System and method for navigating host vehicle on road segment having object on road edge

Abstract

The present disclosure provides a system for navigating a host vehicle relative to a road segment, the system may include at least one processor including circuitry and memory, the memory may include instructions that, when executed by the circuitry, cause the at least one processor to navigate the host vehicle relative to the road segment. Causing the at least one processor to: receive at least one image captured by at least one camera from an environment of the host vehicle, wherein the at least one image comprises a representation of at least two objects having offset edges in the environment of the host vehicle and on one side of the road segment; analyzing the at least one image to determine a free space boundary with respect to the at least two objects, wherein the free space boundary represents an edge of at least one free space zone and follows a path different from that represented by the offset edges of the at least two objects; determining at least one navigation action based on the free space boundary; and causing the host vehicle to perform the at least one navigation action.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of priority to U.S. Provisional Application No. 63 / 561,961, filed on March 6, 2024. The foregoing application is incorporated herein by reference in its entirety. Technical Field

[0003] The present disclosure generally relates to autonomous vehicle navigation. Background Art

[0004] As technology continues to advance, the goal of fully autonomous vehicles capable of navigating roadways is nearing realization. Autonomous vehicles may need to consider a variety of factors and make appropriate decisions based on those factors to safely and accurately reach their intended destination. For example, autonomous vehicles may need to process and interpret visual information (e.g., information captured from a camera) and may also use information obtained from other sources (e.g., from a GPS device, speed sensors, accelerometers, suspension sensors, radar, lidar, etc.).

[0005] To make real-time decisions regarding navigation, speed control, and / or steering, autonomous vehicles that rely on visual information must extract or derive useful data from captured imagery. This process involves acquiring valuable information such as the 3D position and / or relative velocity of various features within the scene in the host vehicle's environment. For example, the vehicle's system may need to detect objects located along the edge of a road segment to accurately identify safe, navigable areas in which the vehicle can travel without accidents. Additionally, the system may need to identify and track dynamic elements such as pedestrians, cyclists, and other vehicles, as well as interpret traffic signs, lane markings, and obstacles. These details are crucial for the vehicle to accurately understand its environment, predict the movement of other objects, and make intelligent decisions for safe navigation. Therefore, autonomous vehicles need to be able to extract and derive valuable information from captured imagery.

[0006] The present disclosure describes solutions that can improve autonomous navigation relative to road segments. The disclosed embodiments include innovative systems, methods, and non-transitory computer-readable media for deriving valuable information from one or more captured images. Summary of the Invention

[0007] Embodiments consistent with the present disclosure provide systems and methods for autonomous vehicle navigation. Disclosed embodiments may use cameras to provide autonomous vehicle navigation features. For example, consistent with disclosed embodiments, the disclosed system may include one, two, or more cameras that monitor the vehicle's environment. The disclosed system may provide navigation responses based on, for example, analysis of images captured by one or more of the cameras.

[0008] In one embodiment, a system for navigating a host vehicle relative to a road segment is disclosed. The system may include at least one processor including circuitry and memory, wherein the memory includes instructions that, when executed by the circuitry, cause the at least one processor to: receive at least one image captured by at least one camera from the host vehicle's environment, wherein the at least one image includes representations of at least two objects with offset edges in the host vehicle's environment and located on one side of the road segment; analyze the at least one image to determine a free space boundary relative to the at least two objects, wherein the free space boundary represents an edge of at least one free space zone and follows a path different from a path represented by the offset edges of the at least two objects; determine at least one navigation action based on the free space boundary; and cause the host vehicle to perform the at least one navigation action.

[0009] In another embodiment, a method for navigating a host vehicle relative to a road segment is disclosed. The method may include: receiving at least one image captured by at least one camera from an environment of the host vehicle, wherein the at least one image includes representations of at least two objects with offset edges in the environment of the host vehicle and located on one side of the road segment; analyzing the at least one image to determine a free space boundary relative to the at least two objects, wherein the free space boundary represents an edge of at least one free space zone and follows a path different from a path represented by the offset edges of the at least two objects; determining at least one navigation action based on the free space boundary; and causing the host vehicle to perform the at least one navigation action.

[0010] Consistent with other disclosed embodiments, a non-transitory computer-readable storage medium may store program instructions that are executed by at least one processing device to perform any of the methods described herein.

[0011] The foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various disclosed embodiments. In the drawings:

[0013] Figure 1 is a schematic representation of an exemplary system consistent with the disclosed embodiments.

[0014] Figure 2A is a schematic side view representation of an exemplary vehicle including a system consistent with the disclosed embodiments.

[0015] Figure 2B In accordance with the disclosed embodiments Figure 2A Schematic top view representation of the vehicle and systems shown in .

[0016] Figure 2C is a schematic top view representation of another embodiment of a vehicle including a system consistent with the disclosed embodiments.

[0017] Figure 2D is a schematic top view representation of yet another embodiment of a vehicle including a system consistent with the disclosed embodiments.

[0018] Figure 2E is a schematic top view representation of yet another embodiment of a vehicle including a system consistent with the disclosed embodiments.

[0019] Figure 2F is a schematic representation of an exemplary vehicle control system consistent with the disclosed embodiments.

[0020] Figure 3A is a schematic representation of the interior of a vehicle including a rearview mirror and a user interface for a vehicle imaging system, consistent with the disclosed embodiments.

[0021] Figure 3B is an illustration of an example of a camera mount configured to be positioned behind a rearview mirror and against a vehicle windshield, consistent with the disclosed embodiments.

[0022] Figure 3C From different perspectives consistent with the disclosed embodiments Figure 3B Illustration of the camera mount shown in .

[0023] Figure 3D is an illustration of an example of a camera mount configured to be positioned behind a rearview mirror and against a vehicle windshield, consistent with the disclosed embodiments.

[0024] Figure 4 An exemplary block diagram of a memory configured to store instructions for performing one or more operations consistent with the disclosed embodiments.

[0025] Figure 5A A flowchart illustrating an exemplary process for eliciting one or more navigation responses based on monocular image analysis, consistent with the disclosed embodiments.

[0026] Figure 5B A flow chart illustrating an exemplary process for detecting one or more vehicles and / or pedestrians in a set of images, consistent with the disclosed embodiments.

[0027] Figure 5C A flow chart illustrating an exemplary process for detecting road markings and / or lane geometry in a set of images, consistent with the disclosed embodiments.

[0028] Figure 5D A flow chart illustrating an exemplary process for detecting traffic lights in a set of images, consistent with the disclosed embodiments.

[0029] Figure 5E A flow chart illustrating an exemplary process for eliciting one or more navigation responses based on a vehicle path, consistent with the disclosed embodiments.

[0030] Figure 5F 1 is a flow chart illustrating an example process for determining whether a leading vehicle is changing lanes, consistent with the disclosed embodiments.

[0031] Figure 6 A flow chart illustrating an exemplary process for eliciting one or more navigation responses based on stereo image analysis, consistent with the disclosed embodiments.

[0032] Figure 7 A flow chart illustrating an exemplary process for eliciting one or more navigational responses based on analysis of three sets of images, consistent with the disclosed embodiments.

[0033] Figure 8 A sparse map for providing autonomous vehicle navigation consistent with the disclosed embodiments is shown.

[0034] Figure 9A A polynomial representation of a portion of a road segment consistent with disclosed embodiments is shown.

[0035] Figure 9B A curve representing a target trajectory of a vehicle for a road segment in three-dimensional space is shown, the curve being included in a sparse map consistent with the disclosed embodiments.

[0036] Figure 10 Example landmarks that may be included in a sparse map consistent with the disclosed embodiments are shown.

[0037] Figure 11A Polynomial representations of trajectories consistent with the disclosed embodiments are shown.

[0038] Figure 11B and Figure 11C A target trajectory along a multi-lane road is shown consistent with the disclosed embodiments.

[0039] Figure 11D Example road signature profile curves consistent with disclosed embodiments are shown.

[0040] Figure 12 A schematic illustration of a system for autonomous vehicle navigation using crowdsourced data received from multiple vehicles, consistent with the disclosed embodiments.

[0041] Figure 13 An example autonomous vehicle road navigation model represented by a plurality of three-dimensional splines is presented, consistent with the disclosed embodiments.

[0042] Figure 14 A map skeleton generated from combined location information from multiple drives, consistent with the disclosed embodiments, is shown.

[0043] Figure 15 An example of a longitudinal alignment of two vehicles, with an example sign serving as a landmark, is shown, consistent with disclosed embodiments.

[0044] Figure 16 An example of a longitudinal alignment of multiple vehicles with example signs serving as landmarks is shown, consistent with the disclosed embodiments.

[0045] Figure 17 is a schematic illustration of a system for generating driving data using a camera, a vehicle, and a server, consistent with the disclosed embodiments.

[0046] Figure 18 is a schematic illustration of a system for crowdsourcing sparse maps consistent with the disclosed embodiments.

[0047] Figure 19 A flow chart illustrating an exemplary process for generating a sparse map for autonomous vehicle navigation along a road segment, consistent with the disclosed embodiments.

[0048] Figure 20 A block diagram of a server consistent with the disclosed embodiments is shown.

[0049] Figure 21 A block diagram of a memory consistent with the disclosed embodiments is shown.

[0050] Figure 22 A process for clustering vehicle trajectories associated with vehicles consistent with the disclosed embodiments is presented.

[0051] Figure 23 A navigation system for a vehicle consistent with the disclosed embodiments is presented, which can be used for autonomous navigation.

[0052] Figure 24A 、 Figure 24B 、 Figure 24C and Figure 24D Exemplary lane markings that may be detected consistent with the disclosed embodiments are shown.

[0053] Figure 24E Exemplary mapped lane markings consistent with the disclosed embodiments are shown.

[0054] Figure 24F Exemplary anomalies associated with detecting lane markings consistent with the disclosed embodiments are shown.

[0055] Figure 25A Exemplary images of a vehicle's surroundings for navigation based on mapped lane markings, consistent with the disclosed embodiments, are shown.

[0056] Figure 25B Lateral positioning correction of a vehicle based on mapped lane markings in a road navigation model consistent with the disclosed embodiments is demonstrated.

[0057] Figure 25C and Figure 25D A conceptual representation of a localization technique for localizing a host vehicle along a target trajectory using mapped features included in a sparse map is provided.

[0058] Figure 26A A flow chart illustrating an exemplary process for mapping lane markings for use in autonomous vehicle navigation, consistent with the disclosed embodiments.

[0059] Figure 26B A flow chart illustrating an exemplary process for autonomously navigating a host vehicle along a road segment using mapped lane markings, consistent with the disclosed embodiments.

[0060] Figure 27 is a flow chart illustrating an exemplary process for navigating a host vehicle relative to a road segment, consistent with the disclosed embodiments.

[0061] Figure 28A 、 Figure 28B 、 Figure 28C and Figure 28D 1 shows exemplary images captured by an onboard camera in a host vehicle traveling on a road segment, wherein an object is located in the host vehicle's environment, consistent with the disclosed embodiments.

[0062] Figure 29 1 shows exemplary images captured by an onboard camera in a host vehicle traveling on a road segment with at least one object overhanging a surface of the road segment, consistent with the disclosed embodiments. DETAILED DESCRIPTION

[0063] The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numerals are used in the drawings and the following description to refer to the same or similar parts. Although several illustrative embodiments are described herein, modifications, adaptations, and other implementations are possible. For example, the components shown in the drawings may be replaced, added to, or modified, and the illustrative methods described herein may be modified by replacing, reordering, removing, or adding steps to 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.

[0064] Autonomous Vehicle (AV) Overview

[0065] As used throughout this disclosure, the term "autonomous vehicle" refers to a vehicle that is capable of implementing 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 autonomous (e.g., fully operational without a driver or without driver input). Rather, an autonomous vehicle includes a vehicle that can operate under driver control during certain time periods and without driver control during other time periods. An autonomous vehicle may also include a vehicle that controls only some aspects of vehicle navigation, such as steering (e.g., to maintain the vehicle's course between vehicle lane constraints), but may leave other aspects to the driver (e.g., braking). In some cases, an autonomous vehicle may handle some or all aspects of the vehicle's braking, speed control, and / or steering.

[0066] Because human drivers typically rely on visual cues and observation to control vehicles, the transportation infrastructure is also built accordingly, with lane markings, traffic signs, and traffic lights all designed to provide visual information to drivers. Given these design features of the transportation infrastructure, autonomous vehicles may include cameras and processing units that analyze visual information captured from the vehicle's environment. The visual information may include, for example, components of the transportation infrastructure observable by the driver (e.g., lane markings, traffic signs, traffic lights, etc.) and other obstacles (e.g., other vehicles, pedestrians, debris, etc.). In addition, autonomous vehicles may also use stored information, such as information that provides a model of the vehicle's environment when navigating. For example, a 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 its environment as the vehicle travels, and the vehicle (and other vehicles) may use this information to locate itself on the model.

[0067] In some embodiments of the present disclosure, an autonomous vehicle may use information obtained while navigating (e.g., from a camera, a GPS device, an accelerometer, a velocity sensor, a suspension sensor, etc.). In other embodiments, an autonomous vehicle may use information obtained from past navigations by the vehicle (or by 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 navigations. The following sections provide an overview of a system consistent with the disclosed embodiments, followed by an overview of a forward imaging system and method consistent with the system. The following sections disclose systems and methods for building, using, and updating sparse maps for autonomous vehicle navigation.

[0068] System Overview

[0069] Figure 1 is a block diagram representation of a system 100 consistent with exemplary disclosed embodiments. 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 image capture devices (e.g., cameras), such as image capture device 122, image capture device 124, and image capture device 126. System 100 may also include a data interface 128 that communicatively connects processing unit 110 to image acquisition unit 120. For example, the data interface 128 may include any one or more wired and / or wireless links for transmitting image data acquired by the image acquisition unit 120 to the processing unit 110 .

[0070] The wireless transceiver 172 may include one or more devices configured to exchange transmissions with one or more networks (e.g., a cellular network, the Internet, etc.) over an air interface using radio frequencies, infrared frequencies, magnetic fields, or electric fields. The wireless transceiver 172 may transmit and / or receive data using any known standard (e.g., Wi-Fi, Such transmissions may include communications from the host vehicle to one or more remotely located servers. Such transmissions may also include (one-way or two-way) communications between the host vehicle and one or more target vehicles in the host vehicle's environment (e.g., to facilitate coordination of the host vehicle's navigation with or in conjunction with target vehicles in the host vehicle's environment), or even broadcast transmissions to unspecified recipients in the transmitting vehicle's vicinity.

[0071] Both the application processor 180 and the image processor 190 may include various types of processing devices. For example, either or both of the application processor 180 and the image processor 190 may include a microprocessor, a preprocessor (such as an image preprocessor), a graphics processing unit (GPU), a central processing unit (CPU), support circuits, a digital signal processor, an integrated circuit, a memory, or any other type of device suitable for running application programs and suitable for image processing and analysis. In some embodiments, the application processor 180 and / or the image processor 190 may include any type of single-core or multi-core processor, a mobile device microcontroller, a central processing unit, etc. Various processing devices may be used, including, for example, those available from manufacturers such as processors such as , or purchased from manufacturers such as and other GPUs, and may include various architectures (e.g., x86 processors, wait).

[0072] In some embodiments, the application processor 180 and / or the image processor 190 may include a processor purchased from Any of the EyeQ series of processor chips. These processor designs each include multiple processing units with local memory and instruction sets. Such processors may include video inputs for receiving image data from multiple image sensors and may also include video output capabilities. In one example, Using 90nm micron technology operating at 332Mhz. The architecture consists of two floating point hyperthreaded 32-bit RISC CPUs ( cores), five Visual Compute Engines (VCEs), and three vector microcode processors Denali 64-bit mobile DDR controller, 128-bit internal Sonics interconnect, dual 16-bit video input and 18-bit video output controllers, 16-channel DMA and several peripherals. MIPS34K CPU manages five VCEs, three VMPs TM and DMA, a second MIPS34K CPU and multi-channel DMA and other peripherals. Five VCEs, three and MIPS34K CPUs can perform the intensive visual computations required for multi-function bundled applications. In another example, (It is a third generation processor and its performance is Six times of ) can be used in the disclosed embodiments. In other examples, and / or Of course, any newer or future EyeQ processing device can also be used with the disclosed embodiments.

[0073] Any processing device disclosed herein can be configured to perform certain functions. Configuring a processing device (such as any described EyeQ processor or other controller or microprocessor) to perform certain functions can include programming computer-executable instructions and making those instructions available for execution by the processing device during operation of the processing device. In some embodiments, configuring the processing device can include directly programming the processing device using architecture instructions. For example, a processing device such as a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), etc. can be configured using, for example, one or more hardware description languages ​​(HDLs).

[0074] In other embodiments, configuring the processing device may include storing executable instructions on a memory accessible to the processing device during operation. For example, the processing device may access the memory during operation to obtain and execute the stored instructions. In either case, a processing device configured to perform the sensing, image analysis, and / or navigation functions disclosed herein represents a specialized hardware-based system that controls multiple hardware-based components of a host vehicle.

[0075] Although Figure 1 Two separate processing devices are depicted as being included in processing unit 110, but more or fewer processing devices may be used. For example, in some embodiments, a single processing device may be used to perform the tasks of application processor 180 and image processor 190. In other embodiments, these tasks may be performed by more than two processing devices. Furthermore, in some embodiments, system 100 may include one or more processing units 110 without including other components, such as image acquisition unit 120.

[0076] 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 for image processing and analysis. The image preprocessor may include a video processor for capturing, digitizing, and processing images from an image sensor. The CPU may include any number of microcontrollers or microprocessors. The GPU may also include any number of microcontrollers or microprocessors. The support circuits may include any number of circuits known in the art, including caches, 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 a database and image processing software. The memory may include any number of random access memories, read-only memories, flash memories, disk drives, optical storage devices, tape storage devices, removable storage devices, and other types of storage devices. In one embodiment, the memory may be separate from the processing unit 110. In another embodiment, the memory may be integrated into the processing unit 110.

[0077] Each memory 140, 150 may include software instructions that, when executed by a processor (e.g., application processor 180 and / or image processor 190), may control various aspects of the operation of system 100. For example, 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, the memory units 140, 150 may be separate from the application processor 180 and / or image processor 190. In other embodiments, these memory units may be integrated into the application processor 180 and / or image processor 190.

[0078] 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 can determine the user's location and velocity by processing signals broadcast by global positioning system satellites. Position information from position sensor 130 may be made available to application processor 180 and / or image processor 190.

[0079] In some embodiments, system 100 may include components such as a speed sensor (eg, tachometer, speedometer) for measuring the speed of vehicle 200 and / or an accelerometer (single-axis or multi-axis) for measuring the acceleration of vehicle 200 .

[0080] The user interface 170 may include any device suitable for providing information to or receiving input from one or more users of the system 100. In some embodiments, the user interface 170 may include user input devices, including, for example, a touch screen, a microphone, a keyboard, a pointing device, a scroll wheel, a camera, knobs, buttons, etc. Using such input devices, a user may be able to provide information input or commands to the system 100 by typing instructions or information, providing voice commands, selecting menu options on a screen (using buttons, a pointer, or eye tracking capabilities), or via any other suitable technique for communicating information to the system 100.

[0081] The user interface 170 may be equipped with one or more processing devices configured to provide information to and receive information from a user and process the information for use, for example, by the application processor 180. In some embodiments, such processing devices may execute instructions for recognizing and tracking eye movements, receiving and interpreting voice commands, recognizing and interpreting touches and / or gestures made on a touch screen, responding to keyboard inputs or menu selections, etc. In some embodiments, the user interface 170 may include a display, a speaker, a haptic device, and / or any other device for providing output information to a user.

[0082] The map database 160 may comprise any type of database for storing map data useful to the system 100. In some embodiments, the map database 160 may include data relating to the locations of various items (including roads, water features, geographic features, businesses, points of interest, restaurants, gas stations, etc.) in a reference coordinate system. The map database 160 may store not only the locations of such items, but also descriptors associated with those items, including, for example, names associated with any stored features. In some embodiments, the map database 160 may be physically located with the other components of the system 100. Alternatively or additionally, the map database 160 or a portion thereof may be remotely located relative to the other components of the system 100 (e.g., the processing unit 110). In such embodiments, information from the map database 160 may be downloaded via a wired or wireless data connection to a network (e.g., via a cellular network and / or the Internet, etc.). In some cases, the map database 160 may store sparse data models, including polynomial representations of certain road features (e.g., lane markings) or target trajectories for the host vehicle. Systems and methods for generating such maps are described below with reference to Figures 8 to 19 Have a discussion.

[0083] Image capture devices 122, 124, and 126 may each include any type of device suitable for capturing at least one image from an environment. Furthermore, any number of image capture devices may be used to acquire images for input to the image processor. Some embodiments may include only a single image capture device, while other embodiments may include two, three, or even four or more image capture devices. Image capture devices 122, 124, and 126 will be referred to hereinafter. Figures 2B to 2E Further description is given.

[0084] The system 100 or its various components may be incorporated into a variety of different platforms. In some embodiments, the system 100 may be included on a vehicle 200, such as Figure 2A For example, vehicle 200 may be equipped with processing unit 110 and any other components of system 100, as described above with respect to Figure 1 While in some embodiments, the vehicle 200 may be equipped with only a single image capture device (e.g., a camera), in other embodiments, such as a combination of Figures 2B to 2E As discussed above, multiple image capture devices may be used. For example, Figure 2A As shown, either of the image capture devices 122 and 124 of vehicle 200 may be part of an ADAS (Advanced Driver Assistance System) imaging set.

[0085] The image capture device included on the vehicle 200 as part of the image acquisition unit 120 may be positioned at any suitable location. In some embodiments, such as Figures 2A to 2E and Figures 3A to 3C As shown, the image capture device 122 can be located in the vicinity of the rearview mirror. This location can provide a line of sight similar to the line of sight of the driver of the vehicle 200, which can assist in determining what is and is not visible to the driver. The image capture device 122 can be positioned anywhere near the rearview mirror, but placing the image capture device 122 on the driver's side of the mirror can further assist in obtaining an image representative of the driver's field of view and / or line of sight.

[0086] Other locations for the image capture devices of image acquisition unit 120 may also be used. For example, image capture device 124 may be located on or in the bumper of vehicle 200. Such locations may be particularly suitable for image capture devices with a wide field of view. The line of sight of an image capture device located on the bumper may differ from the driver's line of sight, and therefore the bumper image capture device and the driver may not always see the same objects. Image capture devices (e.g., image capture devices 122, 124, and 126) may also be located in other locations. For example, the image capture device may be located on or in one or both of the side mirrors of vehicle 200, on the roof of vehicle 200, on the hood of vehicle 200, on the trunk of vehicle 200, on the side of vehicle 200, mounted on, positioned behind, or positioned in front of any of the windows of vehicle 200, mounted in or near the front and / or rear light patterns of vehicle 200, and the like.

[0087] In addition to the image capture device, the vehicle 200 may also include various other components of the system 100. For example, the processing unit 110 may be included on the vehicle 200, or integrated with or separate from the vehicle's engine control unit (ECU). The vehicle 200 may also be equipped with a location sensor 130, such as a GPS receiver, and may also include a map database 160 and memory units 140 and 150.

[0088] As previously discussed, wireless transceiver 172 can transmit and / or receive data via one or more networks (e.g., a cellular network, the Internet, etc.). For example, wireless transceiver 172 can upload data collected by system 100 to one or more servers and download data from the one or more servers. Via wireless transceiver 172, system 100 can receive, for example, periodic or on-demand updates to data stored in map database 160, memory 140, and / or storage 150. Similarly, wireless transceiver 172 can upload any data received by system 100 (e.g., images captured by image acquisition unit 120, data received by position sensor 130 or other sensors, vehicle control systems, etc.) and / or any data processed by processing unit 110 to one or more servers.

[0089] The system 100 may upload data to a server (e.g., to the cloud) based on a privacy level setting. For example, the system 100 may implement a privacy level setting to regulate or limit the type of data (including metadata) sent to the server that may uniquely identify the vehicle and / or the driver / owner of the vehicle. Such settings may be set by a user via, for example, the wireless transceiver 172, by factory default settings, or by data received by the wireless transceiver 172.

[0090] In some embodiments, the system 100 may upload data according to a "high" privacy level, and under the set setting, the system 100 may transmit data (e.g., location information related to a route, captured images, etc.) without any details about a specific vehicle and / or driver / owner. For example, when uploading data according to the "high" privacy setting, the system 100 may not include the vehicle identification number (VIN) or the name of the driver or owner of the vehicle, and may instead transmit data such as captured images and / or restricted location information related to a route.

