System, method, and computer program for generating road surface models

Abstract

This system provides a vehicle navigation system that correlates driving information from multiple road sections. [Solution] In the system, server 2810 includes memory and a processor that receive driving information from vehicles 2710 crossing a first road section 2720 and vehicles 2820 crossing a second road section 2730. The processor correlates the driving information from the vehicles and provides a first road model section representing the first road section and a second road model section representing the second road section. If the distance d1 between a first point 2802 associated with the first road section and a second point 2804 associated with the second road section is shorter than or equal to a predetermined distance threshold, the processor correlates the first road model section with the second road model section to provide a correlated road section model and stores the correlated road section model as part of a sparse navigation map.

Description

[Technical Field]

[0001] [Cross-reference of related applications] This application claims priority to U.S. Provisional Application No. 63 / 072,597, filed on 31 August 2020. The aforementioned application is incorporated herein by reference in its entirety. [Background technology]

[0002] This disclosure relates, in general terms, to vehicle navigation.

[0003] As technology continues to advance, the goal of fully autonomous vehicles capable of navigating roads is becoming a reality. 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 also need to process and interpret visual information (e.g., information captured by cameras) and use information obtained from other sources (e.g., GPS devices, speed sensors, accelerometers, suspension sensors, etc.). At the same time, in order to navigate to a destination, autonomous vehicles may need to identify their own position on a specific road (e.g., a specific lane on a multi-lane road), navigate alongside other vehicles, avoid obstacles and pedestrians, observe traffic signals and signs, and travel from one road to another at appropriate intersections or interchanges. Utilizing and interpreting the vast amount of information collected by autonomous vehicles as they travel to their destination presents many design challenges. The vast amounts of data that autonomous vehicles may need to analyze, access, and / or store (e.g., captured image data, map data, GPS data, sensor data, etc.) present challenges that can actually limit or negatively impact autonomous navigation. Furthermore, if autonomous vehicles rely on conventional mapping technologies for navigation, the enormous amount of data required to store and update maps presents a significant challenge. [Overview of the project]

[0004] Embodiments provided in this disclosure provide a vehicle navigation system and method.

[0005] In one embodiment, a system for correlating driving information from multiple road sections may include at least one processor having circuits and memory. The memory, when executed by the circuits, causes at least one processor to receive driving information from each of a first plurality of vehicles crossing a first road section, wherein the driving information from each of the first plurality of vehicles includes at least a representation of the actual trajectory traveled by a particular vehicle among the first plurality of vehicles while crossing the first road section; and may include instructions for correlating the driving information from each of the first plurality of vehicles to provide a first road model section representing the first road section. At least one processor may further receive driving information from each of a second plurality of vehicles crossing a second road section, wherein the driving information from each of the second plurality of vehicles includes at least a representation of the actual trajectory traveled by a particular vehicle among the second plurality of vehicles while crossing the second road section; and may correlate the driving information from each of the second plurality of vehicles to provide a second road model section representing the second road section. At least one processor may further correlate a first road model segment with a second road model segment to provide a correlated road segment model if the drivable distance between a first point associated with a first road segment and a second point associated with a second road segment is shorter than or equal to a predetermined distance threshold; and may store the correlated road segment model as part of a sparse navigation map used for navigating a vehicle along the first and second road segments.

[0006] In one embodiment, a method for correlating driving information from multiple road sections may include the steps of: receiving driving information from each of a first plurality of vehicles crossing a first road section, wherein the driving information from each of the first plurality of vehicles includes at least a representation of the actual trajectory traveled by a particular vehicle among the first plurality of vehicles while crossing the first road section; and correlating the driving information from each of the first plurality of vehicles to provide a first road model section representing the first road section. The method may further include the steps of: receiving driving information from each of a second plurality of vehicles crossing a second road section, wherein the driving information from each of the second plurality of vehicles includes at least a representation of the actual trajectory traveled by a particular vehicle among the second plurality of vehicles while crossing the second road section; and correlating the driving information from each of the second plurality of vehicles to provide a second road model section representing the second road section. The method may further include the steps of: correlating a first road model division with a second road model division in order to provide a correlated road division model if the drivable distance between a first point associated with a first road division and a second point associated with a second road division is shorter than or equal to a predetermined distance threshold; and storing the correlated road division model as part of a sparse navigation map used for navigating a vehicle along the first and second road divisions.

[0007] In one embodiment, a system for generating a road surface model may include at least one processor having circuitry and memory. The memory may include instructions, when executed by the circuitry, that cause at least one processor to access a plurality of points associated with one or more drivable roads; and to generate a road surface model based on the plurality of points, the road surface model including a mesh representing the surface of one or more drivable roads. Generating the road surface model may include determining the drivable distance between a first point of the plurality of points and a second point of the plurality of points; and meshing the first point and the second point together based on the determination that the drivable distance between the first point and the second point is shorter than or equal to a predetermined distance threshold. At least one processor may further store the road surface model for use in navigating a vehicle along one or more drivable roads.

[0008] In one embodiment, a method for generating a road surface model may include accessing a plurality of points associated with one or more drivable roads; and generating a road surface model based on the plurality of points, the road surface model including a mesh representing the surface of one or more drivable roads. Generating a road surface model may include determining the drivable distance between a first point of the plurality of points and a second point of the plurality of points; and meshing the first point and the second point together based on the determination that the drivable distance between the first point and the second point is shorter than or equal to a predetermined distance threshold. The method may further include storing the road surface model for use in navigating a vehicle along one or more drivable roads.

[0009] According to other embodiments disclosed, a non-temporary computer-readable storage medium may store program instructions that are executed by at least one processor and perform any of the methods described herein.

[0010] The foregoing general description and the following detailed description are merely illustrative and explanatory and are not restrictive of the claims.

Brief Description of the Drawings

[0011] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate various embodiments disclosed.

[0012] [Figure 1] It is a diagrammatic representation of an exemplary system according to the disclosed embodiments.

[0013] [Figure 2A] It is a side view diagrammatic representation of an exemplary vehicle including a system according to the disclosed embodiments.

[0014] [Figure 2B] It is a top view diagrammatic representation of the vehicle and system shown in FIG. 2A according to the disclosed embodiments.

[0015] [Figure 2C] It is a top view diagrammatic representation of another embodiment of a vehicle including a system according to the disclosed embodiments.

[0016] [Figure 2D] It is a top view diagrammatic representation of yet another embodiment of a vehicle including a system according to the disclosed embodiments.

[0017] [Figure 2E] It is a top view diagrammatic representation of yet another embodiment of a vehicle including a system according to the disclosed embodiments.

[0018] [Figure 2F] It is a diagrammatic representation of an exemplary vehicle control system according to the disclosed embodiments.

[0019] [Figure 3A] It is a diagrammatic representation of the interior of a vehicle including a rearview mirror and a user interface of a vehicle imaging system according to the disclosed embodiments.

[0020] [Figure 3B] This is a diagram of an example of a camera mount configured to be positioned behind the rearview mirror and opposite the vehicle's windshield, according to the disclosed embodiments.

[0021] [Figure 3C] Figure 3B shows a camera mount from a different viewpoint according to the disclosed embodiment.

[0022] [Figure 3D] This is a diagram of an example of a camera mount configured to be positioned behind the rearview mirror and opposite the vehicle's windshield, according to the disclosed embodiments.

[0023] [Figure 4] This is an exemplary block diagram of a memory configured to store instructions for performing one or more operations according to the disclosed embodiments.

[0024] [Figure 5A] This flowchart shows an exemplary process according to the disclosed embodiments for generating one or more navigation responses based on monocular image analysis.

[0025] [Figure 5B] This flowchart shows an exemplary process for detecting one or more vehicles and / or pedestrians within a set of images, according to the disclosed embodiments.

[0026] [Figure 5C] This flowchart shows an exemplary process for detecting road mark and / or lane geometry information within a set of images, according to the disclosed embodiments.

[0027] [Figure 5D] A flowchart shows an exemplary process for detecting a signal light in a set of images according to the disclosed embodiments.

[0028] [Figure 5E] This flowchart shows an exemplary process for generating one or more navigation responses based on a vehicle path, according to the disclosed embodiments.

[0029] [Figure 5F] This flowchart shows an exemplary process for determining whether a preceding vehicle is changing lanes, according to the disclosed embodiments.

[0030] [Figure 6] This flowchart shows an exemplary process according to the disclosed embodiments for generating one or more navigation responses based on stereoscopic image analysis.

[0031] [Figure 7] This flowchart shows an exemplary process according to the disclosed embodiments for generating one or more navigation responses based on the analysis of three sets of images.

[0032] [Figure 8] The disclosed embodiments show a sparse map for providing autonomous vehicle navigation.

[0033] [Figure 9A] The polynomial representation of a road section according to the disclosed embodiment is shown.

[0034] [Figure 9B] The disclosed embodiments show curves in three-dimensional space representing the target trajectory of a vehicle for a specific road section, included in a sparse map.

[0035] [Figure 10] Examples of landmarks that may be included in a sparse map according to the disclosed embodiments are shown.

[0036] [Figure 11A] The polynomial representation of the orbital according to the disclosed embodiments is shown.

[0037] [Figure 11B] The disclosed embodiments show a target trajectory along a multi-lane road. [Figure 11C] The disclosed embodiments show a target trajectory along a multi-lane road.

[0038] [Figure 11D] An exemplary road signature profile according to the disclosed embodiments is shown.

[0039] [Figure 12] This is a schematic diagram of a system, according to the disclosed embodiment, that uses crowdsourced data received from multiple vehicles for autonomous vehicle navigation.

[0040] [Figure 13] An exemplary autonomous vehicle road navigation model represented by a plurality of three-dimensional splines, according to the disclosed embodiments, is shown.

[0041] [Figure 14] The disclosed embodiment shows a map skeleton generated by combining location information from many journeys.

[0042] [Figure 15] An example of longitudinal alignment of two routes by exemplary landmark signs according to the disclosed embodiments is shown.

[0043] [Figure 16] The disclosed embodiments illustrate an example of longitudinal alignment of many routes using exemplary signs as landmarks.

[0044] [Figure 17]This is a schematic diagram of a system for generating driving data using a camera, a vehicle, and a server, according to the disclosed embodiments.

[0045] [Figure 18] This is a schematic diagram of a system for crowdsourcing sparse maps according to the disclosed embodiment.

[0046] [Figure 19] This flowchart shows an exemplary process for generating a sparse map for autonomous vehicle navigation along road divisions, according to the disclosed embodiments.

[0047] [Figure 20] A block diagram of the server according to the disclosed embodiment is shown.

[0048] [Figure 21] A block diagram of the memory according to the disclosed embodiment is shown.

[0049] [Figure 22] The disclosed embodiments illustrate a process for clustering vehicle tracks associated with a vehicle.

[0050] [Figure 23] The disclosed embodiments illustrate a navigation system for a vehicle that may be used for autonomous navigation.

[0051] [Figure 24A] The following are exemplary lane marks that may be detected according to the disclosed embodiments. [Figure 24B] The following are exemplary lane marks that may be detected according to the disclosed embodiments. [Figure 24C] The following are exemplary lane marks that may be detected according to the disclosed embodiments. [Figure 24D] The following are exemplary lane marks that may be detected according to the disclosed embodiments.

[0052] [Figure 24E] An exemplary mapped lane mark is shown according to the disclosed embodiment.

[0053] [Figure 24F] The disclosed embodiments illustrate exemplary anomalies associated with lane mark detection.

[0054] [Figure 25A] The disclosed embodiments show exemplary images of the vehicle's surrounding environment for navigation based on mapped lane marks.

[0055] [Figure 25B] The disclosed embodiments demonstrate corrections for vehicle lateral positioning based on mapped lane marks in a road navigation model.

[0056] [Figure 25C] This provides a conceptual representation of a localization technique for determining the position of a host vehicle along a target trajectory using mapped features contained in a sparse map. [Figure 25D] This provides a conceptual representation of a localization technique for determining the position of a host vehicle along a target trajectory using mapped features contained in a sparse map.

[0057] [Figure 26A] This flowchart shows an exemplary process for mapping lane marks for use in autonomous vehicle navigation, according to the disclosed embodiments.

[0058] [Figure 26B] This flowchart shows an exemplary process for autonomously navigating a host vehicle along a road section using mapped lane marks, according to the disclosed embodiments.

[0059] [Figure 27] This figure shows an exemplary overpass having overlapping road sections according to the disclosed embodiments.

[0060] [Figure 28] Figure 27 is an overhead view of the overpass according to the disclosed embodiment.

[0061] [Figure 29] An exemplary intersection from which driving information can be collected according to the disclosed embodiment is shown.

[0062] [Figure 30] This flowchart shows an exemplary process for correlating driving information according to the disclosed embodiments.

[0063] [Figure 31A] An exemplary road surface model of an overpass according to the disclosed embodiments is shown.

[0064] [Figure 31B] The disclosed embodiments illustrate exemplary anomalies that may occur when generating a road surface model.

[0065] [Figure 32] This flowchart shows an exemplary process for generating a road surface model according to the disclosed embodiments. [Modes for carrying out the invention]

[0066] The following detailed description refers to the accompanying drawings. Where possible, the same reference numerals are used in the drawings and the following description to refer to the same or similar parts. While several exemplary embodiments are described herein, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the components shown in the drawings, and the exemplary methods described herein may be modified by substitution, rearrangement, deletion, or addition of steps in the disclosed method. Therefore, the following detailed description is not limited to the disclosed embodiments and examples. Rather, the appropriate scope is defined by the appended claims.

[0067] [Overview of Autonomous Vehicles]

[0068] As used throughout this disclosure, the term “autonomous vehicle” means a vehicle capable of performing at least one navigation change without driver input. “Navigation change” means one or more changes to the vehicle’s steering, braking, or acceleration. To be autonomous, a vehicle does not need to be fully automatic (e.g., fully operational without a driver or driver input). Rather, an autonomous vehicle includes vehicles that can operate under driver control during certain periods of time and without driver control during other periods of time. An autonomous vehicle may also include a vehicle that controls only certain aspects of vehicle navigation, such as steering (e.g., to maintain a vehicle course between vehicle lane constraints), but leaves other aspects (e.g., braking) to the driver. In some cases, an autonomous vehicle may handle some or all aspects of the vehicle’s braking, speed control, and / or steering.

[0069] Since human drivers typically rely on visual cues and observations to control their vehicles, traffic infrastructure is constructed accordingly, with lane markings, traffic signs, and traffic lights designed to provide drivers with all the necessary visual information. In light of these design characteristics of traffic infrastructure, autonomous vehicles may include cameras and processing units that analyze visual information captured from the vehicle's environment. This visual information may include, for example, components of the traffic infrastructure observable by the driver (e.g., lane markings, traffic signs, traffic lights, etc.) and other obstacles (e.g., other vehicles, pedestrians, debris, etc.). Furthermore, 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 the vehicle's environment while it is in motion, and the vehicle (and other vehicles) may use this information to determine its own position in the model.

[0070] In some embodiments of this disclosure, an autonomous vehicle may use information obtained during navigation (e.g., from cameras, GPS devices, accelerometers, speed sensors, suspension sensors, etc.). In other embodiments, an autonomous vehicle may use information obtained from past navigation by the vehicle (or other vehicles) during navigation. In yet another embodiment, an autonomous vehicle may use a combination of information obtained during navigation and information obtained from past navigation. The following sections provide an overview of a system according to the disclosed embodiments, followed by an overview of a forward-looking imaging system and method by that system. The following sections disclose a system and method for constructing, using, and updating sparse maps for autonomous vehicle navigation.

[0071] [System Overview]

[0072] Figure 1 is a block diagram representation of System 100 according to an exemplary embodiment disclosed. 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. The processing unit 110 may include one or more processing devices. In some embodiments, the processing unit 110 may include an application processor 180, an image processor 190, or any other suitable processing device. Similarly, the 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, the image acquisition unit 120 may include one or more imaging devices (e.g., cameras), such as imaging device 122, imaging device 124, imaging device 126, etc. System 100 may also include a data interface 128 that connects the processing device 110 to the image acquisition device 120 in a communicative manner. For example, the data interface 128 may include one or more arbitrary wired and / or wireless links for transmitting image data acquired by the image acquisition device 120 to the processing unit 110.

[0073] The wireless transceiver 172 may include one or more devices configured to exchange transmissions with one or more networks (e.g., cellular or the Internet) via a wireless 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®, Bluetooth®, Bluetooth Smart, 802.15.4, ZigBee®, etc.). Such transmissions may include communication from a host vehicle to one or more remotely located servers. Such transmissions may also include (unidirectional or bidirectional) communication between the host vehicle and one or more target vehicles in the host vehicle's environment (e.g., to facilitate the adjustment of the host vehicle's navigation in consideration of or with such target vehicles), as well as broadcast transmissions to unspecified receivers near the transmitting vehicle.

[0074] 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 circuitry, a digital signal processor, an integrated circuit, memory, or any other type of device suitable for running applications and processing and analyzing images. In some embodiments, 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 are available, including processors available from manufacturers such as Intel®, AMD®, etc., or GPUs available from manufacturers such as NVIDIA®, ATI®, etc., and may include various architectures (e.g., x86 processor, ARM®, etc.).

[0075] In some embodiments, the application processor 180 and / or image processor 190 may include any EyeQ series processor chip available from Mobileye®. These processor designs include multiple processing units, each having 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, EyeQ2® uses 90nm-micron technology operating at 332MHz. The EyeQ2® architecture consists of two floating-point hyperthreaded 32-bit RISC CPUs (MIPS32® 34K® cores), five vision computing engines (VCEs), three vector microcode processors (VMP®), a Denali 64-bit mobile DDR controller, a 128-bit internal acoustic interconnect, dual 16-bit video input and 18-bit video output controllers, a 16-channel DMA, and several peripherals. The MIPS34K CPU manages five VCEs, three VMPs® and DMAs, a second MIPS34K CPU and multi-channel DMA, and other peripherals. The five VCEs, three VMPs® and MIPS34K CPUs can perform the intensive vision computations required by multi-function bundled applications. In another example, the disclosed embodiments may use a third-generation processor, EyeQ3®, which is six times more powerful than EyeQ2®. In yet another example, EyeQ4® and / or EyeQ5® may be used in the disclosed embodiments. Naturally, newer or future EyeQ processing devices may also be used with the disclosed embodiments.

[0076] Any of the processing devices disclosed herein can be configured to perform a specific function. Configuring a processing device, such as one of the EyeQ processors or other controllers or microprocessors described herein, to perform a specific function may include programming computer executable instructions and providing those instructions to the processing device for execution during the operation of the processing device. In some embodiments, configuring a processing device may include directly programming architectural instructions into the processing device. For example, processing devices such as field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and the like may be configured, for example, using one or more hardware description languages ​​(HDLs).

[0077] In other embodiments, configuring a processing device may include storing executable instructions in memory accessible to the processing device during operation. For example, the processing device may access the memory during operation to retrieve and execute the stored instructions. In any case, a processing device configured to perform sensing, image analysis, and / or navigation functions disclosed herein represents a dedicated hardware-based system that controls multiple hardware-based components of a host vehicle.

[0078] Figure 1 shows two separate processing devices 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 accomplish the tasks of application processor 180 and image processor 190. In other embodiments, these tasks may be performed by three or more processing devices. Furthermore, in some embodiments, system 100 may include one or more processing units 110 and not include other components such as image acquisition unit 120.

[0079] The processing unit 110 may include various types of devices. For example, the processing unit 110 may include various devices such as a controller, an image preprocessor, a central processing unit (CPU), a graphics processing unit (GPU), support circuits, a digital signal processor, an integrated circuit, memory, or any other type of device that processes and analyzes images. The image preprocessor may include a video processor that captures, digitizes, and processes images from an image sensor. The CPU may include any number of microcontrollers or microprocessors. The GPU may also include any number of microcontrollers or microprocessors. The support circuits may include any number of circuits commonly 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 databases and image processing software. The memory may include any number of random access memories, read-only memories, flash memories, disk drives, optical memory devices, tape memory devices, removable memory devices, and other types of memory devices. In one example, the memory may be separate from the processing unit 110. In another example, the memory may be integrated into the processing unit 110.

[0080] Each memory unit 140, 150 may contain software instructions that, when executed by a processor (e.g., an application processor 180 and / or an image processor 190), can control various aspects of the operation of the system 100. These memory units may contain various database 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 memory, tape memory, removable memory, and / or any other type of memory. In some embodiments, the memory units 140, 150 may be separate from the application processor 180 and / or the image processor 190. In other embodiments, these memory units may be integrated into the application processor 180 and / or the image processor 190.

[0081] The position sensor 130 may include any type of device suitable for determining the position associated with at least one component of the system 100. In some embodiments, the position sensor 130 may include a GPS receiver. Such a receiver can determine the user's position and speed by processing signals broadcast by Global Positioning System satellites. Position information from the position sensor 130 may be provided to the application processor 180 and / or the image processor 190.

[0082] In some embodiments, the system 100 may include components such as a speed sensor (e.g., a tachometer, a speedometer) for measuring the speed of the vehicle 200 and / or an accelerometer (either single-axis or multi-axis) for measuring the acceleration of the vehicle 200.

[0083] The user interface 170 may include any device suitable for providing information or receiving input from one or more users of the system 100. In some embodiments, the user interface 170 may include user input devices, such as a touchscreen, microphone, keyboard, pointer device, track wheel, camera, knob, button, etc. Using such input devices, a user may be able to provide information input or commands to the system 100 by typing commands or information, providing voice commands, selecting menu options on a screen using buttons, pointers or eye-tracking functions, or through any other suitable technique for communicating information to the system 100.

[0084] The user interface 170 may include one or more processing devices configured to provide information to or receive information from the user and process that information for use, for example, by the application processor 180. In some embodiments, such processing devices may execute commands to recognize and track eye movements, commands to receive and interpret voice commands, commands to recognize and interpret touches and / or gestures made on a touchscreen, commands to respond to keyboard input or menu selections, and so on. In some embodiments, the user interface 170 may include a display, a speaker, a haptic device and / or any other device that provides output information to the user.

[0085] The map database 160 may include any type of database that stores map data useful to system 100. In some embodiments, the map database 160 may include data relating to the location in a reference coordinate system of various items, including roads, water features, geographical features, businesses, points of interest, restaurants, gas stations, etc. The map database 160 may store not only the locations of such items but also descriptors associated with those items, including, for example, names associated with any of the stored features. In some embodiments, the map database 160 may be physically located together with other components of system 100. Alternatively or additionally, the map database 160 or a part thereof may be located remotely from other components of system 100 (e.g., processing unit 110). In such embodiments, information from the map database 160 may be downloaded to a network via a wired or wireless data connection (e.g., via a cellular network and / or the Internet, etc.). In some cases, the map database 160 may store a sparse data model that includes a polynomial representation of specific road features (e.g., lane markers) or the target trajectory of a host vehicle. The systems and methods for generating such maps will be discussed below with reference to Figures 8 to 19.

[0086] The imaging devices 122, 124, and 126 may each include any type of device suitable for capturing at least one image from the environment. Furthermore, any number of imaging devices may be used to acquire images to input to the image processor. Some embodiments may include only a single imaging device, while others may include two, three, or even four or more imaging devices. The imaging devices 122, 124, and 126 are further described below with reference to Figures 2B to 2E.

[0087] System 100 or various components of System 100 can be incorporated into various different platforms. In some embodiments, System 100 may be included in a vehicle 200, as shown in Figure 2A. For example, the vehicle 200 may comprise the processing unit 110 and any other components of System 100, as described above with respect to Figure 1. In some embodiments, the vehicle 200 may comprise only a single imaging device (e.g., a camera), while in other embodiments, such as those considered in relation to Figures 2B-2E, multiple imaging devices may be available. For example, as shown in Figure 2A, either imaging devices 122 and 124 of the vehicle 200 may be part of an Advanced Driver Assistance Systems (ADAS) imaging set.

[0088] The imaging device included in the vehicle 200 as part of the image acquisition unit 120 can be positioned at any suitable location. In some embodiments, as shown in Figures 2A-2E and 3A-3C, the imaging device 122 may be positioned near the rearview mirror. This position can provide a similar line of sight to the driver of the vehicle 200 and can help the driver determine what is visible and what is not. While the imaging device 122 can be positioned at any location near the rearview mirror, positioning the imaging device 122 on the driver side of the mirror can further assist in acquiring images representing the driver's field of view and / or line of sight.

[0089] Other positions can also be used for the imaging device of the image acquisition unit 120. For example, imaging device 124 may be placed on or inside the bumper of the vehicle 200. Such a position may be particularly suitable for imaging devices with a wide field of view. The line of sight of an imaging device placed on the bumper may differ from the line of sight of the driver, and therefore the bumper imaging device and the driver are not always looking at the same object. The imaging devices (e.g., imaging devices 122, 124 and 126) may also be placed in other positions. For example, the imaging devices may be placed on or inside one or both side mirrors of the vehicle 200, on the roof of the vehicle 200, on the hood of the vehicle 200, on the trunk of the vehicle 200, on the side of the vehicle 200, mounted on any window of the vehicle 200, positioned behind or in front, mounted inside or near the front and / or rear lights of the vehicle 200, etc.

[0090] In addition to the imaging device, the vehicle 200 may include various other components of the system 100. For example, the processing unit 110 may be integrated into the vehicle's engine control unit (ECU) or included in the vehicle 200 separately from the ECU. The vehicle 200 may also be equipped with position sensors 130 such as a GPS receiver, and may also include a map database 160 and memory units 140 and 150.

