieve a portion of the map data associated with the potential travel envelope from local storage and load the retrieved data into memory for processing.

[0368] In step 3201, navigation information may be received from a vehicle. For example, server 2701 may receive navigation information from vehicle 2702, for example, via network 2705. In some embodiments, the navigation information received from the vehicle may include an indication of the vehicle's position, an indication of the vehicle's speed, and an indication of the vehicle's heading. For example, vehicle 2702 may be configured to receive data from one or more sensors, including, for example, a GPS device, a speed sensor, an accelerometer, a suspension sensor, or the like, or a combination thereof. Vehicle 2702 may also be configured to determine navigation information, such as the vehicle's position, speed, and / or driving direction, based on the received data. Vehicle 2702 may also be configured to transmit the navigation information to server 2701, for example, via network 2705. Alternatively or additionally, vehicle 2702 may be configured to transmit sensor data to server 2701. Server 2701 may be configured to determine navigation information, which may include the vehicle's 2702's position, the vehicle's 2702's speed, and / or the vehicle's 2702's heading, based on the received sensor data.

[0369] In some embodiments, vehicle 2702 may continuously transmit navigation information (and / or sensor data) to server 2701. Alternatively, vehicle 2702 may intermittently transmit navigation information (and / or sensor data) to server 2701. For example, vehicle 2702 may transmit navigation information (and / or sensor data) to server 2701 multiple times over a period of time. By way of example, vehicle 2702 may transmit navigation information (and / or sensor data) to server 2701 once per minute. Alternatively, the vehicle may transmit navigation information if it has access to a more reliable and / or faster network (e.g., having a stronger radio signal via a WiFi connection, etc.).

[0370] In step 3202, the received navigation information may be analyzed to determine a potential travel envelope for the vehicle. For example, server 2701 may analyze the position, speed, and / or driving direction of vehicle 2702 to determine a potential travel envelope that may include the determined area for vehicle 2702. By way of example, as shown in FIG. 28A , server 2701 may determine an area that encompasses the position of vehicle 2702 and, based on the determined area, determine a potential travel envelope having boundary 2811.

[0371] In some embodiments, server 2701 may be configured to determine a potential movement envelope extending from and surrounding the location of vehicle 2702. For example, as shown in FIG. 28A , server 2701 may determine line 2802 passing through the location of vehicle 2702 (or the center of gravity of vehicle 2702). Server 2701 may also be configured to determine a side of the boundary of the potential movement envelope in the direction of travel of vehicle 2702, and another side of the boundary of the potential movement envelope in the direction opposite the direction of travel. As an example, server 2701 may determine an upper boundary of the potential movement envelope in the direction of travel of vehicle 2702, and a lower boundary of the potential movement envelope in the direction opposite the direction of travel of vehicle 2702. In some embodiments, the potential movement envelope may extend further along the direction of travel of the vehicle than in the direction opposite the direction of travel of the vehicle. 28A, an upper boundary of the potential travel envelope may have a distance 2803 from line 2802 (or a first distance from the position of vehicle 2702), and a lower boundary of the potential travel envelope may have a distance 2804 from line 2802 (or a second distance from the position of vehicle 2702). Distance 2803 may be greater than distance 2804 (and / or the first distance may be greater than the second distance). In some embodiments, the location of the center of gravity of the boundary may be offset from the position of vehicle 2702 along the direction of travel of vehicle 2702.