[0091] Other privacy levels are contemplated. For example, the system 100 may transmit data to a server according to a "medium" privacy level and include additional information not included at a "high" privacy level, such as the make and / or model of the vehicle and / or the type of vehicle (e.g., passenger car, sport utility vehicle, truck, etc.). In some embodiments, the system 100 may upload data according to a "low" privacy level. At the "low" privacy level setting, the system 100 may upload data and include information sufficient to uniquely identify a particular vehicle, owner / driver, and / or part or all of the route traveled by the vehicle. Such "low" privacy level data may include, for example, one or more of the VIN, the driver / owner's name, the vehicle's starting point prior to departure, the vehicle's intended destination, the vehicle's make and / or model, the vehicle's type, and the like.

[0092] Figure 2A is a schematic side view representation of an exemplary vehicle imaging system consistent with the disclosed embodiments. Figure 2B for Figure 2A A schematic top view of the embodiment shown is shown. Figure 2B As shown, the disclosed embodiments may include a vehicle 200 including a system 100 in its body having a first image capture device 122 positioned in the vicinity of a rearview mirror and / or near a driver of the vehicle 200, a second image capture device 124 positioned on or in a bumper area (e.g., one of the bumper areas 210) of the vehicle 200, and a processing unit 110.

[0093] like Figure 2C As shown, both image capture devices 122 and 124 may be positioned in the vicinity of a rearview mirror and / or near the driver of vehicle 200. Additionally, although both image capture devices 122 and 124 are shown in FIG. Figure 2B and Figure 2C However, it should be understood that other embodiments may include more than two image capture devices. For example, Figure 2D and Figure 2E In the illustrated embodiment, first, second, and third image capture devices 122 , 124 , and 126 are included in the system 100 of a vehicle 200 .

[0094] like Figure 2D As shown, image capture device 122 may be positioned in the vicinity of a rearview mirror and / or near the driver of vehicle 200, and image capture devices 124 and 126 may be positioned on or in a bumper area (e.g., one of bumper areas 210) of vehicle 200. Figure 2E As shown, image capture devices 122, 124, and 126 may be positioned in the vicinity of rearview mirrors and / or near the driver's seat of vehicle 200. The disclosed embodiments are not limited to any particular number and configuration of image capture devices, and the image capture devices may be positioned in any suitable location within and / or on vehicle 200.

[0095] It should be understood that the disclosed embodiments are not limited to vehicles and may be applied in other contexts. It should also be understood that the disclosed embodiments are not limited to a specific type of vehicle 200 and may be applicable to all types of vehicles, including cars, trucks, trailers, and other types of vehicles.

[0096] The first image capture device 122 may include any suitable type of image capture device. The image capture device 122 may include an optical axis. In one embodiment, the image capture device 122 may include an Aptina M9V024WVGA sensor with a global shutter. In other embodiments, the image capture device 122 may provide a resolution of 1280x960 pixels and may include a rolling shutter. The image capture device 122 may include various optical elements. In some embodiments, one or more lenses may be included, for example, to provide a desired focal length and field of view for the image capture device. In some embodiments, the image capture device 122 may be associated with a 6 mm lens or a 12 mm lens. In some embodiments, the image capture device 122 may be configured to capture an image having a desired field of view (FOV) 202, such as Figure 2DAs shown. For example, the image capture device 122 may be configured to have a regular FOV, such as in the range of 40 degrees to 56 degrees, including a 46 degree FOV, a 50 degree FOV, a 52 degree FOV, or larger. Alternatively, the image capture device 122 may be configured to have a narrow FOV in the range of 23 degrees to 40 degrees, such as a 28 degree FOV or a 36 degree FOV. In addition, the image capture device 122 may be configured to have a wide FOV in the range of 100 degrees to 180 degrees. In some embodiments, the image capture device 122 may include a wide-angle bumper camera or a camera with a FOV of up to 180 degrees. In some embodiments, the image capture device 122 may be a 7.2M pixel image capture device with an aspect ratio of approximately 2:1 (e.g., HxV=3800x1900 pixels) with a horizontal FOV of approximately 100 degrees. Such an image capture device may be used to replace a three-image capture device configuration. Due to significant lens distortion, in implementations where the image capture device uses radially symmetric lenses, the vertical FOV of such an image capture device may be significantly less than 50 degrees. For example, such a lens may not be radially symmetric, which would allow for a vertical FOV greater than 50 degrees and a horizontal FOV of 100 degrees.

[0097] The first image capture device 122 may capture a plurality of first images relative to a scene associated with the vehicle 200. Each of the plurality of first images may be captured as a series of image scan lines, which may be captured using a rolling shutter. Each scan line may include a plurality of pixels.

[0098] The first image capture 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 may acquire image data associated with each pixel included in a particular scan line.

[0099] For example, image capture devices 122, 124, and 126 may include any suitable type and number of image sensors, including CCD sensors or CMOS sensors. In one embodiment, a CMOS image sensor may be employed with a rolling shutter, such that each pixel in a row is read one at a time, and the rows are scanned on a row-by-row basis until the entire image frame has been captured. In some embodiments, the rows may be captured sequentially from top to bottom relative to the frame.

[0100] In some embodiments, one or more of the image capture devices disclosed herein (e.g., image capture devices 122, 124, and 126) may constitute a high-resolution imager and may have a resolution greater than 5 M pixels, 7 M pixels, 10 M pixels, or greater.

[0101] The use of a rolling shutter can cause pixels in different rows to be exposed and captured at different times, which can result in skew and other image artifacts in the captured image frame. On the other hand, when image capture device 122 is configured to operate with a global or synchronized shutter, all pixels can be exposed for the same amount of time and during a common exposure period. Therefore, the image data in a frame collected from a system employing a global shutter represents a snapshot of the entire FOV (such as FOV 202) at a specific time. In contrast, in a rolling shutter application, each row in the frame is exposed and the data is captured at a different time. As a result, moving objects may appear distorted in an image capture device with a rolling shutter. This phenomenon is described in more detail below.

[0102] Second image capture device 124 and third image capture device 126 may be any type of image capture device. Similar to first image capture device 122, each of image capture devices 124 and 126 may include an optical axis. In one embodiment, each of image capture devices 124 and 126 may include an Aptina M9V024 WVGA sensor with a global shutter. Alternatively, each of image capture devices 124 and 126 may include a rolling shutter. Similar to image capture device 122, image capture devices 124 and 126 may be configured to include various lenses and optical elements. In some embodiments, the lenses associated with image capture devices 124 and 126 may provide a FOV (such as FOVs 204 and 206) that is the same as or narrower than the FOV associated with image capture device 122 (such as FOV 202). For example, image capture devices 124 and 126 may have a FOV of 40 degrees, 30 degrees, 26 degrees, 23 degrees, 20 degrees, or less.

[0103] Image capture devices 124 and 126 may capture a plurality of second and third images relative to a scene associated with vehicle 200. Each of the plurality of second and third images may be captured 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 the capture of each of the image scan lines included in the second and third series.

[0104] Each image capture device 122, 124, and 126 may be positioned at any suitable location and orientation relative to vehicle 200. The relative positioning of image capture devices 122, 124, and 126 may be selected to facilitate fusing information acquired from the image capture devices. For example, in some embodiments, the FOV associated with image capture device 124 (such as FOV 204) may partially or completely overlap with the FOV associated with image capture device 122 (such as FOV 202) and the FOV associated with image capture device 126 (such as FOV 206).

[0105] Image capture devices 122, 124, and 126 may be located at any suitable relative heights on vehicle 200. In one embodiment, there may be height differences between image capture devices 122, 124, and 126, which may provide sufficient parallax information to enable stereo analysis. Figure 2A As shown, the two image capture devices 122 and 124 are at different heights. For example, there may also be lateral displacement differences between the image capture devices 122, 124, and 126, thereby providing additional parallax information for stereo analysis by the processing unit 110. The lateral displacement differences may be represented by d x Indicates that Figure 2C and Figure 2D In some embodiments, there may be a forward or backward displacement (e.g., a range displacement) between image capture devices 122, 124, and 126. For example, image capture device 122 may be positioned 0.5 meters to 2 meters or more behind image capture device 124 and / or image capture device 126. This type of displacement may enable one of the image capture devices to cover a potential blind spot of the other image capture devices.

[0106] Image capture device 122 may have any suitable resolution capability (e.g., the 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 x 480, 1024 x 768, 1280 x 960, or any other suitable resolution.

[0107] The frame rate (e.g., the rate at which an image capture device acquires a set of pixel data for one image frame before continuing to capture pixel data associated with the next image frame) may be controllable. The frame rate associated with image capture device 122 may be higher, lower, or the same as the frame rates associated with image capture devices 124 and 126. The frame rates associated with image capture devices 122, 124, and 126 may depend on a variety of factors that may affect the timing of the frame rates. For example, one or more of image capture devices 122, 124, and 126 may include a selectable pixel delay period that is applied before or after the acquisition of image data associated with one or more pixels of the image sensors in image capture devices 122, 124, and / or 126. Generally speaking, image data corresponding to each pixel may be acquired according to a clock rate for the device (e.g., one pixel per clock cycle). Additionally, in embodiments including a rolling shutter, one or more of image capture devices 122, 124, and 126 may include a selectable horizontal blanking period applied before or after the acquisition of image data associated with rows of pixels of image sensors in image capture devices 122, 124, and / or 126. Furthermore, one or more of image capture devices 122, 124, and / or 126 may include a selectable vertical blanking period applied before or after the acquisition of image data associated with image frames of image capture devices 122, 124, and 126.

[0108] These timing controls may enable synchronization of the frame rates associated with image capture devices 122, 124, and 126, even when the line scan rate of each image capture device is different. Additionally, as will be discussed in greater detail below, these selectable timing controls, among other factors (e.g., image sensor resolution, maximum line scan rate, etc.), may enable synchronization of image capture from areas where the FOV of image capture device 122 overlaps with one or more of the FOVs of image capture devices 124 and 126, even when the field of view of image capture device 122 is different from the FOVs of image capture devices 124 and 126.

[0109] The frame rate timing in image capture devices 122, 124, and 126 may depend on the resolution of the associated image sensors. For example, assuming similar line scan rates for both devices, if one device includes an image sensor with a resolution of 640 x 480 and the other device includes an image sensor with a resolution of 1280 x 960, it will take more time to acquire a frame of image data from the sensor with the higher resolution.

[0110] Another factor that may affect the timing of image data acquisition in image capture devices 122, 124, and 126 is the maximum line scan rate. For example, acquiring a line of image data from the image sensors included in image capture devices 122, 124, and 126 will require some minimum amount of time. Assuming no pixel delay period is added, this minimum amount of time for acquiring a line of image data will be related to the maximum line scan rate for the particular device. Devices with higher maximum line scan rates may be able to provide higher frame rates than devices with lower maximum line scan rates. In some embodiments, one or more of image capture devices 124 and 126 may have a maximum line scan rate that is higher than the maximum line scan rate associated with image capture device 122. In some embodiments, the maximum line scan rate of image capture devices 124 and / or 126 may be 1.25 times, 1.5 times, 1.75 times, or 2 times, or more, the maximum line scan rate of image capture device 122.

[0111] In another embodiment, image capture devices 122, 124, and 126 may have the same maximum line scan rate, but image capture device 122 may be operated at a scan rate less than or equal to its maximum scan rate. The system may be configured such that one or more of image capture devices 124 and 126 operate at a line scan rate equal to the line scan rate of image capture device 122. In other cases, the system may be configured such that the line scan rate of image capture device 124 and / or image capture device 126 may be 1.25 times, 1.5 times, 1.75 times, or 2 times, or more, the line scan rate of image capture device 122.

[0112] In some embodiments, image capture devices 122, 124, and 126 may be asymmetric. That is, they may include cameras with different fields of view (FOVs) and focal lengths. For example, the fields of view of image capture devices 122, 124, and 126 may include any desired area relative to the environment of vehicle 200. In some embodiments, one or more of image capture devices 122, 124, and 126 may be configured to acquire image data from the environment in front of vehicle 200, behind vehicle 200, to the sides of vehicle 200, or a combination thereof.

[0113] Furthermore, the focal length associated with each image capture 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 range of distances relative to the vehicle 200. For example, in some embodiments, the image capture devices 122, 124, and 126 may capture images of close-up objects within a few meters from the vehicle. The image capture devices 122, 124, and 126 may also be configured to capture images of objects at greater ranges from the vehicle (e.g., 25 m, 50 m, 100 m, 150 m, or more). In addition, the focal lengths of image capture devices 122, 124, and 126 may be selected such that one image capture device (e.g., image capture device 122) may capture images of objects that are relatively close to the vehicle (e.g., within 10 m or within 20 m), while other image capture devices (e.g., image capture devices 124 and 126) may capture images of objects that are farther from the vehicle 200 (e.g., greater than 20 m, 50 m, 100 m, 150 m, etc.).

[0114] According to some embodiments, the FOV of one or more image capture devices 122, 124, and 126 may have a wide angle. For example, having a FOV of 140 degrees may be advantageous, particularly for image capture devices 122, 124, and 126 that may be used to capture images of areas in the vicinity of vehicle 200. For example, image capture 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 image capture device 122 to have a wide FOV (e.g., at least 140 degrees).

[0115] The field of view associated with each of image capture devices 122, 124, and 126 may depend on the respective focal length. For example, as the focal length increases, the corresponding field of view decreases.

[0116] Image capture devices 122, 124, and 126 may be configured to have any suitable field of view. In one specific example, image capture device 122 may have a horizontal FOV of 46 degrees, image capture device 124 may have a horizontal FOV of 23 degrees, and image capture device 126 may have a horizontal FOV between 23 and 46 degrees. In another instance, image capture device 122 may have a horizontal FOV of 52 degrees, image capture device 124 may have a horizontal FOV of 26 degrees, and image capture device 126 may have a horizontal FOV between 26 and 52 degrees. In some embodiments, the ratio of the FOV of image capture device 122 to the FOV of image capture device 124 and / or image capture device 126 may vary from 1.5 to 2.0. In other embodiments, the ratio may vary between 1.25 and 2.25.

[0117] System 100 can be configured such that the field of view of image capture device 122 at least partially or completely overlaps the field of view of image capture device 124 and / or image capture device 126. In some embodiments, system 100 can be configured such that the fields of view of image capture devices 124 and 126, for example, fall within (e.g., are narrower than) the field of view of image capture device 122 and share a common center therewith. In other embodiments, image capture devices 122, 124, and 126 can capture adjacent FOVs or can have partial overlap in their FOVs. In some embodiments, the fields of view of image capture devices 122, 124, and 126 can be aligned such that the center of the narrower FOV image capture devices 124 and / or 126 is located in the lower half of the field of view of the wider FOV image capture device 122.

[0118] Figure 2F FIG. 1 is a schematic representation of an exemplary vehicle control system consistent with the disclosed embodiments. Figure 2F As indicated, vehicle 200 may include a throttle system 220, a braking system 230, and a steering system 240. System 100 may provide input (e.g., control signals) to one or more of throttle system 220, braking system 230, and steering system 240 via one or more data links (e.g., any wired and / or wireless links or links for transmitting data). For example, based on analysis of images acquired by image capture 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 acceleration, turning, lane changing, etc.). In addition, system 100 may receive input from one or more of throttle system 220, braking system 230, and steering system 24 indicating an operating condition of vehicle 200 (e.g., speed, whether vehicle 200 is braking and / or turning, etc.). Further details are provided below in conjunction with Figures 4 to 7 supply.

[0119] like Figure 3AAs shown, vehicle 200 may also include a user interface 170 for interacting with the driver or passengers of vehicle 200. For example, user interface 170 in a vehicle application may include a touch screen 320, a knob 330, buttons 340, and a microphone 350. The driver or passengers of vehicle 200 may also use handles (e.g., located on or near the steering column of vehicle 200, including, for example, a turn signal handle), buttons (e.g., located on the steering wheel of vehicle 200), etc. to interact with system 100. In some embodiments, microphone 350 may be positioned adjacent to rearview mirror 310. Similarly, in some embodiments, image capture 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., alarms) via speakers 360.

[0120] Figures 3B to 3D 3 is an illustration of an exemplary camera mount 370 configured to be positioned behind a rearview mirror (e.g., rearview mirror 310) and against a vehicle windshield, consistent with the disclosed embodiments. Figure 3B As shown, camera mount 370 may include image capture devices 122, 124, and 126. Image capture devices 124 and 126 may be positioned behind glare shield 380, which may be flush against the vehicle windshield and include a combination of film and / or anti-reflective material. For example, glare shield 380 may be positioned so that the shield is aligned against the vehicle windshield having a matching slope. In some embodiments, each of image capture devices 122, 124, and 126 may be positioned behind glare shield 380, such as, for example, Figure 3D The disclosed embodiments are not limited to any particular configuration of image capture devices 122 , 124 , and 126 , camera mount 370 , and glare shield 380 . Figure 3C For the front view Figure 3B An illustration of a camera mount 370 is shown.

[0121] As will be appreciated by those skilled in the art having the benefit of this disclosure, numerous variations and / or modifications may be made to the aforementioned disclosed embodiments. For example, not all components are required for the operation of system 100. Furthermore, any component may be located in any suitable part of system 100, and these components may be rearranged into various configurations while providing the functionality of the disclosed embodiments. Therefore, the aforementioned configurations are examples, and regardless of the configurations discussed above, system 100 may provide a wide range of functionality for analyzing the surrounding environment of vehicle 200 and navigating vehicle 200 in response to that analysis.

[0122] As discussed in further detail below and consistent with the various disclosed embodiments, system 100 can provide various features related to autonomous driving and / or driver assistance technologies. For example, system 100 can 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 can collect data for analysis from, for example, image acquisition unit 120, location sensor 130, and other sensors. Furthermore, system 100 can analyze the collected data to determine whether vehicle 200 should take a specific action, and then automatically take the determined action without human intervention. For example, while vehicle 200 is navigating without human intervention, system 100 can automatically control 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 can analyze the collected data and, based on the analysis of the collected data, issue warnings and / or alerts to vehicle occupants. Additional details regarding various embodiments provided by system 100 are provided below.

[0123] Forward multiple imaging system

[0124] As discussed above, system 100 can provide driver assistance functionality using a multi-camera system. A multi-camera system can use one or more cameras facing forward in the vehicle's forward direction. In other embodiments, the multi-camera system can include one or more cameras facing the side or rear of the vehicle. In one embodiment, for example, system 100 can use a dual-camera imaging system, where a first camera and a second camera (e.g., image capture devices 122 and 124) can be positioned at the front and / or side of a vehicle (e.g., vehicle 200). The first camera can 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 can be connected to a first image processor to perform monocular image analysis on the image provided by the first camera, and the second camera can be connected to a second image processor to perform monocular image analysis on the image provided by the second camera. The outputs (e.g., processed information) of the first and second image processors can be combined. In some embodiments, the second image processor can receive images from both the first and second cameras for stereo analysis. In another embodiment, system 100 can use a three-camera imaging system, where each of the cameras has a different field of view. Thus, such systems can make decisions based on information derived from objects located at varying distances both in front of and to the sides of the vehicle. References to monocular image analysis may refer to situations where image analysis is performed based on images captured from a single viewpoint (e.g., from a single camera). Stereoscopic image analysis may refer to situations where image analysis is performed based on two or more images captured using one or more variations of image capture parameters. For example, captured images suitable for stereoscopic image analysis may include images captured from two or more different positions, images captured from different fields of view, images captured using different focal lengths and parallax information, and the like.

[0125] For example, in one embodiment, system 100 may implement a three-camera configuration using image capture devices 122, 124, and 126. In such a configuration, image capture device 122 may provide a narrow field of view (e.g., 34 degrees, or other values ​​selected from a range of approximately 20 to 45 degrees, etc.), image capture device 124 may provide a wide field of view (e.g., 150 degrees, or other values ​​selected from a range of approximately 100 to approximately 180 degrees), and image capture device 126 may provide an intermediate field of view (e.g., 46 degrees, or other values ​​selected from a range of approximately 35 to approximately 60 degrees). In some embodiments, image capture device 126 may serve as the primary or main camera. Image capture devices 122, 124, and 126 may be positioned behind rearview mirror 310 and positioned substantially side-by-side (e.g., 6 cm apart). Furthermore, in some embodiments, as discussed above, one or more of image capture devices 122, 124, and 126 may be mounted behind a glare shield 380 flush with the windshield of vehicle 200. Such shielding may serve to minimize the effect of any reflections from the interior of the car on image capture devices 122 , 124 , and 126 .

[0126] In another embodiment, as described above in combination with Figure 3B and Figure 3C As discussed, the wide field of view camera (e.g., image capture device 124 in the example above) can be mounted lower than the narrow field of view and main field of view cameras (e.g., image capture devices 122 and 126 in the example above). This configuration provides a clear line of sight from the wide field of view camera. To reduce reflections, the camera can be mounted close to the windshield of vehicle 200 and a polarizer on the camera can be included to reduce reflected light.

[0127] A three-camera system can provide specific performance features. For example, some embodiments may include the ability to verify detection of an object by one camera based on detection results from another camera. In the three-camera configuration discussed above, processing unit 110 may include, for example, three processing devices (e.g., three EyeQ series processor chips, as discussed above), where each processing device is dedicated to processing images captured by one or more of image capture devices 122, 124, and 126.

[0128] In a three-camera system, the first processing device can receive images from both the main camera and the narrow field of view camera and perform vision processing for the narrow FOV camera to, for example, detect other vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. Furthermore, the first processing device can calculate pixel disparity between the images from the main camera and the narrow camera and create a 3D reconstruction of the vehicle 200's environment. The first processing device can then combine the 3D reconstruction with 3D map data or with 3D information calculated based on information from another camera.

[0129] The second processing device can receive images from the primary camera and perform visual processing to detect other vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. Additionally, the second processing device can calculate camera displacement and, based on this displacement, calculate pixel disparity between consecutive images and create a 3D reconstruction of the scene (e.g., structure from motion). The second processing device can send the structure from motion 3D reconstruction to the first processing device for combination with the stereoscopic 3D image.

[0130] 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 to identify moving objects in the images, such as vehicles changing lanes, pedestrians, etc.

[0131] In some embodiments, having image-based information streams captured and processed independently may provide an opportunity to provide redundancy in the system. Such redundancy may include, for example, using a first image capture device and images processed from that device to verify and / or supplement information obtained by capturing and processing image information from at least a second image capture device.

[0132] In some embodiments, system 100 may use two image capture devices (e.g., image capture devices 122 and 124) to provide navigation assistance for vehicle 200, and a third image capture device (e.g., image capture device 126) to provide redundancy and validate the analysis of data received from the other two image capture devices. For example, in such a configuration, image capture devices 122 and 124 may provide images for stereo analysis by system 100 for navigating vehicle 200, while image capture device 126 may provide images for monocular analysis by system 100 to provide redundancy and validation of information derived from images captured by image capture devices 122 and / or 124. That is, image capture device 126 (and a corresponding processing device) may be considered to provide a redundant subsystem for providing a check on the analysis derived from image capture devices 122 and 124 (e.g., to provide an automatic emergency braking (AEB) system). Additionally, in some embodiments, redundancy and verification of the received data may be supplemented based on information received from one or more sensors (e.g., radar, lidar, acoustic sensors, information received from one or more transceivers outside the vehicle, etc.).

[0133] Those skilled in the art will recognize that the camera configurations, camera placements, number of cameras, camera locations, etc. described above are merely examples. These components and other components described with respect to the overall system can 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 a multi-camera system to provide driver assistance and / or autonomous vehicle functionality are as follows.

[0134] Figure 4 An exemplary functional block diagram of memory 140 and / or 150 that may be stored / programmed with instructions for performing one or more operations consistent with the disclosed embodiments. Although the following refers to memory 140, those skilled in the art will recognize that instructions may be stored in memory 140 and / or 150.

[0135] like Figure 4 As shown, 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 appreciate that references to processing unit 110 in the following discussion may refer individually or collectively to application processor 180 and image processor 190. Therefore, the steps of any of the following processes may be performed by one or more processing devices.

[0136] In one embodiment, the monocular image analysis module 402 may store instructions (such as computer vision software) that, when executed by the processing unit 110, perform monocular image analysis on a set of images acquired by one of the image capture devices 122, 124, and 126. In some embodiments, the processing unit 110 may combine information from the set of images with additional sensory information (e.g., information from radar, lidar, etc.) to perform monocular image analysis. As described below in conjunction with 5A to 5D As described, the monocular image analysis module 402 may include instructions for detecting a set of features within the set of images (such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazardous objects, and any other features associated with the vehicle's environment). Based on this analysis, the system 100 (e.g., via the processing unit 110) may cause one or more navigation responses in the vehicle 200, such as turns, lane changes, changes in acceleration, etc., as discussed below in conjunction with the navigation response module 408.