[0091] As described above, the wireless transceiver 172 may receive and / or upload data via one or more networks (e.g., a cellular network, the Internet, etc.). For example, the wireless transceiver 172 may upload data collected by the system 100 to one or more servers and download data from one or more servers. Through the wireless transceiver 172, the system 100 may receive, for example, periodic or on-demand updates to data stored in the map database 160, memory 140, and / or memory 150. Similarly, the wireless transceiver 172 may upload any data from the system 100 (e.g., images captured by the image acquisition unit 120, data received by the position sensor 130, other sensors, or the vehicle control system, etc.) and / or any data processed by the processing unit 110 to one or more servers.

[0092] System 100 may upload data to a server (e.g., the cloud) based on privacy level settings. For example, System 100 may implement privacy level settings that regulate or restrict data (including metadata) that can uniquely identify a vehicle and / or the vehicle's driver / owner, which is transmitted to the server. Such settings may be configured by the user via the wireless transceiver 172, initialized by factory default settings, or initialized by data received by the wireless transceiver 172.

[0093] In some embodiments, system 100 may upload data according to a “high” privacy level, and under the settings, system 100 may transmit data that does not contain any details about a specific vehicle and / or driver / owner (e.g., location information related to a route, captured images, etc.). For example, when uploading data according to the “high” privacy setting, system 100 may not include the vehicle identification number (VIN) or the name of the vehicle's driver or owner, and instead may transmit data such as captured images and / or limited location information related to a route.

[0094] Other privacy levels are intended. For example, system 100 may transmit data to the server according to a “medium” privacy level, which may include additional information not included under a “high” privacy level, such as the manufacturer and / or model of the vehicle and / or vehicle type (e.g., passenger car, sports utility vehicle, truck, etc.). In some embodiments, system 100 may upload data according to a “low” privacy level. Under a “low” privacy level setting, system 100 may upload and include data sufficient to uniquely identify a particular vehicle, its owner / driver and / or part or all of the route the vehicle has traveled. Such “low” privacy level data may include one or more of the following, for example, VIN, driver / owner name, vehicle's starting point before departure, vehicle's intended destination, vehicle's manufacturer and / or model, vehicle type, etc.

[0095] Figure 2A is a side view representation of an exemplary vehicle imaging system according to the disclosed embodiment. Figure 2B is a top view representation of the embodiment shown in Figure 2A. As shown in Figure 2B, the disclosed embodiment may include a vehicle 200 that includes a system 100 within its body, having a first imaging device 122 positioned near the rearview mirror and / or near the driver of the vehicle 200, a second imaging device 124 positioned on or within the bumper area (e.g., one of the bumper areas 210) of the vehicle 200, and a processing unit 110.

[0096] As shown in Figure 2C, both imaging devices 122 and 124 can be positioned near the rearview mirror of the vehicle 200 and / or near the driver. Furthermore, although two imaging devices 122 and 124 are shown in Figures 2B and 2C, it should be understood that other embodiments may include three or more imaging devices. For example, in the embodiments shown in Figures 2D and 2E, a first imaging device 122, a second imaging device 124, and a third imaging device 126 are included in the system 100 of the vehicle 200.

[0097] As shown in Figure 2D, imaging device 122 may be positioned near the rearview mirror of the vehicle 200 and / or near the driver, and imaging devices 124 and 126 may be positioned on or within the bumper area of ​​the vehicle 200 (e.g., one of the bumper areas 210). Also, as shown in Figure 2E, imaging devices 122, 124 and 126 may be positioned near the rearview mirror of the vehicle 200 and / or near the driver's seat. The disclosed embodiments are not limited to any particular number and configuration of imaging devices, and imaging devices may be positioned in and / or at any suitable location on the vehicle 200.

[0098] It should be understood that the disclosed embodiments are not limited to vehicles and may be applicable in other situations. 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 automobiles, trucks, trailers and other types of vehicles.

[0099] The first imaging device 122 may include any suitable type of imaging device. The imaging device 122 may include an optical axis. In one example, the imaging device 122 may include an Aptina M9V024 WVGA sensor with a global shutter. In other embodiments, the imaging device 122 may provide a resolution of 1280 × 960 pixels and may include a rolling shutter. The imaging device 122 may include various optical elements. In some embodiments, one or more lenses may be included to provide, for example, a desired focal length and field of view of the imaging device. In some embodiments, a 6 mm lens or a 12 mm lens may be associated with the imaging device 122. In some embodiments, the imaging device 122 may be configured to capture an image having a desired field of view (FOV) 202, as shown in Figure 2D. For example, the imaging device 122 may be configured to have a normal FOV, such as in the range of 40 to 56 degrees, including 46-degree FOV, 50-degree FOV, 52-degree FOV, or degrees greater than 52-degree FOV. Alternatively, the imaging device 122 may be configured to have a narrow FOV in the range of 23 to 40 degrees, such as a 28-degree FOV or a 36-degree FOV. In addition, the imaging device 122 may be configured to have a wide FOV in the range of 100 to 180 degrees. In some embodiments, the imaging device 122 may include a wide-angle bumper camera or a bumper camera having an FOV of up to 180 degrees. In some embodiments, the imaging device 122 may be a 7.2M pixel imaging device with an aspect ratio of about 2:1 (e.g., H×V=3800×1900 pixels) and a horizontal FOV of about 100 degrees. Such an imaging device may be used instead of a 3-imaging device configuration. Due to significant lens distortion, the vertical FOV of such an imaging device may be much smaller than 50 degrees in implementations where the imaging device uses a radially symmetric lens. For example, such a lens may not be radially symmetric, thereby allowing a vertical FOV greater than 50 degrees with a horizontal FOV of 100 degrees.

[0100] The first imaging device 122 can acquire multiple first images of a scene associated with the vehicle 200. Each of the multiple first images can be acquired as a series of image scan lines, which can be imaged using a rolling shutter. Each scan line may contain multiple pixels.

[0101] The first imaging device 122 may have a scan rate associated with the acquisition of each of the first series of image scan lines. The scan rate may refer to the rate at which the image sensor can acquire image data associated with each pixel contained in a particular scan line.

[0102] The imaging devices 122, 124, and 126 may include any suitable type and number of image sensors, for example, a CCD sensor or a CMOS sensor. In one embodiment, a CMOS image sensor may be used with a rolling shutter, so that each pixel in a row is read one at a time, and the scanning of the rows proceeds row by row until the entire image frame is captured. In some embodiments, the rows may be captured sequentially from top to bottom relative to the frame.

[0103] In some embodiments, one or more of the imaging devices disclosed herein (e.g., imaging devices 122, 124, and 126) may constitute a high-resolution imager and may have a resolution of more than 5 megapixels, more than 7 megapixels, more than 10 megapixels, or even higher.

[0104] The use of a rolling shutter can result in pixels within different rows being exposed and captured at different times, potentially leading to skew and other image artifacts in the captured image frame. On the other hand, if the imaging device 122 is configured to operate with a global or synchronous shutter, all pixels may be exposed over the same amount of time during a common exposure period. As a result, image data within a frame collected from a system using a global shutter represents a snapshot of the entire FOV (FOV 202, etc.) at a specific time. Conversely, when a rolling shutter is applied, each row within the frame is exposed, and the data is captured at different times. Therefore, moving objects may appear distorted in imaging devices with a rolling shutter. This phenomenon is described in more detail below.

[0105] The second imaging device 124 and the third imaging device 126 can be any type of imaging device. Like the first imaging device 122, each of the imaging devices 124 and 126 may include an optical axis. In one embodiment, each of the imaging devices 124 and 126 may include an Aptina M9V024 WVGA sensor with a global shutter. Alternatively, each of the imaging devices 124 and 126 may include a rolling shutter. Like the imaging device 122, the imaging devices 124 and 126 may be configured to include various lenses and optical elements. In some embodiments, the lenses associated with imaging devices 124 and 126 may be the same as the FOV associated with imaging device 122 (FOV 202, etc.) or provide a narrower FOV (FOV 204 and 206, etc.). For example, imaging devices 124 and 126 may have FOVs of 40 degrees, 30 degrees, 26 degrees, 23 degrees, 20 degrees, or less than 20 degrees.

[0106] The imaging devices 124 and 126 may acquire a plurality of second and third images for a scene associated with the vehicle 200. Each of the plurality of second and third images may be acquired as a second and third series of image scan lines, which may be imaged using a rolling shutter. Each scan line or each line may have a plurality of pixels. The imaging devices 124 and 126 may have second and third scan rates associated with acquiring each image scan line contained within the second and third series.

[0107] Each imaging device 122, 124, and 126 can be positioned in any suitable position and orientation relative to the vehicle 200. The relative positions of the imaging devices 122, 124, and 126 can be selected to assist in fusing the information acquired from the imaging devices. For example, in some embodiments, the FOV associated with imaging device 124 (FOV 204, etc.) may partially or completely overlap with the FOV associated with imaging device 122 (FOV 202, etc.) and the FOV associated with imaging device 126 (FOV 206, etc.).

[0108] The imaging devices 122, 124, and 126 can be positioned on the vehicle 200 at any appropriate relative height. In one example, there may be height differences between the imaging devices 122, 124, and 126, and these height differences may provide sufficient parallax information to enable stereoscopic analysis. For example, as shown in Figure 2A, the two imaging devices 122 and 124 are at different heights. There may also be lateral displacement differences between the imaging devices 122, 124, and 126, which provide additional parallax information for stereoscopic analysis by the processing unit 110, for example. The lateral displacement difference is as shown in Figures 2C and 2D, d x This can be shown as follows. In some embodiments, a frontal or rearward displacement (e.g., range displacement) may exist between imaging devices 122, 124, and 126. For example, imaging device 122 may be positioned 0.5 to 2 meters or more behind imaging devices 124 and / or imaging device 126. With this type of displacement, one of the imaging devices may be able to cover a potential blind spot of the other imaging device.

[0109] The imaging 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 imaging device 122 may be higher, lower, or the same as the resolution of the image sensors associated with imaging devices 124 and 126. In some embodiments, the image sensors associated with imaging device 122 and / or imaging devices 124 and 126 may have a resolution of 640×480, 1024×768, 1280×960, or any other suitable resolution.

[0110] The frame rate (e.g., the rate at which an imaging device acquires a set of pixel data for one image frame before moving on to capturing the pixel data associated with the next image frame) may be controllable. The frame rate associated with imaging device 122 may be higher, lower, or the same as the frame rates associated with imaging devices 124 and 126. The frame rates associated with imaging devices 122, 124, and 126 may depend on various factors that may affect the timing of the frame rate. For example, one or more of imaging devices 122, 124, and 126 may include a selectable pixel delay period that is imposed before or after the acquisition of image data associated with one or more pixels of the image sensors within imaging devices 122, 124, and / or 126. Generally, the image data corresponding to each pixel may be acquired according to the device's clock rate (e.g., one pixel per clock cycle). Furthermore, in embodiments including a rolling shutter, one or more of the imaging devices 122, 124, and 126 may include a selectable horizontal blanking period that is imposed before or after acquisition of image data associated with pixel rows of image sensors within imaging devices 122, 124, and / or 126. Furthermore, one or more of the imaging devices 122, 124, and / or 126 may include a selectable vertical blanking period that is imposed before or after acquisition of image data associated with image frames of imaging devices 122, 124, and 126.

[0111] These timing controls make it possible to synchronize the frame rates associated with imaging devices 122, 124, and 126, even if the line scan rates of each imaging device are different. Furthermore, as will be discussed in more detail below, these selectable timing controls, in particular among other factors (e.g., image sensor resolution, maximum line scan rate, etc.), make it possible to synchronize imaging from areas where the FOV of imaging device 122 overlaps with one or more FOVs of imaging devices 124 and 126, even if the field of view of imaging device 122 is different from the FOV of imaging devices 124 and 126.

[0112] The frame rate timing in imaging devices 122, 124, and 126 may depend on the resolution of the associated image sensors. For example, assuming that both devices have similar line scan rates, and one device includes an image sensor with a resolution of 640 × 480 and the other device includes an image sensor with a resolution of 1280 × 960, acquiring frames of image data from the sensor with a higher resolution will require a longer time.

[0113] Another factor that can affect the timing of image data acquisition in imaging 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 imaging devices 122, 124, and 126 requires some minimum amount of time. Assuming no pixel delay period is added, this minimum amount of time to acquire a line of image data will be related to the maximum line scan rate of a particular device. Devices that offer a higher maximum line scan rate have the potential to offer a higher frame rate than devices with a lower maximum line scan rate. In some embodiments, one or both of imaging devices 124 and 126 may have a higher maximum line scan rate than the maximum line scan rate associated with imaging device 122. In some embodiments, the maximum line scan rate of imaging device 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 imaging device 122.

[0114] In another embodiment, imaging devices 122, 124, and 126 may have the same maximum line scan rate, but imaging device 122 may operate at a scan rate lower than or equal to its maximum scan rate. The system may be configured so that one or both of imaging devices 124 and 126 operate at a line scan rate equal to that of imaging device 122. In other examples, the system may be configured so that the line scan rates of imaging device 124 and / or imaging device 126 are 1.25 times, 1.5 times, 1.75 times, or 2 times or more the line scan rate of imaging device 122.

[0115] In some embodiments, the imaging devices 122, 124, and 126 may be asymmetric. That is, these imaging devices may include cameras having different fields of view (FOV) and focal lengths. The fields of view of the imaging devices 122, 124, and 126 may include, for example, any desired area of ​​the environment of the vehicle 200. In some embodiments, one or more of the imaging devices 122, 124, and 126 may be configured to acquire image data from the environment in front of the vehicle 200, the environment behind the vehicle 200, the environments on both sides of the vehicle 200, or a combination thereof.

[0116] Furthermore, the focal lengths associated with each imaging device 122, 124, and / or 126 may be selectable (e.g., by incorporating an appropriate lens) so that each device acquires images of objects within a desired distance range relative to the vehicle 200. For example, in some embodiments, imaging devices 122, 124, and 126 may acquire images of nearby objects within a few meters of the vehicle. Imaging devices 122, 124, and 126 may also be configured to acquire images of objects at a greater distance from the vehicle (e.g., 25m, 50m, 100m, 150m, or beyond). Furthermore, the focal lengths of the imaging devices 122, 124, and 126 can be selected so that one imaging device (e.g., imaging device 122) can acquire images of objects relatively close to the vehicle (e.g., within 10m or 20m), while the other imaging devices (e.g., imaging devices 124 and 126) can acquire images of objects further away from the vehicle 200 (e.g., beyond 20m, beyond 50m, beyond 100m, beyond 150m, etc.).

[0117] According to several embodiments, the field of view (FOV) of one or more imaging devices 122, 124, and 126 may be wide-angle. For example, it may be advantageous for imaging devices 122, 124, and 126, which can be used to capture images of a near-field area of ​​the vehicle 200 in particular, to have a 140-degree FOV. For example, imaging device 122 may be used to capture images of the right or left area of ​​the vehicle 200, and in such embodiments, it may be desirable for imaging device 122 to have a wide FOV (e.g., at least 140 degrees).

[0118] The fields of view associated with each of the imaging devices 122, 124, and 126 may depend on their respective focal lengths. For example, as the focal length increases, the corresponding field of view decreases.

[0119] The imaging devices 122, 124, and 126 can be configured to have any suitable field of view. In one particular example, imaging device 122 may have a horizontal FOV of 46 degrees, imaging device 124 may have a horizontal FOV of 23 degrees, and imaging device 126 may have a horizontal FOV of 23 to 46 degrees. In another example, imaging device 122 may have a horizontal FOV of 52 degrees, imaging device 124 may have a horizontal FOV of 26 degrees, and imaging device 126 may have a horizontal FOV of 26 to 52 degrees. In some embodiments, the ratio of the FOV of imaging device 122 to the FOV of imaging device 124 and / or imaging device 126 may vary from 1.5 to 2.0. In other embodiments, this ratio may vary from 1.25 to 2.25.

[0120] System 100 may be configured such that the field of view of imaging device 122 at least partially or completely overlaps with the fields of view of imaging device 124 and / or imaging device 126. In some embodiments, System 100 may be configured such that the fields of view of imaging devices 124 and 126 fall within the field of view of imaging device 122 (e.g., smaller than the field of view of imaging device 122) and share a common center with the field of view of imaging device 122. In other embodiments, imaging devices 122, 124 and 126 may image adjacent FOVs or have partially overlapping FOVs. In some embodiments, the fields of view of imaging devices 122, 124 and 126 may be aligned such that the centers of imaging device 124 and / or 126 with narrower FOVs are located in the lower half of the field of view of device 122 with wider FOVs.

[0121] Figure 2F is a graphical representation of an exemplary vehicle control system according to the disclosed embodiment. As shown in Figure 2F, the vehicle 200 may include a throttle system 220, a brake system 230, and a steering system 240. System 100 may provide input (e.g., control signals) to one or more of the throttle system 220, brake system 230, and steering system 240 via one or more data links (e.g., one or more arbitrary wired and / or wireless links for transmitting data). For example, based on the analysis of images acquired by imaging devices 122, 124, and / or 126, System 100 may provide control signals to one or more of the throttle system 220, brake system 230, and steering system 240 to navigate the vehicle 200 (e.g., by causing it to accelerate, turn, shift lanes, etc.). Furthermore, system 100 may receive inputs indicating the operating conditions of the vehicle 200 (e.g., speed, whether the vehicle 200 is braking and / or turning, etc.) from one or more of the throttle system 220, brake system 230, and steering system 24. Further details are provided below in reference to Figures 4 to 7.

[0122] As shown in Figure 3A, the vehicle 200 may also include a user interface 170 for interacting with the driver or occupant of the vehicle 200. For example, the user interface 170 in the vehicle application may include a touchscreen 320, a knob 330, buttons 340, and a microphone 350. The driver or occupant of the vehicle 200 may also interact with the system 100 using a steering wheel (e.g., including a turn signal handle, located on or near the steering column of the vehicle 200) and buttons (e.g., located on the steering wheel of the vehicle 200) and similar. In some embodiments, the microphone 350 may be positioned adjacent to the rearview mirror 310. Similarly, in some embodiments, the imaging device 122 may be positioned near the rearview mirror 310. In some embodiments, the user interface 170 may also include one or more speakers 360 (e.g., speakers of the vehicle audio system). For example, the system 100 may provide various notifications (e.g., alerts) via the speakers 360.

[0123] Figures 3B to 3D illustrate an exemplary camera mount 370 according to a disclosed embodiment, configured to be positioned behind a rearview mirror (e.g., rearview mirror 310) and facing the vehicle windshield. As shown in Figure 3B, the camera mount 370 may include imaging devices 122, 124, and 126. The imaging devices 124 and 126 may be positioned behind a glare shield 380, which may be in direct contact with the vehicle windshield and may include a composition of film and / or anti-reflective material. For example, the glare shield 380 may be positioned so that the shield aligns with the vehicle windshield having a matching incline. In some embodiments, each of the imaging devices 122, 124, and 126 may be positioned behind the glare shield 380, as shown, for example, in Figure 3D. The disclosed embodiments are not limited to any particular configuration of the imaging devices 122, 124, and 126, the camera mount 370, and the glare shield 380. Figure 3C is a front view of the camera mount 370 shown in Figure 3B.

[0124] As will be understood by those skilled in the art who benefit from this disclosure, many modifications and / or changes can be made to the disclosed embodiments described above. For example, not all components are essential for the operation of system 100. Furthermore, any component may be placed in any suitable part of system 100, and the components may be rearranged into various configurations while providing the functionality of the disclosed embodiments. Thus, the configurations discussed above are examples, and regardless of the configurations described above, system 100 can provide a wide range of functionality for analyzing the surroundings of vehicle 200 and navigating vehicle 200 in response to the analysis.

[0125] As will be discussed in more detail below, through various embodiments disclosed, 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 the vehicle 200. System 100 can collect data for analysis from, for example, an image acquisition unit 120, a location sensor 130 and other sensors. Furthermore, System 100 can analyze the collected data to determine whether the vehicle 200 should take a particular action, and then automatically take the determined action without human intervention. For example, if the vehicle 200 is navigating without human intervention, System 100 can automatically control the braking, acceleration and / or steering of the vehicle 200 (for example, by transmitting control signals to one or more of the throttle system 220, brake system 230 and steering system 240). Furthermore, System 100 can analyze the collected data and issue warnings and / or alerts to the vehicle's occupants based on the analysis of the collected data. Further details regarding the various embodiments provided by System 100 are provided below.

[0126] [Forward-facing multi-imaging system]

[0127] As discussed above, system 100 may provide a driving assistance function using a multi-camera system. The multi-camera system may use one or more cameras facing forward of the vehicle. In other embodiments, the multi-camera system may include one or more cameras facing side or rear of the vehicle. In one embodiment, for example, system 100 may use a two-camera imaging system, in which case the first and second cameras (e.g., imaging devices 122 and 124) may be positioned at the front and / or side of the vehicle (e.g., vehicle 200). The first camera may have a field of view that is larger than, smaller than, or partially overlaps with that of the second camera. Furthermore, the first camera may be connected to a first image processor to perform monocular image analysis of the image provided by the first camera, and the second camera may be connected to a second image processor to perform monocular image analysis of the image provided by the second camera. The outputs of the first and second image processors (e.g., processed information) may be combined. In some embodiments, a second image processor may receive images from both the first and second cameras and perform stereoscopic analysis. In another embodiment, system 100 may use a three-camera imaging system in which each camera has a different field of view. Thus, such a system may make decisions based on information derived from objects located at various distances both in front of and to the sides of the vehicle. A reference to monocular image analysis may refer to a case in which the image analysis is performed based on images captured from a single viewpoint (e.g., from a single camera). Stereoscopic image analysis may refer to a case in which the image analysis is performed based on two or more images captured with one or more of the imaging parameters changed. For example, captured images suitable for performing stereoscopic image analysis may include images captured from two or more different locations, images captured from different fields of view, images captured using different focal lengths, images captured with parallax information, etc.

[0128] For example, in one embodiment, system 100 may implement a three-camera configuration using imaging devices 122, 124, and 126. In such a configuration, imaging device 122 may provide a narrow field of view (e.g., 34 degrees or other values ​​selected from the range of about 20 to 45 degrees), imaging device 124 may provide a wide field of view (e.g., 150 degrees or other values ​​selected from the range of about 100 to about 180 degrees), and imaging device 126 may provide a medium field of view (e.g., 46 degrees or other values ​​selected from the range of about 35 to about 60 degrees). In some embodiments, imaging device 126 may operate as a primary or first-order camera. The imaging devices 122, 124, and 126 may be positioned behind the rearview mirror 310 and substantially side by side (e.g., 6 cm apart). Furthermore, in some embodiments, as discussed above, one or more of the imaging devices 122, 124, and 126 may be mounted behind a glare shield 380 that is coplanar with the windshield of the vehicle 200. Such a shield may operate to minimize the impact of any reflections from inside the vehicle on the imaging devices 122, 124, and 126.

[0129] In another embodiment, as discussed above in relation to Figures 3B and 3C, a wide-field camera (e.g., imaging device 124 in the above example) may be mounted lower than a narrow primary-field camera (e.g., imaging devices 122 and 126 in the above example). This configuration may provide a free line of sight from the wide-field camera. To reduce reflections, the camera may be mounted near the windshield of the vehicle 200 and may include a polarizer to reduce reflected light.

[0130] A three-camera system can offer specific performance characteristics. For example, some embodiments may include a function to verify object detection by one camera based on detection results from another camera. In the three-camera configuration discussed above, the processing unit 110 may include, for example, three processing devices (e.g., three EyeQ series processor chips as discussed above), each processing device dedicated to processing images captured by one or more of the imaging devices 122, 124, and 126.

[0131] 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 on the narrow-field-of-view camera to detect, for example, other vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. Furthermore, the first processing device can calculate the pixel mismatch between the image from the main camera and the image from the narrow-field-of-view camera and create a 3D reconstruction of the vehicle 200's environment. Next, the first processing device can combine the 3D reconstruction with 3D map data or 3D information calculated based on information from another camera.

[0132] The second processing device may receive images from the main camera, perform vision processing, and detect other vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. Furthermore, the second processing device may calculate camera displacement, calculate pixel mismatches between consecutive images based on the displacement, and create a 3D reconstruction of the scene (e.g., structure from motion). The second processing device may transmit the structure from motion based on the 3D reconstruction to the first processing device and combine the structure from motion with the stereoscopic 3D image.

[0133] A third processing device may receive images from a wide-field-of-view 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 commands to analyze the images and identify moving objects in the images, such as vehicles changing lanes or pedestrians.

[0134] In some embodiments, independently capturing and processing image-based information streams may provide an opportunity to offer redundancy in the system. Such redundancy may include, for example, using a first imaging device and the images processed from that device to verify and / or complement information obtained by capturing and processing image information from at least a second imaging device.

[0135] In some embodiments, system 100 may use two imaging devices (e.g., imaging devices 122 and 124) to provide navigation assistance to vehicle 200, and a third imaging device (e.g., imaging device 126) may be used to provide redundancy and verify the analysis of data received from the other two imaging devices. For example, in such a configuration, imaging devices 122 and 124 may provide images for stereoscopic analysis by system 100 for navigating vehicle 200, and imaging device 126 may provide images for monocular analysis by system 100 to provide redundancy and verification of information obtained based on images captured from imaging devices 122 and / or imaging devices 124. That is, imaging device 126 (and corresponding processing device) may be considered to provide a redundant subsystem for providing checks on the analysis derived from imaging devices 122 and 124 (e.g., providing an automatic emergency braking (AEB) system). Furthermore, in some embodiments, the redundancy and verification of the received data can be supplemented based on information received from one or more sensors (e.g., radar, lidar, acoustic sensors, information received from one or more transceivers outside the vehicle).

[0136] Those skilled in the art will recognize that the above-described camera configuration, camera arrangement, number of cameras, camera positions, etc., are merely illustrative. These components, described in relation 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 the multi-camera system to provide driver assistance and / or autonomous vehicle functions follow below.