[0372] Alternatively or additionally, in determining the potential travel envelope for vehicle 2702, server 2701 may take into account potential travel distances over a period of time (or time window). For example, server 2701 may determine potential travel distances over a predetermined amount of time and determine a potential travel envelope including the potential travel distances. In some embodiments, the potential travel distances over a predetermined amount of time may be determined based on the location of vehicle 2702 and / or the speed of vehicle 2702. In some embodiments, server 2701 may determine the potential travel envelope further based on a selected or predetermined time window. The time window may be selected or determined based on an indicator of the vehicle's speed. The predetermined amount of time (or time window) may range from 0.1 seconds to 24 hours. In some embodiments, the predetermined amount of time (or time window) may be limited to a subrange of 0.1 seconds to 1 second, 1 second to 5 seconds, 5 seconds to 10 seconds, 10 seconds to 60 seconds, 1 minute to 5 minutes, 5 minutes to 10 minutes, 10 minutes to 60 minutes, 1 hour to 5 hours, 5 hours to 10 hours, or 10 hours to 24 hours. In some embodiments, the predetermined amount of time (or time window) may be determined based on the frequency of transmission of navigation information from vehicle 2702 to server 2701. For example, server 2701 may determine the predetermined amount of time (or time window) based on the interval between two transmissions of navigation information from vehicle 2702. Server 2701 may determine a longer period to determine the potential travel distance for longer transmission intervals.

[0373] In some embodiments, a potential travel envelope may include a boundary. The boundary of a potential travel envelope may have a shape including, for example, a triangular shape, a square shape, a parallelogram shape, a rectangular shape, a square (or substantially square) shape, a trapezoidal shape, a diamond shape, a hexagonal shape, an octagonal shape, a circular (or substantially circular) shape, an elliptical shape, an oval shape, an irregular shape, or the like, or a combination thereof. Figures 28A through 28D show example potential travel envelopes for a vehicle within an area 2800, according to disclosed embodiments. As shown in Figure 28A, server 2701 may determine a potential travel envelope having boundary 2811 that may include the trapezoidal shape of vehicle 2702. As another example, as shown in Figure 28B, server 2701 may determine a potential travel envelope having boundary 2812 that may include the elliptical shape of vehicle 2702. 28C, server 2701 may determine a potential travel envelope having boundary 2813 that may include the triangular shape of vehicle 2702. As another example, as shown in FIG. 28D, server 2701 may determine a potential travel envelope having boundary 2814 that may include the rectangular shape of vehicle 2702.

[0374] In some embodiments, the vehicle 2702 (and / or vehicle device 2703) may determine potential travel envelopes based on navigation information.

[0375] In step 3203, one or more map segments may be transmitted to vehicle 2702. In some embodiments, the map segments may include map information for a geographic region that at least partially overlaps with the potential travel envelope of vehicle 2702. For example, server 2701 may transmit, via network 2705, one or more map segments that include map data for a geographic region that at least partially overlaps with the potential travel envelope of vehicle 2702.

[0376] In some embodiments, one or more map sections include one or more tiles that represent areas of predetermined dimensions. For example, as shown in Figure 28E, server 2701 may determine one or more tiles 2831 that at least partially overlap with the potential travel envelope of vehicle 2702 (i.e., the potential travel envelope having boundary 2821) and transmit map data associated with tiles 2831 to vehicle 2702 over network 2705.

[0377] In some embodiments, the dimensions of the tiles transmitted to the vehicle 2702 may vary. For example, as shown in FIGS. 29A and 29B , an area (or map) may be divided into different levels, and tiles at particular levels may have particular dimensions. In some embodiments, the predetermined dimensions of the tiles transmitted to the vehicle 2702 may range from 0.25 square kilometers to 100 square kilometers, which may be limited to subranges of 0.25 square kilometers to 1 square kilometer, 1 square kilometer to 10 square kilometers, 10 square kilometers to 25 square kilometers, 25 square kilometers to 50 square kilometers, and 50 square kilometers to 100 square kilometers. In some embodiments, the predetermined dimensions of the tiles may be less than or equal to 10 square kilometers. Alternatively, the predetermined dimensions of the tiles may be less than or equal to 1 square kilometer. Alternatively, the predetermined dimensions of the tiles may be less than or equal to 10 square kilometers. In some embodiments, the tiles may have a rectangular shape, a square shape, a hexagonal shape, or the like, or a combination thereof.