[0137] In one embodiment, stereo image analysis module 404 may store instructions (such as computer vision software) that, when executed by processing unit 110, perform stereo image analysis of a first set of images and a second set of images acquired by a combination of image capture devices selected from any one of image capture devices 122, 124, and 126. In some embodiments, processing unit 110 may combine information from the first set of images and the second set 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 for performing stereo image analysis based on a first set of images acquired by image capture device 124 and a second set of images acquired by image capture device 126. As described below in conjunction with Figure 6 As described, stereo image analysis module 404 may include instructions for detecting a set of features (such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazardous objects, etc.) within the first and second sets of images. Based on this analysis, processing unit 110 may cause one or more navigation responses in vehicle 200, such as a turn, lane change, change in acceleration, etc., as discussed below in conjunction with navigation response module 408. Furthermore, in some embodiments, stereo image analysis module 404 may implement techniques associated with a trained system (such as a neural network or deep neural network) or an untrained system (such as a system that may be configured to use computer vision algorithms to detect and / or label objects in the environment from which sensory information is captured 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.

[0138] In one embodiment, the velocity and acceleration module 406 may store software configured to analyze data received from one or more computing and electromechanical devices in the vehicle 200 that are configured to cause changes in the velocity and / or acceleration of the vehicle 200. For example, the processing unit 110 may execute instructions associated with the velocity and acceleration module 406 to calculate a target velocity for the vehicle 200 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, for example, the target position, velocity, and / or acceleration, the position and / or velocity of the vehicle 200 relative to nearby vehicles, pedestrians, or road objects, position information for the vehicle 200 relative to lane markings on the road, etc. Among other things, the processing unit 110 may calculate the target velocity for the vehicle 200 based on sensory input (e.g., information from radar) and input from other systems of the vehicle 200, such as the throttle system 220, the braking system 230, and / or the steering system 240 of the vehicle 200. Based on the calculated target speed, the processing unit 110 may transmit 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 by, for example, physically depressing the brakes or releasing the accelerator of the vehicle 200.

[0139] In one embodiment, navigation response module 408 may store software executable by processing unit 110 to determine a desired navigation response based on data derived from the execution of monocular image analysis module 402 and / or stereo image analysis module 404. Such data may include position and velocity information associated with nearby vehicles, pedestrians, and road objects, target position information for vehicle 200, and the like. Additionally, in some embodiments, the navigation response may be based (in part or in whole) on map data, a predetermined position of vehicle 200, and / or a relative velocity or acceleration between vehicle 200 and one or more objects detected from the execution of monocular image analysis module 402 and / or stereo image analysis module 404. Navigation response module 408 may also determine the desired navigation response 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 steering system 240 of vehicle 200. Based on the desired navigation response, processing unit 110 may transmit electronic signals to throttle system 220, brake system 230, and steering system 240 of vehicle 200 to trigger the desired navigation response to achieve a predetermined angle of rotation, for example, by turning the steering wheel of vehicle 200. 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 for calculating the change in velocity of vehicle 200.

[0140] Furthermore, any modules disclosed herein (e.g., modules 402, 404, and 406) may implement techniques associated with trained systems (such as neural networks or deep neural networks) or untrained systems.

[0141] Figure 5A A flow chart illustrating an exemplary process 500A for causing one or more navigation responses based on monocular image analysis consistent with the disclosed embodiments is provided. At step 510, the processing unit 110 may receive a plurality of images via the data interface 128 between the processing unit 110 and the image acquisition unit 120. For example, a camera included in the image acquisition unit 120 (such as the image capture device 122 having the field of view 202) may capture a plurality of images of an area in front of the vehicle 200 (or, for example, to the side or rear of the vehicle) and transmit them to the processing unit 110 via a data connection (e.g., digital, wired, USB, wireless, Bluetooth, etc.). The processing unit 110 may execute the monocular image analysis module 402 at step 520 to analyze the plurality of images, as described below in conjunction with Figures 5B to 5D By performing this 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, traffic lights, etc.

[0142] Processing unit 110 may also execute monocular image analysis module 402 at step 520 to detect various road hazards, such as, for example, parts of truck tires, fallen road signs, loose cargo, small animals, etc. Road hazards can vary in structure, shape, size, and color, which may make detecting such hazards more challenging. 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 disparity between frames to construct a 3D map of the road. Processing unit 110 may then use the 3D map to detect hazards on the road surface and above the road surface.

[0143] At step 530, the processing unit 110 may execute the navigation response module 408 to perform the navigation response based on the analysis performed at step 520 and the above-mentioned combination. Figure 4The described techniques are used to cause one or more navigation responses in vehicle 200. Navigation responses may include, for example, turns, lane changes, changes in acceleration, etc. In some embodiments, processing unit 110 may use data derived from execution of rate and acceleration module 406 to cause one or more navigation responses. In addition, multiple navigation responses may occur simultaneously, sequentially, or any combination thereof. For example, processing unit 110 may cause vehicle 200 to change out of a lane and then accelerate by, for example, sequentially transmitting control signals to steering system 240 and throttle system 220 of vehicle 200. Alternatively, processing unit 110 may cause vehicle 200 to brake while changing lanes by, for example, simultaneously transmitting control signals to braking system 230 and steering system 240 of vehicle 200.

[0144] Figure 5B Flowchart illustrating an exemplary process 500B for detecting one or more vehicles and / or pedestrians in a set of images consistent with the disclosed embodiments. Processing unit 110 may execute monocular image analysis module 402 to implement process 500B. At step 540, processing unit 110 may determine 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 possible locations within each image that may contain objects of interest (e.g., vehicles, pedestrians, or portions thereof). The predetermined patterns may be designed to achieve a high "false hit" rate and a low "miss" rate. For example, processing unit 110 may use a low threshold for similarity to the predetermined patterns to identify a candidate object as a possible vehicle or pedestrian. Doing so may allow processing unit 110 to reduce the probability of missing (e.g., not identifying) a candidate object representing a vehicle or pedestrian.

[0145] At step 542, processing unit 110 may filter the set of candidate objects based on classification criteria to exclude certain candidates (e.g., irrelevant or less relevant objects). Such criteria may be derived from various attributes associated with object types stored in a database (e.g., a database stored in memory 140). Attributes may include object shape, size, texture, location (e.g., relative to vehicle 200), etc. Thus, processing unit 110 may use one or more sets of criteria to reject incorrect candidates from a set of candidate objects.

[0146] At step 544, processing unit 110 may analyze the plurality of image frames to determine whether an object in a set of candidate objects represents a vehicle and / or a pedestrian. 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 for the detected objects and compare the frame-by-frame position data of the objects with the predicted positions.

[0147] At step 546, processing unit 110 may construct a set of measurements for the detected objects. Such measurements may include, for example, position, velocity, and acceleration values ​​associated with the detected objects (relative to vehicle 200). In some embodiments, processing unit 110 may construct the measurements based on an estimation technique such as a Kalman filter or linear quadratic estimation (LQE) using a series of time-based observations and / or based on available modeling data for different object types (e.g., cars, trucks, pedestrians, bicycles, road signs, etc.). The Kalman filter may be based on a measurement of the scale of the object, where the scale measurement is proportional to the time to collision (e.g., the amount of time it takes for vehicle 200 to reach the object). Thus, by performing steps 540 to 546, processing unit 110 may identify vehicles and pedestrians that appear within a set of captured images and derive information related to the vehicles and pedestrians (e.g., position, velocity, size). Based on this identification and derived information, processing unit 110 may cause one or more navigation responses in vehicle 200, as described above in conjunction with Figure 5A described.

[0148] At step 548, processing unit 110 may perform an optical flow analysis on 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, for example, analyzing motion patterns relative to vehicle 200 in one or more images associated with other vehicles and pedestrians, and where the motion patterns are different from the motion of the road surface. Processing unit 110 may calculate the motion of candidate objects by observing the 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 into a mathematical model to calculate the motion of the candidate objects. Therefore, optical flow analysis may provide another method for detecting vehicles and pedestrians near vehicle 200. Processing unit 110 may perform optical flow analysis in conjunction with steps 540 to 546 to provide redundancy for detecting vehicles and pedestrians and increase the reliability of system 100.

[0149] Figure 5CFlowchart illustrating an exemplary process 500C for detecting road markings and / or lane geometry in a set of images consistent with the disclosed embodiments. Processing unit 110 may execute monocular image analysis module 402 to implement process 500C. At step 550, processing unit 110 may detect a set of objects by scanning one or more images. In order to detect lane markings, lane geometry, and other relevant road marking segments, processing unit 110 may filter the set of objects to exclude those determined to be irrelevant (e.g., small potholes, small rocks, etc.). At step 552, processing unit 110 may group together the segments detected in step 550 that belong to the same road marking or lane marking. Based on the grouping, processing unit 110 may develop a model, such as a mathematical model, representing the detected segments.

[0150] 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 to the real-world plane. The projection may be characterized using a cubic polynomial with coefficients corresponding to physical properties such as the position, slope, curvature, and curvature derivatives of the detected road. When generating the projection, processing unit 110 may take into account changes in the road surface and the pitch rate and roll rate associated with vehicle 200. In addition, processing unit 110 may model the road elevation by analyzing position and motion cues present on the road surface. Furthermore, processing unit 110 may estimate the pitch rate and roll rate associated with vehicle 200 by tracking a set of feature points in one or more images.

[0151] At step 556, processing unit 110 may perform a multi-frame analysis by, for example, tracking the detected segments across consecutive image frames and accumulating frame-by-frame data associated with the detected segments. As processing unit 110 performs a multi-frame analysis, the set of measurements constructed at step 554 may become more reliable and associated with increasingly higher confidence levels. Thus, by performing steps 550, 552, 554, and 556, processing unit 110 may identify road markings that appear within a set of captured images and derive lane geometry information. Based on this identified and derived information, processing unit 110 may cause one or more navigation responses in vehicle 200, as described above in conjunction with Figure 5A described.

[0152] At step 558, processing unit 110 may consider additional information sources to further develop a safety model for vehicle 200 in the context of its surroundings. Processing unit 110 may use the safety model to define scenarios in which system 100 can safely perform autonomous control of vehicle 200. To develop the safety model, in some embodiments, processing unit 110 may consider the positions and motions of other vehicles, detected road edges and barriers, and / or a general road shape description extracted from map data (such as data from map database 160). By considering additional information sources, processing unit 110 can provide redundancy for detecting road markings and lane geometry and increase the reliability of system 100.

[0153] Figure 5D A flow chart illustrating an exemplary process 500D for detecting traffic lights in a set of images consistent with the disclosed embodiments is provided. Processing unit 110 may execute monocular image analysis module 402 to implement process 500D. At step 560, processing unit 110 may scan the set of images and identify objects that appear at locations in the images that may contain traffic lights. For example, processing unit 110 may filter the identified objects to construct a set of candidate objects, excluding those that are unlikely to correspond to traffic lights. This filtering may be performed based on various attributes associated with traffic lights, such as shape, size, texture, and position (e.g., relative to vehicle 200). Such attributes may be based on multiple examples of traffic lights and traffic control signals and stored in a database. In some embodiments, processing unit 110 may perform a multi-frame analysis on the set of candidate objects that reflect possible traffic lights. For example, processing unit 110 may track candidate objects across consecutive image frames, estimate the real-world positions of the candidate objects, and filter out objects that are moving (which are unlikely to be traffic lights). In some embodiments, processing unit 110 may perform color analysis on the candidate objects and identify the relative positions of the detected colors that appear within the possible traffic lights.

[0154] At step 562, processing unit 110 may analyze the geometric features of the intersection. This analysis may be based on any combination of: (i) the number of lanes detected on either side of vehicle 200, (ii) detected markings on the road (such as arrows), and (iii) a description of the intersection extracted from map data (such as data from map database 160). Processing unit 110 may use information derived from the execution of monocular analysis module 402 to perform the analysis. Additionally, processing unit 110 may determine a correspondence between the traffic lights detected at step 560 and the lanes present near vehicle 200.

[0155] As the vehicle 200 approaches the intersection, at step 564, the processing unit 110 may update the confidence level associated with the analyzed intersection geometry and detected traffic lights. For example, the number of traffic lights estimated to be present at the intersection compared to the number actually present at the intersection may affect the confidence level. Therefore, based on the confidence level, the processing unit 110 may delegate control to the driver of the vehicle 200 in order to improve safety conditions. By performing steps 560, 562, and 564, the processing unit 110 may identify the traffic lights that appear within the set of captured images and analyze the intersection geometry information. Based on this identification and analysis, the processing unit 110 may cause one or more navigation responses in the vehicle 200, as described above in conjunction with Figure 5A described.

[0156] Figure 5E Flowchart showing an exemplary process 500E for inducing one or more navigation responses in vehicle 200 based on a vehicle path consistent with the disclosed embodiments. At step 570, processing unit 110 may construct an initial vehicle path associated with vehicle 200. The vehicle path may be represented by a set of points expressed as coordinates (x, z), and the distance d between two points in the set of points may be i May fall within the range of 1 meter to 5 meters. In one embodiment, the processing unit 110 may use two polynomials (such as a left road polynomial and a right road polynomial) to construct an initial vehicle path. The processing unit 110 may calculate the geometric midpoint between the two polynomials and, if available, offset each point included in the resulting vehicle path by a predetermined offset (e.g., a smart lane offset) (a zero offset may correspond to driving in the middle of the lane). The offset may be in a direction perpendicular to the segment between any two points in the vehicle path. In another embodiment, the processing unit 110 may use a polynomial and an estimated lane width to offset each point of the vehicle path by half the estimated lane width plus a predetermined offset (e.g., a smart lane offset).

[0157] At step 572, the processing unit 110 may update the vehicle path constructed at step 570. The processing unit 110 may reconstruct the vehicle path constructed at step 570 using a higher resolution so that the distance d between two points in the set of points representing the vehicle path is k Smaller than the distance d mentioned above i For example, the distance d k The processing unit 110 may reconstruct the vehicle path using a parabolic spline algorithm, which may generate a cumulative distance vector S corresponding to the total length of the vehicle path (ie, based on a set of points representing the vehicle path).

[0158] At step 574, the processing unit 110 may determine a look-ahead point (expressed as coordinates (x l ,z l )). Processing unit 110 may extract a look-ahead point from the accumulated distance vector S, and the look-ahead point may be associated with a look-ahead distance and a look-ahead time. The look-ahead distance (which may have a lower limit in the range of 10 meters to 20 meters) 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 it reaches the lower limit). The look-ahead time (which may be in the range of 0.5 seconds to 1.5 seconds) may be inversely proportional to the gain of one or more control loops associated with inducing 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, the lateral dynamics of the car, etc. Therefore, the higher the gain of the heading error tracking control loop, the shorter the look-ahead time.

[0159] At step 576, the processing unit 110 may determine the heading error and yaw rate command based on the look-ahead point determined at step 574. The processing unit 110 may calculate the heading error and yaw rate command by calculating the arc tangent of the look-ahead point, e.g., arctan(x l / z l ) to determine the heading error. Processing unit 110 may determine the yaw rate command as the product of the heading error and the high-level control gain. If the look-ahead distance is not at the lower limit, the high-level control gain may be equal to: (2 / look-ahead time). Otherwise, the high-level control gain may be equal to: (2*vehicle 200 speed / look-ahead distance).

[0160] Figure 5F FIGURE 5 is a flow chart illustrating an exemplary process 500F for determining whether a leading vehicle is changing lanes consistent with the disclosed embodiments. At step 580, the processing unit 110 may determine navigation information associated with a leading vehicle (e.g., a vehicle traveling in front of the vehicle 200). For example, the processing unit 110 may use the above-described method in conjunction with the vehicle 200 to determine whether the leading vehicle is changing lanes. Figure 5A and Figure 5B The described techniques can be used to determine the position, velocity (e.g., direction and speed), and / or acceleration of the vehicle ahead. The processing unit 110 can also use the above combined Figure 5E The described techniques determine one or more road polynomials, look-ahead points (associated with vehicle 200 ), and / or snail trajectories (eg, a set of points describing a path taken by a leading vehicle).

[0161] At step 582, processing unit 110 may analyze the navigation information determined at step 580. In one embodiment, processing unit 110 may calculate the distance between the snail trajectory and the road polynomial (e.g., along the trajectory). If the variance of this distance along the trajectory exceeds a predetermined threshold (e.g., 0.1 to 0.2 meters on a straight road, 0.3 to 0.4 meters on a moderately curving road, and 0.5 to 0.6 meters on a road with a sharp curve), processing unit 110 may determine that the preceding vehicle may be changing lanes. In the event that multiple vehicles are detected traveling ahead of vehicle 200, processing unit 110 may compare the snail trajectories associated with each vehicle. Based on this comparison, processing unit 110 may determine that a vehicle whose snail trajectory does not match the snail trajectories of the other vehicles may be changing lanes. Processing unit 110 may also compare the curvature of the snail trajectory (associated with the preceding vehicle) with the expected curvature of the road segment on which the preceding vehicle is traveling. The expected curvature may be extracted from map data (e.g., data from map database 160 ), from a road polynomial, from snail trajectories of other vehicles, from prior knowledge about the road, etc. If the curvature of the snail trajectory differs from the expected curvature of the road segment by more than a predetermined threshold, processing unit 110 may determine that the leading vehicle may be changing lanes.

[0162] In another embodiment, the processing unit 110 may compare the instantaneous position of the preceding vehicle with a look-ahead point (associated with the vehicle 200) over a specific time period (e.g., 0.5 seconds to 1.5 seconds). If the distance between the instantaneous position of the preceding vehicle and the look-ahead point changes over the specific time period, and the cumulative sum of the changes exceeds a predetermined threshold (e.g., 0.3 meters to 0.4 meters on a straight road, 0.7 meters to 0.8 meters on a moderately curved road, and 1.3 meters to 1.7 meters on a road with a sharp curve), the processing unit 110 may determine that the preceding vehicle is likely changing lanes. In another embodiment, the processing unit 110 may analyze the geometric characteristics of the snail's trajectory by comparing the lateral distance traveled along the snail's trajectory with the expected curvature of the snail's trajectory. The expected curvature radius may be determined according to the following calculation: (δ z 2 +δ x 2 ) / 2 / (δ x ), where δ x represents the lateral distance traveled, and δ zIndicates the longitudinal distance traveled. If the difference between the lateral distance traveled and the expected curvature exceeds a predetermined threshold (e.g., 500 meters to 700 meters), the processing unit 110 may determine that the leading vehicle is likely changing lanes. In another embodiment, the 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 is overlaid on top of the road polynomial), the 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 in front of the leading vehicle and the snail trajectories of the two vehicles are not parallel, the processing unit 110 may determine that the (closer) leading vehicle is likely changing lanes.

[0163] At step 584, processing unit 110 may determine whether the leading vehicle 200 is changing lanes based on the analysis performed at step 582. For example, processing unit 110 may make a determination based on a weighted average of the individual analyses performed at step 582. Under such an approach, for example, a determination by processing unit 110 that the leading vehicle is likely changing lanes based on a particular type of analysis may be assigned a value of "1" (and "0" to indicate a determination that the leading vehicle is unlikely to be changing lanes). Different analyses performed at step 582 may be assigned different weights, and the disclosed embodiments are not limited to any particular combination of analyses and weights.

[0164] Figure 6 A flow chart illustrating an exemplary process 600 for inducing one or more navigation responses based on stereo image analysis consistent with the disclosed embodiments is shown. At step 610, processing unit 110 may receive a first plurality of images and a second plurality of images via data interface 128. For example, cameras included in image acquisition unit 120 (such as image capture devices 122 and 124 having fields of view 202 and 204) may capture a first plurality of images and a second plurality of images of the area in front 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 plurality of images and the second plurality of images via two or more data interfaces. The disclosed embodiments are not limited to any particular data interface configuration or protocol.

[0165] At step 620, the processing unit 110 may execute the stereo image analysis module 404 to perform stereo image analysis on the first plurality of images and the second plurality of images to create a 3D map of the road in front of the vehicle and detect features within the image, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, road hazards, etc. The stereo image analysis may be combined with the above 5A to 5DThe steps described above are performed in a similar manner. For example, processing unit 110 may execute stereo image analysis module 404 to detect candidate objects (e.g., vehicles, pedestrians, road markings, traffic lights, road hazards, etc.) within the first and second pluralities of images, filter out a subset of candidate objects based on the respective objects, perform multi-frame analysis, construct measurements, and determine confidence levels for the remaining candidate objects. When performing the above steps, processing unit 110 may consider information from both the first and second pluralities of images, rather than information from a single 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) for candidate objects appearing in both the first and second pluralities of 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 whether the candidate object appears in one of the multiple images but not the other, or by observing other differences that would exist relative to the object if it appeared in both image streams. For example, position, velocity, and / or acceleration relative to vehicle 200 may be determined based on the trajectory, position, movement characteristics, etc. of features associated with objects appearing in one or both of the image streams.

[0166] At step 630, the processing unit 110 may execute the navigation response module 408 to perform the navigation response based on the analysis performed at step 620 and the above-mentioned combination. Figure 4 The described techniques may be used to cause one or more navigation responses in the vehicle 200. The navigation responses may include, for example, turns, lane changes, changes in acceleration, changes in speed, braking, etc. In some embodiments, the processing unit 110 may use data derived from execution of the speed and acceleration module 406 to cause one or more navigation responses. Additionally, multiple navigation responses may occur simultaneously, sequentially, or any combination thereof.

[0167] Figure 7A flow chart illustrating an exemplary process 700 for inducing one or more navigation responses based on analysis of three sets of images, consistent with the disclosed embodiments, is shown. At step 710, processing unit 110 may receive a first plurality of images, a second plurality of images, and a third plurality of images via data interface 128. For example, cameras included in image acquisition unit 120 (such as image capture devices 122, 124, and 126 having fields of view 202, 204, and 206) may capture a first plurality of images, a second plurality of images, and a third plurality of images of areas 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 plurality of images, the second plurality of images, and the third plurality of images via three or more data interfaces. For example, each of image capture devices 122, 124, and 126 may have an associated data interface for transmitting data to processing unit 110. The disclosed embodiments are not limited to any particular data interface configuration or protocol.

[0168] At step 720, the processing unit 110 may analyze the first plurality of images, the second plurality of images, and the third plurality of images to detect features within the images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, road hazards, etc. This analysis may be combined with the above 5A to 5D and Figure 6 For example, the processing unit 110 may perform a monocular image analysis on each of the first plurality of images, the second plurality of images, and the third plurality of images (e.g., via execution of the monocular image analysis module 402 and based on the above combined 5A to 5D Alternatively, the processing unit 110 may perform stereoscopic image analysis on the first plurality of images and the second plurality of images, the second plurality of images and the third plurality of images, and / or the first plurality of images and the third plurality of images (e.g., via execution of the stereoscopic image analysis module 404 and based on the above in combination with Figure 64 and 5. The processing unit 110 may perform a combination of monocular image analysis and stereo image analysis. For example, the processing unit 110 may perform monocular image analysis on the first plurality of images (e.g., via execution by the monocular image analysis module 402) and stereo image analysis on the second and third pluralities of images (e.g., via execution by the stereo image analysis module 404). The configuration of the image capture devices 122, 124, and 126—including their respective locations and fields of view 202, 204, and 206—may affect the type of analysis performed on the first, second, and third pluralities of images. The disclosed embodiments are not limited to a particular configuration of the image capture devices 122, 124, and 126, or the type of analysis performed on the first, second, and third pluralities of images.

[0169] In some embodiments, processing unit 110 may test system 100 based on the images acquired and analyzed at steps 710 and 720. Such testing may provide an indicator of the overall performance of system 100 for a particular configuration of image capture devices 122, 124, and 126. For example, processing unit 110 may determine the proportion of "false hits" (e.g., instances in which system 100 incorrectly determines the presence of a vehicle or pedestrian) and "misses."

[0170] At step 730, the processing unit 110 may cause one or more navigation responses in the vehicle 200 based on information derived from both the first plurality of images, the second plurality of images, and the third plurality of images. The selection of both the first plurality of images, the second plurality of images, and the third plurality of images may depend on various factors, such as, for example, the number, type, and size of objects detected in each of the plurality of images. The processing unit 110 may also base its selection on image quality and resolution, the effective field of view reflected in the image, the number of frames captured, the extent to which one or more objects of interest actually appear in the frames (e.g., the percentage of frames in which the objects appear, the proportion of the objects appearing in each such frame, etc.), etc.

[0171] In some embodiments, processing unit 110 may select information derived from both the first, second, and third pluralities of images by determining the extent to which information derived from one image source is consistent with information derived from other image sources. For example, processing unit 110 may combine processed information derived from each of image capture devices 122, 124, and 126 (whether through monocular analysis, stereo analysis, or any combination thereof) and determine visual indicators (e.g., lane markings, detected vehicles and their locations and / or paths, detected traffic lights, etc.) that are consistent across the images captured by each of image capture devices 122, 124, and 126. Processing unit 110 may also exclude information that is inconsistent across the captured images (e.g., vehicles changing lanes, lane models indicating vehicles that are too close to vehicle 200, etc.). Thus, processing unit 110 may select information derived from both the first, second, and third pluralities of images based on the determination of consistent and inconsistent information.