[0137] Figure 4 is an exemplary functional block diagram of memories 140 and / or 150 in which instructions for performing one or more operations according to the disclosed embodiments may be stored / programmed. Hereafter, we will refer to memory 140, but those skilled in the art will recognize that instructions can be stored in memories 140 and / or 150.

[0138] As shown in Figure 4, memory 140 may store a monocular image analysis module 402, a stereoscopic 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, the application processor 180 and / or the image processor 190 may execute instructions stored in any of the modules 402, 404, 406, and 408 contained in memory 140. Those skilled in the art will understand that references to processing unit 110 in the following discussion may refer to the application processor 180 and the image processor 190 individually or collectively. Thus, any step of the following process may be performed by one or more processing devices.

[0139] In one embodiment, the monocular image analysis module 402 may store instructions (such as computer vision software) to perform monocular image analysis of a set of images acquired by one of the imaging devices 122, 124, and 126, when executed by the processing unit 110. In some embodiments, the processing unit 110 may perform monocular image analysis by combining information from the set of images with additional sensory information (e.g., information from radar or LiDAR). As described below in relation to Figures 5A to 5D, 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 materials, and any other features associated with the vehicle's environment. Based on the analysis, the system 100 may (e.g., via the processing unit 110) cause one or more navigation responses in the vehicle 200, such as turns, lane shifts, and acceleration changes, and so on, as discussed below in relation to the navigation response module 408.

[0140] In one embodiment, the stereoscopic image analysis module 404 may store instructions (such as computer vision software) that, when executed by the processing unit 110, perform stereoscopic image analysis of first and second sets of images acquired by a combination of imaging devices selected from among imaging devices 122, 124, and 126. In some embodiments, the processing unit 110 may perform stereoscopic image analysis by combining information from the first and second sets of images with additional sensory information (e.g., information from radar). For example, the stereoscopic image analysis module 404 may include instructions to perform stereoscopic image analysis based on a first set of images acquired by imaging device 124 and a second set of images acquired by imaging device 126. As will be described below with reference to Figure 6, the stereoscopic image analysis module 404 may include instructions to detect sets of features in the first and second sets of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazardous materials, and the like. Based on the analysis, the processing unit 110 may cause one or more navigation responses in the vehicle 200, such as turns, lane shifts, and acceleration changes, and the like, as discussed below in relation to the navigation response module 408. Furthermore, in some embodiments, the stereoscopic image analysis module 404 may implement techniques related to untrained systems, such as a system that can use a trained system (such as a neural network or deep neural network) or a computer vision algorithm to detect and / or label objects in an environment where sensory information has been captured and processed. In one embodiment, the stereoscopic image analysis module 404 and / or other image processing modules may be configured to use a combination of trained and untrained systems.

[0141] 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 within the vehicle 200 that are configured to change the velocity and / or acceleration of the vehicle 200. For example, the processing unit 110 may execute commands associated with the velocity and acceleration module 406 to calculate the target velocity of the vehicle 200 based on data derived from the execution of the monocular image analysis module 402 and / or stereoscopic image analysis module 404. Such data may include, for example, target position, velocity and / or acceleration, the position and / or speed of the vehicle 200 relative to nearby vehicles, pedestrians or road objects, and the position information of the vehicle 200 relative to road lane markings, and so on. In addition, the processing unit 110 may calculate the target velocity of the vehicle 200 based on sensory input (e.g., information from radar) and input from other systems of the vehicle 200, such as the vehicle's throttle system 220, brake system 230 and / or steering system 240. Based on the calculated target speed, the processing unit 110 can send electronic signals to the vehicle 200's throttle system 220, brake system 230 and / or steering system 240 to trigger changes in speed and / or acceleration, for example, by physically pressing the brakes of the vehicle 200 or releasing the accelerator.

[0142] In one embodiment, the navigation response module 408 may store software executable by the processing unit 110 to determine a desired navigation response based on data derived from the execution of the monocular image analysis module 402 and / or the stereoscopic image analysis module 404. Such data may include position and speed information associated with nearby vehicles, pedestrians and road objects, and target position information for the vehicle 200, and so on. Furthermore, in some embodiments, the navigation response may be based (partially or entirely) on map data, a predetermined position of the vehicle 200, and / or relative velocity or relative acceleration between the vehicle 200 and one or more objects detected from the execution of the monocular image analysis module 402 and / or the stereoscopic image analysis module 404. The navigation response module 408 may also determine a desired navigation response based on sensory input (e.g., information from radar) and input from other systems of the vehicle 200, such as the vehicle's throttle system 220, brake system 230, and steering system 240. Based on the desired navigation response, the processing unit 110 may trigger the desired navigation response by sending electronic signals to the vehicle 200's throttle system 220, brake system 230, and steering system 240, for example, by turning the steering wheel of the vehicle 200 to achieve a predetermined angle of rotation. In some embodiments, the processing unit 110 may use the output of the navigation response module 408 (e.g., the desired navigation response) as input to the execution of the speed and acceleration module 406 for calculating changes in the vehicle 200's speed.

[0143] Furthermore, any of the modules disclosed herein (e.g., modules 402, 404, and 406) can implement techniques related to trained systems (such as neural networks or deep neural networks) or untrained systems.

[0144] Figure 5A is a flowchart illustrating an exemplary process 500A according to a disclosed embodiment that generates one or more navigation responses based on monocular image analysis. In step 510, the processing unit 110 may receive multiple images via a data interface 128 between the processing unit 110 and the image acquisition unit 120. For example, a camera included in the image acquisition unit 120 (e.g., an imaging device 122 having a field of view 202) may capture multiple images of an area in front of the vehicle 200 (or, for example, the side or rear of the vehicle) and transmit them to the processing unit 110 via a data connection (e.g., digital, wired, USB, wireless, Bluetooth, etc.). The processing unit 110 may perform a monocular image analysis module 402 to analyze the multiple images in step 520, as will be described in more detail below in relation to Figures 5B to 5D. By performing the analysis, the processing unit 110 may detect sets of features within the image set, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, and the like.

[0145] In step 520, the processing unit 110 may also run the monocular image analysis module 402 to detect various road hazards, such as truck tire parts, fallen road signs, loose cargo, small animals, and similar objects. The structure, shape, size, and color of road hazards can vary, which can make the detection of such hazards more difficult. In some embodiments, the processing unit 110 may run the monocular image analysis module 402 to perform multi-frame analysis on multiple images to detect road hazards. For example, the processing unit 110 may estimate the camera movement between consecutive image frames and calculate the pixel mismatch between frames to construct a 3D map of the road. The processing unit 110 may then use the 3D map to detect the road surface and any hazards present on the road surface.

[0146] In step 530, the processing unit 110 may execute the navigation response module 408 to produce one or more navigation responses in the vehicle 200 based on the analysis performed in step 520 and the techniques described above in relation to Figure 4. Navigation responses may include, for example, turns, lane shifts and acceleration changes, and the like. In some embodiments, the processing unit 110 may use data derived from the execution of the speed and acceleration module 406 to produce one or more navigation responses. Furthermore, the multiple navigation responses may occur simultaneously, sequentially, or in any combination thereof. For example, the processing unit 110 may cause the vehicle 200 to cross one lane and then accelerate by sequentially sending control signals to the steering system 240 and throttle system 220 of the vehicle 200. Alternatively, the processing unit 110 may cause the vehicle 200 to brake and simultaneously shift lanes by simultaneously sending control signals to the brake system 230 and steering system 240 of the vehicle 200.

[0147] Figure 5B is a flowchart illustrating an exemplary process 500B for detecting one or more vehicles and / or pedestrians in a set of images according to a disclosed embodiment. Processing unit 110 may perform process 500B by running monocular image analysis module 402. In 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 and compare the images to one or more predetermined patterns to identify locations in each image that may contain a target object (e.g., a vehicle, pedestrian, or part thereof). The predetermined patterns may be specified to achieve a high rate of "false hits" and a low rate of "misses". For example, processing unit 110 may use a low similarity threshold to a predetermined pattern to identify a candidate object as a possible vehicle or pedestrian. In doing so, processing unit 110 may reduce the probability of missing (e.g., not identifying) a candidate object representing a vehicle or pedestrian.

[0148] In step 542, the processing unit 110 may filter the set of candidate objects to exclude certain candidates (e.g., irrelevant or unrelated objects) based on classification criteria. Such criteria may be derived from various characteristics associated with object types stored in a database (e.g., a database stored in memory 140). Characteristics may include the shape, dimensions, texture and position (e.g., relative to the vehicle 200) of the object, and similar characteristics. Thus, the processing unit 110 may use one or more sets of criteria to reject false candidates from the set of candidate objects.

[0149] In step 544, the processing unit 110 may analyze multiple image frames to determine whether an object in a set of candidate objects represents a vehicle and / or a pedestrian. For example, the processing unit 110 may track candidate objects detected across consecutive frames and accumulate frame-by-frame data associated with the detected objects (e.g., size, position relative to the vehicle 200, etc.). Furthermore, the processing unit 110 may estimate the parameters of the detected objects and compare the frame-by-frame position data of the objects with a predicted position.

[0150] In step 546, the processing unit 110 may construct a set of measurements of the detected object. Such measurements may include, for example, position, velocity, and acceleration values ​​associated with the detected object (relative to the vehicle 200). In some embodiments, the processing unit 110 may construct measurements based on estimation techniques that use a series of time-based observations, such as a Kalman filter or linear quadratic estimation (LQE), and / or based on modeling data available for different object types (e.g., cars, trucks, pedestrians, bicycles, road signs, etc.). The Kalman filter is obtained based on a measurement of the object's scale, where the scale measurement is proportional to the time to collision (e.g., the amount of time until the vehicle 200 reaches the object). Thus, by performing steps 540-546, the processing unit 110 may identify vehicles and pedestrians appearing in the set of captured images and derive information associated with the vehicles and pedestrians (e.g., position, velocity, size). Based on the identification and derived information, the processing unit 110 may produce one or more navigation responses in the vehicle 200, as described above in relation to Figure 5A.

[0151] In step 548, the processing unit 110 may perform optical flow analysis on one or more images to reduce the probability of detecting a "false hit" and missing a candidate object representing a vehicle or pedestrian. Optical flow analysis may refer to, for example, analyzing a motion pattern different from the motion of the road surface relative to the vehicle 200 in one or more images associated with other vehicles and pedestrians. The processing unit 110 may calculate the motion of a candidate object by observing the different positions of the object across multiple image frames captured at different times. The processing unit 110 may calculate the motion of a candidate object by using position and time values ​​as input to a mathematical model. Thus, optical flow analysis may provide an alternative method for detecting vehicles and pedestrians near the vehicle 200. The processing unit 110 may perform optical flow analysis in combination with steps 540-546 to provide redundancy in detecting vehicles and pedestrians and increase the reliability of the system 100.

[0152] Figure 5C is a flowchart illustrating an exemplary process 500C for detecting road mark and / or lane geometry information within a set of images according to a disclosed embodiment. Processing unit 110 may perform process 500C by executing monocular image analysis module 402. In step 550, processing unit 110 may detect sets of objects by scanning one or more images. To detect lane mark segments, lane geometry information and other related road marks, processing unit 110 may filter the sets of objects to exclude those determined to be irrelevant (e.g., small holes, small stones, etc.). In step 552, processing unit 110 may group together segments detected in step 550 that belong to the same road mark or lane mark. Based on the grouping, processing unit 110 may develop a model, such as a mathematical model, to represent the detected segments.

[0153] In step 554, the processing unit 110 may construct a set of measurements associated with the detected segment. In some embodiments, the processing unit 110 may create a projection of the detected segment from the image plane onto a real-world plane. The projection may be characterized using a cubic polynomial with coefficients corresponding to physical properties such as the detected road position, slope, curvature, and derivative of curvature. In generating the projection, the processing unit 110 may take into account changes in the road surface as well as the pitch and roll rates associated with the vehicle 200. In addition, the processing unit 110 may model the road height by analyzing the position and motion cues present on the road surface. Furthermore, the processing unit 110 may estimate the pitch and roll rates associated with the vehicle 200 by tracking a set of feature points in one or more images.

[0154] In step 556, the processing unit 110 may perform a multi-frame analysis, for example, by tracking the detected segments across consecutive image frames and accumulating frame-by-frame data associated with the detected segments. When the processing unit 110 performs a multi-frame analysis, the set of measurements constructed in step 554 may become more reliable and can be associated with increasingly higher confidence. Thus, by performing steps 550, 552, 554, and 556, the processing unit 110 may identify road marks appearing in the set of captured images and derive lane geometry information. Based on the identified and derived information, the processing unit 110 may generate one or more navigation responses in the vehicle 200, as described above in relation to Figure 5A.

[0155] In step 558, the processing unit 110 may further develop a safety model of the vehicle 200 in the surrounding environment by considering additional information sources. Using the safety model, the processing unit 110 may define the conditions under which the system 100 can safely perform autonomous control of the vehicle 200. To develop the safety model, in some embodiments, the processing unit 110 may consider the positions and movements of other vehicles, detected road edges and barriers, and / or general road shape descriptions extracted from map data (such as data from the map database 160). By considering additional information sources, the processing unit 110 may provide redundancy for detecting road marks and lane geometry, thereby increasing the reliability of the system 100.

[0156] Figure 5D is a flowchart illustrating an exemplary process 500D for detecting a traffic light in a set of images according to a disclosed embodiment. Processing unit 110 may perform process 500D by running monocular image analysis module 402. In step 560, processing unit 110 may scan the set of images and identify objects appearing at locations in the images that are likely to contain traffic lights. For example, processing unit 110 may filter the identified objects to construct a set of candidate objects that exclude objects that are less likely to correspond to traffic lights. Filtering may be based on various characteristics associated with traffic lights, such as shape, dimensions, texture and location (e.g., relative to vehicle 200), and similar. Such characteristics may be obtained based on multiple examples of traffic lights and traffic control signals and stored in a database. In some embodiments, processing unit 110 may perform multi-frame analysis on the set of candidate objects that reflect possible traffic lights. For example, processing unit 110 may track candidate objects across consecutive image frames, estimate the real-world location of the candidate objects, and filter out objects that are moving (less likely to be traffic lights). In some embodiments, the processing unit 110 may perform color analysis on candidate objects to identify the relative positions of detected colors that may be displayed inside a traffic light.

[0157] In step 562, the processing unit 110 may analyze the geometry of the intersection. The analysis may be based on any combination of (i) the number of lanes detected on both sides of the vehicle 200, (ii) marks detected on the road (such as arrow marks), and (iii) a description of the intersection extracted from map data (such as data from the map database 160). The processing unit 110 may perform the analysis using information derived from the execution of the monocular analysis module 402. In addition, the processing unit 110 may determine the correspondence between the traffic lights detected in step 560 and the lanes that appear near the vehicle 200.

[0158] As the vehicle 200 approaches the intersection, in step 564, the processing unit 110 may update the confidence level associated with the analyzed intersection geometry and detected traffic signals. For example, the number of traffic signals estimated to appear at the intersection compared to the number actually appearing at the intersection may affect the confidence level. Based on the confidence level, the processing unit 110 may delegate control to the driver of the vehicle 200 to improve safety conditions. By performing steps 560, 562, and 564, the processing unit 110 may identify the traffic signals appearing in the set of captured images and analyze the intersection geometry information. Based on the identification and analysis, the processing unit 110 may cause one or more navigation responses in the vehicle 200 as described above in relation to Figure 5A.

[0159] Figure 5E is a flowchart illustrating an exemplary process 500E according to a disclosed embodiment for generating one or more navigation responses in a vehicle 200 based on a vehicle path. In step 570, the processing unit 110 may construct an initial vehicle path associated with the vehicle 200. The vehicle path may be represented using a set of points represented by coordinates (x,y), where d is the distance between two points in the set of points. iThis can be in the range of 1 to 5 meters. In one embodiment, the processing unit 110 may construct an initial vehicle path using two polynomials, such as left and right road polynomials. The processing unit 110 calculates the geometric midpoint between the two polynomials and, if there is a predetermined offset (offset 0 may correspond to driving in the center of the lane), may offset each point included in the resulting vehicle path by the predetermined offset (e.g., smart lane offset). The offset may be in a direction perpendicular to the division between any two points in the vehicle path. In another embodiment, the processing unit 110 may use one polynomial and the estimated lane width to offset each point in the vehicle path by half the estimated lane width plus a predetermined offset (e.g., smart lane offset).

[0160] In step 572, the processing unit 110 may update the vehicle route constructed in step 570. The processing unit 110 updates the distance d between two points in the set of points representing the vehicle route. k However, the distance d mentioned above i The vehicle path constructed in step 570 can be reconstructed using a higher resolution so that it is shorter than d. For example, distance d k This can be in the range of 0.1 to 0.3 meters. The processing unit 110 may reconstruct the vehicle path using a parabolic spline algorithm, which may yield a cumulative distance vector S corresponding to the total length of the vehicle path (i.e., based on a set of points representing the vehicle path).

[0161] In step 574, the processing unit 110 reads the look-ahead points ((x l ,z l) that can be represented by coordinates). The processing unit 110 can extract a look-ahead point from the cumulative distance vector S, and can associate a look-ahead distance and a look-ahead time with the look-ahead point. The look-ahead distance can have a lower limit range of 10 to 20 meters and can be calculated as the product of the speed of the vehicle 200 and the look-ahead time. For example, as the speed of the vehicle 200 decreases, the look-ahead distance can also become shorter (e.g., until it reaches the lower limit). The look-ahead time, which can be in the range of 0.5 to 1.5 seconds, can be inversely proportional to the gain of one or more control loops associated with generating a navigation response in the vehicle 200, such as a tracking control loop for progress error. For example, the gain of the tracking control loop for progress error can depend on the bandwidths of a yaw rate loop, a steering actuator loop, and the lateral dynamics of the vehicle, and the like. Therefore, the higher the gain of the tracking control loop for progress error, the shorter the look-ahead time.

[0162] In step 576, the processing unit 110 can determine a progress error and a yaw rate command based on the look-ahead point specified in step 574. The processing unit 110 can identify the progress error by calculating the arctangent of the look-ahead point, e.g., arctan(x l / z l ). The processing unit 110 can determine the yaw rate command as the product of the progress error and a high-level control gain. The high-level control gain can be a value equal to (2 / look-ahead time) when the look-ahead distance is not at the lower limit. Otherwise, when the look-ahead distance is at the lower limit, the high-level control gain can be a value equal to (2 * speed of the vehicle 200 / look-ahead distance).

[0163] Figure 5F is a flowchart illustrating an exemplary process 500F according to a disclosed embodiment for determining whether a preceding vehicle is changing lanes. In step 580, the processing unit 110 may determine navigation information associated with the preceding vehicle (e.g., a vehicle traveling ahead of vehicle 200). For example, the processing unit 110 may determine the position, speed (e.g., direction and velocity) and / or acceleration of the preceding vehicle using the techniques described above in relation to Figures 5A and 5B. The processing unit 110 may also determine one or more road polynomials, look-ahead points (associated with vehicle 200) and / or snail trails (e.g., sets of points describing the path taken by the preceding vehicle) using the techniques described above in relation to Figure 5E.

[0164] In step 582, the processing unit 110 may analyze the navigation information identified in step 580. In one embodiment, the processing unit 110 may calculate the distance (e.g., along the trail) between the snail trail and the road polynomial. If the difference in this distance along the trail exceeds a predetermined threshold (e.g., 0.1 to 0.2 meters for straight roads, 0.3 to 0.4 meters for gently curving roads, and 0.5 to 0.6 meters for sharply curving roads), the processing unit 110 may determine that the preceding vehicle is likely changing lanes. If it is detected that multiple vehicles are traveling ahead of vehicle 200, the processing unit 110 may compare the snail trails associated with each vehicle. Based on the comparison, the processing unit 110 may determine that a vehicle whose snail trail does not match the snail trail of another vehicle is likely changing lanes. The processing unit 110 may further compare the curvature of the snail trail (associated with the preceding vehicle) with the expected curvature of the road section on which the preceding vehicle is traveling. The expected curvature can be extracted from map data (e.g., data from map database 160), road polynomials, snail trails of other vehicles, prior knowledge about the road, and similar sources. If the difference between the curvature of the snail trail and the expected curvature of the road section exceeds a predetermined threshold, the processing unit 110 may determine that there is a high probability that the preceding vehicle is changing lanes.

[0165] In another embodiment, the processing unit 110 may compare the instantaneous position of a preceding vehicle with a look-ahead point (associated with the vehicle 200) over a specific time period (e.g., 0.5 to 1.5 seconds). If the difference in distance between the instantaneous position of the preceding vehicle and the look-ahead point, and the cumulative sum of the branches, during the specific time period exceeds a predetermined threshold (e.g., 0.3 to 0.4 meters for straight roads, 0.7 to 0.8 meters for gently curving roads, and 1.3 to 1.7 meters for sharply curving roads), the processing unit 110 may determine that the preceding vehicle is likely changing lanes. In another embodiment, the processing unit 110 may analyze the geometry of a snail trail by comparing the lateral distance traveled along the trail with the expected curvature of the snail trail. The expected radius of curvature is calculated as: (δ z 2 +δ x 2 ) / 2 / (δ x ) can be determined according to the formula, where σ x σ represents the lateral movement distance. z represents the vertical travel distance. If the difference between the horizontal travel distance and the expected curvature exceeds a predetermined threshold (e.g., 500-700 meters), the processing unit 110 may determine that the preceding vehicle is likely to be changing lanes. In another embodiment, the processing unit 110 may analyze the position of the preceding vehicle. If the position of the preceding vehicle obscures the road polynomial (e.g., the preceding vehicle overlaps the road polynomial), the processing unit 110 may determine that the preceding vehicle is likely to be changing lanes. If the position of the preceding vehicle is such that another vehicle is detected ahead of the preceding vehicle and the snail trails of the two vehicles are not parallel, the processing unit 110 may determine that the (closer) preceding vehicle is likely to be changing lanes.

[0166] In step 584, the processing unit 110 may determine whether the preceding vehicle 200 is changing lanes based on the analysis performed in step 582. For example, the processing unit 110 may make this determination based on a weighted average of the individual analyses performed in step 582. Under such a scheme, for example, the processing unit 110 may assign a value of "1" to its determination that the preceding vehicle is likely to be changing lanes based on a particular type of analysis (where "0" represents a determination that the preceding vehicle is unlikely to be changing lanes). Different weights may be assigned to different analyses performed in step 582, and the disclosed embodiments are not limited to any particular combination of analyses and weights.

[0167] Figure 6 is a flowchart illustrating an exemplary process 600 that generates one or more navigation responses based on stereoscopic image analysis according to a disclosed embodiment. In step 610, the processing unit 110 may receive a plurality of first and second images via the data interface 128. For example, a camera included in the image acquisition unit 120 (e.g., imaging devices 122 and 124 having fields of view 202 and 204) may capture a plurality of first and second images of the area in front of the vehicle 200 and transmit them to the processing unit 110 via a digital connection (e.g., USB, wireless, Bluetooth, etc.). In some embodiments, the processing unit 110 may receive a plurality of first and second images via two or more data interfaces. The disclosed embodiments are not limited to any particular data interface configuration or protocol.

[0168] In step 620, the processing unit 110 may execute the stereoscopic image analysis module 404 to perform stereoscopic image analysis on the first and second sets of images to create a 3D map of the road ahead of the vehicle and to detect features in the images such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights and road hazards, and similar objects. The stereoscopic image analysis may be performed in the same manner as the steps described above in relation to Figures 5A to 5D. For example, the processing unit 110 may execute the stereoscopic image analysis module 404 to detect candidate objects (e.g., vehicles, pedestrians, road markings, traffic lights, road hazards, etc.) in the first and second sets of images, filter and exclude subsets of candidate objects based on various criteria, perform multi-frame analysis, construct measurements, and determine the confidence level of the remaining candidate objects. In performing the above steps, the processing unit 110 may consider information from both the first and second sets of images, rather than information from only one set of images. For example, the processing unit 110 may analyze the difference in pixel-level data (or other data subsets from the two streams of captured images) of a candidate object that appears in both the first and second sets of images. As another example, the processing unit 110 may estimate the position and / or velocity (e.g., relative to the vehicle 200) of a candidate object by observing that an object appears in one of the sets of images but not in the other, or by other differences that may exist for an object that appears in the two image streams. For example, the position, velocity, and / or acceleration relative to the vehicle 200 may be determined based on the trajectory, position, motion characteristics, etc., of features associated with the object that appears in one or both of the image streams.

[0169] In step 630, the processing unit 110 may execute the navigation response module 408 to generate one or more navigation responses in the vehicle 200 based on the analysis performed in step 620 and the techniques described above in relation to Figure 4. Navigation responses may include, for example, turns, lane shifts, acceleration changes, speed changes and brakes, and the like. In some embodiments, the processing unit 110 may use data derived from the execution of the speed and acceleration module 406 to generate one or more navigation responses. Furthermore, the multiple navigation responses may occur simultaneously, sequentially, or in any combination thereof.

[0170] Figure 7 is a flowchart illustrating an exemplary process 700 that generates one or more navigation responses based on the analysis of three sets of images according to the disclosed embodiments. In step 710, the processing unit 110 may receive first, second, and third sets of images via the data interface 128. For example, cameras included in the image acquisition unit 120 (e.g., imaging devices 122, 124, and 126 having fields of view 202, 204, and 206) may capture first, second, and third sets of images of the front and / or side areas of the vehicle 200 and transmit them to the processing unit 110 via a digital connection (e.g., USB, wireless, Bluetooth, etc.). In some embodiments, the processing unit 110 may receive first, second, and third sets of images via three or more data interfaces. For example, each of the imaging devices 122, 124, and 126 may have an associated data interface for communicating data to the processing unit 110. The disclosed embodiments are not limited to any particular data interface configuration or protocol.