[0378] In some embodiments, the map information transmitted to the vehicle 2702 may include a polynomial representation of a target trajectory along one or more road segments, as described elsewhere in this disclosure. For example, the map information may include a polynomial representation of a portion of a road segment according to the disclosed embodiments shown in Figures 9A, 9B, and 11A. For example, the map information may include a polynomial representation of a target trajectory determined based on two or more reconstructed trajectories of previous trajectories of the vehicle along one or more road segments.

[0379] In some embodiments, as described elsewhere in this disclosure, vehicle 2702 may receive one or more road segments and then navigate according to the one or more road segments. For example, vehicle 2702 may be configured to perform one or more navigation actions (e.g., make a turn, stop at a location, etc.) based on the received one or more road segments. Alternatively or additionally, vehicle 2702 may be configured to perform one or more navigation actions based on a polynomial representation of a target trajectory along one or more road segments.

[0380] In some embodiments, vehicle 2702 may receive one or more road segments and store the one or more road segments in a storage device. Vehicle 2702 may also load one or more tiles included in the one or more road segments into memory for processing. For example, as shown in FIG. 30 , vehicle 2702 (and / or server 2701) may be configured to determine that vehicle 2702's location is in tile 5 at time point 1. Vehicle 2702 may also be configured to retrieve (or load) adjacent tiles 1 through 4 and 6 through 9. At time point 2, vehicle 2702 (and / or server 2701) may be configured to determine that vehicle 2702's location will move from tile 5 to tile 3. Vehicle 2702 may be configured to retrieve (or load) new tiles 10 through 14 adjacent to tile 3. Vehicle 2702 may also be configured to retain tiles 2, 3, 5, and 6 and delete tiles 1, 4, and 7 through 9.

[0381] Alternatively or additionally, vehicle 2702 may determine a portion of a tile where the vehicle's location falls within the tile and load tiles adjacent to that portion. As an example, as shown in FIG. 31A , vehicle 2702 may determine that the vehicle's location falls within a subtile (the subtile having the pattern of dots in tile 5) and load map data for tiles adjacent to the subtile (i.e., tiles 4, 7, and 8) into memory for processing. As another example, as shown in FIG. 31B , vehicle 2702 may determine that the vehicle's location falls within the top-left subtile of tile 5. Vehicle 2702 may also load tiles adjacent to the top-left subtile of tile 5 (i.e., tiles 1, 2, and 4). In some embodiments, vehicle 2702 may be configured to decode tiles before loading the data into memory.

[0382] In some embodiments, rather than receiving one or more road segments from server 2701 over network 2705, vehicle 2702 may retrieve one or more road segments from local storage. For example, vehicle 2702 may determine a potential travel envelope based on an analysis of navigation information and determine one or more road segments that include map information for a geographic area that at least partially overlaps with the potential travel envelope of vehicle 2702. Vehicle 2702 may also retrieve data for one or more road segments from local storage. In some embodiments, vehicle 2702 may load data for one or more road segments into its memory for processing.

[0383] Bandwidth Management for Map Generation and Refinement

[0384] As described elsewhere in this disclosure, utilizing and interpreting the vast amount of data collected by vehicles (e.g., captured image data, map data, GPS data, sensor data, etc.) poses many design challenges. For example, data collected by vehicles may need to be uploaded to a server. The vast amount of uploaded data can easily overwhelm or overwhelm the vehicle's transmission bandwidth. Furthermore, it may be difficult for the server to analyze the new data and update relevant portions of the map based on the new data. Furthermore, different density levels may be used to map different types of features. For example, a density level of 330 kB per kilometer may be required for mapping non-semantic features, compared to a density level of approximately 20 kB per kilometer for semantic features. Given the computational resources required to collect non-semantic feature information and certain hard-limit bandwidth caps that may be imposed on each vehicle (e.g., 100 MB per year), the resources available to the vehicle may be insufficient to consistently collect non-semantic feature information.