[0172] The navigation response may include, for example, a turn, a lane change, a change in acceleration, etc. The processing unit 110 may, based on the analysis performed at step 720 and the above combined Figure 4 The processing unit 110 may also use data derived from execution of the velocity and acceleration module 406 to cause one or more navigation responses. In some embodiments, the processing unit 110 may cause one or more navigation responses based on the relative position, relative velocity, and / or relative acceleration between the vehicle 200 and an object detected within any of the first plurality of images, the second plurality of images, and the third plurality of images. The multiple navigation responses may occur simultaneously, sequentially, or any combination thereof.

[0173] Sparse road models for autonomous vehicle navigation

[0174] In some embodiments, the disclosed systems and methods may utilize sparse maps for autonomous vehicle navigation. Specifically, sparse maps may be used for autonomous vehicle navigation along road segments. For example, a sparse map may provide sufficient information for autonomous vehicle navigation without requiring the storage and / or updating of large amounts of data. As discussed in further detail below, an autonomous vehicle may utilize a sparse map to navigate one or more roads based on one or more stored trajectories.

[0175] Sparse maps for autonomous vehicle navigation

[0176] In some embodiments, the disclosed systems and methods may generate a sparse map for autonomous vehicle navigation. For example, a 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 use the sparse map to navigate one or more roads. For example, in some embodiments, a sparse map may include data related to roads and potential landmarks along the roads that is sufficient for vehicle navigation, but the data also exhibits a small data footprint. For example, the sparse data maps described in detail below may require significantly less storage space and data transfer bandwidth than digital maps that include detailed map information (such as image data collected along the roads).

[0177] For example, a sparse data map may store a three-dimensional polynomial representation of a preferred vehicle path along a road, rather than storing detailed representations of road segments. These paths may require very little data storage space. In addition, in the described sparse data map, landmarks may be identified and included in the sparse map road model to assist navigation. These landmarks may be located at any spacing suitable for achieving vehicle navigation, but in some cases, it is not necessary to identify such landmarks at high density and short spacing and include them in the model. Instead, in some cases, navigation 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 is possible. As will be discussed in more detail in other sections, a sparse map may be generated based on data collected or measured by vehicles equipped with various sensors and devices (such as image capture devices, global positioning system sensors, motion sensors, etc.) as the vehicles travel along a roadway. In some cases, a sparse map may be generated based on data collected during multiple drives of one or more vehicles along a particular roadway. Generating a sparse map using multiple drives of one or more vehicles may be referred to as a "crowdsourced" sparse map.

[0178] Consistent with the disclosed embodiments, an autonomous vehicle system may use a sparse map for navigation. For example, the disclosed systems and methods may distribute a sparse map for use in generating a road navigation model for an autonomous vehicle, and the autonomous vehicle may be navigated along a road segment using the sparse map and / or the generated road navigation model. A sparse map consistent with the present disclosure may include one or more three-dimensional shapes that may represent predetermined trajectories that the autonomous vehicle may traverse as it moves along the associated road segment.

[0179] Sparse maps consistent with the present disclosure may also include data representing one or more road features. Such road features may include recognized landmarks, road signature outlines, and any other road-related features useful in navigating a vehicle. Sparse maps consistent with 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, rather than including a detailed representation of a road, such as road edges, road curvature, images associated with road segments, or data detailing other physical features associated with road segments, disclosed embodiments of sparse maps may require a relatively small amount of storage space (and relatively small bandwidth when portions of the sparse map are transferred to a vehicle), but may still adequately provide autonomous vehicle navigation. The small data footprint of the disclosed sparse maps, discussed in further 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.

[0180] For example, rather than storing detailed representations of various aspects of a road, the disclosed sparse map may store polynomial representations of one or more trajectories that a vehicle may follow along a road. Thus, rather than storing (or having to transmit) details about the physical properties of a road to enable navigation along the road, using the disclosed sparse map, a vehicle may navigate along a particular road segment without, in some cases, having to interpret the physical aspects of the road, but rather by aligning its travel path with a trajectory (e.g., a polynomial spline) along the particular road segment. In this way, a vehicle may be navigated primarily based on stored trajectories (e.g., polynomial splines), which may require significantly less storage space than approaches that involve the storage of roadway images, road parameters, road layouts, and the like.

[0181] In addition to the stored polynomial representations of trajectories along a road segment, the disclosed sparse map may also include small data objects that can 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 a vehicle traveling along the road segment. The digital signatures may have a reduced size relative to the signals acquired by the sensors. In some embodiments, the digital signatures may be created to be compatible with a classifier function configured to detect and identify road features from signals acquired by the sensors, for example, during subsequent driving. In some embodiments, the digital signatures may be created to have the smallest possible footprint while maintaining the ability to correlate or match road features with stored signatures based on images of the road features (or, if the stored signatures are not image-based and / or include other data, based on digital signals generated by the sensors) captured at a subsequent time by a camera mounted on a vehicle traveling along the same road segment.

[0182] In some embodiments, the size of the data object may be further correlated with the uniqueness of the road feature. For example, for a road feature detectable by a camera mounted on a vehicle, and when the camera system mounted on the vehicle is coupled to a classifier capable of distinguishing image data corresponding to the road feature as being associated with a particular type of road feature (e.g., a road sign), and when such road sign is locally unique in the area (e.g., there are no identical road signs or road signs of the same type nearby), it may be sufficient to store data indicating the type of road feature and its location.

[0183] As will be discussed in further detail below, road features (e.g., landmarks along a road segment) can be stored as small data objects that can represent road features using relatively few bytes while providing sufficient information for identifying such features and using such features for navigation. In one example, road signs can be identified as recognizable landmarks on which a vehicle's navigation can be based. Representations of road signs can be stored in a sparse map to include, for example, a few bytes of data indicating the type of landmark (e.g., a stop sign) and a few bytes of data indicating the location of the landmark (e.g., coordinates). Navigation based on such data-based representations of landmarks (e.g., using representations sufficient for positioning, identification, and navigation based on the landmarks) can provide the desired level of navigation functionality associated with sparse maps without significantly increasing the data overhead associated with sparse maps. Such condensed representations of landmarks (and other road features) can be utilized by sensors and processors, including those mounted on such vehicles, that are configured to detect, identify, and / or classify specific road features.

[0184] When, for example, a sign or even a particular type of sign is locally unique in a given area (e.g., when there are no other signs, nor any other signs of the same type), a sparse map can use data indicating a class of landmarks (signs or particular types of signs), and during navigation (e.g., autonomous navigation), when a camera mounted on an autonomous vehicle captures an image of an area that includes the sign (or particular type of sign), a processor can process the image, detect the sign (if indeed present in the image), classify the image as a sign (or particular type of sign), and associate the location of the image with the location of the sign as stored in the sparse map.

[0185] The sparse map may include any suitable representation of objects identified along a road segment. In some cases, objects may be referred to as semantic objects or non-semantic objects. Semantic objects may include, for example, objects associated with a predetermined type classification. Such type classification may be useful in reducing the amount of data required to describe semantic objects identified in the environment, which may be beneficial both during the acquisition phase (e.g., reducing costs associated with bandwidth usage for transmitting driving information from multiple driving vehicles to a server) and during the navigation phase (e.g., reduction in map data may speed up the transmission of map tiles from a server to a navigation vehicle and may also reduce costs associated with bandwidth usage for such transmissions). A semantic object classification type may be assigned to any type of object or feature expected to be encountered along a roadway.

[0186] Semantic objects can be further divided into two or more logical groups. For example, in some cases, the semantic object type of a group can be associated with a predetermined dimension. Such semantic objects may include specific speed limit signs, yield signs, merge signs, stop signs, traffic lights, directional arrows on the roadway, manhole covers, or any other type of object that can be associated with a standardized size. One benefit given by such semantic objects is that very little data may be required to represent / completely define the object. For example, if the standardized size of the speed limit size is known, the acquisition vehicle may only need to identify (by analyzing the captured image) the presence of the speed limit sign (identified type) together with an indication of the position of the detected speed limit sign (e.g., the 2D position in the captured image of the center of the sign or a corner of the sign (or, alternatively, the 3D position in real-world coordinates)) to provide sufficient information for map generation on the server side. In the case of transmitting the 2D image position to the server, the position associated with the captured image in which the sign was detected can also be transmitted, so that the server can determine the real-world position of the sign (e.g., by using a structure-in-motion technique from multiple captured images of one or more acquisition vehicles). Even with limited information (only a few bytes are needed to define each detected object), the server can build a map including fully represented speed limit signs based on the type classifications (representing speed limit signs) received from one or more collecting vehicles together with the location information for the detected signs.

[0187] Semantic objects may also include other recognized object or feature types that are not associated with specific standardized characteristics. Such objects or features may include potholes, tar seams, light poles, non-standardized signs, curbs, trees, branches, or any other type of recognized object type with one or more variable characteristics (e.g., variable size). In such cases, in addition to transmitting to the server an indication of the type of object or feature detected (e.g., pothole, pole, etc.) and location information for the detected object or feature, the collection vehicle may also transmit an indication of the size of the object or feature. The size may be expressed in terms of 2D image size (e.g., using a bounding box or one or more size values) or real-world size (determined by structure-in-motion calculations, based on LiDAR or radar system output, based on trained neural network output, etc.).

[0188] Non-semantic objects or features can include any detectable object or feature that falls outside of a recognized category or type, but can still provide valuable information in map generation. In some cases, such non-semantic features can include the detected corner of a building or the corner of a detected window on a building, a single rock or object near a roadway, concrete splatter in the shoulder of the roadway, or any other detectable object or feature. When such an object or feature is detected, one or more acquisition vehicles can transmit the location of one or more points (2D image points or 3D real-world points) associated with the detected object / feature to a map generation server. In addition, a compressed or simplified image segment (e.g., an image hash) can be generated for the area of ​​the captured image that includes the detected object or feature. This image hash can be calculated based on a predetermined image processing algorithm and can form a valid signature for the detected non-semantic object or feature. Such a signature can be useful for navigation relative to sparse maps that include non-semantic features or objects, as vehicles traveling through the roadway can apply an algorithm similar to the algorithm used to generate the image hash to confirm / verify the presence of the mapped non-semantic feature or object in the captured image. Using this technique, non-semantic features can be added to the richness of a sparse map (e.g., to enhance its usefulness in navigation) without adding significant data overhead.

[0189] As noted, target trajectories can be stored in a sparse map. These target trajectories (e.g., 3D splines) can represent preferred or recommended paths for each available lane on a roadway, each valid path through an intersection, merges and exits, etc. In addition to target trajectories, other road features can also be detected, collected, and incorporated into the sparse map in the form of representative splines. Such features can include, for example, road edges, lane markings, curbs, guardrails, or any other objects or features extending along a roadway or road segment.

[0190] Generate sparse maps

[0191] In some embodiments, the sparse map may include at least one line representation of road surface features extending along a road segment and a plurality of landmarks associated with the road segment. In certain aspects, the sparse map may be generated via "crowd sourcing," such as by image analysis of a plurality of images acquired as one or more vehicles traverse the road segment.

[0192] Figure 8 A sparse map 800 is shown that one or more vehicles (e.g., vehicle 200 (which may be an autonomous vehicle)) may access to provide autonomous vehicle navigation. The 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, an optical disk, flash memory, a magnetic-based storage device, an optical-based storage device, etc. In some embodiments, the sparse map 800 may be stored in a database (e.g., map database 160), which may be stored in memory 140 or 150 or other types of storage devices.

[0193] In some embodiments, sparse map 800 may be stored on a storage device or non-transitory computer-readable medium that is installed on vehicle 200 (e.g., a storage device included in a navigation system installed on vehicle 200). A processor provided on vehicle 200 (e.g., processing unit 110) may access sparse map 800 stored on the storage device or computer-readable medium installed on vehicle 200 to generate navigation instructions for guiding autonomous vehicle 200 as the vehicle traverses a road segment.

[0194] However, the sparse map 800 does not need to be stored locally relative to the vehicle. In some embodiments, the sparse map 800 may be stored on a storage device or computer-readable medium provided on a remote server that communicates with the vehicle 200 or a device associated with the vehicle 200. A processor provided on the vehicle 200 (e.g., the processing unit 110) may receive the data included in the sparse map 800 from the remote server and may execute the data for guiding the autonomous driving of the vehicle 200. In such embodiments, the remote server may store all or only a portion of the sparse map 800. Therefore, a storage device or computer-readable medium provided to be loaded on the vehicle 200 and / or loaded on one or more additional vehicles may store the remaining portion of the sparse map 800.

[0195] Furthermore, in such embodiments, the sparse map 800 may be made accessible to a plurality of vehicles (e.g., tens, hundreds, thousands, or millions of vehicles, etc.) traversing various road segments. It should also be noted that the sparse map 800 may include a plurality of sub-maps. For example, in some embodiments, the sparse map 800 may include hundreds, thousands, millions, or more sub-maps (e.g., map tiles) that may be used to navigate a vehicle. Such sub-maps may be referred to as local maps or map tiles, and a vehicle traveling along a roadway may access any number of local maps relevant to the location where the vehicle is traveling. The local map segments of the sparse map 800 may be stored along with a global navigation satellite system (GNSS) key as an index to a database of the sparse map 800. Thus, while the calculation of the steering angle for navigating the main vehicle in the present system may be performed without relying on the GNSS position of the main vehicle, road features, or landmarks, such GNSS information may be used for retrieval of relevant local maps.

[0196] Generally speaking, the sparse map 800 can be generated based on data (e.g., driving information) collected from one or more vehicles as they travel along a roadway. For example, using sensors mounted on one or more vehicles (e.g., cameras, speedometers, GPS, accelerometers, etc.), the trajectories of one or more vehicles traveling along the roadway can be recorded, and a polynomial representation of the preferred trajectory of a vehicle for subsequent travel along the roadway can be determined based on the collected trajectories traveled by one or more vehicles. Similarly, data collected by one or more vehicles can help identify potential landmarks along a particular roadway. Data collected from passing vehicles can also be used to identify road profile information, such as road width profiles, road roughness profiles, traffic line spacing profiles, road conditions, etc. Using the collected information, the sparse map 800 can be generated and distributed (e.g., for local storage or via in-flight data transmission) for use in navigating one or more autonomous vehicles. However, in some embodiments, map generation may not end at the initial generation of the map. As will be discussed in greater detail below, the sparse map 800 may be continuously or periodically updated based on data collected from vehicles as those vehicles continue to traverse roadways included in the sparse map 800 .

[0197] 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 contour locations, etc.). The locations of the map elements included in the sparse map 800 can be obtained using GPS data collected from vehicles crossing the roadway. For example, a vehicle passing an identified landmark can determine the location of the identified landmark using GPS location information associated with the vehicle and a determination of the location of the identified landmark relative to the vehicle (e.g., based on image analysis of data collected from one or more cameras mounted on the vehicle). Such location determinations of the identified landmark (or any other feature included in the sparse map 800) can be repeated as additional vehicles pass through the location of the identified landmark. Some or all of the additional location determinations can be used to refine the location information relative to the identified landmark stored in the sparse map 800. For example, in some embodiments, multiple location measurements relative to a particular feature stored in the sparse map 800 can be averaged together. However, any other mathematical operation may also be used to refine the stored location of a map element based on a plurality of determined locations for the map element.

[0198] In one specific example, collection vehicles may traverse a specific road segment. Each collection vehicle captures images of its respective environment. Images may be collected at any suitable frame capture rate (e.g., 9 Hz, etc.). An image analysis processor onboard each collection vehicle analyzes the captured images to detect the presence of semantic and / or non-semantic features / objects. At a high level, the collection vehicles transmit indications of the detection of semantic and / or non-semantic objects / features, along with the locations associated with those objects / features, to a mapping server. More specifically, type indicators, size indicators, and the like may be transmitted along with the location information. The location information may include any suitable information for enabling the mapping server to aggregate the detected objects / features into a sparse map useful for navigation. In some cases, the location information may include one or more 2D image locations (e.g., XY pixel locations) in the captured images where the semantic or non-semantic features / objects were detected. Such image locations may correspond to the center, corners, and the like of the features / objects. In this scenario, to assist the mapping server in reconstructing and aligning driving information from multiple collection vehicles, each collection vehicle may also provide the server with the location (e.g., GPS location) at which each image was captured.

[0199] In other cases, the acquisition vehicle may provide one or more 3D real-world points associated with the detected objects / features to the server. Such 3D points may be relative to a predetermined origin (such as the origin of the driving segment) and may be determined by any suitable technique. In some cases, structure-in-motion techniques may be used to determine the 3D real-world position of the detected objects / features. For example, a particular object such as a particular speed limit sign may be detected in two or more captured images. Using information such as the known ego-motion of the acquisition vehicle between captured images (speed, trajectory, GPS position, etc.), together with observed changes in the speed limit sign in the captured images (changes in XY pixel location, changes in size, etc.), the real-world position of one or more points associated with the speed limit sign may be determined and passed to the mapping server. Such an approach is optional because it requires more computation on the part of the acquisition vehicle system. The sparse maps of the disclosed embodiments may enable autonomous navigation of the vehicle using a relatively small amount of stored data. In some embodiments, the sparse map 800 may have a data density (e.g., including data representing target tracks, landmarks, and any other stored road features) of less than 2MB per kilometer of road, less than 1MB per kilometer of road, less than 500kB per kilometer of road, or less than 100kB per kilometer of road. In some embodiments, the data density of the sparse map 800 may be less than 10kB per kilometer of road or even less than 2kB per kilometer of road (e.g., 1.6kB per kilometer), or no more than 10kB per kilometer of road, or no more than 20kB per kilometer of road. In some embodiments, most (if not all) roadways in the United States can be autonomously navigated using a sparse map with a total of 4GB or less of data. These data density values ​​may represent average values ​​over the entire sparse map 800, over a local map within the sparse map 800, and / or over a specific road segment within the sparse map 800.

[0200] As noted, 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 three-dimensional splines. For example, the target trajectories stored in the sparse map 800 may be determined based on two or more reconstructed trajectories of a vehicle previously traveling 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 expected path of travel along the road in a first direction, and a second target trajectory may be stored to represent an expected path of travel along the road in another direction (e.g., opposite to the first direction). Additional target trajectories may be stored relative to a particular road segment. For example, on a multi-lane road, one or more target trajectories may be stored that represent the expected path of travel of the vehicle in one or more lanes associated with the multi-lane road. In some embodiments, each lane of the multi-lane road may be associated with its own target trajectory. In other embodiments, there may be fewer target trajectories stored than there are lanes on the multi-lane road. In such cases, a vehicle navigating on a multi-lane road can use any stored target trajectory to guide its navigation by taking into account the lane offset from the lane for which the target trajectory is stored (for example, if the vehicle is traveling in the leftmost lane of a three-lane highway and target trajectories are stored only for the middle lane of the highway, the vehicle can navigate using the target trajectory for the middle lane by taking into account the lane offset between the middle lane and the leftmost lane when generating navigation instructions).

[0201] In some embodiments, the target trajectory may represent an ideal path that the vehicle should take when traveling. The target trajectory may be located, for example, at the approximate center of the driving lane. In other cases, the target trajectory may be located at other locations 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, etc. In such cases, navigation based on the target trajectory may include a determined offset to be maintained relative to the location of the target trajectory. In addition, in some embodiments, the determined offset to be maintained relative to the location of the target trajectory may be different based on the type of vehicle (for example, a passenger vehicle including two axles may have a different offset along at least a portion of the target trajectory than a truck including more than two axles).

[0202] The sparse map 800 may also include data related to a plurality of predetermined landmarks 820 associated with specific road segments, local maps, and the like. As discussed in greater detail below, these landmarks can be used for navigation of the autonomous vehicle. For example, in some embodiments, the landmarks can be used to determine the vehicle's current position relative to a stored target track. Using this position information, the autonomous vehicle may be able to adjust its heading to match the direction of the target track at the determined location.

[0203] A plurality of landmarks 820 may be identified and stored in the sparse map 800 at any suitable spacing. In some embodiments, the 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 employed. For example, in the sparse map 800, the identified (or recognized) landmarks may be spaced 10 meters, 20 meters, 50 meters, 100 meters, 1 kilometer, or 2 kilometers apart. In some cases, the identified landmarks may be located at a distance of more than 2 kilometers apart.

[0204] Between landmarks, and therefore between determinations of the vehicle's position relative to the target trajectory, the vehicle can navigate based on dead reckoning, where the vehicle uses sensors to determine its ego-motion and estimate its position relative to the target trajectory. Because errors can accumulate during navigation by dead reckoning, position determinations relative to the target trajectory can become increasingly less accurate over time. The vehicle can use the landmarks present in sparse map 800 (and their known locations) to remove dead reckoning-induced errors from the position determination. In this way, the identified landmarks included in sparse map 800 can serve as navigation anchors from which the vehicle's accurate position relative to the target trajectory can be determined. Because a certain amount of error in positional locations is acceptable, identified landmarks need not always be available to the autonomous vehicle. Instead, suitable navigation can even be based on landmark spacings of 10 meters, 20 meters, 50 meters, 100 meters, 500 meters, 1 kilometer, 2 kilometers, or more meters, as noted above. In some embodiments, a density of one identified landmark per 1 km of roadway may be sufficient to maintain longitudinal position determination accuracy within 1 meter. Therefore, not every potential landmark occurring along a road segment needs to be stored in the sparse map 800 .

[0205] Furthermore, in some embodiments, lane markings may be used to localize the vehicle during landmark intervals. By using lane markings during landmark intervals, the accumulation of errors during navigation by dead reckoning may be minimized.

[0206] In addition to target tracks and identified landmarks, the sparse map 800 may also include information related to various other road features. For example, Figure 9A 800 along a particular road segment. 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 a polynomial representing the left and right sides of a single lane is represented in Figure 9A Regardless of how many lanes a road may have, it can be similar to Figure 9A The method shown in uses polynomials to represent roads. For example, the left and right sides of a multi-lane road can be represented by something like Figure 9A, and including intermediate lane markings on multi-lane roads (e.g., dashed markings indicating lane boundaries, solid yellow lines indicating boundaries between lanes traveling in different directions, etc.) may also be represented using polynomials such as Figure 9A It is represented by the polynomials shown.

[0207] like Figure 9A As shown, lane 900 can be represented using a polynomial (e.g., a first-order, second-order, third-order, or any suitable order polynomial). For illustration purposes, lane 900 is shown as a two-dimensional lane and the polynomial is shown as a two-dimensional polynomial. Figure 9A As depicted, lane 900 includes a left side 910 and a right side 920. In some embodiments, more than one polynomial may be used to represent locations on each side of a road or lane boundary. For example, each of left side 910 and right side 920 may be represented by multiple polynomials of any suitable length. In some cases, the polynomials may have a length of approximately 100 meters, although other lengths greater or less than 100 meters may also be used. Additionally, the polynomials may overlap to facilitate seamless transitions when navigating based on subsequently encountered polynomials as the host vehicle travels along the roadway. For example, each of left side 910 and right side 920 may be represented by multiple third-order polynomials divided into segments of approximately 100 meters in length (an example of a first predetermined range) and overlapping by approximately 50 meters. The polynomials representing left side 910 and right side 920 may or may not have 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.

[0208] exist Figure 9A In the example shown, 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, while substantially parallel to each other, follow the locations of their respective sides of the road. Polynomial segments 911, 912, 913, 914, 915, and 916 have a length of approximately 100 meters and overlap with adjacent segments in the series by approximately 50 meters. However, as previously noted, polynomials of different lengths and different amounts of overlap may also be used. For example, the polynomial may have a length of 500m, 1km, or longer, and the amount of overlap may vary from 0m to 50m, 50m to 100m, or greater than 100m. In addition, although Figure 9A are shown as representing polynomials extending in 2D space (e.g., on the surface of paper), but it will be appreciated that these polynomials may represent curves extending in three dimensions (e.g., including a height component) to represent changes in elevation of a road segment in addition to XY curvature. Figure 9AIn the example shown, the right side 920 of lane 900 is further represented by a first grouping having polynomial segments 921 , 922 , and 923 and a second grouping having polynomial segments 924 , 925 , and 926 .

[0209] Returning to the target track of the sparse map 800, Figure 9B 3D polynomials representing target trajectories for a vehicle traveling along a particular road segment are shown. The target trajectory represents not only the XY path that the host vehicle should travel along the particular road segment, but also the elevation changes that the host vehicle will experience while traveling along that road segment. Thus, each target trajectory in the sparse map 800 can be represented by one or more 3D polynomials, such as Figure 9B The three-dimensional polynomial 950 is shown. The sparse map 800 may include a plurality of trajectories (e.g., millions or billions or more to represent the trajectories of vehicles along various road segments of the world's roadways). In some embodiments, each target trajectory may correspond to a spline connecting the three-dimensional polynomial segments.

[0210] Regarding the data footprint of the polynomial curves stored in the sparse map 800, in some embodiments, each cubic polynomial can be represented by four parameters, each requiring four bytes of data. A suitable representation can be obtained for a cubic polynomial, requiring approximately 192 bytes of data per 100 meters. This translates to a data usage / transmission requirement of approximately 200 kB per hour for a host vehicle traveling approximately 100 km / hr.