[0171] In step 720, the processing unit 110 may analyze the first, second, and third plurality of images to detect features in the images such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, road hazards, and similar objects. The analysis may be performed in the same manner as the steps described above in relation to Figures 5A to 5D and Figure 6. For example, the processing unit 110 may perform monocular image analysis on each of the first, second, and third plurality of images (for example, via the execution of the monocular image analysis module 402 and based on the steps described above in relation to Figures 5A to 5D). Alternatively, the processing unit 110 may perform stereoscopic image analysis on the first and second plurality of images, the second and third plurality of images and / or the first and third plurality of images (for example, via the execution of the stereoscopic image analysis module 404 and based on the steps described above in relation to Figure 6). The processed information corresponding to the analysis of the first, second, and / or third plurality of images may be combined. In some embodiments, the processing unit 110 may perform a combination of monocular image analysis and stereoscopic image analysis. For example, the processing unit 110 may perform monocular image analysis on a first set of images (e.g., via the execution of the monocular image analysis module 402) and stereoscopic image analysis on second and third sets of images (e.g., via the execution of the stereoscopic image analysis module 404). The configuration of the imaging devices 122, 124, and 126—including their respective positions and fields of view 202, 204, and 206—may affect the type of analysis performed on the first, second, and third sets of images. The disclosed embodiments are not limited to the specific configuration of the imaging devices 122, 124, and 126 or the type of analysis performed on the first, second, and third sets of images.

[0172] In some embodiments, the processing unit 110 may perform tests on the system 100 based on the images acquired and analyzed in steps 710 and 720. Such tests may provide an indicator of the overall performance of the system 100 in a particular configuration of the imaging devices 122, 124, and 126. For example, the processing unit 110 may identify the percentage of "false hits" (e.g., when the system 100 incorrectly determines the presence of a vehicle or pedestrian) and "misses."

[0173] In step 730, the processing unit 110 may generate one or more navigation responses in the vehicle 200 based on information derived from two of the first, second, and third images. The selection of two of the first, second, and third images may depend on various factors, such as the number, type, and size of objects detected in each of the images. The processing unit 110 may also make selections based on image quality and resolution, the effective field of view reflected in the image, the number of imaging frames and the extent to which one or more objects in question actually appear in the frames (e.g., the percentage of frames in which objects appear, the percentage of such frames in which objects appear, etc.), and similar factors.

[0174] In some embodiments, the processing unit 110 may select information derived from two of a plurality of first, second, and third images by determining the degree to which the information derived from one image source is consistent with the information derived from other image sources. For example, the processing unit 110 may combine the processed information derived from each of the imaging devices 122, 124, and 126 (whether monocular analysis, stereoscopic analysis, or any combination of the two) to identify visual indicators (e.g., lane markings, detected vehicles and / or their positions and / or routes, detected traffic lights, etc.) that are consistent across images captured by each of the imaging devices 122, 124, and 126. The processing unit 110 may also exclude information that is inconsistent across the captured images (e.g., vehicles changing lanes, lane models indicating vehicles too close to vehicle 200, etc.). Thus, based on the determination of consistent and inconsistent information, the processing unit 110 may select information derived from two of a plurality of first, second, and third images.

[0175] Navigation responses may include, for example, turns, lane shifts, and acceleration changes, and similar ones. Processing unit 110 may generate one or more navigation responses based on the analysis performed in step 720 and the techniques described above in relation to Figure 4. Processing unit 110 may also generate one or more navigation responses using data derived from the performance of the velocity and acceleration module 406. In some embodiments, processing unit 110 may generate one or more navigation responses based on the relative position, relative velocity, and / or relative acceleration between the vehicle 200 and an object detected in any of the first, second, and third images. Multiple navigation responses may occur simultaneously, sequentially, or in any combination thereof.

[0176] [Sparse road model for autonomous vehicle navigation]

[0177] In some embodiments, the disclosed systems and methods may use sparse maps for autonomous vehicle navigation. Specifically, sparse maps may be for autonomous vehicle navigation along road divisions. For example, sparse maps may provide sufficient information for navigating an autonomous vehicle without storing and / or updating large amounts of data. As will be discussed in more detail below, an autonomous vehicle may use a sparse map to navigate one or more roads based on one or more stored trajectories.

[0178] [Sparse maps for autonomous vehicle navigation]

[0179] In some embodiments, the disclosed systems and methods may generate sparse maps for autonomous vehicle navigation. For example, a sparse map may provide sufficient information for navigation without requiring excessive data storage or data transfer speeds. As will be discussed in more detail below, a vehicle (which may be an autonomous vehicle) may use a sparse map to navigate one or more roads. For example, in some embodiments, a sparse map may include road-related data and potential landmarks along the road that may be sufficient for vehicle navigation but also show a small data footprint. For example, a sparse data map, as described in detail below, may require significantly less storage space and data transfer bandwidth compared to a digital map that includes detailed map information such as image data collected along the road.

[0180] For example, instead of storing a detailed representation of road divisions, a sparse data map may store a three-dimensional polynomial representation of a preferred vehicle path along a road. These paths may require little data storage space. Furthermore, in the sparse data maps described, landmarks may be identified and included in the sparse map road model to aid navigation. These landmarks may be placed at arbitrary intervals suitable for enabling vehicle navigation, but in some cases, it is not necessary to identify and include such landmarks in the model at high density and short intervals. Rather, in some cases, navigation may be possible based on landmarks that are separated by at least 50 meters, at least 100 meters, at least 500 meters, at least 1 kilometer, or at least 2 kilometers. As will be discussed in more detail in other sections, sparse maps may be generated based on data collected or measured by a vehicle equipped with various sensors and devices such as imaging devices, global positioning system sensors, motion sensors, etc., as the vehicle moves along a road. In some cases, a sparse map may be generated based on data collected during multiple runs of one or more vehicles along a particular road. Using multiple runs of one or more vehicles to generate a sparse map can be referred to as "crowdsourcing" of sparse maps.

[0181] According to the disclosed embodiments, an autonomous vehicle system may use a sparse map for navigation. For example, the disclosed system and method may deliver a sparse map for generating a road navigation model for an autonomous vehicle, and may use the sparse map and / or the generated road navigation model to navigate the autonomous vehicle along road segments. The sparse map according to the Disclosure may include one or more three-dimensional contours that may represent predetermined trajectories that the autonomous vehicle may traverse as it moves along the associated road segments.

[0182] The sparse maps provided in this disclosure may also include data representing one or more road features. Such road features may include recognized landmarks, road signature profiles, and any other road-related features useful for vehicle navigation. The sparse maps provided in this disclosure may enable autonomous vehicle navigation based on a relatively small amount of data contained in the sparse maps. Even without including detailed representations of roads, such as road edges, road curvature, images associated with road divisions, or other physical features associated with road divisions, the disclosed embodiments of sparse maps may require relatively little memory (and relatively little bandwidth when portions of the sparse maps are transferred to the vehicle), but may still be able to provide sufficient autonomous vehicle navigation. The small data footprint of the disclosed sparse maps, which will be discussed in more detail below, may be achieved in some embodiments by storing representations of road-related elements that require a small amount of data but still enable autonomous navigation.

[0183] For example, rather than storing detailed representations of various road configurations, the disclosed sparse map may store polynomial representations of one or more trajectories that a vehicle can follow along the road. Thus, using the disclosed sparse map, rather than needing to store (or transfer) details about the physical properties of the road to enable navigation along the road, a vehicle may, in some cases, navigate along a particular road section by aligning its travel path with a trajectory (e.g., a polynomial spline) along that particular road section, without needing to interpret the physical configuration of the road. In this way, a vehicle may be able to navigate based on a stored trajectory (e.g., a polynomial spline), which may require far less memory than methods that involve storing road images, road parameters, road layouts, etc.

[0184] In addition to the stored polynomial representation of the trajectory along the road division, 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 division. The digital signatures may be smaller in size than the signals acquired by the sensors. In some embodiments, the digital signatures may be constructed to be compatible with a classifier function configured to detect and identify road features from signals acquired by sensors during subsequent travel, for example. In some embodiments, the digital signatures may be constructed such that they have the smallest possible footprint while maintaining the ability to correlate or match road features with the stored signatures based on images of road features captured by a camera mounted on a vehicle traveling later along the same road division (or, if the stored signatures are not image-based and / or contain other data, digital signals generated by sensors).

[0185] In some embodiments, the size of the data object may be further associated with the uniqueness of the road feature. For example, if a camera mounted on a vehicle is used to detect road features, and the vehicle-mounted camera system is coupled with a classifier that can distinguish image data corresponding to a particular type of road feature, such as one associated with a road sign, and such a road sign is locally unique in that area (e.g., there are no identical or similar road signs nearby), then it may be sufficient to store only data indicating the type of road feature and its location.

[0186] As will be discussed in more detail below, road features (e.g., landmarks along road divisions) can be stored as small data objects that represent road features in relatively few bytes, while simultaneously providing sufficient information for recognizing and using such features for navigation. For example, road signs can be identified as recognized landmarks on which vehicle navigation can be based. A representation of a road sign can be stored in a sparse map, for example, containing 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). Navigating based on such a data perspective representation of a landmark (e.g., using a representation sufficient to locate, recognize, and navigate based on a landmark) can provide the desired level of navigation capability associated with a sparse map without significantly increasing the data overhead associated with the sparse map. Such lean representations of landmarks (and other road features) can utilize sensors and processors mounted on such a vehicle, configured to detect, identify, and / or classify specific road features.

[0187] For example, if a sign or a particular type of sign is locally unique in a given area (e.g., if there are no other signs or no other signs of the same type), a sparse map may use data indicating the type of landmark (sign or particular type of sign). During navigation (e.g., autonomous navigation) when a camera mounted on an autonomous vehicle captures images of an area containing a sign (or particular type of sign), the processor may process the images, detect the sign (if it actually exists in the image), classify the image as a sign (or particular type of sign), and correlate the location of the image with the location of the sign stored in the sparse map.

[0188] A sparse map may contain any appropriate representation of objects identified along road divisions. In some cases, objects may be referred to as semantic or non-semantic objects. Semantic objects may include, for example, objects associated with a predetermined type classification. This type classification can be useful in reducing the amount of data required to describe semantic objects perceived in the environment, which can be beneficial both during the data collection phase (e.g., reducing the cost associated with using bandwidth to transfer driving information from multiple collection vehicles to a server) and during the navigation phase (e.g., reducing map data can speed up the transfer of map tiles from the server to the navigating vehicle, and the cost associated with using bandwidth for such transfers can also be reduced). The classification type of a semantic object may assign any type of object or feature expected to be encountered along a road.

[0189] Semantic objects can be further divided into two or more logical groups. For example, in some cases, one group of semantic object types may be associated with predetermined dimensions. Such semantic objects may include specific speed limit signs, yield signs, merge signs, stop signs, traffic lights, road directional arrows, manhole covers, or any other type of object that can be associated with a standardized size. One of the advantages provided by such semantic objects is that little data may be required to represent / fully define the object. For example, if the standardized size of a speed limit sign is known, the collecting vehicle may only need to identify the presence (recognized type) of the speed limit sign (by analysis of the captured image) along with a representation of the location of the detected speed limit sign (e.g., the 2D location in the captured image of the center of the sign or a specific corner of the sign (or, instead, the 3D location in real-world coordinates)) to provide sufficient information for generating a map on the server side. When 2D image locations are sent to the server, the location associated with the detected image of the sign is also sent so that the server can determine the real-world location of the sign (for example, by a moving structure technique using multiple images captured from one or more collection vehicles). Even with this limited information (only a few bytes are needed to define each detected object), the server can construct a map containing fully represented speed limit signs based on the detected sign location information and the type classification (representation of speed limit signs) received from one or more collection vehicles.

[0190] Semantic objects may also include other types of recognized objects or features that are not associated with specific standardized properties. Such objects or features may include potholes in the road, tar joints, lampposts, non-standardized signs, curbs, trees, tree branches, or any other type of recognized object with one or more variable properties (e.g., variable dimensions). In such cases, in addition to transmitting a description of the type of detected object or feature (e.g., pothole, pole, etc.) and location information of the detected object or feature to the server, the collection vehicle may also transmit a description of the size of the object or feature. The size may be represented by the dimensions of a 2D image (e.g., bounding box or one or more dimensional values) or by real-world dimensions (determined by in-motion structural calculations, based on LIDAR or RADAR system output, based on trained neural network output, etc.).

[0191] Non-semantic objects or features may include any detectable objects or features that fall outside the range of recognized categories or types but can still provide valuable information for map generation. In some cases, such non-semantic features may include detected building corners or window corners, unique stones or objects near roads, concrete debris on road shoulders, or any other detectable objects or features. Upon detection of such objects or features, one or more collection vehicles may transmit the locations of one or more points (2D image points or 3D real-world points) associated with the detected object / feature to the map generation server. Furthermore, compressed or simplified image segments (e.g., image hashes) may be generated for the regions of the captured image containing the detected objects or features. These image hashes may be calculated based on predetermined image processing algorithms and can form an effective signature for the detected non-semantic objects or features. Such signatures can be useful for navigation related to sparse maps containing non-semantic features or objects, because they can apply algorithms similar to those used to generate image hashes to confirm / verify the presence of mapped non-semantic features or objects in captured images when a vehicle crosses a road. Using this technique, non-semantic features can be enriched into sparse maps (for example, to improve their usefulness in navigation) without adding significant data overhead.

[0192] As mentioned above, target trajectories can be stored in a sparse map. These target trajectories (e.g., 3D splines) may represent preferred or recommended routes for each available lane on a road, each valid route through intersections, merges and exits, etc. In addition to target trajectories, other road features may also be detected and collected and incorporated into the sparse map in the form of representative splines. Such features may include, for example, road edges, lane marks, curbs, guardrails, or any other objects or features that extend along the road or road section.

[0193] [Generating sparse maps]

[0194] In some embodiments, a sparse map may include at least one linear representation of road surface features extending along road divisions and a plurality of landmarks associated with the road divisions. In certain embodiments, a sparse map may be generated via "crowdsourcing," for example, by image analysis of a plurality of images taken as one or more vehicles cross a road division.

[0195] Figure 8 shows a sparse map 800 that one or more vehicles, for example, vehicle 200 (which may be an autonomous vehicle), can access to provide autonomous vehicle navigation. The sparse map 800 may be stored in memory such as memory 140 or 150. Such memory devices may include any type of non-temporary storage device or computer-readable medium. For example, in some embodiments, memory 140 or 150 may include a hard drive, compact disk, flash memory, magnetic-based memory device, optical-based memory 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.

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

[0197] However, the sparse map 800 does not need to be stored locally with respect to the vehicle. In some embodiments, the sparse map 800 may be stored in a storage device or computer-readable medium mounted on a remote server that communicates with the vehicle 200 or a device associated with the vehicle 200. A processor mounted on the vehicle 200 (e.g., processing unit 110) may receive the data contained in the sparse map 800 from the remote server and execute the data to guide 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. Thus, a storage device or computer-readable medium mounted on the vehicle 200 and / or on one or more additional vehicles may store the remaining portion of the sparse map 800.

[0198] Furthermore, in such embodiments, the sparse map 800 can be accessible to multiple vehicles (e.g., tens, hundreds, thousands, or millions of vehicles) traversing various road sections. It should also be noted that the sparse map 800 may include multiple submaps. For example, in some embodiments, the sparse map 800 may include hundreds, thousands, millions, or more submaps (e.g., map tiles) that can be used when navigating a vehicle. Such submaps may be called local maps or map tiles, and a vehicle traveling along a road may access any number of local maps relevant to the vehicle's location. Local map areas of the sparse map 800 may be stored as an index to the database of the sparse map 800, along with Global Navigation Satellite System (GNSS) keys. Thus, the calculation of steering angles for navigating a host vehicle in this system may be performed independently of the host vehicle's GNSS position, road features, or landmarks, although such GNSS information may be used to retrieve relevant local maps.

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

[0200] The data recorded in the sparse map 800 may include location information based on Global Positioning System (GPS) data. For example, location information may be included in the sparse map 800 for various map elements, such as landmark locations and road profile locations. The locations of map elements included in the sparse map 800 may be obtained using GPS data collected from vehicles crossing the road. For example, a vehicle passing over an identified landmark may determine the location of the identified landmark using GPS location information associated with the vehicle, and may determine the location of the identified landmark relative to the vehicle (for example, by analyzing image data collected from one or more cameras mounted on the vehicle). Such location determination of an identified landmark (or any other feature included in the sparse map 800) may be repeated when additional vehicles pass over the location of the identified landmark. Some or all of the additional location determinations may be used to refine the location information stored in the sparse map 800 in relation to the identified landmark. For example, in some embodiments, multiple location measurements related to a particular feature stored in the sparse map 800 may be averaged together. However, it is also possible to refine the stored locations of map elements based on multiple determined locations of map elements using any other mathematical operations.

[0201] In a specific example, a collection vehicle may traverse a particular road section. Each collection vehicle captures images of its respective environment. Images may be collected at any appropriate frame rate (e.g., 9 Hz, etc.). An image analysis processor mounted on each collection vehicle analyzes the captured images to detect the presence of semantic and / or non-semantic features / objects. At a high level, the collection vehicle transmits instructions for the detection of semantic and / or non-semantic objects / features to a mapping server, along with the locations associated with those objects / features. More specifically, type indices, dimensional indices, etc., may be transmitted along with the location information. The location information may include any appropriate information to enable 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) within the captured images where semantic or non-semantic features / objects were detected. Such image locations may correspond to the center, corners, etc., of the feature / object. In this scenario, the mapping server reconstructs driving information from multiple collection vehicles, and to assist in the alignment of the driving information, each collection vehicle may also provide the server with the location where each image was taken (e.g., GPS location).

[0202] In other cases, the data collection vehicle may provide the server with one or more 3D real-world points associated with detected objects / features. Such 3D points may be associated with predetermined starting points (such as the starting point of a driving section) and may be determined by any appropriate technique. In some cases, structural techniques in motion may be used to determine the 3D real-world location of the detected objects / features. For example, a specific object, such as a particular speed limit sign, may be detected in two or more captured images. Information such as the known self-motion of the data collection vehicle between the captured images (speed, trajectory, GPS position, etc.), along with changes in the speed limit sign observed in the captured images (changes in XY pixel position, changes in size, etc.), may be used to determine the real-world location of one or more points associated with the speed limit sign and pass them to the mapping server. Such methods are optional as they require more computation on the data collection vehicle system side. 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 of less than 2 MB per kilometer of road, less than 1 MB per kilometer of road, less than 500 KB per kilometer of road, or less than 100 KB per kilometer of road (e.g., including data representing target trajectories, landmarks, and any other stored road features). In some embodiments, the data density of the sparse map 800 may be less than 10 KB per kilometer of road, or less than 2 KB per kilometer of road (e.g., 1.6 KB per kilometer), or 10 KB or less per kilometer of road, or 20 KB or less per kilometer of road. In some embodiments, most, if not all, of the roads in the United States may be autonomously navigated using a sparse map with a total data of 4 GB or less. These data density values ​​may represent the average across the entire sparse map 800, across local maps within the sparse map 800, and / or across specific road sections within the sparse map 800.

[0203] As described above, the sparse map 800 may include representations of multiple target trajectories 810 for guiding autonomous driving or navigation along road divisions. Such target trajectories may be stored as three-dimensional splines. The target trajectories stored in the sparse map 800 may be determined, for example, based on two or more reconstructed trajectories of a vehicle's previous crossing along a particular road division. A road division may be associated with a single or multiple target trajectories. For example, on a two-lane road, a first target trajectory may be stored to represent the intended driving path along the road in a first direction, and a second target trajectory may be stored to represent the intended driving path along the road in another direction (e.g., the opposite direction to the first direction). Additional target trajectories may be stored for a particular road division. For example, on a multi-lane road, one or more target trajectories may be stored representing the intended driving paths of vehicles in one or more lanes associated with the multi-lane road. In some embodiments, each lane of a multi-lane road may be associated with its own target trajectory. In other embodiments, there may be fewer stored target trajectories than lanes present on a multi-lane road. In such cases, a vehicle navigating a multi-lane road may use one of the stored target trajectories to guide navigation, taking into account the amount of lane offset from the lane in which the target trajectory is stored (for example, if a vehicle is traveling in the leftmost lane of a three-lane highway and the target trajectory is stored only for the center lane of the highway, the vehicle may use the target trajectory for the center lane to navigate, taking into account the amount of lane offset between the center lane and the leftmost lane when generating navigation instructions).

[0204] In some embodiments, the target trajectory may represent the ideal path that a vehicle should take when traveling. The target trajectory may be located, for example, approximately in the center of a travel lane. In other cases, the target trajectory may be located elsewhere relative to the road division. For example, the target trajectory may approximately coincide with the center of the road, the edge of the road, or the edge of a lane. In such cases, navigation based on the target trajectory may include a determined amount of offset to be maintained relative to the position of the target trajectory. Furthermore, in some embodiments, the determined amount of offset to be maintained relative to the position of the target trajectory may differ depending on the type of vehicle (for example, a passenger car with two axles may have a different offset along at least part of the target trajectory than a truck with three or more axles).

[0205] The sparse map 800 may also include data related to a number of predetermined landmarks 820 associated with specific road divisions, local maps, etc. These landmarks can be used to navigate an autonomous vehicle, as will be discussed in more detail below. For example, in some embodiments, landmarks can be used to determine the vehicle's current position relative to a stored target trajectory. Using this position information, the autonomous vehicle may be able to adjust its direction of travel to match the direction of the target trajectory at the determined position.

[0206] Multiple landmarks 820 can be identified at any appropriate interval and stored in a sparse map 800. In some embodiments, landmarks can be stored at a relatively high density (e.g., every few meters or more). However, in some embodiments, significantly large landmark interval values ​​can be used. For example, in a sparse map 800, identified (or recognized) landmarks may be spaced 10 meters, 20 meters, 50 meters, 100 meters, 1 kilometer, or 2 kilometers apart. In some cases, identified landmarks may be located at distances greater than 2 kilometers apart.

[0207] Between landmarks, and therefore while determining the vehicle's position relative to a target trajectory, the vehicle may navigate based on dead reckoning, where the vehicle uses sensors to determine its own motion and estimate its position relative to the target trajectory. Because errors can accumulate during dead reckoning navigation, the accuracy of position determination relative to the target trajectory may gradually decrease over time. The vehicle may use landmarks present in a sparse map 800 (and their known locations) to eliminate errors induced by dead reckoning in position determination. In this way, identified landmarks included in the sparse map 800 can function as navigation anchors, from which the vehicle's precise position relative to the target trajectory can be determined. Since some degree of error may be acceptable in position determination, identified landmarks do not always need to be available to the autonomous vehicle. Rather, as mentioned above, adequate navigation may be possible even based on landmark intervals of 10 meters, 20 meters, 50 meters, 100 meters, 500 meters, 1 kilometer, 2 kilometers, or more. In some embodiments, a density of one identified landmark per kilometer of road may be sufficient to maintain longitudinal position determination accuracy of less than 1 meter. Therefore, it is not always necessary to store all potential landmarks that appear along the road divisions in the sparse map 800.

[0208] Furthermore, in some embodiments, lane marks may be used to locate the vehicle between landmark intervals. By using lane marks between landmark intervals, the accumulation of errors during dead reckoning navigation can be minimized.

[0209] In addition to target trajectories and identifiable landmarks, the sparse map 800 may contain information related to various other road features. For example, Figure 9A shows a representation of a curve along a particular road section that may be stored in the sparse map 800. In some embodiments, a single lane of a road may be modeled by a three-dimensional polynomial description of the left and right sides of the road. Such polynomials representing the left and right sides of a single lane are shown in Figure 9A. Regardless of the number of lanes a road may have, roads can be represented using polynomials in a similar manner to that shown in Figure 9A. For example, the left and right sides of a multi-lane road can be represented by polynomials similar to those shown in Figure 9A, and intermediate lane markers included in a multi-lane road (e.g., dashed lines representing lane boundaries, solid yellow lines representing boundaries between lanes traveling in different directions, etc.) can also be represented using polynomials as shown in Figure 9A.

[0210] As shown in Figure 9A, lane 900 can be represented using a polynomial (e.g., a first-order, second-order, third-order, or any appropriate degree polynomial). For illustrative purposes, lane 900 is shown as a two-dimensional lane, and the polynomial is shown as a two-dimensional polynomial. As shown in Figure 9A, lane 900 includes left lane 910 and right lane 920. In some embodiments, multiple polynomials can be used to represent the position on each side of the road or lane boundary. For example, left lane 910 and right lane 920 can each be represented by multiple polynomials of any appropriate length. In some cases, the polynomials may be about 100m long, but other lengths longer or shorter than 100m can also be used. Furthermore, polynomials can be superimposed on each other to facilitate seamless transitions when a host vehicle is traveling along the road and navigating based on the polynomials it encounters later. For example, each of the left 910 and the right 920 can be separated into sections of approximately 100 meters in length (an example of a first predetermined range) and represented by multiple cubic polynomials that overlap each other by approximately 50 meters. The polynomials representing the left 910 and the right 920 may or may not be in the same order. For example, in some embodiments, some polynomials may be quadratic, some cubic, and some quartic.

[0211] In the example shown in Figure 9A, the left side 910 of lane 900 is represented by two groups of cubic polynomials. The first group includes polynomial divisions 911, 912, and 913. The second group includes polynomial divisions 914, 915, and 916. The two groups are substantially parallel to each other, but follow the position on each side of the road. Polynomial divisions 911, 912, 913, 914, 915, and 916 are approximately 100 meters long and overlap by approximately 50 meters with adjacent divisions within the series. However, as mentioned above, polynomials of different lengths and different amounts of overlap can also be used. For example, the polynomials could be 500m, 1km, or more in length, and the amount of overlap could vary from 0 to 50m, 50m to 100m, or more than 100m. Furthermore, Figure 9A is shown as representing polynomials spread in 2D space (e.g., on the surface of paper), and it should be understood that these polynomials, in addition to XY curvature, can represent curves that spread in 3 dimensions (e.g., including a height component) to represent changes in elevation of the road section. In the example shown in Figure 9A, the right side 920 of lane 900 is further represented by a first group having polynomial sections 921, 922, and 923, and a second group having polynomial sections 924, 925, and 926.