[0385] The systems and methods may enable control not only of whether driving data is collected, but also when and how the driving data is collected. For example, the disclosed systems and methods may enable a server to determine whether a host vehicle is entering an area that includes a region of interest. Once the vehicle has entered the area, the server can cause the vehicle to begin collecting high-density non-semantic feature information. If the server determines that the vehicle has passed a point of interest, the server can cause the vehicle to upload the collected non-semantic feature information. If the server determines that the vehicle has not passed through the region of interest, the server can cause the host vehicle to discard the collected non-semantic feature information. The disclosed systems and methods may also enable the server to update a map based on the non-semantic feature information collected by the vehicle.

[0386] 33 illustrates an exemplary system for automatically generating a navigation map associated with one or more road segments according to the disclosed embodiments. As shown in FIG. 33 , the system 3300 may include a server 3301, one or more vehicles 3302, one or more vehicle devices 3303 associated with the vehicles, a database 3304, and a network 3305. For example, the vehicle 3302 and / or the vehicle device 3303 may be configured to collect first navigation information associated with an environment traversed by the vehicle 3302 at a first density level when the vehicle 3302 travels outside a predetermined distance from a geographical area of ​​interest. The vehicle 3302 and / or the vehicle device 3303 may also be configured to collect second navigation information associated with an environment traversed by the vehicle 3302 at a second density level, which may be greater than the first density level, when the vehicle 3302 travels in the geographical area of ​​interest or within a predetermined distance from the geographical area of ​​interest.

[0387] The server 3301 may be configured to receive first and / or second navigation information associated with an environment traversed by the vehicle 3302. The database 3304 may be configured to store information about the components of the system 3300 (e.g., the server 3301, the vehicle 3302, and / or the vehicle device 3303). The network 3305 may be configured to facilitate communication between the components of the system 3300.

[0388] The server 3301 may be configured to trigger collection of first navigation information associated with an environment traversed by the vehicle 3302. The first navigation information may be collected at a first density level. The server 3301 may also be configured to determine a location of the vehicle 3302 based on an output associated with a GPS sensor associated with the vehicle 3302. The server 3301 may further be configured to determine whether the vehicle 3302 is in a geographical area of ​​interest (or a boundary thereof) or within a predetermined distance from the geographical area of ​​interest. The server 3301 may also be configured to trigger collection of second navigation information associated with an environment traversed by the vehicle 3302 based on a determination that the location of the vehicle 3302 is in the geographical area of ​​interest or within a predetermined distance from the geographical area of ​​interest. The second navigation information may be collected at a second density level that may be greater than the first density level. The server 3301 may further be configured to cause the vehicle 3302 to upload at least one of the first navigation information collected from the vehicle 3302 or the second navigation information collected (or a portion thereof). The server 3301 may also be configured to update a navigation map based on the uploaded at least one of the collected first navigation information or the collected second navigation information. In some embodiments, the server 3301 may be a cloud server that performs the functions disclosed herein. The term "cloud server" refers to a computer platform that provides services over a network such as the Internet. In this example configuration, the server 3301 may use a virtual machine that ma...

Claims

1. 1. A system for navigating a vehicle, the system comprising: At least one processor having a circuit and a memory, the memory, when executed by the circuit, causing the at least one processor to: receiving navigation information associated with the vehicle, the navigation information including at least an indication of a location of the vehicle; determining a plurality of target navigation map segments from a map database, the map database including a plurality of stored navigation map segments, each corresponding to an area in the real world, the determination of the plurality of target navigation map segments based on the indication of vehicle position and map segment connectivity information associated with the plurality of stored navigation map segments, the map segment connectivity information indicating whether one or more boundaries between the plurality of stored navigation map segments can be crossed by the vehicle traveling along at least one road segment; determining a priority for retrieving the plurality of target navigation map segments from the map database, the priority being based on connectivity between a current navigation map segment and the plurality of target navigation map segments as indicated by the map segment connectivity information; initiating a download of the plurality of target navigation map segments from the map database based on the determined priorities; and navigating the vehicle along at least one target trajectory included in one or more of the plurality of target navigation map segments downloaded from the map database.