[0211] The sparse map 800 can use a combination of geometric feature descriptors and metadata to describe the lane network. The geometric features can be described by polynomials or splines as described above. The metadata can describe the number of lanes, special features (such as carpool lanes), and possibly other sparse labels. The total footprint of such indicators may be negligible.

[0212] Thus, a sparse map according to an embodiment of the present disclosure may include at least one line representation of a road surface feature extending along a road segment, each line representation representing a path along the road segment that substantially corresponds to the road surface feature. In some embodiments, as discussed above, the at least one line representation of a road surface feature may include a spline, a polynomial representation, or a curve. Furthermore, 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," road surface features may be identified by image analysis of multiple images acquired as one or more vehicles traverse a road segment.

[0213] As previously indicated, the sparse map 800 may include a plurality of predetermined landmarks associated with road segments. Instead of storing an actual image of the landmark and relying on image recognition analysis based on captured and stored images, each landmark in the sparse map 800 may be represented and identified using less data than would be required for a stored actual image. The data representing the landmark may still include sufficient information to describe or identify the landmark along the road. Storing data describing the characteristics of the landmark rather than the actual image of the landmark may reduce the size of the sparse map 800.

[0214] Figure 10 Examples of the types of landmarks that can be represented in the sparse map 800 are shown. Landmarks can include any visible and identifiable objects along a road segment. Landmarks can be selected so that they are fixed and do not change frequently with respect to their location and / or content. When a vehicle traverses a particular road segment, the landmarks included in the sparse map 800 can be useful in determining the location of the vehicle 200 relative to the target trajectory. Examples of landmarks can include traffic signs, directional signs, general signs (e.g., rectangular signs), roadside fixtures (e.g., lampposts, reflectors, etc.), and any other suitable categories. In some embodiments, lane markings on the road can also be included in the sparse map 800 as landmarks.

[0215] Figure 10 Examples of landmarks shown include traffic signs, directional signs, roadside fixtures, 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 that include one or more arrows indicating one or more directions to different locations. For example, directional signs may include highway signs 1025 with arrows directing vehicles to different roads or locations, exit signs 1030 with arrows directing vehicles off a road, and the like. Thus, at least one of the plurality of landmarks may include a road sign.

[0216] General signs may not be related to traffic. For example, general signs may include billboards used for advertising, or welcome signs adjacent to a border between two countries, states, counties, cities, or towns. Figure 10 A general sign 1040 ("Joe's Restaurant") is shown. Although the general sign 1040 may have a rectangular shape, such as Figure 10 As shown, however, generally the logo 1040 may have other shapes, such as square, circle, triangle, etc.

[0217] Landmarks may also include roadside fixtures. Roadside fixtures may be objects that are not signs and may not be related to traffic or directions. For example, roadside fixtures may include lampposts (e.g., lamppost 1035), power poles, traffic light poles, etc.

[0218] Landmarks may also include beacons specifically designed for use with autonomous vehicle navigation systems. For example, such beacons may include independent structures placed at predetermined intervals to assist in navigating the host vehicle. Such beacons may also include visual / graphic information added to existing road signs (e.g., icons, symbols, barcodes, etc.) that can be identified or recognized by vehicles traveling along the 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 identification and / or landmark location information that the host vehicle can use to determine its position along the target trajectory.

[0219] In some embodiments, the landmarks included in the sparse map 800 can be represented by data objects of a predetermined size. The data representing the landmarks can include any suitable parameters for identifying a specific landmark. For example, in some embodiments, the landmarks stored in the sparse map 800 can include parameters such as the physical size of the landmark (e.g., to support estimation of the distance to the landmark based on a known size / scale), the distance to the previous landmark, the lateral offset, the altitude, the type code (e.g., the landmark type - what type of direction sign, traffic sign, etc.), GPS coordinates (e.g., to support global positioning), and any other suitable parameters. Each parameter can be associated with a data size. For example, 8 bytes of data can be used to store the landmark size. The distance to the previous landmark, the lateral offset, and the altitude can be specified using 12 bytes of data. The type code associated with a landmark such as a direction sign or traffic sign can require approximately 2 bytes of data. For general landmarks, 50 bytes of data storage can be used to store an image signature that can identify a general landmark. The GPS location of a landmark can be associated with 16 bytes of data storage. These data sizes for each parameter are merely examples, and other data sizes can also be used. Representing landmarks in the sparse map 800 in this manner can provide a lean solution for efficiently representing landmarks in a database. In some embodiments, objects may be referred to as standard semantic objects or non-standard semantic objects. Standard semantic objects may include any kind of object for which a standardized set of characteristics exists (e.g., speed limit signs, warning signs, directional signs, traffic lights, etc. with known dimensions or other characteristics). Non-standard semantic objects may include any object that is not associated with a standardized set of characteristics (e.g., general advertising signs, signs identifying commercial establishments, potholes, trees, etc., which may have variable dimensions). Each non-standard semantic object can be represented using 38 bytes of data (e.g., 8 bytes for size; 12 bytes for distance, lateral offset, and altitude to the previous landmark; 2 bytes for type code; and 16 bytes for location coordinates). Standard semantic objects can be represented using even less data because the mapping server may not need size information to fully represent the objects in the sparse map.

[0220] The sparse map 800 may use a tag system to represent landmark types. In some cases, each traffic sign or directional sign may be associated with its own tag, which is stored in the database as part of the landmark identification. For example, the database may include nearly 1,000 different tags to represent various traffic signs and nearly 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. In some embodiments, a common landmark may be represented using less than about 100 bytes (e.g., about 86 bytes, including size of 8 bytes; 12 bytes for the distance, lateral offset, and altitude to the previous landmark; 50 bytes for the image signature; and 16 bytes for the GPS coordinates).

[0221] Thus, for semantic road signs that do not require image signatures, even at a relatively high landmark density of approximately 1 per 50m, the data density impact on the sparse map 800 may be close to 760 bytes per kilometer (e.g., 20 landmarks per km x 38 bytes per landmark = 760 bytes). Even for generic signs that include image signature components, the data density impact is approximately 1.72kB per km (e.g., 20 landmarks per km x 86 bytes per landmark = 1,720 bytes). For semantic road signs, this equates to approximately 76kB of data usage per hour for a vehicle traveling at 100 km / hr. For generic signs, this equates to approximately 170kB per hour for a vehicle traveling at 100 km / hr. It should be noted that in some environments (e.g., urban environments), the density of detected objects available for inclusion in the sparse map may be much higher (perhaps more than one per meter). In some embodiments, a roughly rectangular object (such as a rectangular sign) may be represented in the sparse map 800 by no more than 100 bytes of data. The representation of a generally rectangular object (e.g., general sign 1040) in the sparse map 800 may include a compressed image signature or image hash (e.g., compressed image signature 1045) associated with the generally rectangular object. This compressed image signature / image hash may be determined using any suitable image hashing algorithm and may be used, for example, to help identify the general sign as a recognized landmark. Such a compressed 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 to perform comparative image analysis on the actual images in order to identify landmarks.

[0222] refer to Figure 10, the sparse map 800 may include or store a compressed image signature 1045 associated with the generic sign 1040, rather than an actual image of the generic sign 1040. For example, after an image capture device (e.g., image capture device 122, 124, or 126) captures an image of the generic sign 1040, a processor (e.g., image processor 190 or any other processor that can process images onboard or remotely located relative to the host vehicle) may perform image analysis to extract / create the compressed image signature 1045, which includes a unique signature or pattern associated with the generic sign 1040. In one embodiment, the compressed image signature 1045 may include a shape, a color pattern, a brightness pattern, or any other feature that can be extracted from the image of the generic sign 1040 to describe the generic sign 1040.

[0223] For example, in Figure 10 , the circles, triangles, and stars shown in the compressed image signature 1045 may represent areas of different colors. The patterns represented by the circles, triangles, and stars may be stored in the sparse map 800, for example, within the 50 bytes designated as comprising the image signature. It is worth noting that the circles, triangles, and stars are not necessarily meant to indicate that such shapes are stored as part of the image signature. Instead, these shapes are meant to conceptually represent recognizable areas with discernible color differences, text areas, graphic shapes, or other variations of characteristics that may be associated with a generic sign. Such compressed image signatures may be used to identify landmarks in the form of generic signs. For example, the compressed image signatures may be used to perform a same-and-different analysis based on a comparison of the stored compressed image signatures with image data captured, for example, using a camera mounted on an autonomous vehicle.

[0224] Thus, multiple landmarks can be identified by analyzing multiple images acquired as one or more vehicles traverse a road segment. As explained below with respect to "crowdsourcing," in some embodiments, image analysis to identify multiple landmarks may include accepting a potential landmark when the ratio of images in which the landmark appears to images in which the landmark does not appear exceeds a threshold. Additionally, in some embodiments, image analysis to identify multiple landmarks may include rejecting a potential landmark when the ratio of images in which the landmark does not appear to images in which the landmark appears exceeds a threshold.

[0225] Returning to a target trajectory that the host vehicle can use to navigate a specific road segment, Figure 11AA polynomial representation of a trajectory captured during the process of establishing or maintaining a sparse map 800 is shown. The polynomial representation of a target trajectory included in the sparse map 800 may be determined based on two or more reconstructed trajectories of a vehicle's previous travel along the same road segment. In some embodiments, the polynomial representation of a target trajectory included in the sparse map 800 may be an aggregation of two or more reconstructed trajectories of a vehicle's previous travel along the same road segment. In some embodiments, the polynomial representation of a target trajectory included in the sparse map 800 may be an average of two or more reconstructed trajectories of a vehicle's previous travel 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 traveling along a road segment.

[0226] like Figure 11A As shown, a road segment 1100 may be traveled by multiple vehicles 200 at different times. Each vehicle 200 may collect data related to the path traveled by the vehicle along the road segment. The path traveled by a particular vehicle may be determined based on camera data, accelerometer information, velocity sensor information, and / or GPS information, among other potential sources. Such data may be used to reconstruct the trajectory of the vehicle traveling along the road segment, and based on these reconstructed trajectories, a target trajectory (or multiple target trajectories) may be determined for the particular road segment. Such target trajectories may represent a preferred path for the host vehicle as it travels along the road segment (e.g., as guided by an autonomous navigation system).

[0227] exist Figure 11A In the example shown, 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 trajectory, such as a three-dimensional polynomial trajectory. It should be noted that in some embodiments, any of the reconstructed trajectories may be assembled and loaded onto a vehicle traversing road segment 1100.

[0228] Additionally or alternatively, such reconstructed trajectories may be determined on the server side based on information received from vehicles traversing the road segment 1100. For example, in some embodiments, the vehicles 200 may transmit data related to their movement along the road segment 1100 (e.g., steering angle, heading, time, location, speed, sensed road geometry and / or sensed landmarks, etc.) to one or more servers. The server may reconstruct a trajectory for the vehicle 200 based on the received data. The server may also generate a target trajectory for guiding navigation of an autonomous vehicle that will travel along the same road segment 1100 at a later time based on the first trajectory 1101, the second trajectory 1102, and the third trajectory 1103. Although a target trajectory may be associated with a single previous travel of a road segment, in some embodiments, each target trajectory included in the sparse map 800 may be determined based on two or more reconstructed trajectories of vehicles traversing the same road segment. Figure 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 trajectory 1101, the second trajectory 1102, and the third trajectory 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.

[0229] At a mapping server, the server may receive actual trajectories for a particular road segment from multiple collection vehicles that traverse that road segment. To generate a target trajectory for each valid path along the road segment (e.g., each lane, each driving direction, each path through an intersection, etc.), the received actual trajectories may be aligned. The alignment process may include correlating the actual, collected trajectories with each other using the detected objects / features identified along the road segment and the collected locations of those detected objects / features. Once aligned, an average or "best fit" target trajectory, etc., may be determined for each available lane based on the aggregated, correlated / aligned actual trajectories.

[0230] Figure 11B and Figure 11C The concept of target trajectories associated with road segments present within the geographic area 1111 is further illustrated. Figure 11BAs shown, a first road segment 1120 within geographic area 1111 may include a multi-lane road including two lanes 1122 designated for vehicles traveling in a first direction and two additional lanes 1124 designated for vehicles traveling in a second direction opposite the first direction. Lanes 1122 and 1124 may be separated by a double yellow line 1123. Geographic area 1111 may also include a branch road segment 1130 intersecting road segment 1120. Road segment 1130 may include a two-lane road, with each lane designated for a different direction of travel. Geographic area 1111 may also include other road features such as a stop line 1132, a stop sign 1134, a speed limit sign 1136, and a hazard sign 1138.

[0231] like Figure 11C As shown, sparse map 800 may include local map 1140 that includes a road model for assisting autonomous navigation of a vehicle within geographic area 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 area 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. Furthermore, 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 the preferred path that the autonomous vehicle should follow when transitioning from lane 1120 (and specifically, relative to target trajectory 1141 associated with the rightmost lane of lane 1120) to road segment 1130 (and specifically, relative to target trajectory 1145 associated with the first side of road segment 1130). Similarly, target trajectory 1148 represents the preferred path that the autonomous vehicle should follow when transitioning from road segment 1130 (and specifically, relative to target trajectory 1146) to a portion of road segment 1124 (and specifically, as shown, relative to target trajectory 1143 associated with the left lane in lane 1124).

[0232] Sparse map 800 may also include representations of other road-related features associated with geographic area 1111. For example, sparse map 800 may also include representations of one or more landmarks identified in geographic area 1111. Such landmarks may include a first landmark 1150 associated with stop line 1132, a second landmark 1152 associated with stop sign 1134, a third landmark 1154 associated with a speed limit sign, and a fourth landmark 1156 associated with hazard sign 1138. Such landmarks may be used, for example, to assist the autonomous vehicle in determining its current position relative to any of the illustrated target tracks so that the vehicle can adjust its heading to match the direction of the target track at the determined location.

[0233] In some embodiments, the sparse map 800 may also include road signature profiles. Such road signature profiles may be associated with any discernible / measurable change in at least one parameter associated with a road. For example, in some cases, such profiles may be associated with changes in road surface information, such as changes in surface roughness of a particular road segment, changes in road width above a particular road segment, changes in the distance between dashed lines drawn along a particular road segment, changes in road curvature along a particular road segment, and the like. Figure 11D An example of a road signature profile 1160 is shown. While profile 1160 may represent any of the parameters mentioned above or other parameters, in one example, profile 1160 may represent a measure of road surface roughness, such as obtained, for example, by monitoring one or more sensors that provide outputs indicative of the amount of suspension displacement when a vehicle travels over a particular road segment.

[0234] Alternatively or concurrently, profile 1160 may represent variations in road width, as determined based on image data obtained via a camera mounted on a vehicle traveling along a particular road segment. For example, such profiles can be used to determine a particular location of an ego vehicle relative to a particular target trajectory. That is, as the ego vehicle traverses a road segment, it 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 predetermined profile that plots variations in parameters relative to position along the road segment, the measured and predetermined profiles can be used (e.g., by superimposing corresponding segments of the measured and predetermined profiles) to determine the current position along the road segment, and therefore relative to the target trajectory for the road segment.

[0235] In some embodiments, the sparse map 800 may include different trajectories based on different characteristics associated with users of the autonomous vehicle, environmental conditions, and / or other parameters related to driving. For example, in some embodiments, different trajectories may be generated based on different user preferences and / or profiles. A 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 there are toll roads on the route. The disclosed system may generate different sparse maps with different trajectories based on such different user preferences or profiles. As another example, some users may prefer to drive in a fast-moving lane, while other users may prefer to always maintain a position in the center lane.

[0236] Different trajectories can be generated and included in sparse map 800 based on different environmental conditions (such as day and night, snow, rain, fog, etc.). Sparse map 800 generated based on these different environmental conditions can be provided to autonomous vehicles driving under different environmental conditions. In some embodiments, a camera installed on the autonomous vehicle can detect environmental conditions and provide such information back to the server that generates and provides the sparse map. For example, the server can generate or update an already generated sparse map 800 to include trajectories that may be more suitable or safer for autonomous driving under the detected environmental conditions. As the autonomous vehicle travels along the road, the sparse map 800 can be updated dynamically based on the environmental conditions.

[0237] Other different parameters related to driving can also be used as a basis for generating different sparse maps and providing them to different autonomous vehicles. For example, when the autonomous vehicle is traveling at high speeds, turns may be tighter. Tracks associated with specific lanes rather than roads can be included in sparse map 800 so that the autonomous vehicle can maintain itself within a specific lane when the vehicle follows a specific track. When images captured by a camera mounted on the autonomous vehicle indicate that the vehicle has deviated from the lane (e.g., crossed a lane marking), an action can be triggered within the vehicle to bring the vehicle back to the designated lane according to the specific track.

[0238] Crowdsourcing sparse maps

[0239] The disclosed sparse maps can be efficiently (and passively) generated through the power of crowdsourcing. For example, any private or commercial vehicle equipped with a camera (e.g., a simple, low-resolution camera commonly included on today's vehicles as OEM equipment) and an appropriate image analysis processor can be used as a collection vehicle. No special equipment (e.g., high-resolution imaging and / or positioning systems) is required. Due to the disclosed crowdsourcing technology, the generated sparse maps can be extremely accurate and can include extremely fine position information (achieving navigation error limits of 10 cm or less) without requiring any specialized imaging or sensing equipment as input to the map generation process. Crowdsourcing also enables faster (and inexpensive) updates to the generated maps because new driving information is continuously available to the mapping server system from any road traversed by a private or commercial vehicle that is minimally equipped to also serve as a collection vehicle. No designated vehicle equipped with high-resolution imaging and mapping sensors is required. Therefore, the expenses associated with setting up such specialized vehicles can be avoided. Furthermore, updates to the currently disclosed sparse maps can be much faster than systems that rely on dedicated, specialized mapping vehicles (which, due to their expense and specialized equipment, are typically limited to fleets of specialized vehicles that are far fewer in number than the number of private or commercial vehicles already available for the disclosed collection techniques).

[0240] The disclosed sparse maps generated by crowdsourcing can be extremely accurate because they can be generated based on many inputs from multiple (tens, hundreds, millions, etc.) collection vehicles that have collected driving information along a particular road segment. For example, each collection vehicle driving along a particular road segment can record its actual trajectory and can determine position information relative to detected objects / features along the road segment. This information is transmitted from multiple collection vehicles to a server. The actual trajectories are aggregated to generate a refined target trajectory for each valid driving path along the road segment. In addition, the position information collected from multiple collection vehicles for each detected object / feature (semantic or non-semantic) along the road segment can also be aggregated. Therefore, the mapped position of each detected object / feature can constitute the average of hundreds, thousands, or millions of individually determined positions for each detected object / feature. Such techniques can produce extremely accurate mapped positions for detected objects / features.

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

[0242] The vehicle trajectory data that a vehicle may upload to a server may correspond to an actual reconstructed trajectory for the vehicle or may correspond to a recommended trajectory, which may be based on or related to the actual reconstructed trajectory of the vehicle, but may differ from the actual reconstructed trajectory. For example, a vehicle may modify its actual, reconstructed trajectory and submit (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 that may be used in building the sparse data map 800 may include information related to potential landmark candidates. For example, by crowdsourcing information, the disclosed systems and methods can identify potential landmarks in the environment and refine the landmark locations. The landmarks can be used by the autonomous vehicle's navigation system to determine and / or adjust the vehicle's position along a target trajectory.

[0244] The reconstructed trajectory that a vehicle may generate as the vehicle 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 the vehicle's motion using, for example, ego-motion estimates (e.g., three-dimensional translation and three-dimensional rotation of the camera, and therefore the body of the vehicle). The rotation and translation estimates may be determined based on analysis of images captured by one or more image capture devices in conjunction with information from other sensors or devices, such as inertial sensors and velocity sensors. For example, the inertial sensors may include accelerometers or other suitable sensors configured to measure changes in translation and / or rotation of the body of the vehicle. The vehicle may include a velocity sensor that measures the velocity of the vehicle.

[0245] In some embodiments, the camera's (and therefore the vehicle's) ego-motion can be estimated based on optical flow analysis of captured images. Optical flow analysis of an image sequence identifies the movement of pixels within the image sequence and determines the vehicle's motion based on the identified movement. The ego-motion can be integrated over time and along road segments to reconstruct a trajectory associated with the road segment that the vehicle has followed.

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

[0247] The geometric features of the reconstructed trajectory (and also the target trajectory) along the road segment can be represented by a curve in three-dimensional space, which can be a spline connecting three-dimensional polynomials. The reconstructed trajectory curve can 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 a few meters ahead of the vehicle's current location is identified in each frame or image. This location is the location to which the vehicle is expected to travel within a predetermined time period. This operation can be repeated frame by frame, and the vehicle can simultaneously calculate the camera's ego motion (rotation and translation). At each frame or image, a short-range model for the desired path is generated by the vehicle in a reference frame attached to the camera. The short-range models can be pieced together to obtain a three-dimensional model of the road in a certain coordinate system, which can be an arbitrary or predetermined coordinate system. The three-dimensional model of the road can then be fitted by a spline, which can include or connect one or more polynomials of appropriate order.

[0248] To derive a short-range road model for each frame, one or more detection modules can be used. For example, a bottom-up lane detection module can be used. This can be useful when drawing lane markings on a road. This module finds edges in the image and assembles them together to form lane markings. A second module can be used in conjunction with the bottom-up lane detection module. The second module is an end-to-end deep neural network that can be trained to predict the correct short-range path from the input image. In both modules, the road model can be detected in the image coordinate system and converted into a three-dimensional space that can be virtually attached to the camera.

[0249] Although the reconstructed trajectory modeling method may introduce an accumulation of errors due to the integration of ego motion over long periods of time, which may include a noise component, such errors may be insignificant because the generated model can provide sufficient accuracy for navigation on a local scale. In addition, it is possible to eliminate the integrated errors by using external information sources (such as satellite imagery or geodetic measurements). For example, the disclosed systems and methods can use a GNSS receiver to eliminate the accumulated errors. However, GNSS positioning signals may not always be available and accurate. The disclosed systems and methods can implement steering applications that are weakly dependent on the availability and accuracy of GNSS positioning. In such systems, the use of GNSS signals may be limited. For example, in some embodiments, the disclosed system may only use GNSS signals for database indexing purposes.

[0250] In some embodiments, the distance scale (e.g., local scale) relevant to autonomous vehicle navigation and steering applications can be approximately 50 meters, 100 meters, 200 meters, 300 meters, etc. Such distances can be used because the geometric road model is primarily used for two purposes: planning the trajectory ahead and localizing the vehicle on the road model. In some embodiments, when the control algorithm steers the vehicle based on 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 can use the model within a typical range of 40 meters ahead (or any other suitable distance ahead, such as 20 meters, 30 meters, 50 meters). According to a method called "tail alignment" described in more detail in another section, the localization task uses the road model within a typical range of 60 meters behind the car (or any other suitable distance, such as 50 meters, 100 meters, 150 meters, etc.). The disclosed systems and methods can generate a geometric model with sufficient accuracy within a specific range (such as 100 meters) so that the planned trajectory will not deviate from the lane center by more than, for example, 30 cm.

[0251] As explained above, a three-dimensional road model can be constructed by detecting short-range road segments and stitching them together. Stitching can be achieved by calculating a six-degree ego-motion model using video and / or images captured by a camera, data from inertial sensors reflecting the vehicle's motion, and the host vehicle's velocity signal. The accumulated error can be sufficiently small at certain local scales (such as approximately 100 meters). All of this can be accomplished in a single drive on a specific road segment.

[0252] In some embodiments, multiple drives can be used to average the resulting model and further improve its accuracy. The same car may travel the same route multiple times, or multiple cars may send their collected model data to a central server. In any case, a matching process can be performed to identify overlapping models and average them to generate a target trajectory. Once convergence criteria are met, the constructed model (e.g., including the target trajectory) can be used for steering. Subsequent drives can be used to further improve the model and to adapt to infrastructure changes.

[0253] If multiple vehicles are connected to a central server, sharing the driving experience (such as sensed data) between them becomes feasible. Each vehicle client can store a partial copy of a general road model, which may be relevant to its current location. A bidirectional update process can be performed between the vehicle and the server. The small footprint concept discussed above enables the disclosed system and method to perform bidirectional updates using very little bandwidth.

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

[0255] The vehicle may determine the distance from the vehicle to a landmark or the location associated with the landmark (e.g., any semantic or non-semantic object or feature along a road segment) 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 a suitable image analysis method (such as a scaling method and / or an optical flow method). As previously indicated, the location of the object / feature may include the 2D image location of one or more points associated with the object / feature (e.g., XY pixel locations in one or more captured images), or may include the 3D real-world location of one or more points (e.g., determined by structure from motion / optical flow techniques, lidar or radar information, etc.). In some embodiments, the disclosed systems and methods may be configured to determine the type or classification of a potential landmark. In the event that the vehicle determines that a potential landmark corresponds to a predetermined type or classification stored in a sparse map, it may be sufficient for the vehicle to transmit an indication of the type or classification of the landmark along with the location of the landmark to a server. The server may store such indication. At a later time, during navigation, the navigation vehicle may capture an image including representations of the landmarks, process the image (e.g., using a classifier) ​​and compare the resulting landmarks to confirm detection of the mapped landmarks and to use the mapped landmarks to position the navigation vehicle relative to the sparse map.