[0212] Returning to the target trajectories of the sparse map 800, Figure 9B shows a three-dimensional polynomial representing the target trajectory of a vehicle traveling along a particular road section. The target trajectory represents not only the XY path that the host vehicle should travel along a particular road section, but also the changes in elevation that the host vehicle will experience as it travels along the road section. Thus, each target trajectory in the sparse map 800 can be represented by one or more three-dimensional polynomials, such as the three-dimensional polynomial 950 shown in Figure 9B. The sparse map 800 may contain multiple trajectories (e.g., millions or billions or more to represent vehicle trajectories along various road sections along roads around the world). In some embodiments, each target trajectory may correspond to a spline connecting the three-dimensional polynomial sections.

[0213] Regarding the data footprint of the polynomial curves stored in the sparse map 800, in some embodiments, each cubic polynomial may be represented by four parameters, each requiring 4 bytes of data. A suitable representation can be obtained with a cubic polynomial requiring approximately 192 bytes of data per 100m. This can be rephrased as approximately 200KB per hour in terms of the data usage / transfer requirements of a host vehicle traveling at approximately 100km / hr.

[0214] A sparse map 800 can describe a lane network using a combination of geometry descriptors and metadata. The geometry can be described as a polynomial or spline, as described above. The metadata can describe the number of lanes, special properties (such as carpool lanes), and possibly other sparse labels. The total footprint of such metrics can be very small.

[0215] Accordingly, the sparse map according to embodiments of the present disclosure may include at least one linear representation of a road surface feature extending along a road division, each linear representation representing a path along the road division substantially corresponding to the road surface feature. In some embodiments, as discussed above, the at least one linear 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 road edges or lane marks. In addition, as discussed below with respect to “crowdsourcing,” the road surface feature may be identified by image analysis of multiple images acquired when one or more vehicles cross a road division.

[0216] As mentioned above, the sparse map 800 may include multiple predetermined landmarks associated with road divisions. Rather than storing actual images of the landmarks and relying, for example, on image recognition analysis based on captured and stored images, each landmark in the sparse map 800 may be represented and recognized using less data than would be required for the stored actual images. Nevertheless, the data representing the landmarks may contain sufficient information to describe or identify the landmarks along the roads. The size of the sparse map 800 can be reduced by storing data that describes the characteristics of the landmarks rather than actual images of the landmarks.

[0217] Figure 10 shows examples of types of landmarks that may be represented in the sparse map 800. Landmarks may include any visible and identifiable objects along the road division. Landmarks may be selected so that they are fixed and do not change frequently in terms of location and / or content. Landmarks included in the sparse map 800 may be useful in determining the position of a vehicle 200 relative to a target trajectory when a vehicle crosses a particular road division. Examples of landmarks may include traffic signs, directional signs, general signs (e.g., rectangular signs), roadside fixtures (e.g., lampposts, reflectors, etc.), and any other appropriate categories. In some embodiments, lane marks on the road may also be included as landmarks in the sparse map 800.

[0218] Examples of landmarks shown in Figure 10 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 indicating a vehicle to a different road or location, and exit signs 1030 with arrows indicating a vehicle to exit a road, etc. Therefore, at least one of the multiple landmarks may include a road sign.

[0219] General signs may be unrelated to traffic. For example, general signs may include billboards used for advertising, or welcome signs adjacent to the boundary between two countries, states, counties, cities, or towns. Figure 10 shows general sign 1040 ("Joe's Restaurant"). As shown in Figure 10, general sign 1040 may have a rectangular shape, but general sign 1040 may have other shapes such as square, circular, or triangular.

[0220] Landmarks may also include roadside fixtures. Roadside fixtures can be objects that are not signs and may not be related to traffic or direction. For example, roadside fixtures may include lampposts (e.g., lamppost 1035), power line poles, traffic signal poles, etc.

[0221] Landmarks may also include beacons that can be specifically designed for use in autonomous vehicle navigation systems. For example, such beacons may include freestanding structures placed at predetermined intervals to assist in the navigation of a host vehicle. Such beacons may also include visual / graphic information that can be added to existing road signs (e.g., icons, emblems, barcodes, etc.) that can be identified or recognized by vehicles traveling along a road section. Such beacons may also include electronic components. In such embodiments, non-visual information may be transmitted to a host vehicle using electronic beacons (e.g., RFID tags, etc.). Such information may include, for example, landmark identification and / or landmark location information that can be used by the host vehicle to determine its position along a target trajectory.

[0222] In some embodiments, landmarks included in the sparse map 800 may be represented by data objects of predetermined size. The data representing a landmark may include any appropriate parameters for identifying a particular landmark. For example, in some embodiments, a landmark stored in the sparse map 800 may include parameters such as the physical size of the landmark (e.g., to support estimation of distance to the landmark based on known size / scale), distance to the previous landmark, lateral offset, height, type code (e.g., landmark type - type of directional sign, traffic sign, etc.), GPS coordinates (e.g., to support global localization), and any other appropriate parameters. Each parameter may be associated with a data size. For example, the landmark size may be stored using 8 bytes of data. The distance to the previous landmark, lateral offset, and height may be specified using 12 bytes of data. A type code associated with a landmark such as a directional sign or traffic sign may require approximately 2 bytes of data. In the case of a general sign, an image signature enabling the identification of the general sign may be stored using 50 bytes of data storage. The GPS location of a landmark may be associated with 16 bytes of data storage. These data sizes for each parameter are merely examples, and other data sizes may also be used. Representing landmarks in a sparse map 800 in this way may provide an efficient 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 class of objects for which a standardized set of characteristics exists (e.g., speed limit signs, warning signs, directional signs, traffic lights, etc., having known dimensions or other characteristics). Non-standard semantic objects may include any objects not associated with a standardized set of characteristics (e.g., general advertising signs, signs identifying businesses, potholes in the road that may have variable dimensions, trees, etc.).Each non-standard semantic object can be represented by 38 bytes of data (e.g., 8 bytes for size; 12 bytes for distance to previous landmark, lateral offset, and height; 2 bytes for type code; and 16 bytes for position coordinates). Standard semantic objects can be represented with even less data, as the mapping server may not need size information to fully represent objects in a sparse map.

[0223] A sparse map 800 may use a tagging system to represent landmark types. In some cases, each traffic sign or directional sign may be associated with its own tag and stored in a database as part of landmark identification. For example, the database may contain as many as 1,000 different tags to represent various traffic signs and about 10,000 different tags to represent directional signs. Naturally, any appropriate number of tags can be used, and additional tags can be created as needed. A generic sign may be represented using less than about 100 bytes in some embodiments (e.g., about 86 bytes including 8 bytes for size; 12 bytes for distance to the previous landmark, lateral offset, and height; 50 bytes for the image signature; and 16 bytes for GPS coordinates).

[0224] Therefore, for semantic road signs that do not require image signatures, the impact of data density on a sparse Map 800 can be as much as 760 bytes per kilometer, even with a relatively high landmark density of about 1 per 50m (e.g., 20 landmarks per kilometer x 38 bytes per landmark = 760 bytes). Even for general-purpose signs that include image signature components, the impact of data density is about 1.72 KB per kilometer (e.g., 20 landmarks per kilometer x 86 bytes per landmark = 1,720 bytes). For semantic road signs, this corresponds to about 76 KB of data usage per hour for a vehicle traveling at 100 km / hr. For general-purpose signs, this corresponds to about 170 KB per hour for a vehicle traveling at 100 km / hr. Note that in some environments (e.g., urban environments), the density of detected objects that can be included in a sparse map can be much higher (perhaps more than 2 per meter). In some embodiments, generally rectangular objects, such as rectangular signs, can be represented in a sparse map 800 with data of 100 bytes or less. The representation of a generally rectangular object (e.g., general sign 1040) in the sparse map 800 may include a condensed image signature or image hash (e.g., condensed image signature 1045) associated with the generally rectangular object. This condensed image signature / image hash can be determined using any suitable image hash algorithm and can be used, for example, as a recognized landmark to assist in the identification of a general sign. Such a condensed image signature (e.g., image information derived from actual image data representing the object) can avoid the need to store actual images of the object or the need to perform comparative image analysis on actual images in order to recognize landmarks.

[0225] Referring to Figure 10, the sparse map 800 may contain or store a condensed image signature 1045 associated with the general sign 1040, rather than an actual image of the general sign 1040. For example, after an imaging device (e.g., imaging devices 122, 124, or 126) has captured an image of the general sign 1040, a processor (e.g., image processor 190, or any other processor capable of processing the image, whether mounted on the host vehicle or remotely installed) may perform image analysis to extract / create a condensed image signature 1045 containing a unique signature or pattern associated with the general sign 1040. In one embodiment, the condensed image signature 1045 may include shapes, color patterns, brightness patterns, or any other features that can be extracted from an image of the general sign 1040 to describe the general sign 1040.

[0226] For example, in Figure 10, the circles, triangles, and stars shown in the condensed image signature 1045 may represent areas of different colors. The patterns represented by the circles, triangles, and stars may be stored in a sparse map 800, for example, within 50 bytes designated to contain the image signature. In particular, the presence of circles, triangles, and stars does not necessarily mean that such shapes are stored as part of the image signature. Rather, these shapes are intended to conceptually represent recognizable areas having identifiable color differences, text areas, graphic shapes, or other variations of characteristics that may be associated with general signs. Such condensed image signatures can be used to identify landmarks in the form of general signs. For example, condensed image signatures can be used to perform an analysis of whether they are the same as, for example, image data captured using a camera mounted on an autonomous vehicle, based on a comparison with the stored condensed image signature.

[0227] Therefore, multiple landmarks can be identified by image analysis of multiple images taken when one or more vehicles cross a road section. As described below with respect to "crowdsourcing," in some embodiments, the image analysis for identifying multiple landmarks may include accepting potential landmarks if the ratio of images in which the landmark appears to images in which the landmark does not appear exceeds a threshold. Furthermore, in some embodiments, the image analysis for identifying multiple landmarks may include rejecting potential landmarks if the ratio of images in which the landmark does not appear to images in which the landmark appears exceeds a threshold.

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

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

[0230] In the example shown in Figure 11A, the first reconstructed track 1101 may be determined based on data received from a first vehicle crossing road section 1100 during a first period (e.g., day 1), the second reconstructed track 1102 may be obtained from a second vehicle crossing road section 1100 during a second period (e.g., day 2), and the third reconstructed track 1103 may be obtained from a third vehicle crossing road section 1100 during a third period (e.g., day 3). Each track 1101, 1102, and 1103 can be represented by a polynomial, such as a three-dimensional polynomial. Note that in some embodiments, one of the reconstructed tracks may be mounted and assembled on a vehicle crossing road section 1100.

[0231] In addition, or otherwise, such reconstructed trajectories may be determined on the server side based on information received from vehicles crossing the road section 1100. For example, in some embodiments, a vehicle 200 may transmit data related to its movements along the road section 1100 (e.g., among other things, steering angle, direction of travel, time, position, speed, detected road geometry, and / or detected landmarks) to one or more servers. The servers may reconstruct the trajectories of the vehicle 200 based on the received data. The servers may also generate target trajectories based on the first, second, and third trajectories 1101, 1102, and 1103 to guide autonomous vehicle navigation later traveling along the same road section 1100. The target trajectory may be associated with a single previous crossing of the road section, but in some embodiments, each target trajectory included in a sparse map 800 may be determined based on two or more reconstructed trajectories of vehicles crossing the same road section. In Figure 11A, the target trajectory is represented by 1110. In some embodiments, the target trajectory 1110 may be generated based on the average of the first, second, and third trajectories 1101, 1102, and 1103. In some embodiments, the target trajectory 1110 included in the sparse map 800 may be an aggregation (e.g., a weighted combination) of two or more reconstructed trajectories.

[0232] On the mapping server, the server may receive actual trajectories for a particular road section from multiple collection vehicles traversing the road section. The received actual trajectories may be aligned to generate target trajectories for each valid route along the road section (e.g., each lane, each direction of travel, each route through intersections, etc.). The alignment process may involve correlating the actual collected trajectories with each other, using the detected objects / features identified along the road section, along with the collected locations of those detected objects / features. Once aligned, the average or "optimal" target trajectory per available lane may be determined based on the aggregated and correlated / aligned actual trajectories.

[0233] Figures 11B and 11C further illustrate the concept of target trajectories associated with road divisions existing within geographical area 1111. As shown in Figure 11B, a first road division 1120 within geographical area 1111 may include a multi-lane road with two lanes 1122 designated for vehicle travel in a first direction, and two additional lanes 1124 designated for vehicle travel in a second direction opposite to the first direction. Lanes 1122 and 1124 may be separated by a double yellow line 1123. Geographical area 1111 may also include a branch road division 1130 intersecting road division 1120. Road division 1130 may include a two-lane road, each lane designated for a different direction of travel. Geographical area 1111 may also include other road features such as a stop line 1132, a stop sign 1134, a speed limit sign 1136, and a hazard sign 1138.

[0234] As shown in Figure 11C, the sparse map 800 may include a local map 1140 containing a road model to assist in the autonomous navigation of a vehicle within the geographical region 1111. For example, the local map 1140 may include target trajectories for one or more lanes associated with road divisions 1120 and / or 1130 within the geographical region 1111. For example, the local map 1140 may include target trajectories 1141 and / or 1142 that an autonomous vehicle may access or rely on when crossing lane 1122. Similarly, the local map 1140 may include target trajectories 1143 and / or 1144 that an autonomous vehicle may access or rely on when crossing lane 1124. Furthermore, the local map 1140 may include target trajectories 1145 and / or 1146 that an autonomous vehicle may access or rely on when crossing road division 1130. Target trajectory 1147 represents the preferred path that the autonomous vehicle should take when transitioning from lane 1120 (specifically, corresponding to target trajectory 1141 associated with the rightmost lane of lane 1120) to road section 1130 (specifically, corresponding to target trajectory 1145 associated with the first side of road section 1130). Similarly, target trajectory 1148 represents the preferred path that the autonomous vehicle should take when transitioning from road section 1130 (specifically, corresponding to target trajectory 1146) to a portion of road section 1124 (specifically, as shown, corresponding to target trajectory 1143 associated with the left lane of lane 1124).

[0235] The sparse map 800 may also include representations of other road-related features associated with the geographical area 1111. For example, the sparse map 800 may also include representations of one or more landmarks identified in the geographical area 1111. Such landmarks may include a first landmark 1150 associated with a stop line 1132, a second landmark 1152 associated with a stop sign 1134, a third landmark associated with a speed limit sign 1154, and a fourth landmark 1156 associated with a hazard sign 1138. Such landmarks may be used, for example, to help an autonomous vehicle determine its current position relative to one of the indicated target trajectories, so that the vehicle can adjust its direction of travel to match the direction of the target trajectory at a determined location.

[0236] In some embodiments, the sparse map 800 may also include a road signature profile. Such a road signature profile may be associated with any identifiable / measurable change in at least one parameter associated with a road. For example, in some cases, such a profile may be associated with changes in road surface information such as changes in surface roughness of a particular road section, changes in road width across a particular road section, changes in distance between dashed lines drawn along a particular road section, or changes in road curvature along a particular road section. Figure 11D shows an example of a road signature profile 1160. The profile 1160 may represent one or more of the parameters described above, but in one example, the profile 1160 may represent a measure of road surface roughness, obtained, for example, by monitoring one or more sensors that provide an output indicating the amount of suspension displacement when a vehicle is traveling on a particular road section.

[0237] Alternatively, or simultaneously, profile 1160 may represent changes in road width determined based on image data acquired via a camera mounted on a vehicle traveling along a particular road section. Such a profile may be useful, for example, in determining a specific position of an autonomous vehicle relative to a particular target trajectory. That is, as an autonomous vehicle crosses a road section, it may measure a profile associated with one or more parameters associated with the road section. If the measured profile can be correlated / matched with a predetermined profile that plots changes in parameters with respect to the position along the road section, then the measured predetermined profile may be used (for example, by superimposing the corresponding sections of the measured predetermined profile) to determine the current position along the road section, and therefore the current position relative to the target trajectory of the road section.

[0238] In some embodiments, the sparse map 800 may include different trajectories based on different characteristics associated with the user of the autonomous vehicle, environmental conditions, and / or other parameters related to driving. For example, in some embodiments, different trajectories may be generated based on the preferences and / or profiles of different users. Sparse maps 800 containing such different trajectories may be provided to different autonomous vehicles of different users. For example, some users may prefer to avoid toll roads, while others may prefer to take the shortest or fastest route, regardless of whether the route includes toll roads. The disclosed system may generate different sparse maps with different trajectories based on the preferences or profiles of such different users. As another example, some users may prefer to drive in the lane with the fastest traffic, while others may prefer to always stay in the center lane.

[0239] Different trajectories may be generated based on different environmental conditions such as day and night, snow, rain, fog, etc., and may be included in the sparse map 800. An autonomous vehicle traveling in different environmental conditions may provide the sparse map 800 generated based on such different environmental conditions. In some embodiments, cameras mounted on the autonomous vehicle may detect environmental conditions and provide such information to a server that generates and provides the sparse map. For example, the server may generate or update an already generated sparse map 800 to include trajectories that may be more suitable or safer for autonomous driving under the detected environmental conditions. Updating the sparse map 800 based on environmental conditions may be performed dynamically as the autonomous vehicle travels along the road.

[0240] Other different parameters related to driving can also be used as a basis for generating and providing different sparse maps for different autonomous vehicles. For example, turning can be difficult when an autonomous vehicle is traveling at high speed. When an autonomous vehicle is traveling on a particular trajectory, the sparse map 800 may include trajectories associated with a specific lane rather than a road so that the vehicle can stay within that lane. If images captured by a camera mounted on the autonomous vehicle indicate that the vehicle has drifted outside of its lane (e.g., crossed a lane mark), an action may be triggered within the vehicle to return the vehicle to the designated lane according to the specific trajectory.

[0241] [Crowdsourcing for sparse maps]

[0242] 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 fitted as OEM equipment in current vehicles) and a suitable image analysis processor can function as a data collection vehicle. No special equipment (e.g., high-resolution imaging systems and / or positioning systems) is required. As a result of the disclosed crowdsourcing techniques, the generated sparse maps can be highly accurate and may include highly refined positional information (enabling a navigation error limit of 10 cm or less) as input to the map generation process, without requiring any special imaging or detection equipment. Crowdsourcing also allows the mapping server system to continuously utilize new driving information from any road traversed by a minimally equipped private or commercial vehicle that also functions as a data collection vehicle, thus enabling the generated maps to be updated much more quickly (and cheaply). Designated vehicles equipped with high-resolution imaging sensors and mapping sensors are not required. Thus, the costs associated with manufacturing such specialized vehicles can be avoided. Furthermore, updating the currently disclosed sparse maps can be done much faster than with systems that rely on dedicated specialized mapping vehicles (which, due to cost and specialized equipment, are typically limited to a group of specialized vehicles far fewer in number than the number of private or commercial vehicles already available to perform the disclosed collection techniques).

[0243] The sparse maps disclosed through crowdsourcing can be highly accurate because they can be generated based on numerous inputs from multiple (tens, hundreds, millions, etc.) data collection vehicles that collect driving information along a specific road section. For example, all data collection vehicles traveling along a specific road section can record their actual trajectory and determine the location information associated with objects / features detected along the road section. This information is passed from multiple data collection vehicles to a server. The actual trajectories are aggregated, and refined target trajectories are generated for each valid driving path along the road section. Furthermore, location information (semantic or non-semantic) collected from multiple data collection vehicles for each object / feature detected along the road section can also be aggregated. As a result, the mapped location of each detected object / feature may constitute the average of hundreds, thousands, or millions of individually determined locations for each detected object / feature. Such techniques can result in highly accurate mapped locations of detected objects / features.

[0244] In some embodiments, the disclosed systems and methods may generate sparse maps for autonomous vehicle navigation. For example, the disclosed systems and methods may use crowdsourced data to generate a sparse map that one or more autonomous vehicles can use to navigate along a system of roads. As used herein, “crowdsourcing” means receiving data from various vehicles (e.g., autonomous vehicles) traveling along a road segment at different times and using such data to generate and / or update a road model, including sparse map tiles. Either the model or its sparse map tiles may then be transmitted to a vehicle or other vehicle traveling along the road segment at a later time to assist in autonomous vehicle navigation. The road model may include a plurality of target trajectories representing preferred trajectories that an autonomous vehicle should follow when crossing a road segment. The target trajectories may be the same as reconstructed actual trajectories collected from vehicles crossing the road segment and may be transmitted from the vehicles to a server. In some embodiments, the target trajectories may differ from actual trajectories previously taken by one or more vehicles when crossing a road segment. The target trajectories may be generated based on actual trajectories (e.g., by averaging or any other appropriate action).

[0245] The vehicle trajectory data that a vehicle may upload to the server may correspond to the vehicle's actual reconstructed trajectory, or may correspond to a recommended trajectory that is based on or related to the vehicle's actual reconstructed trajectory, but may differ from the actual reconstructed trajectory. For example, a vehicle may modify its actual reconstructed trajectory and send the modified actual trajectory to the server (e.g., as a recommendation). The road model may use the recommended modified trajectory as the target trajectory for the autonomous navigation of other vehicles.

[0246] In addition to trajectory information, other information potentially for use in constructing the sparse data map 800 may include information related to potential landmark candidates. For example, by crowdsourcing information, the disclosed systems and methods may identify potential landmarks in the environment and refine landmark locations. Landmarks may be used by autonomous vehicle navigation systems to determine and / or adjust the position of a vehicle along a target trajectory.

[0247] The reconstructed trajectory that a vehicle may generate as it travels along a road can 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, self-motion estimation (e.g., three-dimensional translation and three-dimensional rotation of a camera, and by extension, the vehicle body). The estimation of rotation and translation may be determined based on the analysis of images captured by one or more imaging devices, along with information from other sensors or devices such as inertial sensors and velocity sensors. For example, the inertial sensor may include an accelerometer or other suitable sensor configured to measure changes in the translation and / or rotation of the vehicle body. The vehicle may include a velocity sensor to measure the vehicle's speed.

[0248] In some embodiments, the self-motion of the camera (and consequently the vehicle body) can be estimated based on an optical flow analysis of the captured images. The optical flow analysis of a series of images identifies the movement of pixels from the series of images, and based on the identified movement, the motion of the vehicle is determined. The self-motion can be integrated over time along the road section to reconstruct the trajectory associated with the road section the vehicle traveled.

[0249] Using data collected by multiple vehicles during multiple runs along road divisions at different times (e.g., reconstructed trajectories), a road model (e.g., including target trajectories, etc.) can be constructed within a sparse data map 800.

[0250] The geometry of the reconstructed track (and also the target track) along the road division can be represented by a curve in three-dimensional space, which can be a spline connecting cubic polynomials. The reconstructed track curve can be determined from the analysis of a plurality of images captured by a video stream or a camera attached to the vehicle. In some embodiments, the position is identified in each frame or image several meters ahead of the current position of the vehicle. This position is where the vehicle is expected to travel within a predetermined period. This operation can be repeated for each frame, and at the same time, the vehicle can calculate the self-motion (rotation and translation) of the camera. In each frame or image, a short-distance model of the desired path is generated by the vehicle within the reference frame attached to the camera. By stitching together the short-distance models, a three-dimensional model of the road in several coordinate frames, which can be any coordinate frame or a predetermined coordinate frame, can be obtained. Then, the three-dimensional model of the road can be fitted by a spline that can include or connect one or more polynomials of an appropriate degree.

[0251] One or more detection modules can be used to conclude the short-distance road model in each frame. For example, a bottom-up lane detection module can be used. The bottom-up lane detection module can be useful when lane marks are drawn on the road. This module can search for the ends in the image and assemble them together to form lane marks. A second module can be used together 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-distance path from the input image. In either module, the road model is detected in the image coordinate frame and can be converted to a three-dimensional space that is virtually connected to the camera.

[0252] The reconstructed orbital modeling method may introduce cumulative errors due to the integration of long-term self-motion, which may contain noise components; however, such errors may not be significant because the generated model can provide sufficient accuracy for navigation on a local scale. In addition, the integrated errors can be canceled out using external sources such as satellite imagery or geodetic measurements. For example, the disclosed system and method may use a GNSS receiver to cancel out cumulative errors. However, GNSS positioning signals are not always available and accurate. The disclosed system and method may enable steering applications that are weakly dependent on the availability and accuracy of GNSS positioning. In such systems, the use of GNSS signals may be limited. For example, in some embodiments, the disclosed system may use GNSS signals only for the purpose of indexing a database.

[0253] In some embodiments, the range scale (e.g., local scale) that may be relevant to an autonomous vehicle navigation steering application could be around 50 meters, 100 meters, 200 meters, 300 meters, etc. Such distances can be used because the road model of geometry is used primarily for two purposes: planning the trajectory ahead and locating the vehicle on the road model. In some embodiments, when a control algorithm steers the vehicle according to a target point located 1.3 seconds ahead (or any other time such as 1.5 seconds, 1.7 seconds, 2 seconds, etc.), the planning task may use the model over a typical range of 40 meters ahead (or any other appropriate distance ahead such as 20 meters, 30 meters, 50 meters, etc.). For the positioning task, the road model is used over a typical range of 60 meters behind the vehicle (or any other appropriate distance such as 50 meters, 100 meters, 150 meters, etc.), according to a method called "tail alignment," which will be described in more detail in another section. The disclosed system and method can generate a geometric model with sufficient accuracy over a specific range, such as 100 meters, so that the planned trajectory does not deviate by more than 30 cm from the center of the lane.