2. The system of claim 1 , wherein the map database is located remotely relative to the vehicle.

3. 3. The system of claim 1, wherein the memory includes instructions that, when executed by the circuitry, cause the at least one processor to perform a procedure for determining the plurality of target navigation map segments to obtain based on the map segment connectivity information.

4. 4. The system of claim 3, wherein if the map segment connectivity information indicates that the vehicle cannot navigate directly from a current navigation map segment to one or more navigation map segments adjacent to the current map segment, the obtained multiple target navigation map segments exclude the one or more navigation map segments adjacent to the current navigation map segment on which the vehicle is located.

5. 5. The system of claim 1, wherein the memory includes instructions that, when executed by the circuitry, cause the at least one processor to perform a procedure for determining the plurality of target navigation map segments to obtain based on at least one of an indication of the speed of the vehicle or an indication of the direction of travel of the vehicle.

6. 6. The system of claim 1, wherein a first navigation map segment is assigned a higher priority than a second navigation map segment based on an indication by the map segment connectivity information that the first navigation map segment has navigable connectivity to a current navigation map segment where the vehicle is located at a shorter distance than a navigable connection between the second navigation map segment and the current navigation map segment.

7. The system of claim 6 , wherein the first navigation map segment does not share a boundary with the current navigation map segment, but the second navigation map segment does share a boundary with the current navigation map segment.

8. The system of claim 1 , wherein each of the plurality of stored navigation map segments includes a field in which map segment connectivity information is stored.

9. 10. The system of claim 8, wherein the plurality of target navigation map segments are prioritized by accessing at least two fields of the plurality of stored navigation map segments and analyzing map segment connectivity information stored in the accessed fields.

10. The system of claim 9 , wherein the accessed field is associated with a predetermined number of navigation map segments that are proximate to a current navigation map segment in which the vehicle is located.

11. The system of claim 10 , wherein the predetermined number of navigation map segments are arranged in tiles, and the accessed fields are associated with eight tiles surrounding a central tile in which the vehicle is located.

12. 11. The system of claim 10, wherein the predetermined number of navigation map segments are arranged in tiles, and the accessed fields are associated with 24 tiles surrounding a central tile in which the vehicle is located.

13. 13. A system according to any one of claims 9 to 12, wherein the field to be accessed is selected based on at least one of a direction of travel of the vehicle or a speed of the vehicle.

14. The system of claim 1 , wherein the map segment connectivity information describes available directions along road segments that cross boundaries of a navigation map segment.

15. 15. The system of claim 1, wherein the map segment connectivity information is stored by logical bits associated with one or more boundaries associated with each of the plurality of target navigation map segments.

16. 16. The system of claim 1, wherein each of the plurality of stored navigation map segments includes a plurality of map sub-segments, and each of the plurality of map sub-segments includes map segment connectivity information specific to that map sub-segment.

17. 17. The system of claim 1, wherein the memory includes instructions that, when executed by the circuitry, cause the at least one processor to perform a procedure for periodically updating the plurality of target navigation map segments obtained from the map database.

18. 20. The system of claim 17, wherein the periodic updates occur at a rate of more than twice per second.

19. 18. The system of claim 17, wherein the periodic update occurs before all of the determined plurality of target navigation map segments are retrieved from the map database.

20. 20. The system of claim 1, wherein the indication of the location of the vehicle is accurate to within 5 centimeters of the actual location of the vehicle.