[0256] In some embodiments, multiple autonomous vehicles traveling on a road segment can communicate with a server. The vehicles (or clients) can generate curves describing their driving in an arbitrary coordinate system (e.g., by integrating ego motion). The vehicles can detect landmarks and locate them in the same frame. The vehicles can upload the curves and landmarks to the server. The server can collect data from the vehicles over multiple drives and generate a unified road model. For example, as described below with respect to Figure 19 As discussed, the server can 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 a sparse map to one or more vehicles. The server may continuously or periodically update the model as new data is received from the vehicles. For example, the server may process the new data to assess whether the data includes information that should trigger an update or creation of new data on the server. The server may distribute the updated model or the update to the vehicles for use in providing autonomous vehicle navigation.

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

[0259] The server can distribute the updated model (or updated portion of the model) to one or more vehicles traveling on the road segment associated with the update to the model. The server can also distribute the updated model to vehicles that are about to travel on the road segment, or whose planned trips include the road segment associated with the update to the model. For example, if an autonomous vehicle travels along another road segment before reaching the road segment associated with the update, the server can 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 public road segment). The server may use the landmarks to match curves and create an average road model based on the trajectories collected from multiple vehicles. The server may also calculate a road map and the most likely path for each node or intersection of the 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 attributes received from multiple vehicles traveling along a public road segment (such as the distance from one landmark to another landmark (e.g., the previous landmark along the road segment) measured by multiple vehicles) to determine arc length parameters and support positioning and speed calibration for each client vehicle along the path. The server may average the physical dimensions of the landmarks measured by multiple vehicles traveling along the public road segment and identifying 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 the lateral positions of the landmarks measured by multiple vehicles traveling along the public road segment and identifying the same landmark (e.g., from the lane in which the vehicle is traveling to the position in the landmark). The averaged lateral portion may be used to support lane allocation. The server may average the GPS coordinates of the landmarks measured by multiple vehicles traveling along the same road segment and identifying the same landmark. The averaged GPS coordinates of the landmarks may be used to support global localization or positioning of the landmarks in the road model.

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

[0263] In some embodiments, the server may analyze driver interventions during autonomous driving. The server may analyze data received from the vehicle at the time and location of the intervention and / or data received before the time of the intervention. The server may identify certain portions of the data that caused or were closely related to the intervention, such as data indicating a temporary lane closure or data indicating pedestrians 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] Figure 12 Schematic illustration of a system for generating sparse maps using crowdsourcing (and for distributing and navigating using the crowdsourced sparse maps). Figure 12 A road segment 1200 is shown that includes one or more lanes. Multiple vehicles 1205, 1210, 1215, 1220, and 1225 may travel on the road segment 1200 at the same time or at different times (although Figure 12 1200 at the same time). At least one of vehicles 1205, 1210, 1215, 1220, and 1225 may be an autonomous vehicle. To simplify this example, all vehicles 1205, 1210, 1215, 1220, and 1225 are assumed to be autonomous vehicles.

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

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

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

[0268] In some embodiments, the 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 in road segment 1200, the lane type (e.g., one-way lane, two-way lane, driving lane, passing lane, etc.), lane markings, lane width, etc. In some embodiments, the navigation information may include a lane assignment, such as which lane of a plurality of lanes the vehicle is traveling in. For example, the lane assignment may be associated with a numeric value "3" indicating that the vehicle is traveling in the third lane from the left or right. As another example, the lane assignment may be associated with a text value "Center Lane" indicating that the vehicle is traveling in the center lane.

[0269] Server 1230 may store navigation information on a non-transitory computer-readable medium (such as a hard drive, optical disk, 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 for public road segment 1200 based on navigation information received from multiple vehicles 1205, 1210, 1215, 1220, and 1225, and may store the model as part of the sparse map. Server 1230 may determine a track 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 on lanes of the road segment at different times. Server 1230 may generate an autonomous vehicle road navigation model, or a portion (e.g., an updated portion) of the model, based on the multiple tracks determined based on the crowdsourced navigation data. Server 1230 can transmit the model, or updated portion 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 traveling on the road segment at a later time, for use in updating an existing autonomous vehicle road navigation model provided in the vehicle's navigation system. The autonomous vehicle road navigation model can be used by the autonomous vehicle when autonomously navigating along public road segment 1200.

[0270] As explained above, the autonomous vehicle road navigation model may include a sparse map (e.g., Figure 8 sparse map 800). Sparse map 800 may include a sparse record of data related to road geometry and / or landmarks along the road, which may provide sufficient information for guiding autonomous navigation of an autonomous vehicle without requiring excessive data storage. In some embodiments, an autonomous vehicle road navigation model may be stored separately from sparse map 800, and map data from sparse map 800 may be used when the model is executed 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 for guiding autonomous navigation of autonomous vehicles 1205, 1210, 1215, 1220, and 1225 or other vehicles that later travel along road segment 1200. For example, when the autonomous vehicle road navigation model is executed by a processor included in a navigation system of vehicle 1205, the model may enable the processor to compare a trajectory determined based on navigation information received from vehicle 1205 with a predetermined trajectory included in the sparse map 800 to verify and / or correct the current driving route of vehicle 1205.

[0271] In the autonomous vehicle road navigation model, the geometric features of the road features or target trajectory can be encoded by a curve in three-dimensional space. In one embodiment, the curve can be a three-dimensional spline including one or more connected three-dimensional polynomials. As will be understood by those skilled in the art, a spline can be a numerical function defined by a series of polynomials for fitting data. The spline for fitting the three-dimensional geometric feature data of the road may include a linear spline (first order), a quadratic spline (second order), a cubic spline (third order), or any other spline (other orders), or a combination thereof. The spline may include one or more three-dimensional polynomials of different orders connecting (e.g., fitting) the data points of the three-dimensional geometric feature data of the road. In some embodiments, the autonomous vehicle road navigation model may include a three-dimensional spline corresponding to the target trajectory along a public road segment (e.g., road segment 1200) or the lane of road segment 1200.

[0272] As explained 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 the road segment 1200. The landmark may be visible within the field of view of a camera (e.g., camera 122) mounted on each of the vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, camera 122 may capture an image of the landmark. A processor (e.g., processor 180, 190, or processing unit 110) disposed on vehicle 1205 may process the image of the landmark to extract identification information for the landmark. The landmark identification information, rather than the actual image of the landmark, may be stored in the sparse map 800. The landmark identification information may require much less storage space than the actual image. Other sensors or systems (e.g., a GPS system) may also provide certain identification information of the landmark (e.g., the location of the landmark). Landmarks may include at least one of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, 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 place), a landmark beacon, or a lamppost. 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 on a vehicle, such that when the vehicle passes by the device, the beacon and the location of the device (e.g., determined from the GPS location of the device) received by the vehicle can be used as a landmark to be included in the autonomous vehicle road navigation model and / or the sparse map 800.

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

[0274] The identification of a landmark may include the size of the landmark. A processor provided on a vehicle (e.g., 1205) may estimate the physical size of the landmark based on analysis of the image. Server 1230 may receive multiple estimates of the physical size of the same landmark from different vehicles passing through different routes. Server 1230 may average the different estimates to arrive at a physical size for 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 current speed of the vehicle and an expanded scale based on the position of the landmark appearing in the image relative to the expanded focus of the camera. For example, the distance to the landmark may be estimated by Z = V*dt*R / D, where V is the speed of the vehicle, R is the distance from the landmark to the expanded focus in the image at time t1, and D is the change in the distance of the landmark in the image from t1 to t2. dt represents (t2-t1). For example, the distance to a landmark can be estimated using Z = V * dt * R / D, where V is the vehicle's speed, R is the distance between the landmark and the expanded focus in the image, dt is the time interval, and D is the image displacement of the landmark along the epipolar line. Equivalent equations to the above equation, such as Z = V * ω / Δω, can be used to estimate the distance to the landmark. Here, V is the vehicle's speed, ω is the image length (e.g., the object's width), and Δω is the change in image length per unit time.

[0275] When the physical size of the landmark is known, the distance to the landmark can also be determined based on the following equation: Z = f*W / ω, where f is the focal length, W is the size of the landmark (e.g., height or width), and ω is the number of pixels when the landmark is out of the image. According to the above equation, the change in distance Z can be calculated using ΔZ = f*W*Δω / ω2 + f*ΔW / ω, where ΔW decays to zero by averaging, and where Δω is the number of pixels representing the accuracy of the bounding box in the image. The value of the estimated physical size of the landmark can be calculated by averaging multiple observations at the server side. The error in the resulting distance estimate can be very small. There are two sources of error that may appear when using the above formula, namely ΔW and Δω. Their contribution to the distance error is given by ΔZ = f*W*Δω / ω2 + f*ΔW / ω. However, ΔW decays to zero by averaging; therefore, ΔZ is determined by Δω (e.g., the inaccuracy of the bounding box in the image).

[0276] For landmarks of unknown size, the distance to the landmark can be estimated by tracking feature points on the landmark between consecutive frames. For example, certain features appearing 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 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] Figure 13 An example autonomous vehicle road navigation model represented by a plurality of three-dimensional splines 1301 , 1302 , and 1303 is shown. Figure 13 Curves 1301, 1302, and 1303 are shown for illustrative purposes only. Each spline may include one or more three-dimensional polynomials connecting a plurality of data points 1310. Each polynomial may be a first-order polynomial, a second-order polynomial, a third-order polynomial, or any combination of polynomials of varying 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 a landmark (e.g., the landmark's size, location, and identification information) and / or a road signature profile (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 a landmark, while other data points may be associated with data related to a road signature profile.

[0278] Figure 14 Raw location data 1410 (e.g., GPS data) received from five separate drives is shown. A drive can be separated from another drive if it is traversed by separate vehicles at the same time, by the same vehicle at separate times, or by separate vehicles at separate times. To account for errors in location data 1410 and for different locations of vehicles in the same lane (e.g., one vehicle may be driving closer to the left side of the lane than another), server 1230 may use one or more statistical techniques to generate a map skeleton 1420 to determine whether variations in raw location data 1410 represent actual deviations or statistical errors. Each path within skeleton 1420 can be linked back to the raw data 1410 that formed the path. For example, the path between A and B within skeleton 1420 is linked to raw data 1410 from drives 2, 3, 4, and 5, but not from drive 1. Skeleton 1420 may not be detailed enough to be used for navigating a vehicle (e.g., because, unlike the spline described above, it combines drives from multiple lanes on the same road), but it can provide useful topological information and can be used to define intersections.

[0279] Figure 15 An example of additional detail that can be generated for a sparse map within a segment of a map skeleton (e.g., segment A to B within skeleton 1420) is shown. Figure 15As depicted, data (e.g., ego-motion data, road marking data, etc.) can be shown as a function of position S (or S1 or S2) along the driving. Server 1230 can identify landmarks for the sparse map by identifying unique matches between landmarks 1501, 1503, and 1505 of driving 1510 and landmarks 1507 and 1509 of driving 1520. Such a matching algorithm can result in the identification of landmarks 1511, 1513, and 1515. However, those skilled in the art will recognize that other matching algorithms can be used. For example, probabilistic optimization can be used instead of or in conjunction with unique matching. Server 1230 can align the driving longitudinally to align the matching landmarks. For example, server 1230 can select a driving (e.g., driving 1520) as a reference driving and then transform and / or elastically stretch the other driving (e.g., driving 1510) for alignment.

[0280] Figure 16 An example of aligned landmark data for use in a sparse map is shown. Figure 16 In the example shown in FIG. 1 , landmark 1610 includes a road sign. Figure 16 The example further depicts data from multiple drivers 1601, 1603, 1605, 1607, 1609, 1611, and 1613. Figure 16 In the example of , data from drive 1613 consists of a "ghost" landmark, and server 1230 may identify the landmark as such because none of drives 1601, 1603, 1605, 1607, 1609, and 1611 include identifications of landmarks in the vicinity of the landmark identified in drive 1613. Accordingly, server 1230 may accept a potential landmark when the ratio of images in which the landmark appears to images in which the landmark does not appear exceeds a threshold, and / or may reject a potential landmark when the ratio of images in which the landmark does not appear to images in which the landmark appears exceeds a threshold.

[0281] Figure 17 A system 1700 is depicted for generating driving data that can be used to crowdsource sparse maps. Figure 17 As depicted, system 1700 may include camera 1701 and positioning device 1703 (e.g., a GPS locator). Camera 1701 and positioning device 1703 may be mounted on a vehicle (e.g., one of vehicles 1205, 1210, 1215, 1220, and 1225). Camera 1701 may generate a plurality of types of data, such as ego-motion data, traffic sign data, road data, etc. The camera data and location data may be segmented into driving segments 1705. For example, driving segments 1705 may each include camera data and location data from a driving distance of less than 1 km.

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

[0283] System 1700 also includes a server (e.g., server 1230). Server 1230 can receive driving segments 1705 from the vehicle and reassemble the driving segments 1705 into a single driving 1707. Such an arrangement can reduce bandwidth requirements when transmitting data between the vehicle and the server, while also allowing the server to store data related to the entire driving.

[0284] Figure 18 Describes a method further configured to crowdsource sparse maps Figure 17 System 1700. As in Figure 17 In FIG, system 1700 includes a vehicle 1810 and a positioning device (e.g., a GPS locator) that uses, for example, a camera (which generates, for example, ego-motion data, traffic sign data, road data, etc.) to capture driving data. Figure 17 In the example, the vehicle 1810 divides the collected data into driving segments (in Figure 18 The server 1230 then receives the driving segments and reconstructs the driving from the received segments (in Figure 18 is depicted as “driving 1”).

[0285] As in Figure 18 As further depicted, system 1700 also receives data from additional vehicles. For example, vehicle 1820 also uses, for example, a camera (which generates, for example, ego-motion data, traffic sign data, road data, etc.) and a positioning device (e.g., a GPS locator) to capture driving data. Similar to vehicle 1810, vehicle 1820 segments the collected data into driving segments (in Figure 18 2”, “DS2 2”, “DSN 2”). The server 1230 then receives the driving segments and reconstructs the driving from the received segments (in Figure 18 Any number of additional vehicles may be used. For example, Figure 18 Also included is "Sedan N", which captures driving data, segments it into driving segments (in Figure 18and sends it to the server 1230 for reconstruction into a driving (depicted as "DS1 N", "DS2 N", "DSN N") Figure 18 depicted as "Driving N").

[0286] like Figure 18 As depicted, server 1230 can construct a sparse map (depicted as “Map”) using reconstructed drives (e.g., “Drive 1,” “Drive 2,” and “Drive N”) collected from multiple vehicles (e.g., “Car 1” (also labeled as vehicle 1810), “Car 2” (also labeled as vehicle 1820), and “Car N”).

[0287] Figure 19 FIG19 is a flow chart illustrating an example process 1900 for generating a sparse map for autonomous vehicle navigation along a road segment. The process 1900 may be performed by one or more processing devices included in the server 1230.

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

[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 plurality of images (step 1910). Each line representation may represent a path along the road segment substantially corresponding to the road surface feature. For example, server 1230 may analyze the environmental imagery received from camera 122 to identify road edges or lane markings and determine a driving trajectory 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 the driving trajectory of vehicle 1205 based on the camera ego-motion (e.g., three-dimensional translation and / or three-dimensional rotational motion) received in step 1905.

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

[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 traveling along the road segment, and process 1900 may include clustering, by server 1230, vehicle trajectories associated with a plurality of 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, a plurality of trajectories associated with vehicles traveling on the road segment into a plurality of 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. By way of further example, process 1900 may include aligning the data received in step 1905. As described above, other processes or steps performed by server 1230 may also be included in process 1900.

[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 present data in world coordinates. For example, longitude and latitude coordinates on the surface of the earth may be used. In order 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 locate the vehicle on the map and to find a rotational transformation between the subject reference frame and the world reference frame (e.g., north, east, and down). Once the subject reference frame is aligned with the map reference frame, the desired route can be expressed in the subject reference frame and steering commands can be calculated or generated.

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

[0294] Figure 20 A block diagram of server 1230 is shown. Server 1230 may include a communication unit 2005, which may include both hardware components (e.g., communication control circuitry, switches, and antennas) and software components (e.g., communication protocols, computer code). For example, communication unit 2005 may include at least one network interface. Server 1230 may communicate with vehicles 1205, 1210, 1215, 1220, and 1225 via communication unit 2005. For example, server 1230 may receive navigation information transmitted from vehicles 1205, 1210, 1215, 1220, and 1225 via communication unit 2005. Server 1230 may distribute autonomous vehicle road navigation models to one or more autonomous vehicles via communication unit 2005.

[0295] The server 1230 may include at least one non-transitory storage medium 2010, such as a hard drive, an optical disk, a magnetic tape, etc. The storage device 1410 may be configured to store data, such as navigation information received from the vehicles 1205, 1210, 1215, 1220, and 1225 and / or an autonomous vehicle road navigation model generated by the server 1230 based on the navigation information. The storage device 2010 may be configured to store any other information, such as a sparse map (e.g., as described above with respect to Figure 8 The sparse map 800 discussed).

[0296] In addition to or in place 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 a non-transitory memory, such as flash memory, random access memory, etc. Memory 2015 may be configured to store data, such as computer code or instructions executable by a processor (e.g., processor 220), map data (e.g., data of sparse map 800), autonomous vehicle road navigation models, 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 code 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 vehicle traveling on road segment 1200 at a later time). Processing device 2020 may be similar to or different from processors 180, 190, or processing unit 110.

[0298] Figure 21 A block diagram of a memory 2015 is shown that can store computer code or instructions for performing one or more operations for generating a road navigation model for use in autonomous vehicle navigation. Figure 21 As shown, the memory 2015 may store one or more modules for performing operations for processing vehicle navigation information. For example, the memory 2015 may include a model generation module 2105 and a model distribution module 2110. The processor 2020 may execute instructions stored in any of the modules 2105 and 2110 included in the memory 2015.

[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 for a public road segment (e.g., road segment 1200) based on navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225. For example, when generating the autonomous vehicle road navigation model, the processor 2020 may cluster vehicle trajectories along the public road segment 1200 into different clusters. The processor 2020 may determine a target trajectory along the public road segment 1200 based on the clustered vehicle trajectories for each of the different clusters. Such operations may include finding a mean or average trajectory of the clustered vehicle trajectories within 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 public 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, which are provided to the autonomous vehicle for autonomous navigation. Target trajectories may be received from multiple vehicles or generated based on actual trajectories received from multiple vehicles or recommended trajectories (actual trajectories with some modifications). Target trajectories included in the road model or sparse map may be continuously updated (e.g., averaged) using new trajectories received from other vehicles.

[0301] Vehicles traveling on a road segment can collect data through various sensors. This data may include landmarks, road signature profiles, vehicle motion (e.g., accelerometer data, speed data), and vehicle location (e.g., GPS data). The actual trajectory itself can be reconstructed, or the data can be transmitted to a server, which will reconstruct the actual trajectory for the vehicle. In some embodiments, the vehicle can transmit data related to the trajectory (e.g., curves in an arbitrary reference frame), landmark data, and lane assignments along the travel path to server 1230. Various vehicles traveling along the same road segment in multiple drives may have different trajectories. Server 1230 can identify the route or trajectory associated with each lane from the trajectories received from the vehicles through a clustering process.

[0302] Figure 22A process for clustering vehicle trajectories associated with vehicles 1205, 1210, 1215, 1220, and 1225 for determining a target trajectory for a common road segment (e.g., road segment 1200) is shown. The target trajectory or multiple target trajectories determined from the clustering process may be included in the 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 landmarks, road geometry, and vehicle motion information received from vehicles 1205, 1210, 1215, 1220, and 1225. To generate the autonomous vehicle road navigation model, server 1230 may cluster vehicle trajectories 1600 into multiple clusters 2205, 2210, 2215, 2220, 2225, and 2230, as shown in FIG. Figure 22 shown.

[0303] Various criteria can be used to cluster. In some embodiments, all of the drives in a cluster may be similar with respect to their absolute heading along road segment 1200. The absolute heading may be obtained from GPS signals received by vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, the absolute heading may be obtained using dead reckoning. As will be appreciated by those skilled in the art, dead reckoning can be used to determine the current position and, therefore, the heading of vehicles 1205, 1210, 1215, 1220, and 1225 by using previously determined positions, estimated speeds, and the like. Trajectories clustered by absolute heading may be useful for identifying routes along a roadway.

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

[0305] In each cluster 2205, 2210, 2215, 2220, 2225 and 2230, the trajectories can be averaged to obtain a target trajectory associated with a particular cluster. For example, trajectories from multiple drives associated with the same lane cluster can be averaged. The averaged trajectory can be a target trajectory associated with a particular lane. In order to average the cluster of trajectories, the server 1230 can select a reference frame for an arbitrary trajectory C0. For all other trajectories (C1, ..., Cn), the server 1230 can find a rigid transformation that maps Ci to C0, where i = 1, 2, ..., n, where n is a positive integer corresponding to the total number of trajectories included in the cluster. The server 1230 can calculate a mean curve or trajectory in the C0 reference frame.

[0306] In some embodiments, landmarks can define arc lengths that match between different drives, which can be used to align the trajectory with the lane. In some embodiments, lane markings before and after the intersection can be used to align the trajectory with the lane.

[0307] To assemble lanes from the trajectory, server 1230 may select a reference frame for any lane. Server 1230 may map partially overlapping lanes to the selected reference frame. Server 1230 may continue mapping until all lanes are in the same reference frame. Adjacent lanes may be aligned as if they were the same lane, and then they may shift laterally.

[0308] Landmarks identified along a road segment can be mapped to a common reference frame, first at the lane level and then at the intersection level. For example, the same landmark may be identified multiple times by multiple vehicles across multiple drives. The data received for the same landmark in different drives may vary slightly. This data can be averaged and mapped to a common reference frame, such as the C0 reference frame. Additionally or alternatively, the variance of the data received for the same landmark across multiple drives can be calculated.

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

[0310] For the localization of an autonomous vehicle, the disclosed systems and methods may use an extended Kalman filter. The location of the vehicle may be determined based on three-dimensional position data and / or three-dimensional orientation data, by predicting a future location ahead of the vehicle's current location through integration of ego motion. The vehicle's localization may be corrected or adjusted by observing images of landmarks. For example, when a vehicle detects a landmark within an image captured by a camera, the landmark may be compared with known landmarks stored in a road model or sparse map 800. The known landmark may have a known location (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 location along the target trajectory may be adjusted based on the distance to the landmark and the known location of the landmark (stored in the road model or sparse map 800). The position / location data of the landmark stored in the road model and / or sparse map 800 (e.g., an average from multiple drives) may be assumed to be accurate.

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

[0312] In some embodiments, poles along the road (such as lampposts and utility or cable poles) can be used as landmarks for locating the vehicle. Other landmarks such as traffic signs, traffic lights, arrows on the road, stop lines, and static features or signatures of objects along the road segment can also be used as landmarks for locating the vehicle. When using poles for positioning, the x-observation value of the pole (i.e., the viewing angle from the vehicle) can be used instead of the y-observation value (i.e., the distance to the pole) because the base of the pole may be obscured and sometimes they are not in the plane of the road.

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

[0314] Navigation system 2300 may include a communication unit 2305 configured to communicate with server 1230 via communication path 1235. Navigation system 2300 may also include a GPS unit 2310 configured to receive and process GPS signals. Navigation system 2300 may further include at least one processor 2315 configured to process data such as GPS signals, map data from sparse map 800 (which may be stored on a storage device configured to be mounted on vehicle 1205 and / or received from server 1230), geometric features sensed by road profile sensor 2330, images captured by camera 122, and / or autonomous vehicle road navigation models received from server 1230. 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, road profile sensor 2330 may include a device that measures the movement of the vehicle 2305's suspension to obtain a road roughness profile. In some embodiments, road profile sensor 2330 may include a radar sensor to measure the distance from vehicle 1205 to the side of the road (e.g., an obstacle on the side of the road), thereby measuring the width of the road. In some embodiments, road profile sensor 2330 may include a device configured to measure the upper and lower elevations of the road. In some embodiments, road profile sensor 2330 may include a device configured to measure the curvature of the road. For example, a camera (e.g., camera 122 or another camera) may be used to capture images of the road showing the curvature of the road. Vehicle 1205 may use such images to detect the curvature of the road.