[0254] As described above, the three-dimensional road model can be constructed by detecting short-distance sections and connecting them together. Connecting them together can be made possible by calculating a six-stage self-motion model using the video and / or images captured by the camera, the data from the inertial sensors reflecting the movement of the vehicle, and the speed signal of the host vehicle. The cumulative error can be small enough over some local range scales, such as about 100 meters. Over all these range scales, it can be completed in a single run over a specific road segment.

[0255] In some embodiments, multiple runs can be used to average the resulting models and further improve their accuracy. The same vehicle can run the same route multiple times, or the model data collected by multiple vehicles can be sent to a central server. In either case, a matching procedure can be performed to identify overlapping models and enable averaging to generate a target trajectory. The constructed model (e.g., including the target trajectory) can be used for steering when the convergence criterion is met. Subsequent runs can be used to further improve the model and to accommodate changes in the infrastructure.

[0256] When multiple vehicles are connected to a central server, it becomes possible to share driving experiences (such as detected data) among the multiple vehicles. Each vehicle client can store a partial copy of the universal road model that may be relevant to its current position. A two-way update procedure between the vehicle and the server can be executed by the vehicle and the server. The small footprint concept discussed above enables the disclosed systems and methods to perform two-way updates using a very narrow bandwidth.

[0257] Information related to potential landmarks may also be determined and transmitted to a central server. For example, the disclosed system and method may determine the physical characteristics of one or more potential landmarks based on one or more images containing the landmarks. These physical characteristics may include the physical size of the landmark (e.g., height, width), the distance from the vehicle to the landmark, the distance from the landmark to the previous landmark, the lateral position of the landmark (e.g., the position of the landmark relative to the driving lane), the GPS coordinates of the landmark, the type of landmark, and the identification of text on the landmark. For example, a vehicle may detect potential landmarks such as speed limit signs by analyzing one or more images captured by a camera.

[0258] A vehicle may determine the distance from the vehicle to a landmark or the location associated with a landmark (e.g., any semantic or non-semantic object or feature along a road section) based on the analysis of one or more images. In some embodiments, the distance may be determined by analyzing images of the landmark using appropriate image analysis methods such as scaling and / or optical flow methods. As previously stated, the location of an object / feature may include the 2D image locations of one or more points associated with the object / feature (e.g., XY pixel locations of one or more captured images) or the 3D real-world locations of one or more points (determined by, for example, structural / optical flow techniques in motion, 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. If the vehicle determines that a particular potential landmark corresponds to a predetermined type or classification stored in a sparse map, it may be sufficient for the vehicle to simply communicate the representation of the landmark's type or classification along with its location to a server. The server may store such representations. Later, during navigation, the navigating vehicle may capture images containing representations of landmarks, process the images (e.g., using a classifier), and compare the resulting landmarks to confirm the detection of mapped landmarks and to use the mapped landmarks for locating the navigating vehicle on a sparse map.

[0259] In some embodiments, multiple autonomous vehicles traveling on a road section may communicate with a server. A vehicle (or client) may generate a curve describing its journey (e.g., by self-motion integration) in an arbitrary coordinate frame. A vehicle may detect landmarks and place them within the same frame. A vehicle may upload curves and landmarks to the server. The server may collect data from the vehicles over multiple journeys and generate a unified road model. For example, as discussed below with respect to Figure 19, the server may use the uploaded curves and landmarks to generate a sparse map with a unified road model.

[0260] The server may also deliver models to clients (e.g., vehicles). For example, the server may deliver a sparse map to one or more vehicles. When the server receives new data from vehicles, it may continuously or periodically update the model. For example, the server may process the new data and evaluate whether it contains information that should trigger an update or the creation of new data on the server. The server may deliver updated models or updates to vehicles in order to provide autonomous vehicle navigation.

[0261] The server may use one or more criteria to determine whether new data received from a vehicle should trigger a model update or the creation of new data. For example, if new data indicates that a previously recognized landmark no longer exists or has been replaced by another landmark at a particular location, the server may determine that the new data should trigger a model update. As another example, if new data indicates that a road section is being closed, and this is supported by data received from other vehicles, the server may determine that the new data should trigger a model update.

[0262] The server may deliver the updated model (or the updated portion of the model) to one or more vehicles traveling on the road section to which the model update is associated. The server may also deliver the updated model to vehicles that are about to travel on the road section to which the model update is associated, or to vehicles on a planned trip that includes the road section. For example, while an autonomous vehicle is traveling along another road section before reaching the road section to which the update is associated, the server may deliver the update or the model to be updated to the autonomous vehicle before the vehicle reaches the road section.

[0263] In some embodiments, a remote server may collect tracks and landmarks from multiple clients (e.g., vehicles traveling along a common road section). The server may use landmarks to match curves and create an average road model based on the tracks collected from multiple vehicles. The server may also calculate the most likely paths for each node or connection point of the road section and the road graph. For example, the remote server may align the tracks to generate a crowdsourced sparse map from the collected tracks.

[0264] The server may average landmark characteristics received from multiple vehicles traveling along a common road section, such as the distance between one landmark and another (e.g., a previous landmark along a road section), measured by multiple vehicles, in order to determine arc length parameters and support localization and speed calibration along the route of each client vehicle. The server may average the physical dimensions of landmarks measured by multiple vehicles traveling along a common road section and recognizing the same landmark. The averaged physical dimensions may be used to support distance estimation, such as the distance from a vehicle to a landmark. The server may average the lateral position of landmarks (e.g., the position from the lane the vehicle is traveling in to the landmark) measured by multiple vehicles traveling along a common road section and recognizing the same landmark. The averaged lateral portion may be used to support lane assignment. The server may average the GPS coordinates of landmarks measured by multiple vehicles traveling along the same road section and recognizing the same landmark. The averaged GPS coordinates of landmarks may be used to support global localization or positioning of landmarks within a road model.

[0265] In some embodiments, the server may identify model changes, such as construction, detours, new signs, or sign removals, based on data received from the vehicle. Upon receiving new data from the vehicle, the server may update the model continuously, periodically, or instantaneously. The server may deliver the updated model or the updated model to the vehicle to provide autonomous navigation. For example, as will be discussed further below, the server may use crowdsourced data to filter out “ghost” landmarks detected by the vehicle.

[0266] 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 the intervention occurred. The server may identify specific parts of the data that triggered or are closely related to the intervention, such as data indicating a temporary lane closure setting or data indicating pedestrians on the road. The server may update the model based on the identified data. For example, the server may correct one or more trajectories stored in the model.

[0267] Figure 12 is a schematic diagram of a system that generates a sparse map using crowdsourcing (and uses a crowdsourced sparse map for delivery and navigation). Figure 12 shows a road section 1200 containing one or more lanes. Multiple vehicles 1205, 1210, 1215, 1220, and 1225 may travel on road section 1200 simultaneously or at different times (however, in Figure 12 they are shown appearing on road section 1200 simultaneously). At least one of the vehicles 1205, 1210, 1215, 1220, and 1225 may be an autonomous vehicle. For simplicity in this embodiment, we assume that all of the vehicles 1205, 1210, 1215, 1220, and 1225 are autonomous vehicles.

[0268] Each vehicle may be similar to a vehicle disclosed in another embodiment (e.g., vehicle 200) and may include components or devices included in or associated with a vehicle disclosed in another embodiment. Each vehicle may be equipped with an imaging device or camera (e.g., imaging device 122 or camera 122). Each vehicle may communicate with a remote server 1230 via a wireless communication path 1235, as shown by the dashed line, over one or more networks (e.g., via a cellular network and / or the Internet, etc.). Each vehicle may transmit data to and receive data from the server 1230. For example, the server 1230 may collect data from multiple vehicles traveling on road section 1200 at different times, process the collected data to generate an autonomous vehicle road navigation model or model update. The server 1230 may transmit the autonomous vehicle road navigation model or model update to the vehicle that transmitted data to the server 1230. The server 1230 may transmit the autonomous vehicle road navigation model or model update to other vehicles traveling on road section 1200 at a later date.

[0269] When vehicles 1205, 1210, 1215, 1220, and 1225 travel along road section 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, the navigation information may be associated with a common road section 1200. The navigation information may include trajectories associated with each of vehicles 1205, 1210, 1215, 1220, and 1225 as each vehicle travels along road section 1200. In some embodiments, the trajectories may be reconstructed based on data detected by various sensors and devices mounted on vehicle 1205. For example, the trajectories may be reconstructed based on at least one of accelerometer data, velocity data, landmark data, road geometry or profile data, vehicle positioning data, and self-motion data. In some embodiments, the trajectory may be reconstructed based on data from inertial sensors such as accelerometers and the speed of the vehicle 1205 detected by velocity sensors. Furthermore, in some embodiments, the trajectory may be determined based on the detected self-motion of the camera, which may exhibit three-dimensional translation and / or three-dimensional rotation (or rotational motion) (for example, by a processor mounted on each of the vehicles 1205, 1210, 1215, 1220, and 1225). The self-motion of the camera (and thus the vehicle body) may be determined from the analysis of one or more images captured by the camera.

[0270] In some embodiments, the trajectory of the vehicle 1205 may be determined by a processor mounted on the vehicle 1205 and transmitted to the server 1230. In other embodiments, the server 1230 may receive data detected by various sensors and devices mounted on the vehicle 1205 and determine the trajectory based on the data received from the vehicle 1205.

[0271] In some embodiments, the navigation information transmitted from vehicles 1205, 1210, 1215, 1220, and 1225 to the server 1230 may include data relating to the road surface, road geometry, or road profile. The geometry of road section 1200 may include lane structure and / or landmarks. The lane structure may include the total number of lanes in road section 1200, lane types (e.g., unidirectional lanes, bidirectional lanes, driving lanes, overtaking lanes, etc.), markings on the lanes, lane width, etc. In some embodiments, the navigation information may include lane assignment, for example, which of several lanes the vehicle is traveling in. For example, the lane assignment may be associated with the numerical 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 the text value "center lane" indicating that the vehicle is traveling in the center lane.

[0272] Server 1230 may store navigation information on non-temporary computer-readable media such as hard drives, compact disks, tapes, and memory. Based on navigation information received from multiple vehicles 1205, 1210, 1215, 1220, and 1225, Server 1230 may generate at least a portion of an autonomous vehicle road navigation model for a common road section 1200 (e.g., via a processor included in Server 1230) and store the model as part of a sparse map. Based on crowdsourced data (e.g., navigation information) received from multiple vehicles (e.g., 1205, 1210, 1215, 1220, and 1225) traveling in the lanes of the road section at different times, Server 1230 may determine the trajectory associated with each lane. Based on the multiple trajectories determined based on the crowdsourced navigation data, Server 1230 may generate an autonomous vehicle road navigation model or a portion of the model (e.g., an updated portion). Server 1230 may transmit a model or the updated portion of a model to one or more autonomous vehicles 1205, 1210, 1215, 1220, and 1225 traveling on road section 1200, or to any other autonomous vehicles later traveling on the road section to update an existing autonomous vehicle road navigation model provided to the vehicle's navigation system. The autonomous vehicle road navigation model may be used when the autonomous vehicles autonomously navigate along the common road section 1200.

[0273] As described above, the autonomous vehicle road navigation model may be contained in a sparse map (for example, the sparse map 800 shown in Figure 8). The sparse map 800 may contain a sparse record of data relating to the road geometry and / or landmarks along the road, and may provide sufficient information to guide the autonomous navigation of the autonomous vehicle without requiring excessive data storage for the time being. In some embodiments, the autonomous vehicle road navigation model may be stored separately from the sparse map 800 and may use map data from the sparse map 800 when the model is run for navigation. In some embodiments, the autonomous vehicle road navigation model may use map data contained in the sparse map 800 to determine a target trajectory along road section 1200 in order to guide the autonomous navigation of autonomous vehicles 1205, 1210, 1215, 1220, and 1225 or other vehicles traveling along road section 1200 later. For example, when an autonomous vehicle road navigation model is executed by a processor included in the navigation system of vehicle 1205, the model can verify and / or correct the current driving course of vehicle 1205 by having the processor compare the trajectory determined based on navigation information received from vehicle 1205 with predetermined trajectories included in a sparse map 800.

[0274] In an autonomous vehicle road navigation model, the geometry of a road or target trajectory can be encoded by a curve in three-dimensional space. In one embodiment, the curve may be a three-dimensional spline containing one or more connected three-dimensional polynomials. As those skilled in the art will understand, a spline may be a numerical function piecewise defined by a set of polynomials for fitting data. A spline for fitting three-dimensional road geometry data may include a linear spline (primary), a quadratic spline (secondary), a cubic spline (cubic), or any other spline (of other degrees), or a combination thereof. A spline may contain one or more three-dimensional polynomials of different degrees that connect (e.g., fit) data points of the three-dimensional road geometry data. In some embodiments, an autonomous vehicle road navigation model may include a three-dimensional spline corresponding to a common road division (e.g., road division 1200) or a target trajectory along the lanes of road division 1200.

[0275] As described above, the autonomous vehicle road navigation model included in the sparse map may include other information, such as the identification of at least one landmark along road segment 1200. The landmark may be visible within the field of view of cameras (e.g., camera 122) installed in each of vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, camera 122 may capture an image of the landmark. A processor (e.g., processor 180, 190, or processing unit 110) installed in vehicle 1205 may process the image of the landmark to extract the identification information of the landmark. Instead of the actual image of the landmark, the landmark identification information may be stored in sparse map 800. The landmark identification information may require much less storage space than the actual image. Other sensors or systems (e.g., GPS system) may also provide specific identification information of the landmark (e.g., the location of the landmark). The landmark may include at least one of a traffic sign, an arrow mark, a lane mark, a broken-line lane mark, a traffic signal, a stop line, a direction sign (e.g., a highway exit sign with an arrow indicating direction, a highway sign with an arrow pointing to a different direction or location), a landmark beacon, or a streetlight pole. The landmark beacon refers to a device (e.g., an RFID device) installed along a road segment that transmits or reflects a signal to a receiver installed in a vehicle. As a result, when the vehicle passes by the device, the beacon and the location of the device received by the vehicle (e.g., determined from the GPS location of the device) may be used as a landmark included in the autonomous vehicle road navigation model and / or sparse map 800.

[0276] The identification of at least one landmark may include the location of at least one landmark. The location of a landmark may be determined based on location measurements performed using sensor systems (e.g., Global Positioning System, inertial-based positioning system, landmark beacon, etc.) associated with a plurality of vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, the location of a 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 a plurality of runs. 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 average location measurement as the location of the landmark. The location of the landmark may be continuously refined by measurements received from the vehicles in subsequent runs.

[0277] Landmark identification may include landmark size. A processor mounted on a vehicle (e.g., 1205) may estimate the physical size of a landmark based on image analysis. Server 1230 may receive multiple estimates of the physical size of the same landmark from different vehicles over different journeys. Server 1230 may average the different estimates to arrive at the physical size of the landmark and store that landmark size in the road model. Using the physical size estimate, the distance from the vehicle to the landmark may be further determined or estimated. The distance to the landmark may be estimated based on the vehicle's current speed and the scale of magnification based on the location of the landmark appearing in the image relative to the camera's magnification focus. For example, the distance to the landmark may be estimated as Z = V * dt * R / D, where V is the vehicle's speed, R is the distance in the image from the landmark to the magnification focus at time t1, D is the change in the distance of the landmark in the image from t1 to t2, and dt represents (t2 - t1). For example, the distance to a landmark can be estimated using the formula Z = V * dt * R / D, where V is the vehicle speed, R is the distance in the image between the landmark and the magnified focus, dt is the time interval, and D is the image displacement of the landmark along the epipolar line. The distance to a landmark can also be estimated using the above formula and other equivalent formulas, such as Z = V * ω / Δω, where V is the vehicle speed, ω is the image length (e.g., object width), and Δω is the change in the image length per unit time.

[0278] If the physical size of a 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 the landmark passes through in the image. From the above equation, the change in distance Z can be calculated using ΔZ = f*W*Δω / ω² + f*ΔW / ω, where ΔW is attenuated to zero by averaging, and Δω is the number of pixels representing the precision of the bounding box in the image. The value that estimates the physical size of the landmark can be calculated on the server side by averaging multiple observations. The error resulting from the distance estimation can be very small. There are two sources of error that can occur when using the above equation, namely ΔW and Δω. The contribution to the distance error is given by ΔZ = f*W*Δω / ω²f*ΔW / ω, where ΔW is attenuated to zero by averaging. Therefore, ΔZ is determined by Δω (e.g., the inaccuracy of the bounding box in the image).

[0279] For landmarks of unknown dimensions, the distance to the landmark can be estimated by tracking feature points on the landmark across consecutive frames. For example, specific features to be displayed on a speed limit sign can be tracked across two or more image frames. Based on these tracked features, a distance distribution per feature point can be generated. Distance estimates can be extracted from this distance distribution. For example, the distance that appears most frequently in the distance distribution can be used as the distance estimate. Alternatively, the mean of the distance distribution can be used as the distance estimate.

[0280] Figure 13 shows an exemplary autonomous vehicle road navigation model represented by a plurality of three-dimensional splines 1301, 1302, and 1303. The curves 1301, 1302, and 1303 shown in Figure 13 are for illustrative purposes only. Each spline may contain one or more three-dimensional polynomials connecting a plurality of data points 1310. Each polynomial may be a linear polynomial, a quadratic polynomial, a cubic polynomial, or any suitable combination of polynomials of different degrees. Each data point 1310 may be associated with navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, each data point 1310 may be associated with data related to landmarks (e.g., size, location, and landmark identification information) and / or road signature profiles (e.g., road geometry, road roughness profile, road curvature profile, road width profile). In some embodiments, some data points 1310 may be associated with data related to landmarks, while other data points may be associated with data related to road signature profiles.

[0281] Figure 14 shows raw location data 1410 (e.g., GPS data) received from five separate runs. A run may be separate from another run if separate vehicles cross simultaneously, if the same vehicle crosses at different times, or if separate vehicles cross at different times. To account for errors in the location data 1410 and different positions of vehicles within the same lane (e.g., one vehicle may be traveling closer to the left side of the lane than another), the remote server 1230 may use one or more statistical techniques to generate a map skeleton 1420 and determine whether changes in the raw location data 1410 represent actual deviations or statistical errors. Each path in the skeleton 1420 may be relinked to the raw data 1410 that formed that path. For example, the path between A and B in the skeleton 1420 is linked to raw data 1410 from runs 2, 3, 4, and 5, but not from run 1. Skeleton 1420 may not be detailed enough to be used for vehicle navigation (unlike the splines mentioned above, for example, because it combines travel from multiple lanes on the same road), but it can provide useful topological information and can be used to define intersections.

[0282] Figure 15 shows an example of how additional detail can be generated for a sparse map within a section of a map skeleton (e.g., section A to B within skeleton 1420). As shown in Figure 15, data (e.g., self-motion data, road mark data, and so on) may be indicated according to a position S (or S1 or S2) along the journey. Server 1230 can identify landmarks in the sparse map by identifying unique matches between landmarks 1501, 1503, and 1505 of journey 1510 and landmarks 1507 and 1509 of journey 1520. Such a matching algorithm may lead to the identification of landmarks 1511, 1513, and 1515. However, those skilled in the art will recognize that other matching algorithms may be used. For example, probability optimization may be used instead of, or in combination with, unique matches. Server 1230 may align the matched landmarks by longitudinally aligning the journey. For example, server 1230 may select one travel (e.g., travel 1520) as the reference travel, and then shift and / or elastically extend the other travel (e.g., travel 1510) for alignment.

[0283] Figure 16 shows an example of aligned landmark data for use in a sparse map. In the example in Figure 16, landmark 1610 includes a road sign. The example in Figure 16 further shows data from multiple runs 1601, 1603, 1605, 1607, 1609, 1611, and 1613. In the example in Figure 16, the data from run 1613 consists of "ghost" landmarks, and therefore, since none of runs 1601, 1603, 1605, 1607, 1609, and 1611 include identification of landmarks in the vicinity of identified landmarks within run 1613, server 1230 may identify the landmark as a "ghost". Accordingly, server 1230 may accept a potential landmark if the ratio of images in which a landmark appears to images in which the landmark does not appear exceeds a threshold, and / or may reject a potential landmark if the ratio of images in which a landmark appears to images in which the landmark does not appear exceeds a threshold.

[0284] Figure 17 shows a system 1700 for generating driving data that can be used to crowdsource sparse maps. As shown in Figure 17, the system 1700 may include a camera 1701 and a location device 1703 (e.g., a GPS locator). The camera 1701 and the location device 1703 may be mounted on a vehicle (e.g., one of vehicles 1205, 1210, 1215, 1220, and 1225). The camera 1701 may generate multiple types of data, e.g., self-motion data, traffic sign data, road data, or similar. The camera data and location data may be divided into driving segments 1705. For example, each driving segment 1705 may have camera data and location data for driving less than 1 km.

[0285] In some embodiments, the system 1700 can eliminate redundancy in the driving segment 1705. For example, if a landmark appears in multiple images from camera 1701, the system 1700 can eliminate redundant data so that the driving segment 1705 contains only one copy of the location of the landmark and any metadata associated with the landmark. As a further example, if lane marks appear in multiple images from camera 1701, the system 1700 can eliminate redundant data so that the driving segment 1705 contains only one copy of the location of the lane marks and any metadata associated with the lane marks.

[0286] System 1700 also includes a server (e.g., server 1230). Server 1230 receives a travel segment 1705 from the vehicle and can recombine the travel segment 1705 into a single travel segment 1707. Such an arrangement may reduce the bandwidth requirements when transferring data between the vehicle and the server, and the server can also store data related to the entire travel segment.

[0287] Figure 18 shows the system 1700 of Figure 17, further configured for crowdsourcing sparse maps. As shown in Figure 17, the system 1700 includes a vehicle 1810 that captures driving data using, for example, a camera (e.g., generating self-motion data, traffic sign data, road data, or the like) and a location device (e.g., a GPS locator). As shown in Figure 17, the vehicle 1810 divides the collected data into driving segments (shown in Figure 18 as "DS1 1", "DS2 1", and "DSN 1"). The server 1230 then receives the driving segments and reconstructs the driving segments (shown in Figure 18 as "Driving 1").

[0288] As further shown in Figure 18, system 1700 also receives data from additional vehicles. For example, vehicle 1820 also captures driving data using, for example, a camera (which generates, for example, self-motion data, traffic sign data, road data, or similar) and a location device (for example, a GPS locator). Similar to vehicle 1810, vehicle 1820 categorizes the collected data into driving segments (shown in Figure 18 as "DS1 2", "DS2 2", and "DSN 2"). Server 1230 then receives the driving segments and reconstructs a drive (shown in Figure 18 as "Driving 2") from the received segments. Any number of additional vehicles may be used. For example, Figure 18 also includes "Vehicle N" which captures driving data, categorizes it into driving segments (shown in Figure 18 as "DS1 N", "DS2 N", and "DSN N"), and transmits it to server 1230 for reconstruction into a drive (shown in Figure 18 as "Driving N").

[0289] As shown in Figure 18, the server 1230 may construct a sparse map (referred to as "Map") using reconstructed mileage (e.g., "Ride 1", "Ride 2", and "Ride N") collected from multiple vehicles (e.g., "Vehicle 1" (also referred to as Vehicle 1810), "Vehicle 2" (also referred to as Vehicle 1820), and "Vehicle N").

[0290] Figure 19 is a flowchart illustrating an exemplary process 1900 for generating a sparse map for autonomous vehicle navigation along road divisions. Process 1900 may be performed by one or more processing devices included in server 1230.

[0291] Process 1900 may include receiving multiple images taken as one or more vehicles cross a road section (step 1905). Server 1230 may receive images from cameras included in one or more of the vehicles 1205, 1210, 1215, 1220, and 1225. For example, camera 122 may capture one or more images of the environment surrounding vehicle 1205 as vehicle 1205 travels along road section 1200. In some embodiments, server 1230 may also receive de-redundant image data, from which redundancy has been removed by a processor on vehicle 1205, as discussed above with respect to Figure 17.

[0292] Process 1900 may further include identifying at least one linear representation of a road surface feature extending along a road section based on multiple images (step 1910). Each linear representation may represent a path along a road section substantially corresponding to a road surface feature. For example, server 1230 may analyze environmental images received from camera 122 to identify road edges or lane marks and determine the trajectory of travel along the road section 1200 associated with the road edges or lane marks. In some embodiments, the trajectory (or linear representation) may include a spline, a polynomial representation, or a curve. Server 1230 may determine the trajectory of travel of vehicle 1205 based on the camera's own motion (e.g., three-dimensional translational motion and / or three-dimensional rotational motion) received in step 1905.

[0293] Process 1900 may also include identifying multiple landmarks associated with a road division based on multiple images (step 1910). For example, server 1230 may analyze environmental images received from camera 122 to identify one or more landmarks, such as road signs, along the road division 1200. Server 1230 may identify landmarks by analyzing multiple images taken when one or more vehicles cross the road division. To enable crowdsourcing, the analysis may include rules for accepting and rejecting landmarks that may be associated with the road division. For example, the analysis may include accepting a potential landmark if the ratio of images in which the landmark appears to images in which the landmark does not appear exceeds a threshold, and / or rejecting a potential landmark if the ratio of images in which the landmark appears to images in which the landmark does not appear exceeds a threshold.