21. The processor receiving navigation information associated with a vehicle, the navigation information including at least an indication of a location of the vehicle; determining a plurality of target navigation map segments from a map database, the map database including a plurality of stored navigation map segments, each corresponding to an area in the real world, the determination of the plurality of target navigation map segments based on the indication of vehicle position and map segment connectivity information associated with the plurality of stored navigation map segments, the map segment connectivity information indicating whether one or more boundaries between the plurality of stored navigation map segments can be crossed by the vehicle traveling along at least one road segment; determining a priority for retrieving the plurality of target navigation map segments from the map database, the priority being based on connectivity between a current navigation map segment and the plurality of target navigation map segments as indicated by the map segment connectivity information; initiating a download of the plurality of target navigation map segments from the map database based on the determined priorities; and navigating the vehicle along at least one target trajectory included in one or more of the plurality of target navigation map segments downloaded from the map database.

22. 22. The computer program of claim 21, wherein the map database is located remotely relative to the vehicle.

23. 23. A computer program product as claimed in claim 21 or 22, wherein the map segment connectivity information describes available directions of travel along road segments that cross boundaries of a navigation map segment.

24. 24. A computer program product as claimed in any one of claims 21 to 23, wherein the map segment connectivity information is stored by means of logical bits associated with one or more boundaries associated with each of the plurality of target navigation map segments.

25. 25. The computer program product of claim 21, wherein each of the plurality of stored navigation map segments includes a plurality of map sub-segments, each of the plurality of map sub-segments including map segment connectivity information specific to that map sub-segment.

26. 26. A computer program product as claimed in any one of claims 21 to 25, causing the processor to periodically update the plurality of target navigation map segments obtained from the map database.

27. 27. The computer program product of claim 26, wherein the periodic updates occur at a rate of more than twice per second.

28. 27. The computer program product of claim 26, wherein the periodic update occurs before all of the determined plurality of target navigation map segments are retrieved from the map database.

29. 1. A non-transitory computer-readable medium comprising instructions that, when executed by at least one processor, cause the at least one processor to perform operations, the operations including:

1. A method for accessing at least one data structure having a digital map, the digital map comprising: a plurality of navigation map segments, each corresponding to an area in the real world; the accessing step including, for each of the plurality of navigation map segments, map segment connectivity information associated with each boundary shared with an adjacent navigation map segment, the map segment connectivity information being stored in a computer-readable storage medium with logic bits indicating whether one or more boundaries between the plurality of navigation map segments can be crossed by a vehicle traveling along at least one road segment; receiving navigation information associated with the vehicle, the navigation information including at least an indication of a location of the vehicle; determining a plurality of target navigation map segments from a map database, the map database including the plurality of navigation map segments, the determination of the plurality of target navigation map segments being based on the indication of vehicle position and map segment connectivity information associated with the plurality of navigation map segments; determining a priority for retrieving the plurality of target navigation map segments from the map database, the priority being based on connectivity between a current navigation map segment and the plurality of target navigation map segments as indicated by the map segment connectivity information; initiating a download of the plurality of target navigation map segments from the map database based on the determined priorities; navigating the vehicle along at least one target trajectory included in one or more of the plurality of target navigation map segments downloaded from the map database; 1. A non-transitory computer-readable medium comprising:

30. 30. The non-transitory computer-readable medium of claim 29, wherein the map segment connectivity information associated with each boundary shared with an adjacent navigation map segment indicates a unidirectional boundary.

31. 31. The non-transitory computer-readable medium of claim 29 or 30, wherein the map segment connectivity information associated with each boundary shared with an adjacent navigation map segment indicates a two-way boundary.

32. 32. The non-transitory computer-readable medium of claim 29, wherein the digital map includes a representation of a target trajectory to be followed by a vehicle navigation system when traversing road segments represented on the digital map.

33. 33. The non-transitory computer-readable medium of claim 32, wherein the digital map includes a representation of recognized landmarks used by a vehicle navigation system to identify a vehicle's position relative to at least one of the target trajectories.

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