[0315] At least one processor 2315 may be programmed to receive at least one environmental image associated with vehicle 1205 from camera 122. At least one processor 2315 may analyze the at least one environmental image to determine navigation information related to vehicle 1205. The navigation information may include a trajectory associated with vehicle 1205's travel along road segment 1200. At least one processor 2315 may determine the trajectory based on movement of camera 122 (and therefore the vehicle), such as three-dimensional translation and three-dimensional rotation. In some embodiments, at least one processor 2315 may determine the translation and rotation of camera 122 based on analysis of multiple images captured by camera 122. In some embodiments, the navigation information may include lane assignment information (e.g., the lane in which vehicle 1205 is traveling along road segment 1200). The 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 back to vehicle 1205 for use in providing autonomous navigation guidance for vehicle 1205.

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

[0317] At least one processor 2315 may be configured to communicate with various sensors and components included in the vehicle 1205, including the communication unit 2305, the GPS unit 2310, 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 via 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 the data or information collected by the sensors or components to the server 1230.

[0318] In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 can communicate with each other and share navigation information with each other, allowing at least one of vehicles 1205, 1210, 1215, 1220, and 1225 to generate an autonomous vehicle road navigation model using crowdsourcing, for example, based on information shared by other vehicles. In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 can share navigation information with each other, and each vehicle can update its own autonomous vehicle road navigation model located within the vehicle. In some embodiments, at least one of vehicles 1205, 1210, 1215, 1220, and 1225 (e.g., vehicle 1205) can serve as a hub vehicle. At least one processor 2315 of the hub vehicle (e.g., vehicle 1205) can perform some or all of the functions performed by server 1230. For example, at least one processor 2315 of the hub vehicle can communicate with other vehicles and receive navigation information from them. 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 for use in providing autonomous navigation guidance.

[0319] Navigation based on sparse maps

[0320] As previously discussed, the autonomous vehicle road navigation model including the sparse map 800 may include multiple mapped lane markings and multiple mapped objects / features associated with road segments. As discussed in more detail below, these mapped lane markings, objects, and features may be used when the autonomous vehicle navigates. For example, in some embodiments, the mapped objects and features may be used to locate the host vehicle relative to the map (e.g., relative to the mapped target track). The mapped lane markings may be used to determine (e.g., as a check) the lateral position and / or orientation relative to the planned or target track. Utilizing this position information, the autonomous vehicle may be able to adjust the heading direction to match the direction of the target track at the determined location.

[0321] Vehicle 200 may be configured to detect lane markings in a given road segment. A road segment may include any markings on a road used to direct vehicular traffic on a roadway. For example, lane markings may be continuous or dashed lines that delineate the edges of a travel lane. Lane markings may also include double lines, such as double continuous lines, double dashed lines, or a combination of continuous and dashed lines, indicating, for example, whether travel in an adjacent lane is permitted. Lane markings may also include highway entrance and exit markings that indicate, for example, a deceleration lane for an exit ramp, or dot-dashed lines that indicate a lane is a turn-only lane or that a lane is ending. Markings may further indicate work zones, temporary lane changes, travel paths through intersections, medians, dedicated lanes (e.g., bike lanes, HOV lanes, etc.), or other miscellaneous markings (e.g., crosswalks, speed bumps, railroad crossings, stop lines, etc.).

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

[0323] 24A to 24D Example points and locations that can be detected by vehicle 200 to represent a particular lane marking are shown. Similar to the landmarks described above, vehicle 200 can use various image recognition algorithms or software to identify points and locations within a captured image. For example, vehicle 200 can identify a series of edge points, corner points, or various other points and locations associated with a particular lane marking. Figure 24A Continuous lane markings 2410 are shown that may be detected by vehicle 200. Lane markings 2410 may represent the outer edge of a roadway and are represented by a continuous white line. Figure 24A As shown, the vehicle 200 may be configured to detect a plurality of edge location points 2411 along the lane markings. The location points 2411 may be collected to represent the lane markings at any spacing sufficient to create mapped lane markings in the sparse map. For example, the lane markings may be represented by one point per meter of the detected edge, one point per five meters of the detected edge, or at other suitable spacings. In some embodiments, the spacing may be determined by other factors, such as, for example, the point based on which the vehicle 200 has the highest confidence rating for the location of the detected point, rather than at a set interval. Although Figure 24AWhile edge points are shown on the inner edge of lane marking 2410, points may be collected on the outer edge of the line or along both edges. Figure 24A A single line is shown in FIG, but similar edge points may be detected for two continuous lines. For example, point 2411 may be detected along the edge of one or more of the continuous lines.

[0324] Vehicle 200 may also represent lane markings differently depending on the type or shape of the lane markings. Figure 24B An exemplary dashed lane marking 2420 is shown that may be detected by the vehicle 200. Figure 24A By identifying edge points as in , the vehicle can detect a series of corner points 2421 representing the corners of the lane dash to define the complete boundary of the dash. Figure 24B Each corner of a given dash mark being located is shown, and the vehicle 200 can detect or upload a subset of the points shown in the figure. For example, the vehicle 200 can detect the front edge or front corner of a given dash mark, or can detect the two corner points closest to the interior of the lane. In addition, not every dash mark can be captured, for example, the vehicle 200 can capture and / or record points representing samples of dash marks (e.g., every other, every third, every fifth, etc.) or points of dash marks at predetermined intervals (e.g., every meter, every 5 meters, every 10 meters, etc.). For similar lane markings, such as markings indicating that a lane is an exit ramp, markings indicating the end of a particular lane, or various other lane markings that may have detectable corner points, corner points can also be detected. Corner points can also be detected for lane markings consisting of a double dashed line or a combination of a continuous line and a dashed line.

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

[0326] In some embodiments, vehicle 200 may identify points representing other features, such as a vertex between two intersecting lane markings. Figure 24D Example points representing the intersection between two lane markings 2460 and 2465 are shown. Vehicle 200 can calculate vertex 2466, which represents the intersection between the two lane markings. For example, one of lane markings 2460 or 2465 can represent a train crossing area or other intersection area in a road segment. Although lane markings 2460 and 2465 are shown as crossing each other perpendicularly, various other configurations can be detected. For example, lane markings 2460 and 2465 can cross at other angles, or one or both lane markings can terminate at vertex 2466. Similar techniques can also be applied to intersections between dashed lines or other types of lane markings. In addition to vertex 2466, various other points 2467 can also be detected to provide further information about the orientation of lane markings 2460 and 2465.

[0327] Vehicle 200 can associate real-world coordinates with each detected point of lane markings. For example, a location identifier can be generated, including the coordinates for each point, to be uploaded to a server for use in mapping lane markings. The location identifier can further include other identifying information about the point, including whether the point represents a corner, edge, center, etc. Vehicle 200 can therefore be configured to determine the real-world location of each point based on image analysis. For example, vehicle 200 can detect other features in the image, such as the various landmarks described above, to locate the real-world location of the lane markings. This can involve determining the location of the lane markings in the image relative to detected landmarks, or determining the vehicle's position based on detected landmarks and then determining the distance from the vehicle (or the vehicle's target trajectory) to the lane markings. If landmarks are unavailable, the location of the lane marking points can be determined relative to the vehicle's position determined based on dead reckoning. The real-world coordinates included in the location identifier can be expressed as absolute coordinates (e.g., latitude / longitude coordinates) or relative to other features, such as based on longitudinal position along the target trajectory and lateral distance from the target trajectory. The location identifiers can then be uploaded to a server for use in generating mapped lane markings in a navigation model, such as the sparse map 800. In some embodiments, the server can construct splines representing lane markings for the road segment. Alternatively, the vehicle 200 can generate the splines and upload them to the server for recording in the navigation model.

[0328] Figure 24E An exemplary navigation model or sparse map for a corresponding road segment including mapped lane markings is shown. The sparse map may include a target trajectory 2475 for the vehicle traveling along the road segment. As described above, the target trajectory 2475 may represent an ideal path for the vehicle to take when traveling the corresponding road segment, or may be located elsewhere on the road (e.g., the centerline of the road). The target trajectory 2475 may be calculated using various methods described above, such as by aggregating (e.g., a weighted combination) two or more reconstructed trajectories of vehicles traversing the same road segment.

[0329] In some embodiments, target trajectories can be generated equally for all vehicle types and for all roads, vehicles, and / or environmental conditions. However, in other embodiments, various other factors or variables may also be considered when generating the target trajectory. Different target trajectories can be generated for different types of vehicles (e.g., sedans, light trucks, and full trailers). For example, a target trajectory with a relatively tighter turning radius can be generated for a small sedan compared to a larger 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, brake conditions or estimated brake conditions, remaining fuel, 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 specific road segment (e.g., speed limit, frequency and size of turns, slope, etc.). In some embodiments, various user settings may also be used to determine the target trajectory, such as a set driving mode (e.g., desired driving aggressiveness, economy mode, etc.).

[0330] The sparse map may also include mapped lane markings 2470 and 2480 representing lane markings along the road segment. The mapped lane markings may be represented by a plurality of location identifiers 2471 and 2481. As described above, the location identifier may include the location in real-world coordinates of a point associated with the detected lane marking. Similar to the target trajectory in the model, the lane markings may also include elevation data and may be represented as curves in three-dimensional space. For example, the curve may be a spline connecting three-dimensional polynomials of appropriate order, which may be calculated based on the location identifier. The mapped lane markings may also include other information or metadata about the lane markings, such as an identifier of the type of lane marking (e.g., between two lanes with the same direction of travel, between two lanes with opposite directions of travel, at the edge of a roadway, etc.) and / or other characteristics of the lane markings (e.g., continuous, dashed, single line, double line, yellow, white, etc.). In some embodiments, the mapped lane markings may be continuously updated within the model, for example using crowdsourcing techniques. The same vehicle may upload location identifiers during multiple occasions while traveling the same road segment, or data may be selected from multiple vehicles (such as 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 vehicle and stored in the system. As the mapped lane markings are updated and refined, the updated road navigation model and / or sparse map may be distributed to multiple autonomous vehicles.

[0331] Generating mapped lane markings in a sparse map may also include detecting and / or reducing errors based on anomalies in the image or in the actual lane markings themselves. Figure 24F An exemplary anomaly 2495 associated with detecting lane markings 2490 is shown. Anomaly 2495 may appear in the image captured by vehicle 200, for example, from objects obstructing the camera's view of the lane markings, debris on the lens, etc. In some cases, the anomaly may be due to the lane markings themselves, which may be damaged or worn, or partially covered by dirt, debris, water, snow, or other materials on the road. Anomaly 2495 may cause vehicle 200 to detect an error point 2491. Sparse map 800 can provide the correct mapped lane markings and eliminate the error. In some embodiments, vehicle 200 may detect error point 2491, for example, by detecting anomaly 2495 in the image or by identifying the error based on lane marking points detected before and after the anomaly. Based on the detected anomaly, the vehicle may ignore point 2491 or adjust it to be consistent with other detected points. In other embodiments, the error may be corrected after the point has been uploaded, for example, by determining that the point is outside an expected threshold based on other points uploaded during the same trip or based on aggregation of data from previous trips along the same road segment.

[0332] The mapped lane markings in the navigation model and / or sparse map can also be used by an autonomous vehicle to navigate through the corresponding roadway. For example, a vehicle navigating along a target trajectory can periodically use the mapped lane markings in the sparse map to align itself with the target trajectory. As mentioned above, a vehicle can navigate between landmarks based on dead reckoning, where the vehicle uses sensors to determine its own motion and estimate its position relative to the target trajectory. Errors may accumulate over time, and the position determination of the vehicle relative to the target trajectory may become increasingly inaccurate. Therefore, the vehicle can use the lane markings (and their known locations) present in the sparse map 800 to reduce dead reckoning-induced errors in the position determination. In this way, the identified lane markings included in the sparse map 800 can serve as navigation anchors from which the vehicle's accurate position relative to the target trajectory can be determined.

[0333] Figure 25A An exemplary image 2500 of a vehicle's surroundings that can be used for navigation based on mapped lane markings is shown. Image 2500 may be captured, for example, by vehicle 200 using image capture devices 122 and 124 included in image acquisition unit 120. Image 2500 may include an image of at least one lane marking 2510, such as Figure 25A The image 2500 may also include one or more landmarks 2521, such as road signs, which are used for navigation as described above. Figure 25ASome elements shown in , such as elements 2511 , 2530 , and 2520 , which do not appear in captured image 2500 , but are detected and / or determined by vehicle 200 , are also shown for reference.

[0334] Use the above 24A to 24D and Figure 24F Using the various techniques described above, the vehicle can analyze image 2500 to identify lane markings 2510. Various points 2511 corresponding to features of lane markings in the image can be detected. For example, point 2511 can correspond to an edge of a lane marking, a corner of a lane marking, a midpoint of a lane marking, a vertex between two intersecting lane markings, or various other features or locations. Point 2511 can be detected as a location corresponding to a point stored in a navigation model received from a server. For example, if a sparse map containing points representing the centerlines of mapped lane markings is received, point 2511 can also be detected based on the centerlines of lane markings 2510.

[0335] The vehicle can also determine a longitudinal position, represented by element 2520, located along the target trajectory. Longitudinal position 2520 can be determined from image 2500, for example, by detecting landmarks 2521 within image 2500 and comparing the measured locations with known landmark locations stored in the road model or sparse map 800. The vehicle's location along the target trajectory can then be determined based on the distance to the landmarks and the known locations of the landmarks. Longitudinal position 2520 can also be determined from images other than those used to determine the locations of lane markings. For example, longitudinal position 2520 can be determined by detecting landmarks in images captured simultaneously or nearly simultaneously with image 2500 from other cameras within image acquisition unit 120. In some cases, the vehicle may not be near any landmarks or other reference points used to determine longitudinal position 2520. In such cases, the vehicle may navigate based on dead reckoning and, therefore, may use sensors to determine its ego-motion and estimate longitudinal position 2520 relative to the target trajectory. The vehicle can also determine distance 2530, which represents the actual distance between the vehicle and lane markings 2510 observed in the captured image. The camera angle, the speed of the vehicle, the width of the vehicle, or various other factors may be considered when determining the distance 2530.

[0336] Figure 25BA lateral positioning correction of a vehicle based on mapped lane markings in a road navigation model is shown. As described above, the vehicle 200 can use one or more images captured by the vehicle 200 to determine the distance 2530 between the vehicle 200 and the lane markings 2510. The vehicle 200 may also have access to a road navigation model, such as a sparse map 800, which may include mapped lane markings 2550 and a target trajectory 2555. The mapped lane markings 2550 may be modeled using the techniques described above (e.g., using crowdsourced location identifiers captured by multiple vehicles). The target trajectory 2555 may also be generated using the various techniques previously described. The vehicle 200 may also determine or estimate the longitudinal position 2520 along the target trajectory 2555, as described above with respect to Figure 25A The vehicle 200 can then determine an expected distance 2540 based on the lateral distance between the target trajectory 2555 and the mapped lane markings 2550 corresponding to the longitudinal position 2520. The lateral positioning of the vehicle 200 can be corrected or adjusted by comparing the actual distance 2530 measured using the captured image to the expected distance 2540 from the model.

[0337] Figure 25C and Figure 25D Illustrations are provided in association with another example for locating a host vehicle during navigation based on mapped landmarks / objects / features in a sparse map. Figure 25C A series of images captured from a vehicle navigating along road segment 2560 is conceptually represented. In this example, road segment 2560 comprises a straight section of a two-lane highway, marked by road edges 2561 and 2562 and a center lane marker 2563. As shown, the host vehicle is navigating along lane 2564 associated with a mapped target track 2565. Therefore, under ideal circumstances (and absent influencing factors such as the presence of a target vehicle or object in the roadway), the host vehicle should closely track mapped target track 2565 as it navigates along lane 2564 of road segment 2560. In practice, the host vehicle may experience drift while navigating along mapped target track 2565. For efficient and safe navigation, this drift should be maintained within acceptable limits (e.g., + / - 10 cm lateral displacement from target track 2565 or any other suitable threshold). To periodically account for drift and make any required course corrections to ensure that the host vehicle follows the target trajectory 2565, the disclosed navigation system may be able to use one or more mapped features / objects included in the sparse map to locate the host vehicle along the target trajectory 2565 (e.g., determine the lateral and longitudinal position of the host vehicle relative to the target trajectory 2565).

[0338] As a simple example, Figure 25CSpeed ​​limit sign 2566 is shown as it may appear in five different, sequentially captured images as the host vehicle travels along road segment 2560. For example, at a first time t0, sign 2566 may appear in a captured image near the horizon. As the host vehicle approaches sign 2566, in subsequently captured images at times t1, t2, t3, and t4, sign 2566 will appear at different 2D-Y pixel locations in the captured images. For example, in captured image space, sign 2566 will move downward and to the right along curve 2567 (e.g., a curve extending through the center of the sign in each of the five captured image frames). As the host vehicle approaches sign 2566, the sign will also appear to increase in size (i.e., it will occupy a larger number of pixels in subsequently captured images).

[0339] These changes in the image space representation of objects, such as sign 2566, can be used to determine the host vehicle's local position along the target trajectory. For example, as described in the present disclosure, any detectable object or feature (such as a semantic feature, such as sign 2566, or a detectable non-semantic feature) can be identified by one or more collecting vehicles of a previously traversed road segment (e.g., road segment 2560). A mapping server can collect collected driving information from multiple vehicles, aggregate and correlate this information, and generate a sparse map that includes, for example, a target trajectory 2565 for lanes 2564 of road segment 2560. The sparse map can also store the location of sign 2566 (along with type information, etc.). During navigation (e.g., before entering road segment 2560), a map tile containing the sparse map for road segment 2560 can be provided to the host vehicle. To navigate within lanes 2564 of road segment 2560, the host vehicle can follow the mapped target trajectory 2565.

[0340] The mapped representation of the marker 2566 can be used by the host vehicle to locate itself relative to the target track. For example, a camera on the host vehicle will capture an image 2570 of the host vehicle's environment, and the captured image 2570 may include an image representation of the marker 2566 having a specific size and a specific XY image location, such as Figure 25D2565 . This size and XY image location can be used to determine the host vehicle's position relative to target trajectory 2565. For example, based on a sparse map including a representation of marker 2566, the host vehicle's navigation processor can determine that, in response to the host vehicle traveling along target trajectory 2565, the representation of marker 2566 should appear in the captured image such that the center of marker 2566 will move (in image space) along line 2567. If a captured image (such as image 2570) shows the center (or other reference point) displaced from line 2567 (e.g., the expected image space trajectory), the host vehicle's navigation system can determine that it was not on target trajectory 2565 at the time the image was captured. However, based on the image, the navigation processor can determine appropriate navigation corrections to return the host vehicle to target trajectory 2565. For example, if analysis shows that the image location of marker 2566 is displaced in the image by a distance 2572 to the left of the expected image space location on line 2567, the navigation processor can cause the host vehicle's heading to change (e.g., by changing the steering angle of the wheels) to move the host vehicle to the left by a distance 2573. In this manner, each captured image can be used as part of a feedback loop process such that the difference between the observed image position of the landmark 2566 and the expected image trajectory 2567 can be minimized to ensure that the host vehicle continues to travel with little or no deviation along the target trajectory 2565. Of course, the more mapped objects available, the more frequently the described positioning techniques can be employed, which can reduce or eliminate drift-induced deviations from the target trajectory 2565.

[0341] The process described above can be used to detect the lateral orientation or displacement of the host vehicle relative to the target trajectory. The positioning of the host vehicle relative to the target trajectory 2565 can also include the determination of the longitudinal position of the target vehicle along the target trajectory. For example, the captured image 2570 includes a representation of the marker 2566 having a particular image size (e.g., a 2D XY pixel area). As the mapped marker 2566 travels through the image space along the line 2567 (e.g., as the size of the marker gradually increases, such as Figure 25C 2565 ).

[0342] Figure 25C and Figure 25DThis provides only one example of the disclosed positioning technique using a single mapped object and a single target track. In other examples, there may be more target tracks (e.g., one target track for each drivable lane of a multi-lane highway, city streets, complex intersections, etc.) and there may be more maps available for positioning. For example, a sparse map representing an urban environment may include many objects per meter that can be used for positioning.

[0343] Figure 26A Flowchart showing an exemplary process 2600A for mapping lane markings for use in autonomous vehicle navigation consistent with the disclosed embodiments. At step 2610, process 2600A may include receiving two or more location identifiers associated with detected lane markings. For example, step 2610 may be performed by server 1230 or one or more processors associated with the server. The location identifier may include the location of a point associated with the detected lane marking in real-world coordinates, as described above with respect to Figure 24E As described. In some embodiments, the location identifier may also include other data, such as additional information about a road segment or lane marking. Additional data, such as accelerometer data, velocity data, landmark data, road geometry or contour data, vehicle positioning data, ego-motion data, or various other forms of data described above, may also be received during step 2610. The location identifier may be generated by a vehicle, such as vehicles 1205, 1210, 1215, 1220, and 1225, based on images captured by the vehicle. For example, the identifier may be determined based on acquiring 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 position of the detected lane markings relative to a location associated with the host vehicle. As described above, lane markings may include a variety of different marking types, and the location identifier may correspond to a variety of points relative to the lane markings. For example, if the detected lane marking is part of a dashed line marking a lane boundary, the point may correspond to a detected corner of the lane marking. In the case where the detected lane marking is part of a continuous line marking the lane boundary, the point may correspond to a detected edge of the lane marking, with various spacings as described above. In some embodiments, the point may correspond to the centerline of the detected lane marking, such as Figure 24C As shown, or may correspond to a vertex between two intersecting lane markings and at least two other points associated with the intersecting lane markings, such as Figure 24D shown.

[0344] At 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 where the lane markings were detected.

[0345] At step 2614, process 2600A may include updating the autonomous vehicle road navigation model relative to the corresponding road segment based on the two or more location identifiers associated with the detected lane markings. For example, the autonomous road navigation model may be the sparse map 800, and server 1230 may update the sparse map to include or adjust the mapped lane markings in the model. Server 1230 may update the sparse map based on the above description of the lane markings. Figure 24E In some embodiments, updating the autonomous vehicle road navigation model may include storing one or more indicators of the positions of detected lane markings in real-world coordinates. The autonomous vehicle road navigation model may also include at least one target trajectory for the vehicle to travel along the corresponding road segment, such as Figure 24E shown.

[0346] At step 2616, process 2600A may include distributing the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles. For example, server 1230 may distribute the updated autonomous vehicle road navigation model to vehicles 1205, 1210, 1215, 1220, and 1225 that may use the model for navigation. The autonomous vehicle road navigation model may be distributed via one or more networks (e.g., via a cellular network and / or the Internet, etc.) via wireless communication paths 1235, such as Figure 12 shown.

[0347] In some embodiments, lane markings may be mapped using data received from multiple vehicles, such as through crowdsourcing techniques, as described above with respect to Figure 24EAs described. For example, process 2600A may include receiving a first communication from a first host vehicle including a location identifier associated with a detected lane marking, and receiving a second communication from a second host vehicle including an additional location identifier associated with the detected lane marking. For example, the second communication may be received from a subsequent vehicle traveling on the same road segment, or from the same vehicle on a subsequent trip along the same road segment. Process 2600A may further include refining the determination of at least one location associated with the detected lane marking 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 marking.

[0348] Figure 26B Flowchart showing an exemplary process 2600B for autonomously navigating a host vehicle along a road segment using mapped lane markings. Process 2600B may be performed, for example, by processing unit 110 of autonomous vehicle 200. At step 2620, process 2600B may include receiving an autonomous vehicle road navigation model from a server-based system. In some embodiments, the autonomous vehicle road navigation model may include a target trajectory for the host vehicle along the road segment and location identifiers associated with one or more lane markings 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 three-dimensional spline, such as Figure 9B As shown above. 24A to 24F As described, the location identifier may include a location in real-world coordinates of a point associated with a lane marking (e.g., a corner point of a dashed lane marking, an edge point of a continuous lane marking, a vertex between two intersecting lane markings and other points associated with intersecting lane markings, a center line associated with a lane marking, etc.).

[0349] At step 2621, process 2600B may include receiving at least one image representing the environment of the vehicle. The image may be received from an image capture device of the vehicle, such as by image capture devices 122 and 124 included in image acquisition unit 120. The image may include an image of one or more lane markings, similar to image 2500 described above.

[0350] At step 2622, process 2600B may include determining the longitudinal position of the host vehicle along the target trajectory. Figure 25A As described, this may be based on other information in the captured image (eg, landmarks, etc.) or by dead reckoning the vehicle between detected landmarks.

[0351] At step 2623, process 2600B may include determining an expected lateral distance to a lane marking based on the determined longitudinal position of the host vehicle along the target trajectory and based on two or more location identifiers associated with at least one lane marking. For example, vehicle 200 may use sparse map 800 to determine the expected lateral distance to the lane marking. Figure 25B As shown, longitudinal position 2520 along target trajectory 2555 may be determined in step 2622. Using sparse map 800, vehicle 200 may determine expected distance 2540 to mapped lane marking 2550 corresponding to longitudinal position 2520.