[0294] Process 1900 may include other operations or steps performed by Server 1230. For example, navigation information may include a target trajectory for a vehicle to travel along a road section, and Process 1900 may include, as will be discussed in more detail below, the server 1230 clustering vehicle trajectories related to multiple vehicles traveling on the road section and determining a target trajectory based on the clustered vehicle trajectories. Clustering vehicle trajectories may include, by Server 1230, clustering multiple trajectories related to vehicles traveling on the road section into multiple clusters based on at least one of the absolute direction of travel of the vehicles or the lane assignment of the vehicles. Generating a target trajectory may include, by Server 1230, averaging the clustered trajectories. As a further example, Process 1900 may include aligning the data received in step 1905. Other processes or steps performed by Server 1230, as described above, may also be included in Process 1900.

[0295] The disclosed systems and methods may include other features. For example, the disclosed systems may use local coordinates rather than global coordinates. In the case of autonomous driving, some systems may display data in world coordinates. For example, they may use longitude and latitude coordinates of the ground surface. To use a map for steering, the host vehicle may determine its position and orientation relative to the map. It seems natural to use an on-board GPS device to position the vehicle on the map and to find rotational transformations between the vehicle's reference frame and the world reference frame (e.g., north, east, and down). Once the vehicle's reference frame is aligned with the map reference frame, the vehicle's reference frame may represent a desired route and calculate or generate steering commands.

[0296] The disclosed systems and methods may enable autonomous vehicle navigation (e.g., steering control) in a low-footprint model that can be collected by the autonomous vehicle itself without the aid of expensive surveying equipment. To support autonomous navigation (e.g., steering applications), the road model may include a sparse map with road geometry, lane structure, and landmarks that can be used to determine the location or position of a vehicle along a trajectory included in the model. As discussed above, the generation of the sparse map may be performed by a remote server that communicates with a vehicle traveling on the road and receives data from the vehicle. The data may include a suggested trajectory that may represent the detected data, a trajectory reconstructed based on the detected data, and / or a modified reconstructed trajectory. As discussed below, the server may transmit the model to a vehicle traveling on the road later or to another vehicle to assist autonomous navigation.

[0297] Figure 20 shows a block diagram of server 1230. Server 1230 may include a communication unit 2005 which may include both hardware components (e.g., communication control circuits, switches, and antennas) and software components (e.g., communication protocols, computer code). For example, the communication unit 2005 may include at least one network interface. Server 1230 may communicate with vehicles 1205, 1210, 1215, 1220, and 1225 through the communication unit 2005. For example, server 1230 may receive navigation information transmitted from vehicles 1205, 1210, 1215, 1220, and 1225 through the communication unit 2005. Server 1230 may distribute an autonomous vehicle road navigation model to one or more autonomous vehicles through the communication unit 2005.

[0298] Server 1230 may include at least one non-temporary storage medium 2010, such as a hard drive, compact disk, or tape. Storage device 1410 may be configured to store navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225, and / or data such as an autonomous vehicle road navigation model generated by server 1230 based on the navigation information. Storage device 2010 may be configured to store any other information, such as a sparse map (e.g., the sparse map 800 discussed above with respect to Figure 8).

[0299] In addition to, or instead of, the storage device 2010, the server 1230 may include memory 2015. Memory 2015 may be similar to or different from memory 140 or 150. Memory 2015 may be non-temporary memory such as flash memory, random access memory, etc. Memory 2015 may be configured to store data such as computer code or instructions executable by a processor (e.g., processor 2020), map data (e.g., data for sparse map 800), autonomous vehicle road navigation models, and / or navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225.

[0300] Server 1230 may include at least one processing device 2020 configured to execute computer code or instructions stored in memory 2015 in order 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 that 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 that later travels on road section 1200). Processing device 2020 may be similar to or different from processors 180, 190, or processing unit 110.

[0301] Figure 21 shows a block diagram of memory 2015, which may store computer code or instructions for performing one or more operations to generate a road navigation model for use in autonomous vehicle navigation. As shown in Figure 21, memory 2015 may store one or more modules for performing operations to process vehicle navigation information. For example, memory 2015 may include a model generation module 2105 and a model distribution module 2110. The processor 2020 may execute instructions stored in either module 2105 or 2110 contained in memory 2015.

[0302] The model generation module 2105, when executed by the processor 2020, can store instructions that can generate at least a portion of an autonomous vehicle road navigation model for a common road section (e.g., road section 1200) based on navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225. For example, in generating an autonomous vehicle road navigation model, the processor 2020 may cluster vehicle trajectories along the common road section 1200 into different clusters. The processor 2020 may determine a target trajectory along the common road section 1200 based on the clustered vehicle trajectories for each of the different clusters. Such operation may include finding the average or average trajectory of the clustered vehicle trajectories in each cluster (e.g., by averaging the data representing the clustered vehicle trajectories). In some embodiments, the target trajectory may be associated with a single lane of the common road section 1200.

[0303] A road model and / or sparse map may store trajectories associated with road sections. These trajectories may be called target trajectories and are provided to autonomous vehicles for autonomous navigation. Target trajectories may be received from multiple vehicles or generated based on actual or recommended trajectories (actual trajectories with some modifications) received from multiple vehicles. Target trajectories contained in a road model or sparse map may be continuously updated (e.g., averaged) with new trajectories received from other vehicles.

[0304] Vehicles traveling on a road section may collect data through various sensors. This data may include landmarks, road signature profiles, vehicle motion (e.g., accelerometer data, velocity data), and vehicle position (e.g., GPS data), which may reconstruct the actual trajectory itself, or the data may be sent to a server, which may reconstruct the actual trajectory of the vehicle. In some embodiments, a vehicle may send data to the server 1230 related to its trajectory (e.g., curves in an arbitrary reference frame), landmark data, and lane assignment along the travel route. Different vehicles traveling along the same road section in multiple trips may have different trajectories. The server 1230 may identify the route or trajectory associated with each lane from the trajectories received from the vehicles through a clustering process.

[0305] Figure 22 illustrates the process of clustering vehicle trajectories associated with vehicles 1205, 1210, 1215, 1220, and 1225 to determine a target trajectory for a common road section (e.g., road section 1200). The target trajectory or multiple target trajectories determined from the clustering process may be included in an autonomous vehicle road navigation model or a sparse map 800. In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 traveling along road section 1200 may transmit multiple trajectories 2200 to a server 1230. In some embodiments, the 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 an autonomous vehicle road navigation model, the server 1230 may cluster the vehicle tracks 1600 into multiple clusters 2205, 2210, 2215, 2220, 2225, and 2230, as shown in Figure 22.

[0306] Clustering can be performed using various criteria. In some embodiments, all travel within a cluster may be similar with respect to the absolute direction of travel along road section 1200. The absolute direction of travel may be obtained from GPS signals received by vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, the absolute direction of travel may be obtained using dead reckoning. As those skilled in the art will understand, dead reckoning can be used to determine the current position, and therefore the direction of travel of vehicles 1205, 1210, 1215, 1220, and 1225, using previously determined positions, estimated speeds, etc. Tracks clustered by absolute direction of travel may be useful for identifying routes along the road.

[0307] In some embodiments, all travel within a cluster may be similar with respect to lane assignments along the road section 1200 (e.g., the same lane before and after an intersection). Tracks clustered by lane assignment may be useful for identifying lanes along the road. In some embodiments, both criteria (e.g., absolute direction of travel and lane assignment) may be used for clustering.

[0308] In each cluster 2205, 2210, 2215, 2220, 2225, and 2230, trajectories can be averaged to obtain a target trajectory associated with a particular cluster. For example, trajectories from multiple runs associated with the same lane cluster can be averaged. The average trajectory may be the target trajectory associated with a particular lane. To average the clusters of trajectories, server 1230 can select a reference frame for any trajectory C0. For all other trajectories (C1, ..., Cn), server 1230 can find a rigid body transformation that maps Ci to C0, where i = 1, 2, ..., n, where n is a positive integer and corresponds to the total number of trajectories in the cluster. Server 1230 can calculate the average curve or trajectory in the C0 reference frame.

[0309] In some embodiments, landmarks may define arc lengths that coincide between different routes, and these arc lengths may be used to align the track with the lane. In some embodiments, lane marks before and after intersections may be used to align the track with the lane.

[0310] To assemble lanes from trajectories, 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 shifted laterally.

[0311] Landmarks recognized along road divisions 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 recognized multiple times by multiple vehicles in multiple laps. Data on the same landmark received in different laps may differ slightly. Such data can be averaged and mapped to the same reference frame, such as the C0 reference frame. In addition, the variance of the same landmark data received in multiple laps can be calculated.

[0312] In some embodiments, each lane of road section 120 may be associated with a target trajectory and a specific landmark. A target trajectory or a set of such target trajectories may be included in an autonomous vehicle road navigation model, which may later be used by other autonomous vehicles traveling along the same road section 1200. While vehicles 1205, 1210, 1215, 1220, and 1225 are traveling along road section 1200, landmarks identified by those vehicles may be recorded in association with the target trajectories. The target trajectory and landmark data may be continuously or periodically updated with new data received from other vehicles in subsequent travels.

[0313] For the localization of an autonomous vehicle, the disclosed systems and methods may use an extended Kalman filter. The vehicle's position may be determined based on three-dimensional positional data and / or three-dimensional directional data, and a prediction of the vehicle's future position beyond its current position by integral of its own motion. The vehicle's localization may be modified or adjusted by image observation of landmarks. For example, if the vehicle detects a landmark in an image captured by a camera, the landmark may be compared to a known landmark stored in a road model or sparse map 800. The known landmark may have a known position (e.g., GPS data) along a target trajectory stored in the road model and / or sparse map 800. The distance from the vehicle to the landmark may be estimated based on the current speed and the image of the landmark. The vehicle's position along the target trajectory may be adjusted based on the distance to the landmark and the known position of the landmark (stored in the road model or sparse map 800). The location / location data of the landmark stored in the road model and / or sparse map 800 (e.g., average value from multiple runs) may be estimated to be accurate.

[0314] In some embodiments, the disclosed system may form a closed-loop subsystem in which the estimation of the position of the vehicle's six degrees of freedom (e.g., 3D position data and 3D orientation data) can be used to navigate the autonomous vehicle (e.g., steer the wheels) to reach a desired point (e.g., 1.3 seconds ahead of a stored point). The position of the six degrees of freedom can then be estimated using data measured from steering and actual navigation.

[0315] In some embodiments, poles along the road, such as lampposts and power line poles or cable poles, may be used as landmarks for vehicle positioning. Other landmarks, such as traffic signs, signals, road arrows, stop lines, and static features or signatures of objects along road markings, may also be used as landmarks for vehicle positioning. When poles are used for positioning, the x-measurement of the pole (i.e., the viewing angle from the vehicle) may be used instead of the y-measurement (i.e., the distance to the pole), because the base of the pole may be obstructed and not on the road plane.

[0316] Figure 23 shows a navigation system for a vehicle that may be used for autonomous navigation using a crowdsourced sparse map. For illustrative purposes, the vehicle is referred to as vehicle 1205. The vehicle shown in Figure 23 may be any other vehicle disclosed herein, including, for example, vehicles 1210, 1215, 1220, and 1225, and vehicle 200 as shown in other embodiments. As shown in Figure 12, vehicle 1205 may communicate with server 1230. Vehicle 1205 may include an imaging device 122 (e.g., camera 122). Vehicle 1205 may include a navigation system 2300 configured to provide navigation guidance for vehicle 1205 to travel on a road (e.g., road section 1200). Vehicle 1205 may also include other sensors, such as a speed sensor 2320 and an accelerometer 2325. The speed sensor 2320 may be configured to detect the speed of vehicle 1205. The accelerometer 2325 may be configured to detect acceleration or deceleration of the vehicle 1205. The vehicle 1205 shown in Figure 23 may be an autonomous vehicle, and the navigation system 2300 may be used to provide navigation guidance for autonomous driving. Alternatively, the vehicle 1205 may also be a non-autonomous, human-controlled vehicle, and the navigation system 2300 may still be used to provide navigation guidance.

[0317] The navigation system 2300 may include a communication unit 2305 configured to communicate with the server 1230 via a communication path 1235. The navigation system 2300 may also include a GPS unit 2310 configured to receive and process GPS signals. The navigation system 2300 may further include at least one processor 2315 configured to process data such as GPS signals, map data from a sparse map 800 (which may be stored in a storage device mounted on the vehicle 1205 and / or received from the server 1230), road geometry detected by the road profile sensor 2330, images captured by the camera 122, and / or an autonomous vehicle road navigation model received from the server 1230. The road profile sensor 2330 may include different types of devices for measuring different types of road profiles, such as road surface roughness, road width, road height, and road curvature. For example, the road profile sensor 2330 may include a device that measures the movement of the vehicle 2305's suspension to derive a road roughness profile. In some embodiments, the road profile sensor 2330 may include a radar sensor that measures the distance from the vehicle 1205 to the roadside (e.g., a roadside barrier), thereby measuring the width of the road. In some embodiments, the road profile sensor 2330 may include a device configured to measure the elevation of the road. In some embodiments, the 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 an image of the road showing the curvature. The vehicle 1205 may use such an image to detect the curvature of the road.

[0318] At least one processor 2315 may be programmed to receive at least one environmental image from camera 122 associated with vehicle 1205. At least one processor 2315 may analyze at least one environmental image to determine navigation information associated with vehicle 1205. Navigation information may include a trajectory related to the vehicle 1205 traveling along road section 1200. At least one processor 2315 may determine the trajectory based on the motion of camera 122 (and therefore the vehicle), such as three-dimensional translational motion and three-dimensional rotational motion. In some embodiments, at least one processor 2315 may determine the translational and rotational motion of camera 122 based on an analysis of multiple images acquired by camera 122. In some embodiments, navigation information may include lane assignment information (e.g., whether vehicle 1205 is traveling in its lane along road section 1200). Navigation information transmitted from vehicle 1205 to server 1230 may be used by server 1230 to generate and / or update an autonomous vehicle road navigation model, which may be transmitted from server 1230 to vehicle 1205 to provide autonomous navigation guidance to vehicle 1205.

[0319] At least one processor 2315 may also be programmed to send navigation information from the vehicle 1205 to the server 1230. In some embodiments, navigation information may be sent to the server 1230 along with road information. Road location information may include at least one of the following: GPS signals received by the GPS unit 2310, landmark information, road geometry, lane information, etc. At least one processor 2315 may receive an autonomous vehicle road navigation model or a portion of a model from the server 1230. The autonomous vehicle road navigation model received from the server 1230 may include at least one update based on navigation information sent from the vehicle 1205 to the server 1230. The portion of the model sent from the server 1230 to the vehicle 1205 may include the updated portion of the model. Based on the received autonomous vehicle road navigation model or the updated portion of a model, at least one processor 2315 may trigger at least one navigation action by the vehicle 1205 (e.g., steering such as making a turn, braking, accelerating, or overtaking another vehicle).

[0320] At least one processor 2315 may be configured to communicate with various sensors and components included in the vehicle 1205, including a communication unit 1705, a GPS unit 2315, a camera 122, a speed sensor 2320, an accelerometer 2325, and a road profile sensor 2330. At least one processor 2315 may collect information or data from the various sensors and components and transmit the information or data to 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 data or information collected by the sensors or components to the server 1230.

[0321] In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 can communicate with each other and share navigation information, so that at least one of the vehicles 1205, 1210, 1215, 1220, and 1225 can 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 provided to the vehicle. In some embodiments, at least one of the vehicles 1205, 1210, 1215, 1220, and 1225 (e.g., vehicle 1205) can function 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 the server 1230. For example, at least one processor 2315 of the hub vehicle may communicate with other vehicles and receive navigation information from them. At least one processor 2315 of the hub vehicle may generate an autonomous vehicle road navigation model or an update to the model based on the shared information received from other vehicles. At least one processor 2315 of the hub vehicle may transmit the autonomous vehicle road navigation model or an update to the model to other vehicles in order to provide autonomous navigation guidance.

[0322] [Navigation based on sparse maps]

[0323] As mentioned above, an autonomous vehicle road navigation model including a sparse map 800 may include multiple mapped lane marks and multiple mapped objects / features associated with road divisions. These mapped lane marks, objects, and features can be used when the autonomous vehicle navigates, as will be discussed in more detail below. For example, in some embodiments, mapped objects and features can be used to determine the host vehicle's position relative to the map (e.g., relative to a mapped target trajectory). Mapped lane marks can be used (e.g., as checks) to determine the lateral position and / or orientation relative to a planned or target trajectory. Using this positional information, the autonomous vehicle may be able to adjust its direction of travel to match the direction of the target trajectory at the determined position.

[0324] Vehicle 200 may be configured to detect lane markings in a given road section. Road sections may include any markings on the road for guiding vehicle traffic on the road. For example, lane markings may be solid or dashed lines indicating the end of a driving lane. Lane markings may also include double lines, such as double solid lines, double dashed lines, or a combination of solid and dashed lines, indicating whether passage in an adjacent lane is permitted. Lane markings may also include, for example, highway entrance and exit markings indicating a deceleration lane for an exit ramp, or dotted lines indicating that a lane is for turning only or where a lane ends. Markings may further indicate work areas, temporary lane shifts, driving routes through intersections, median strips, dedicated lanes (e.g., bicycle lanes, HOV lanes, etc.), or various other miscellaneous markings (e.g., pedestrian crossings, speed humps, railway crossings, stop lines, etc.).

[0325] Vehicle 200 may capture images of surrounding lane marks using cameras such as imaging devices 122 and 124 included in the image acquisition unit 120. Vehicle 200 may analyze the images to detect the locations of points associated with the lane marks based on features identified in one or more of the captured images. The locations of these points may be uploaded to a server to represent the lane marks in a sparse map 800. Depending on the camera position and field of view, lane marks on both sides of the vehicle may be detected simultaneously from a single image. In other embodiments, different cameras may be used to capture images on multiple sides of the vehicle. Instead of uploading actual images of the lane marks, the marks may be stored in the sparse map 800 as splines or a series of points, thus reducing the size of the sparse map 800 and / or the data that must be remotely uploaded by the vehicle.

[0326] Figures 24A to 24D show the locations of exemplary points that may be detected by the vehicle 200 to represent specific lane marks. Similar to the landmarks described above, the vehicle 200 may identify the locations of points in the captured images using various image recognition algorithms or software. For example, the vehicle 200 may recognize the locations of a set of endpoints, corner points, or various other points associated with a particular lane mark. Figure 24A shows a solid lane mark 2410 that may be detected by the vehicle 200. The lane mark 2410 may represent the outer edge of the road, represented by a solid white line. As shown in Figure 24A, the vehicle 200 may be configured to detect multiple endpoint location points 2411 along the lane mark. The location points 2411 may be collected to represent the lane mark at any interval sufficient to create a lane mark mapped in a sparse map. For example, the lane mark may be represented by one point for every meter of detected edge, one point for every 5 meters of detected edge, or other appropriate intervals. In some embodiments, the interval may be determined by factors other than a set interval, such as a point having the highest reliability ranking of the detected point location by the vehicle 200. Figure 24A shows an endpoint location point on the inner end of a lane mark 2410, but points may be collected on the outer end of the line or along both ends. Furthermore, although a single line is shown in Figure 24A, similar endpoints may be detected for a double solid line. For example, point 2411 may be detected along one or both ends of a solid line.

[0327] Vehicle 200 may also represent different lane marks depending on the type or shape of the lane mark. Figure 24B shows an exemplary dashed lane mark 2420 that can be detected by vehicle 200. Rather than identifying endpoints as in Figure 24A, the vehicle may detect a series of corner points 2421 that represent the corners of the dashed lane to define the complete boundary of the dashed line. Although Figure 24B shows that each corner of a given dashed mark is localized, vehicle 200 may detect or upload a subset of the points shown in the figure. For example, vehicle 200 may detect the front end or front corner of a given dashed mark, or the two corner points closest to the interior of the lane. Furthermore, not all dashed marks can be imaged; for example, vehicle 200 may image and / or record points that represent a sample of dashed marks (e.g., every other, every three, every five, etc.) or points that represent dashed marks at predetermined intervals (e.g., every meter, every five meters, every ten meters, etc.). Corner points can also be detected in similar lane markers, such as marks indicating that a lane is for an exit ramp, marks indicating that a particular lane is about to end, or various other lane markers that may have detectable corner points. Corner points can also be detected in lane markers consisting of double dashed lines or a combination of solid and dashed lines.

[0328] In some embodiments, points uploaded to the server to generate mapped lane marks may represent points other than detected endpoints or corner points. Figure 24C shows a set of points that may represent the centerline of a given lane mark. For example, a solid lane 2410 may be represented by a centerline point 2441 along the centerline 2440 of the lane mark. In some embodiments, the vehicle 200 may be configured to detect these center points using various image recognition techniques such as a convolutional neural network (CNN), scale-invariant feature transformation (SIFT), orientation gradient histogram (HOG) features, or other techniques. Alternatively, the vehicle 200 may detect other points such as the endpoint 2411 shown in Figure 24A, and the centerline point 2441 may be calculated, for example, by detecting points along each end and determining the midpoint between the endpoints. Similarly, a dashed lane mark 2420 may be represented by a centerline point 2451 along the centerline 2450 of the lane mark. Centerline points can be located at the ends of dashed lines or at various other positions along the centerline, as shown in Figure 24C. For example, each dashed line may be represented by a single point at the geometric center of the dashed line. Points can also be spaced at predetermined intervals along the centerline (e.g., every 1 meter, every 5 meters, every 10 meters, etc.). Centerline points 2451 can be detected directly by the vehicle 200 or calculated based on other detected reference points, such as corner points 2421, as shown in Figure 24B. Centerlines can also be used to represent other lane mark types, such as double lines, using techniques similar to those described above.

[0329] In some embodiments, the vehicle 200 may identify points representing other features, such as vertices between two intersecting lane marks. Figure 24D shows an exemplary point representing an intersection between two lane marks 2460 and 2465. The vehicle 200 may calculate vertex 2466 representing the intersection between the two lane marks. For example, one of the lane marks 2460 or 2465 may represent a train crossing area or other crossing area within a road section. Although the lane marks 2460 and 2465 are shown intersecting perpendicularly to each other, various other configurations may be detected. For example, the lane marks 2460 and 2465 may intersect at other angles, or one or both of the lane marks may terminate at vertex 2466. Similar techniques may also be applied to intersections between dashed lines or other lane mark types. In addition to vertex 2466, various other points 2467 may also be detected, providing further information about the orientation of the lane marks 2460 and 2465.

[0330] Vehicle 200 may associate real-world coordinates with each detected point of the lane marks. For example, it may generate a position identifier containing the coordinates of each point and upload it to a server for mapping the lane marks. The position identifier may further include other identifying information about the point, including whether the point represents a corner point, endpoint, center point, etc. Thus, vehicle 200 may be configured to determine the real-world position of each point based on the analysis of the image. For example, vehicle 200 may detect other features in the image, such as the various landmarks described above, in order to determine the real-world position of the lane marks. This may include determining the position of the lane marks in the image relative to the detected landmarks, or determining the position of the vehicle based on the detected landmarks and then determining the distance from the vehicle (or the vehicle's target trajectory) to the lane marks. If landmarks are unavailable, the position of the lane mark points relative to the vehicle's position determined by dead reckoning may be determined. The real-world coordinates included in the position identifier may be expressed as absolute coordinates (e.g., latitude / longitude coordinates) or may be associated with other features, such as based on longitudinal position along the target trajectory and lateral distance from the target trajectory. Next, the location identifier can be uploaded to the server to generate lane marks mapped in a navigation model (such as a sparse map 800). In some embodiments, the server may construct splines representing lane marks for road divisions. Alternatively, the vehicle 200 may generate splines, upload them to the server, and record them in the navigation model.

[0331] Figure 24E shows an example of an exemplary navigation model or sparse map of a corresponding road section including mapped lane marks. The sparse map may include a target trajectory 2475 that a vehicle follows along the road section. As described above, the target trajectory 2475 may represent the ideal path that a vehicle would take when traveling through the corresponding road section, or it may be located at other points on the road (e.g., the road's centerline). The target trajectory 2475 may be calculated in the various ways described above, for example, based on the aggregation (e.g., weighted combinations) of two or more reconstructed trajectories of vehicles crossing the same road section.

[0332] In some embodiments, target trajectories may be generated equally for all vehicle types and all roads, vehicles, and / or environmental conditions. However, in other embodiments, various other factors or variables may also be considered when generating target trajectories. Different target trajectories may be generated for different types of vehicles (e.g., passenger cars, light trucks, and full trailers). For example, a target trajectory with a relatively smaller turning radius may be generated for a small passenger car than for a large semi-trailer truck. In some embodiments, roads, vehicles, and environmental conditions may also be considered. For example, different target trajectories may be generated for different road conditions (e.g., wet, snowy, icy, dry, etc.), vehicle conditions (e.g., tire condition or estimated tire condition, brake condition or estimated brake condition, fuel level, etc.), or environmental factors (e.g., time of day, visibility, weather, etc.). The target trajectory may also depend on one or more aspects or characteristics of a particular road section (e.g., speed limit, frequency and size of turns, gradient, etc.). In some embodiments, target trajectories, such as set driving modes (e.g., desired aggressive driving, economy mode, etc.), can also be determined using various user settings.