[0352] At step 2624, process 2600B may include analyzing at least one image to identify at least one lane marking. For example, vehicle 200 may use various image recognition techniques or algorithms to identify lane markings within the image, as described above. For example, lane marking 2510 may be detected by image analysis of image 2500, as described above. Figure 25A shown.

[0353] At 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 representing the actual distance between the vehicle and lane marking 2510, such as Figure 25A The camera angle, 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 when determining the distance 2530.

[0354] At step 2626, process 2600B may include determining an autonomous steering action for the host vehicle based on a difference between the expected lateral distance to the at least one lane marking and the determined actual lateral distance to the at least one lane marking. Figure 25B As described, the vehicle 200 may compare the actual distance 2530 with the expected distance 2540. The difference between the actual distance and the expected distance may indicate the error (and its magnitude) between the actual position of the vehicle and the target trajectory to be followed by the vehicle. Therefore, the vehicle may determine an autonomous steering action or other autonomous action based on the difference. For example, if the actual distance 2530 is less than the expected distance 2540, as shown in FIG. Figure 25B As shown, the vehicle can determine an autonomous steering action to guide the vehicle to the left away from lane marking 2510. As a result, the vehicle's position relative to the target trajectory can be corrected. Process 2600B can be used, for example, to improve the vehicle's navigation between landmarks.

[0355] Processes 2600A and 2600B provide only examples of techniques that may be used to navigate a host vehicle using the disclosed sparse map. In other examples, the same techniques may be employed with respect to Figure 25C and Figure 25D Processes that are consistent with those described.

[0356] In some embodiments, the disclosed systems, methods, and non-transitory computer-readable media may use one or more AV maps. An autonomous vehicle (AV) map (or AV map) may include information for supporting and / or implementing one or more autonomous vehicle (AV) functions of a vehicle in a manner that enables the vehicle to operate in a safe manner and / or navigate in an accurate manner. AV functions supported and / or implemented by an autonomous vehicle (or semi-autonomous vehicle) may include one or more autonomously controlled functions (e.g., functions determined, selected, and / or implemented based on instructions executed by at least one processor) such as steering, accelerating, and / or braking the vehicle. The AV functions may be part of a driving strategy such as RSS, which is developed and implemented, for example, by Mobileye of Jerusalem, Israel. Information used to operate the vehicle in a safe manner may include, but is not limited to, information regarding one or more regulations applicable to the location or jurisdiction of the vehicle (e.g., right-hand traffic or left-hand traffic jurisdiction), information regarding the environment in which the vehicle is located (e.g., information regarding drivable paths, stop signs, traffic lights, speed limits, lane markings, landmarks, free space, virtual or physical stop lines, traffic light dependency information, etc.), and / or information related to adjusting and / or adapting the vehicle's navigation to account for one or more objects in the vehicle's environment (e.g., other vehicles, pedestrians, objects, obstacles, obstructions, hazards, road work zones, traffic cones, etc.). The vehicle may sense objects in the vehicle's environment using one or more sensors (e.g., cameras, radar, lidar), as discussed herein. The AV map may serve as a redundant source of information for the sensed information and, in some cases, may also supplement the sensed information (e.g., providing locations of virtual stop lines where no stop lines are marked on the road). In some embodiments, operating the vehicle in a safe manner may further include operating the vehicle to maintain a comfort level for one or more passengers in the vehicle. The comfort level may include one or more predetermined criteria (e.g., related to speed, acceleration, and / or cornering) selected so that the vehicle operates in a manner that meets or exceeds a specified or selected passenger comfort level. The comfort level may be formalized and expressed using an appropriate mathematical formula. For example, a mathematical formula may be used that limits the degree of jerkiness or acceleration applied to a passenger in different directions. Information used to operate the vehicle in an accurate manner may include, but is not limited to, information related to a planned or specified navigation path (e.g., a trajectory, as discussed herein) or a route (e.g., from a specific location such as a starting location to a destination). In some embodiments, the information included in the AV map may further include information to support and / or implement one or more AV functions in an efficient manner.Information used to operate the vehicle in an efficient manner may include information related to speed, acceleration, lane changes and / or lane positioning, and / or information about traveling and / or selecting a path or route based on traffic conditions (e.g., traveling a route that is longer than a shorter route that experiences traffic conditions) and / or other factors or attributes related to potential routes (e.g., weather conditions, road conditions, or other characteristics of the route, such as traveling on a highway without traffic lights rather than a road with traffic lights). In some embodiments, the AV map may further include at least some information from a high-resolution (HD) map. In some embodiments, the AV map may be a sparse map, as described above. In some embodiments, the AV map may be crowdsourced, as discussed herein. In this disclosure, the terms "AV map" and "sparse map" are used interchangeably.

[0357] Target road edge

[0358] When navigating, an autonomous or semi-autonomous vehicle may need to detect one or more objects located along the edge of a road segment and / or a boundary associated with a free space zone. The free space zone may correspond to a navigable area in which the vehicle can travel without incident, and the boundary may specify at least a portion of the edge of the free space zone. In some cases, the boundary may correspond to the edge of a road (e.g., a curb or the edge of the road pavement). However, in some cases, the boundary of the free space zone may correspond to the edge of one or more objects located along the edge of a road segment. For example, in the case of parked vehicles arranged along the side of a road segment (e.g., parallel parked vehicles), a host vehicle (e.g., an autonomous or semi-autonomous vehicle) navigating the road segment may need to detect the edges of parked cars, curbs, barriers, etc. to determine the boundary of the free space zone in which the host vehicle can safely travel.

[0359] The disclosed systems and methods can identify boundaries associated with multiple objects located along the edge of a road segment. These objects can include parked vehicles, fences, barriers, trash cans, construction signs, or any combination of objects. In some embodiments, the disclosed systems and methods can determine a boundary line (or edge) for all objects sensed along the road segment, rather than determining a separate boundary line or edge for each individual object.

[0360] Figure 27A flow chart is provided to illustrate an exemplary process 2700 for navigating a host vehicle relative to a road segment consistent with the disclosed embodiments. The process 2700 may be performed by at least one processor or processing device, such as the processing unit 110 included in the system 100 carried in the host vehicle 200, or various other devices described herein. For example, the processing unit 110 performing the process 2700 may be configured to navigate the host vehicle 200 relative to the road segment. It should be understood that the term "processor" is used as a shorthand for "at least one processor" above and throughout this specification. In other words, the processor may include one or more structures (e.g., circuits) that perform logical operations, whether such structures are arranged in parallel, connected, or distributed. In some embodiments, a non-transitory computer-readable medium may contain instructions that, when executed by the processor, cause the processor to perform the process 2700. Furthermore, the process 2700 is not necessarily limited to Figure 27 The steps shown in , and any steps or processes of the various embodiments described throughout this disclosure may also be included in process 2700.

[0361] Additionally, some disclosed embodiments may involve communicating with at least one trained system (e.g., a trained model or a trained neural network). Communicating with at least one trained system may include inputting one or more different data entries to the trained system and reading or extracting one or more outputs from the trained system. In some embodiments, at least one trained system may be local to the system. For example, referring to Figure 1 , the trained system may be implemented directly within the processing unit 110. Alternatively, in some embodiments, at least one trained model may be external to the system, and at least one processor may communicate with the at least one trained model using any known means for sending and / or receiving data. For example, referring to Figure 1 , the processing unit 110 may use the wireless transceiver 172 to interact and communicate with external trained systems.

[0362] At step 2702, processing unit 110 receives at least one image captured by at least one camera from an environment of a host vehicle. The at least one image may include representations of at least two objects with offset edges in the environment of the host vehicle. The captured image may be acquired by at least one camera onboard the host vehicle. For example, a camera included in image acquisition unit 120 (such as image capture devices 122, 124, or 126 having fields of view 202, 204, and 206, respectively) may capture an image representing a scene appearing in an area in front of vehicle 200 (or, for example, to the side or rear of the vehicle). Additionally, in some embodiments, the at least one camera may transmit the captured image to at least one processor. For example, image capture device 122 may transmit a first image frame to processing unit 110 via data interface 128 via a data connection (e.g., digital, wired, USB, wireless, Bluetooth, etc.).

[0363] As used herein, an object present in the host vehicle's environment refers to any physical entity that can be represented in a captured image acquired by a camera mounted on the host vehicle and / or detected by one or more of the host vehicle's sensors. Examples of such objects include, but are not limited to, other vehicles (e.g., cars, trucks, motorcycles, bicycles, etc.), pedestrians, infrastructure (e.g., traffic signs, signals, road markings, barriers, and buildings), obstacles (e.g., debris, animals, or any unexpected items on the road), or environmental features (e.g., trees, curbs, and other natural or man-made structures). Consistent with the disclosed embodiments, at least two objects in the host vehicle's environment may have offset edges. In this context, "offset edges" refers to the positioning of the edges of two or more objects within the vehicle's environment that are not aligned in a straight or parallel manner, but are slightly offset or displaced relative to each other. This can occur in various real-world scenarios, such as when objects (e.g., curbs, road signs, or parked vehicles) are not perfectly aligned along the same plane or axis. For example, as the host vehicle travels through its environment, it may encounter objects with edges that are offset due to differences in position or orientation. This offset may be due to objects being placed at different distances from the host vehicle, or objects appearing misaligned in the host vehicle's field of view due to terrain or road conditions. It should be understood that an edge, as used herein, refers to a line or surface that typically demarcates the boundaries of a corresponding object along the vehicle's path (e.g., along the lane in which the vehicle is traveling). In practice, this line or surface may be an approximation and may not necessarily correspond exactly to a surface or edge of an object.

[0364] In some embodiments, the at least two objects may be parked vehicles. Additionally, in some embodiments, the parked vehicles may be positioned parallel to the edge of the road segment. In other embodiments, the at least two objects in the host vehicle's environment may include various objects, such as a construction barrier, a construction waste container, or road debris. It should be understood that the two or more objects in the environment may be different in nature, such as, for example, a vehicle and a road barrier. In some embodiments, the at least two objects are not necessarily static and may include moving objects traveling along the road segment, such as pedestrians or bicycles.

[0365] Figure 28A An exemplary image 2800a is shown that is captured by an onboard camera (e.g., image capture device 122) in a host vehicle (e.g., host vehicle 200) traveling on road segment 2805. Consistent with the disclosed embodiments, image 2800a depicts the environment surrounding host vehicle 200 and includes representations of various objects or features within the environment. Figure 28A In the example shown, four vehicles (2810-1, 2810-2, 2810-3, and 2810-4) are parked parallel to one edge of road segment 2805. The parked vehicles 2810-1, 2810-2, 2810-3, and 2810-4 have different offset edges 2820-1, 2820-2, 2820-3, and 2820-4 as shown by dashed lines.

[0366] In some embodiments, processing unit 110 may be further configured to detect at least two objects by analyzing at least one image, using the output of a radar system, using the output of a lidar system, using the output of a trained system, or a combination thereof. In other words, the at least two objects may be detected and identified by utilizing a variety of sensing and processing techniques, either individually or in combination, to enhance accuracy and robustness. One approach may involve analyzing at least one captured image, where a computer vision algorithm may process the visual data to identify and / or classify objects based on characteristics such as shape, color, texture, and contrast. Another approach may rely on the output of a radar system, which can detect objects by transmitting radio waves and analyzing their reflections to determine distance, speed, and relative position. Radar may be particularly useful in conditions with poor visibility, such as fog, rain, or darkness, where traditional cameras may struggle. Yet another approach may utilize the output of a lidar system, which employs laser pulses to scan an environment and generate a high-resolution 3D point cloud, enabling precise object detection and depth perception. In addition, trained systems, such as AI-driven neural networks or VIDAR (Visual Detection and Ranging) systems, can be used to identify and classify objects based on large amounts of pre-learned data, thereby improving detection in complex environments with overlapping or partially occluded objects. VIDAR can combine elements of both computer vision and lidar, using multiple high-resolution cameras to build a 3D representation of the environment. Unlike lidar, which relies on laser reflections, VIDAR can use image-based triangulation technology to provide a passive and cost-effective alternative for depth perception and object localization. VIDAR may be particularly effective for detecting objects at greater distances while reducing reliance on active laser scanning.

[0367] To maximize detection reliability, the processing unit 110 can also combine these approaches, integrating multiple sensor outputs to compensate for individual limitations, thereby ensuring more comprehensive object recognition and tracking. This multimodal approach can enhance awareness of the surrounding environment, facilitating safe and efficient operation. It should be understood that both the lidar and radar systems can be internal to the host vehicle, while the computer vision algorithm and / or trained system can be internal or external to the host vehicle. In the event that the computer vision algorithm and / or trained system is external to the host vehicle, the processing unit 110 can use the wireless transceiver 172 to interact and communicate with the external computer vision algorithm and / or trained system. Reference Figure 28A , any of the parked vehicles (2810-1 to 2810-4) may be detected using at least one of the aforementioned techniques or any combination thereof. Additionally, the detection process may include identifying and deriving relevant edges (2820-1 to 2820-4) of the parked vehicles, which help define their boundaries within the context of the road segment 2805.

[0368] Furthermore, in some embodiments, the processing unit 110 may be configured to determine whether one or more of the at least two objects are moving. Figure 28A , processing unit 110 can determine whether any of parked vehicles 2810-1 through 2810-4 are leaving their parking spaces. To make this determination, processing unit 110 can receive and analyze one or more additional images in addition to, for example, the initial image received at step 2702 of process 2700. By comparing and tracking the positions of detected objects across these consecutive images, processing unit 110 can apply computer vision algorithms to track movement and identify whether any of the objects are moving, such as a parked vehicle starting to move.

[0369] Additionally, in some embodiments, the processing unit 110 may employ other technologies, such as radar, lidar, or trained systems (e.g., VIDAR systems), alone or in combination, to detect the motion of objects. For example, radar can measure Doppler (from which velocity can be derived) and distance over time, while lidar can provide high-resolution 3D mapping (and advanced lidar systems, such as FMCW lidar, can also provide Doppler measurements), enabling the system to monitor object position and detect movement. VIDAR, which integrates vision and ranging capabilities, can also be used to track the movement of objects using both vision and depth data. These technologies, especially when used in combination, can improve detection accuracy and ensure that extremely subtle movements are identified, such as a vehicle moving slightly within a parking space or a pedestrian walking along a road segment. By utilizing multiple sensor modalities, the processing unit 110 can more reliably determine whether at least two objects are moving.

[0370] At step 2704, processing unit 110 analyzes at least one image to determine a free space boundary relative to at least two objects. The free space boundary may represent the edge of at least one free space zone and may follow a different path than the path represented by the offset edges of the at least two objects. In the context of the present disclosure, a free space zone refers to an open area free of obstacles in which the host vehicle can move undisturbed or navigate safely. A free space zone may be a portion of the environment accessible to the host vehicle based on the detected object locations. The free space boundary may represent the dividing line between the free space zone and areas occupied or blocked by two or more objects. Thus, the free space boundary may mark the limit within which the host vehicle may move freely. In some embodiments, the free space boundary may be a continuous line. A continuous free space boundary line may extend from the first of the detected objects to the last of the detected objects (such as a row of parked vehicles). Additionally, in some embodiments, the free space boundary may be substantially straight. Unlike offset edges, which provide a buffer zone around obstacles, the free space boundary may represent the true extent of navigable space, thereby ensuring efficient and safe motion planning.

[0371] Determining free-space boundaries (e.g., continuous boundary lines or edges) for objects detected along a road segment can provide several advantages over identifying individual boundary lines for each object separately. First, it can reduce computational load and minimize processing resources by simplifying the determination of relevant free-space boundaries, resulting in more efficient navigation. Second, this approach can be much faster than measuring each vehicle or object along the road individually. Third, it can improve detection accuracy in scenarios where one object partially or completely occludes another, ensuring a more reliable representation of the environment. Fourth, continuous boundaries can help facilitate smoother steering of the host vehicle by maintaining a consistent distance from the boundary rather than adjusting position when passing through gaps between objects. Finally, a smoother and more predictable driving experience can improve passenger comfort and reduce unnecessary acceleration, braking, or lateral movement.

[0372] refer to Figure 28A , the processing unit 110 may analyze the image 2800a and determine a free space boundary 2830a that marks the boundary between the free space zone (to the left of the free space boundary 2830a) and the zone occupied by the parked vehicles 2810-1 through 2810-4. Consistent with the disclosed embodiments, the free space boundary 2830a follows a path that is different from the path represented by the offset edges 2820-1 through 2820-4 of the parked vehicles 2810-1 through 2810-4. In some embodiments, the free space boundary may extend along the edges of at least two objects. For example, referring to Figure 28A, free space boundary 2830a extends along edges 2820-1 to 2820-4 of parked vehicles 2810-1 to 2810-4. Additionally or alternatively, in some embodiments, the free space boundary may be substantially parallel to the edge or curb of the road. For example, free space boundary 2830a is substantially parallel to the right edge of road segment 2805. Additionally or alternatively, in some embodiments, the free space boundary may be positioned at an o...

Claims

1. A non-transitory computer-readable medium storing instructions executable by at least one processor to perform a method for navigating a host vehicle relative to a road segment, the method comprising: receiving at least one image captured by at least one camera from an environment of the host vehicle, wherein the at least one image includes representations of at least two objects having offset edges in the environment of the host vehicle; analyzing the at least one image to determine a free space boundary relative to the at least two objects, wherein the free space boundary represents an edge of at least one free space zone and follows a path different from a path represented by the offset edges of the at least two objects; determining at least one navigation action based on the free space boundary; and The host vehicle is caused to perform the at least one navigation action.

2. The non-transitory computer-readable medium of claim 1 , wherein analyzing the at least one image to determine the free space boundary associated with at least two objects comprises identifying a potential free space region based on at least one map and determining the free space boundary relative to the at least two objects based on the potential free space region and the at least one image. The non-transitory computer-readable medium of claim 1 , wherein the at least two objects are parked vehicles. The non-transitory computer-readable medium of claim 3 , wherein the parked vehicle is positioned parallel to an edge of a road segment. The non-transitory computer-readable medium of claim 1 , wherein the at least two objects are spaced a distance apart from each other. The non-transitory computer-readable medium of claim 5 , wherein the distance is less than 1 meter. The non-transitory computer-readable medium of claim 5 , wherein the distance is less than 5 meters. The non-transitory computer-readable medium of claim 5 , wherein the distance is less than 10 meters.

9. The non-transitory computer-readable medium of claim 5, wherein the distance is greater than a threshold distance, thereby enabling the host vehicle to partially or completely enter a free space region between the at least two objects.

10. The non-transitory computer-readable medium of claim 9, wherein the at least one navigation action comprises steering the host vehicle to partially or fully enter the free space region between the at least two objects. 11 . The non-transitory computer-readable medium of claim 9 , wherein the at least one navigation action comprises parking the host vehicle within the free space area between the at least two objects.

12. The non-transitory computer-readable medium of claim 9, wherein the at least one navigation action is determined based on the free space boundary, the distance between the at least two objects, a speed of the host vehicle, a planned angle of entry into the free space area, or a combination thereof. 13 . The non-transitory computer-readable medium of claim 5 , wherein the distance is greater than a predetermined distance, and at least a portion of the free space boundary corresponds to a road edge.

14. The non-transitory computer-readable medium of claim 1, wherein the free space boundary extends along edges of the at least two objects.

15. The non-transitory computer-readable medium of claim 1, wherein the free space boundary is substantially parallel to an edge or curb of a road.

16. The non-transitory computer-readable medium of claim 1, wherein the at least one navigation action comprises steering, braking, or accelerating the host vehicle.

17. The non-transitory computer-readable medium of claim 1 , wherein analyzing the at least one image to determine the free space boundary relative to the at least two objects further comprises receiving output provided by a trained system, wherein the output comprises a height estimate for a plurality of pixels in the at least one image associated with one of the at least two objects.

18. The non-transitory computer readable medium of claim 17, wherein the at least one image is analyzed to determine the free space boundary relative to the at least two objects. Further comprising determining the free space boundary using the height estimate.

19. The non-transitory computer-readable medium of claim 17, wherein determining the at least one navigation action is further based on the altitude estimate.

20. The non-transitory computer-readable medium of claim 17, wherein at least one of the at least two objects is an object overhanging a road surface of a road, and the height estimate is relative to at least a portion of the object overhanging the road surface.

21. The non-transitory computer-readable medium of claim 20, wherein the object overhanging the road surface comprises at least one of a lamppost, a gate, a barrier, a tree branch, or a traffic sign.

22. The non-transitory computer-readable medium of claim 1, wherein the method further comprises detecting the at least two objects by analyzing the at least one image, using output of a radar system, using output of a lidar system, using output of a trained system, or a combination thereof.

23. The non-transitory computer-readable medium of claim 1, wherein the method further comprises determining whether at least one moving object is crossing the free space boundary.

24. The non-transitory computer-readable medium of claim 23, wherein the at least one moving object is a pedestrian or a vehicle.

25. The non-transitory computer-readable medium of claim 23, wherein determining the at least one navigation action is further based on whether the at least one mobile object is crossing the free space boundary. 26 . The non-transitory computer-readable medium of claim 1 , wherein the method further comprises refining the free space boundary based on a driving strategy associated with the host vehicle.

27. The non-transitory computer-readable medium of claim 1, wherein the method further comprises determining whether one or more of the at least two objects are moving.

28. The non-transitory computer-readable medium of claim 1, wherein the at least one navigation action comprises altering a planned trajectory of the host vehicle based on the free space boundary.

29. The non-transitory computer-readable medium of claim 1, wherein the at least two objects are located on one side of the road segment.

30. The non-transitory computer-readable medium of claim 29, wherein the at least one image further includes representations of at least two additional objects with offset edges in the environment of the host vehicle and located on another side of the road segment, and wherein the method further comprises: The at least one image is analyzed to determine an additional free space boundary relative to the at least two additional objects, wherein the additional free space boundary represents an additional edge of the at least one free space zone and follows a path different from a path represented by the offset edges of the at least two additional objects.

31. The non-transitory computer-readable medium of claim 30, wherein determining the at least one navigation action is further based on the additional free space boundary.

32. A method for navigating a host vehicle relative to a road segment, the method comprising: receiving at least one image captured by at least one camera from an environment of the host vehicle, wherein the at least one image includes representations of at least two objects having offset edges in the environment of the host vehicle and located on one side of the road segment; analyzing the at least one image to determine a free space boundary relative to the at least two objects, wherein the free space boundary represents an edge of at least one free space zone and follows a path different from a path represented by the offset edges of the at least two objects; determining at least one navigation action based on the free space boundary; and The host vehicle is caused to perform the at least one navigation action.

33. The method of claim 32, wherein analyzing the at least one image to determine the free space boundary associated with at least two objects comprises identifying a potential free space area based on at least one map and determining the free space boundary relative to the at least two objects based on the potential free space area and the at least one image. The method of claim 32 , wherein the at least two objects are spaced a distance apart from each other.

35. The method of claim 32, wherein analyzing the at least one image to determine the free space boundary relative to the at least two objects further comprises receiving output provided by a trained system, wherein the output comprises a height estimate for a plurality of pixels in the at least one image associated with one of the at least two objects.

36. The method of claim 35, wherein analyzing the at least one image to determine the free space boundary relative to the at least two objects further comprises using the height estimate to determine the free space boundary.

37. The method of claim 32, further comprising determining whether at least one moving object is crossing the free space boundary.

38. The method of claim 32, further comprising refining the free space boundary based on a driving strategy associated with the host vehicle.

39. A system for navigating a host vehicle relative to a road segment, the system comprising at least one processor, the at least one processor comprising circuitry and memory, wherein the memory comprises instructions that, when executed by the circuitry, cause the at least one processor to: receiving at least one image captured by at least one camera from an environment of the host vehicle, wherein the at least one image includes representations of at least two objects having offset edges in the environment of the host vehicle and located on one side of the road segment; analyzing the at least one image to determine a free space boundary relative to the at least two objects, wherein the free space boundary represents an edge of at least one free space zone and follows a path different from a path represented by the offset edges of the at least two objects; determining at least one navigation action based on the free space boundary; and The host vehicle is caused to perform the at least one navigation action.

40. The system of claim 39, wherein analyzing the at least one image to determine the free space boundary associated with at least two objects comprises identifying a potential free space area based on at least one map and determining the free space boundary relative to the at least two objects based on the potential free space area and the at least one image.

41. The system of claim 39, wherein the at least two objects are spaced a distance apart from each other.

42. The system of claim 39, wherein analyzing the at least one image to determine the free space boundary relative to the at least two objects further comprises receiving output provided by a trained system, wherein the output comprises a height estimate for a plurality of pixels in the at least one image associated with one of the at least two objects.

43. The system of claim 42, wherein analyzing the at least one image to determine the free space boundary relative to the at least two objects further comprises using the height estimate to determine the free space boundary.

44. The system of claim 39, wherein the at least one processor is further configured to determine whether at least one moving object is crossing the free space boundary.

45. The system of claim 39, wherein the at least one processor is further configured to refine the free space boundary based on a driving strategy associated with the host vehicle.

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