[0333] The sparse map may also include mapped lane marks 2470 and 2480 representing lane marks along road divisions. The mapped lane marks may be represented by multiple location identifiers 2471 and 2481. As described above, the location identifiers may include the real-world coordinate location of the point associated with the detected lane mark. Similar to the target trajectory of the model, the lane marks may also include elevation data and may be represented as curves in three-dimensional space. For example, the curve may be a spline connecting three-dimensional polynomials of appropriate degree, or the curve may be calculated based on the location identifiers. The mapped lane marks may also include other information or metadata about the lane marks, such as an identifier of the lane mark type (e.g., between two lanes with the same direction of travel, between two lanes with opposite directions of travel, the edge of the road, etc.) and / or other characteristics of the lane marks (e.g., solid line, dashed line, single line, double line, yellow line, white line, etc.). In some embodiments, the mapped lane marks may be continuously updated within the model, for example, using crowdsourcing techniques. The same vehicle may upload location identifiers between multiple occasions of traveling on the same road section, or data may be selected from multiple vehicles traveling on the road section at different times (e.g., 1205, 1210, 1215, 1220, and 1225). The sparse map 800 may then be updated or refined based on subsequent location identifiers received from the vehicles and stored in the system. Once the mapped lane marks are updated and refined, the updated road navigation model and / or sparse map may be distributed to multiple autonomous vehicles.

[0334] Generating lane marks mapped within a sparse map may also include detecting and / or mitigating errors based on anomalies in the image or the actual lane marks themselves. Figure 24F shows an exemplary anomaly 2495 associated with the detection of lane marks 2490. Anomalies 2495 may appear in images captured by the vehicle 200, for example, from an object obstructing the camera's view of the lane marks, debris on the lens, etc. In some cases, the anomaly may be attributable to the lane marks themselves, which may be damaged, worn, or partially covered by, for example, dirt, debris, water, snow, or other material on the road. Anomaly 2495 may result in a false point 2491 detected by the vehicle 200. The sparse map 800 may provide correctly mapped lane marks and eliminate errors. In some embodiments, the vehicle 200 may detect the false point 2491, for example, by detecting anomalies 2495 in the image or by identifying errors based on lane mark points detected before and after the anomaly. Based on the detection of an anomaly, the vehicle may exclude point 2491 or adjust it to match other detected points. In other embodiments, errors may be corrected after the point has been uploaded by determining that the point is outside the expected threshold range, for example, based on other points uploaded during the same trip or based on aggregated data from previous trips along the same road section.

[0335] Navigation models and / or lane marks mapped on sparse maps can also be used for navigation by autonomous vehicles traversing corresponding roads. For example, a vehicle navigating along a target trajectory may periodically use mapped lane marks in a sparse map to orient itself to the target trajectory. As described above, between landmarks, a vehicle may navigate based on dead reckoning, where the vehicle uses sensors to determine its own motion and estimate its position relative to the target trajectory. Errors can accumulate over time, and the accuracy of determining the vehicle's position relative to the target trajectory may gradually decrease. Therefore, a vehicle may use lane marks occurring in a sparse map 800 (and their known locations) to reduce errors induced by dead reckoning in positioning. In this way, identified lane marks included in the sparse map 800 can function as navigation anchors, from which the vehicle's precise position relative to the target trajectory can be determined.

[0336] Figure 25A shows an exemplary image 2500 of the surrounding environment of a vehicle that may be used for navigation based on mapped lane marks. Image 2500 may be captured by a vehicle 200, for example, via imaging devices 122 and 124 included in an image acquisition unit 120. Image 2500 may include an image of at least one lane mark 2510, as shown in Figure 25A. Image 2500 may also include one or more landmarks 2521, such as road signs, used for navigation as described above. Several elements shown in Figure 25A, such as elements 2511, 2530, and 2520, which do not appear in the captured image 2500 but are detected and / or determined by the vehicle 200, are also shown for reference.

[0337] Using the various techniques described above with respect to Figures 24A to 24D and Figure 24F, the vehicle can analyze the image 2500 to identify the lane mark 2510. Various points 2511 corresponding to features of the lane mark in the image can be detected. For example, points 2511 may correspond to the ends of the lane mark, the corners of the lane mark, the midpoint of the lane mark, the vertex between two intersecting lane marks, or various other features or locations. Points 2511 may be detected to correspond to the locations of points stored in a navigation model received from a server. For example, if a sparse map is received that includes points representing the centerlines of mapped lane marks, points 2511 may also be detected based on the centerlines of the lane mark 2510.

[0338] The vehicle is also represented by element 2520, and its longitudinal position along the target trajectory can be determined. The longitudinal position 2520 can be determined from image 2500, for example, by detecting a landmark 2521 in image 2500 and comparing the measured position with a known landmark position stored in a road model or sparse map 800. The vehicle's position along the target trajectory can then be determined based on the distance to the landmark and the known position of the landmark. The longitudinal position 2520 can also be determined from images other than those used to determine the position of lane marks. For example, the longitudinal position 2520 can be determined by detecting a landmark in images from another camera in image acquisition unit 120, which is captured simultaneously or nearly simultaneously with image 2500. In some cases, the vehicle may not be near any landmark or other reference point for determining the longitudinal position 2520. In such cases, the vehicle may navigate based on dead reckoning, and thus use sensors to determine its own motion and estimate the longitudinal position 2520 relative to the target trajectory. The vehicle may also determine a distance 2530, which represents the actual distance between the vehicle and the lane mark 2510 observed in the captured image. The camera angle, vehicle speed, vehicle width, or various other factors may be taken into consideration when determining the distance 2530.

[0339] Figure 25B shows a correction for the lateral positioning of a vehicle based on mapped lane marks in a road navigation model. As described above, vehicle 200 may determine the distance 2530 between vehicle 200 and lane marks 2510 using one or more images captured by vehicle 200. Vehicle 200 may also access a road navigation model, such as a sparse map 800, which may include mapped lane marks 2550 and a target trajectory 2555. The mapped lane marks 2550 may be modeled using the techniques described above, for example, using crowdsourced position identifiers captured by multiple vehicles. The target trajectory 2555 may also be generated using the various techniques described above. Vehicle 200 may also determine or estimate a longitudinal position 2520 along the target trajectory 2555, as described above with respect to Figure 25A. Then, vehicle 200 may determine an estimated distance 2540 based on the lateral distance between the target trajectory 2555 and the mapped lane marks 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 with the predicted distance 2540 from the model.

[0340] Figures 25C and 25D provide diagrams associated with another example of locating the host vehicle during navigation based on mapped landmarks / objects / features in a sparse map. Figure 25C conceptually represents a series of images taken from a vehicle navigating along road section 2560. In this example, road section 2560 includes a straight section of a two-lane divided highway separated by road edges 2561 and 2562 and a center lane mark 2563. As shown, the host vehicle is navigating along lane 2564 associated with a mapped target trajectory 2565. Therefore, in ideal conditions (and without influencing factors such as the presence of a target vehicle or object on the road), the host vehicle should accurately track the mapped target trajectory 2565 as it navigates along lane 2564 of road section 2560. In reality, the host vehicle may experience drift as it navigates along the mapped target trajectory 2565. For effective and safe navigation, this drift must be kept within acceptable limits (e.g., a lateral displacement of + / - 10 cm from the target trajectory 2565 or any other appropriate threshold). To periodically account for the drift and make any necessary course corrections to ensure the host vehicle follows the target trajectory 2565, the disclosed navigation system may allow the use of one or more mapped features / objects contained in a sparse map to determine the host vehicle's position along the target trajectory 2565 (e.g., to determine the host vehicle's lateral and longitudinal positions relative to the target trajectory 2565).

[0341] As a simple example, Figure 25C shows a speed limit sign 2566 that may appear in five different images captured sequentially as a host vehicle navigates along road section 2560. For example, at the first time, t0, sign 2566 may appear in the captured image near the horizon. As the host vehicle approaches sign 2566, in the images captured at subsequent times t1, t2, t3, and t4, sign 2566 appears at different 2D XY pixel positions in the captured images. For example, in the captured image space, sign 2566 moves downward and to the right along a curve 2567 (for example, a curve extending through the center of the sign in each of the five captured image frames). Sign 2566 also appears to increase in size as the host vehicle approaches (i.e., occupies more pixels in the subsequently captured images).

[0342] These changes in the image space representation of objects such as sign 2566 can be used to determine the position of a host vehicle whose position along a target trajectory has been identified. For example, as described in this disclosure, a detectable object or feature such as any semantic feature or detectable non-semantic feature, such as sign 2566, may be identified by one or more collecting vehicles that have previously traversed a road section (e.g., road section 2560). A mapping server may collect driving information collected from multiple vehicles, aggregate and correlate this information to generate a sparse map, for example, that includes a target trajectory 2565 in lane 2564 of road section 2560. The sparse map may also store the position of sign 2566 (along with type information, etc.). During navigation (e.g., before entering road section 2560), the host vehicle may be supplied with map tiles containing the sparse map of road section 2560. To navigate lane 2564 of road section 2560, the host vehicle may follow the mapped target trajectory 2565.

[0343] A mapped representation of marker 2566 can be used by the host vehicle to determine its own position relative to a target trajectory. For example, a camera in the host vehicle may capture an image 2570 of the host vehicle's environment, and the captured image 2570 may include an image representation of marker 2566 having a specific size and a specific XY image position, as shown in Figure 25D. This size and XY image position can be used to determine the position of the host vehicle corresponding to the target trajectory 2565. For example, based on a sparse map containing the representation of marker 2566, the host vehicle's navigation processor may determine that the representation of marker 2566 should appear in the captured image such that the center of marker 2566 moves along line 2567 (in image space) in response to the host vehicle traveling along the target trajectory 2565. If the captured image, such as image 2570, shows a center (or other reference point) that has been displaced from line 2567 (e.g., the expected trajectory in image space), the host vehicle navigation system can determine that it was not located on the target trajectory 2565 at the time the image was captured. However, from the images, the navigation processor can determine appropriate navigation corrections to return the host vehicle to the target trajectory 2565. For example, if the analysis shows that the image position of marker 2566 is displaced in the image by a distance of 2572 to the left of the expected image space position on line 2567, the navigation processor may cause the host vehicle to change direction (e.g., change the steering angle of the wheels) to move the host vehicle by a distance of 2573 to the left. In this way, each captured image can be used as part of a feedback loop process, thereby minimizing the difference between the observed image position of marker 2566 and the expected image trajectory 2567, and ensuring that the host vehicle stays along the target trajectory 2565 with little or no deviation. Naturally, the more mapped objects available, the more frequently the described localization techniques can be used, thereby reducing or eliminating deviations due to drift from the target trajectory 2565.

[0344] The process described above may be useful for detecting the lateral orientation or displacement of the host vehicle relative to the target trajectory. Positioning the host vehicle corresponding to the target trajectory 2565 may also include determining the longitudinal position of the target vehicle along the target trajectory. For example, the captured image 2570 includes a representation of a marker 2566 having a specific image size (e.g., a 2D XY pixel region). This size can be compared to the expected image size of the mapped marker 2566 as it moves through image space along line 2567 (e.g., as the size of the marker gradually increases as shown in Figure 25C). Based on the image size of the marker 2566 in image 2570, and based on the progression of the expected size in image space corresponding to the mapped target trajectory 2565, the host vehicle can determine its longitudinal position corresponding to the target trajectory 2565 (at the time image 2570 was captured). This longitudinal position, combined with any lateral displacement corresponding to the target trajectory 2565, allows for the complete localization of the host vehicle in relation to the target trajectory 2565 as the host vehicle navigates along the road 2560, as described above.

[0345] Figures 25C and 25D provide just one example of the disclosed localization technique using a single mapped object and a single target trajectory. In other examples, many more target trajectories may exist (e.g., one target trajectory per feasible lane in a multi-lane highway, urban street, complex intersection, etc.), and many more mappings may be available for localization. For example, a sparse map representing an urban environment may contain numerous objects available for localization every meter.

[0346] Figure 26A is a flowchart illustrating an exemplary process 2600A for mapping lane marks for use in autonomous vehicle navigation, according to a disclosed embodiment. In step 2610, process 2600A may include receiving two or more location identifiers associated with detected lane marks. For example, step 2610 may be performed by server 1230 or one or more processors associated with the server. Location identifiers may include the real-world coordinate location of a point associated with a detected lane mark, as described above with respect to Figure 24E. In some embodiments, location identifiers may also include other data, such as additional information about road divisions or lane marks. Additional data, such as accelerometer data, velocity data, landmark data, road geometry or profile data, vehicle positioning data, self-motion data, or various other forms of data described above, may also be received during step 2610. Location identifiers may be generated by vehicles such as vehicles 1205, 1210, 1215, 1220, and 1225 based on images captured by the vehicles. For example, the identifier may be determined by acquiring at least one image representing the host vehicle's environment from a camera associated with the host vehicle, analyzing at least one image to detect lane marks in the host vehicle's environment, and analyzing at least one image to determine the location of the detected lane marks relative to the location associated with the host vehicle. As described above, lane marks may include various different mark types, and the location identifier may correspond to various points associated with the lane marks. For example, if the detected lane mark is part of a dashed line marking a lane boundary, the point may correspond to the detected corner of the lane mark. If the detected lane mark is part of a solid line marking a lane boundary, the point may correspond to the detected end of the lane mark at various intervals, as described above.In some embodiments, the points may correspond to the centerline of the detected lane mark, as shown in Figure 24C, or to the vertex between two intersecting lane marks and at least one of two other points associated with the intersecting lane marks, as shown in Figure 24D.

[0347] In step 2612, process 2600A may include associating the detected lane mark with a corresponding road section. For example, server 1230 may analyze real-world coordinates or other information received during step 2610 and compare the coordinates or other information with location information stored in the autonomous vehicle road navigation model. Server 1230 may determine the road section in the model that corresponds to the real-world road section where the lane mark was detected.

[0348] In step 2614, process 2600A may include updating the autonomous vehicle road navigation model associated with the corresponding road section based on two or more location identifiers associated with the detected lane marks. For example, the autonomous vehicle road navigation model may be a sparse map 800, and the server 1230 may update the sparse map to include or adjust the lane marks mapped to the model. The server 1230 may update the model based on the various methods or processes described above with respect to Figure 24E. In some embodiments, updating the autonomous vehicle road navigation model may include storing one or more indicators of the real-world coordinate location of the detected lane marks. The autonomous vehicle road navigation model may also include at least one target trajectory that the vehicle follows along the corresponding road section, as shown in Figure 24E.

[0349] In step 2616, process 2600A may include distributing the updated autonomous vehicle road navigation model to multiple autonomous vehicles. For example, server 1230 may distribute the updated autonomous vehicle road navigation model to vehicles 1205, 1210, 1215, 1220, and 1225 that can 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.) through a wireless communication path 1235, as shown in Figure 12.

[0350] In some embodiments, lane marks may be mapped using data received from multiple vehicles via crowdsourcing technology, etc., as described above with respect to Figure 24E. For example, process 2600A may include receiving a first communication from a first host vehicle containing a location identifier associated with a detected lane mark, and a second communication from a second host vehicle containing an additional location identifier associated with a detected lane mark. For example, the second communication may be received from a following vehicle traveling on the same road section, i.e., from the same vehicle traveling behind along the same road section. Process 2600A may further include refining the determination of at least one location associated with the detected lane mark based on the location identifier received in the first communication and the additional location identifier received in the second communication. This may include using an average of multiple location identifiers and / or excluding “ghost” identifiers that do not reflect the real-world location of the lane mark.

[0351] Figure 26B is a flowchart illustrating an exemplary process 2600B for autonomously navigating a host vehicle along a road section using mapped lane marks. Process 2600B may be performed, for example, by a processing unit 110 of an autonomous vehicle 200. In step 2620, process 2600B may include receiving an autonomous vehicle road navigation model from a server-based system. In some embodiments, the autonomous vehicle road navigation model may include a target trajectory for the host vehicle along a road section and position identifiers associated with one or more lane marks associated with the road section. For example, vehicle 200 may receive a sparse map 800 or another road navigation model developed using process 2600A. In some embodiments, the target trajectory may be represented as a three-dimensional spline, for example, as shown in Figure 9B. As explained above with respect to Figures 24A to 24F, the position identifier may include the real-world coordinates of points associated with lane marks (e.g., corner points of dashed lane marks, endpoints of solid lane marks, vertices between two intersecting lane marks and other points associated with intersecting lane marks, centerlines associated with lane marks, etc.).

[0352] In step 2621, process 2600B may include receiving at least one image representing the vehicle environment. The image may be received from the vehicle's imaging device via imaging devices 122 and 124, etc., included in the image acquisition unit 120. The image may include one or more images of lane marks, similar to the image 2500 described above.

[0353] In step 2622, process 2600B may include determining the longitudinal position of the host vehicle along the target trajectory. As described above with respect to Figure 25A, this may be based on other information in the captured image (e.g., landmarks) or by dead reckoning of the vehicle between detected landmarks.

[0354] In step 2623, process 2600B may include determining the expected lateral distance to a lane mark based on the determined longitudinal position of the host vehicle along the target trajectory and on two or more position identifiers associated with at least one lane mark. For example, vehicle 200 may determine the expected lateral distance to a lane mark using a sparse map 800. As shown in Figure 25B, the longitudinal position 2520 along the target trajectory 2555 may be determined in step 2622. Using the sparse map 800, vehicle 200 may determine the expected distance 2540 to the mapped lane mark 2550 corresponding to the longitudinal position 2520.

[0355] In step 2624, process 2600B may include analyzing at least one image to identify at least one lane mark. The vehicle 200 may identify lane marks in the image using various image recognition techniques or algorithms, for example, as described above. For example, lane mark 2510 may be detected through image analysis of image 2500, as shown in Figure 25A.

[0356] In step 2625, process 2600B may include determining the actual lateral distance to at least one lane mark based on the analysis of at least one image. For example, the vehicle may determine a distance 2530 representing the actual distance between the vehicle and the lane mark 2510, as shown in Figure 25A. The camera angle, the vehicle speed, the vehicle width, the camera's position relative to the vehicle, or various other factors may be taken into consideration when determining the distance 2530.

[0357] In step 2626, process 2600B may include determining an autonomous steering action for the host vehicle based on the difference between the expected lateral distance to at least one lane mark and the determined actual lateral distance to at least one lane mark. For example, as described above with respect to Figure 25B, vehicle 200 may compare the actual distance 2530 with the expected distance 2540. The difference between the actual distance and the expected distance may indicate the error (and its magnitude) between the actual position of the vehicle and the target trajectory the vehicle is following. Thus, the vehicle may determine an autonomous steering action or other autonomous action based on this difference. For example, as shown in Figure 25B, if the actual distance 2530 is shorter than the expected distance 2540, the vehicle may determine an autonomous steering action to move away from lane mark 2510 and turn the vehicle to the left. Thus, the vehicle's position relative to the target trajectory can be corrected. Process 2600B may be used, for example, to improve the vehicle's navigation between landmarks.

[0358] Processes 2600A and 2600B provide only examples of techniques that may be used to navigate a host vehicle using the disclosed sparse map. Other examples may also use processes that correspond to the processes described with respect to Figures 25C and 25D.

[0359] [Map-based real-world modeling]

[0360] A key feature of the navigation mapping system described herein is its ability to generate and / or refine maps associated with road divisions using crowdsourced information collected from multiple runs. These navigation maps (e.g., sparse maps) may include target trajectories (e.g., 3D splines) available to vehicles (e.g., host or target / detected vehicles) traveling along the road lanes associated with the map. The maps may also include mapped landmarks that can correspond to a variety of detectable objects (e.g., road signs, road edges, lane markings, bus stops, or any other recognizable features associated with the road, etc.). The mapped landmarks may be associated within the map with refined locations associated with one or more of the landmarks. The refined locations may be determined based on crowdsourced location information determined during each of multiple individual runs along the road division. Objects / landmarks detected from the map and their locations may be used when navigating an autonomous or semi-autonomous vehicle (e.g., by helping to determine where a vehicle is positioned relative to a target trajectory from the map). The navigation maps may also include surface representations of road divisions that can correspond to drivable areas along the road. For example, in some cases, a mesh may be determined (based on any appropriate algorithm or computational technique) that covers the road surface but does not cover surfaces determined to be outside the road (e.g., beyond a determined road boundary such as the edge of the road, sidewalk, road barrier, or similar). In other examples, a mesh may cover the navigable area of ​​the road, including the road surface (at any given point), and any area beyond the road surface determined to be navigable (e.g., soft shoulders along the roadside), but not surfaces determined to be unavoidable (e.g., areas that vehicles cannot or should not traverse, such as barriers or sidewalks). For example, a navigable area outside the road surface may be marked as navigable in the map so that it can be accessed by a vehicle in a particular scenario.For example, in some embodiments, the navigable area may be accessible only in emergency situations (e.g., when remaining on the road could lead to an unavoidable accident, and it is not reasonably assumed that the host vehicle navigating the navigable area would almost certainly cause an accident), in accordance with a driving policy implemented by the host vehicle.

[0361] To generate a crowdsourced navigation map, travel information can be collected from multiple journeys along a road section. This may include, for example, collecting travel information from a single vehicle repeatedly crossing a road section or other area at different times, and / or collecting travel information from multiple different vehicles crossing a road section or other area. The mapping system can then align and aggregate the collected travel information (e.g., the actual trajectory the vehicle traveled, detection of one or more objects, and the associated determined locations of the detected objects, detected lane marks, detected road edges, detected roadside barriers, distance traveled during the journey, etc.) to provide a crowdsourced navigation map. Such a process can improve the accuracy of the crowdsourced map by refining the location of objects and refining the vehicle trajectory, by filling in gaps in the travel dataset (e.g., caused by occlusion during a particular journey, etc.). Further details regarding the generation and implementation of the navigation map are provided through this disclosure.

[0362] In some cases, crowdsourced map generation may involve aggregating, aligning, and / or smoothing various road features derived from various data collected during various drives (e.g., alignment and smoothing of trajectories traveled along available lanes on various road sections) or from various drive sections (e.g., location of detected objects, location of road edges, location and extent of meshes representing the road surface, etc.). One challenge in properly aggregating data from different drives is that points or other data that are physically close but far apart in terms of travel (or traversable) distance may be included in different drives. For example, in the case of an overpass, travel points or features collected from a vehicle traveling under the overpass may be close to each other, or may intersect on the XY plane with points or features collected from a vehicle traveling on the overpass. Nevertheless, in such examples, it may be necessary for a vehicle to travel several kilometers between the two events to cross the overpass after crossing under it (or vice versa). In other words, while an overhead view of the roads may sometimes show roads intersecting, the roads are separate, and the points where roads overlap on the map do not actually form navigable intersections. As a result, a vehicle traveling on the first road after crossing an overpass cannot turn at a point on the map where roads appear to intersect and navigate to the road below.

[0363] A similar scenario may exist in the case of highway exit or entrance ramps, which may include collected driving points / features that are physically close to the point of exiting or entering the highway, but separated by a considerable distance that would be necessary to traverse both in a single drive.

[0364] In such cases, particularly in other examples, the alignment or other aggregation of data points, objects, road surfaces, etc., collected / determined based on multiple travels, needs to be limited to those data points, objects, road surfaces, etc. (e.g., points, features, surfaces, etc., that can be encountered consecutively within specific predetermined travel distances such as 10m, 20m, 50m, 100m, etc.) that are not only physically close to each other but also close in terms of travel distance. To maintain the accuracy of mapped features, points, objects, features, road surfaces, etc., that are farther away than predetermined travel distances may remain unaggregated, unaligned, unmeshed, etc. In other words, there is a need for systems and methods to efficiently and accurately map road divisions and distinguish between roads that are physically close to each other but cannot be directly and continuously navigated, and roads / road surfaces that can be directly and continuously navigated without involving considerable travel distances (e.g., distances exceeding predetermined travel distance thresholds).

[0365] One method for generating a road division map may include a step of analyzing information collected about the road division (e.g., images captured by cameras mounted on vehicles traveling on the road division, and information collected by other sensors mounted on the vehicle, such as LIDAR or radar information). The map generation process may then include generating road splines and sampling the road splines to create a model of the road division's surface. The process may then include creating a mesh to cover the drivable road surface included in the road division. However, as mentioned above, such a process may encounter certain challenges, for example, when roads appear to intersect but do not actually intersect, or when road points, features, surfaces, etc., are physically close to each other but are separated by a considerable distance. As discussed above, such cases may include overpasses / underpasses; highway exit / entrance ramps; road lanes associated with opposite directions of travel (including lanes separated by partitions or barriers, or lanes separated by road marks); road lanes associated with a common direction of travel but separated by one or more barriers, etc. In such cases, the process of aggregating, aligning, and / or smoothing the generated mesh representing specific road features and associated locations, and / or drivable surfaces, can be particularly susceptible to errors caused by the movement of points, features, etc., that are physically close but far apart in terms of distance traveled.

[0366] The disclosed systems and methods may provide solutions that enable the creation of maps for use in autonomous or partially autonomous vehicle navigation, and such maps are free from inaccuracies resulting from the aggregation of crowdsourced driving information collected from road sections that are physically close to each other but cannot be continuously navigated without first traversing a considerable distance. For example, the disclosed systems and methods may generate maps that distinguish between actually navigable intersections where a vehicle can navigate from one road section to another, and apparent intersections formed by intersecting overpasses and underpasses where a vehicle cannot directly navigate from one to the other without traversing a considerable distance. The disclosed systems may distinguish between divided highways (e.g., highways with driving lanes traveling in opposite directions that may be close to or parallel to each other) and other road sections that travel in the same direction but nevertheless constitute separate road sections (e.g., toll lanes or carpool lanes separated from normal driving lanes by pillars or concrete bulkheads) that do not merge or intersect within a certain drivable distance.

[0367] Figure 27 shows an exemplary overpass 2700 having overlapping road sections according to a disclosed embodiment. In this example, the host vehicle 2710 may travel along a road section 2720 that may pass under another road, as shown in Figure 27. The host vehicle 2710 may be configured to capture images from the environment of the vehicle 2710 along the road section 2720 using one or more cameras (e.g., camera 2712 or more cameras 2712). In some embodiments, the host vehicle 2710 may correspond to the vehicle 200 discussed above. Therefore, any feature or embodiment described herein with respect to vehicle 200 may also be applicable to the host vehicle 2710. For example, camera 2712 may correspond to one or more of the image capturing devices 122, 124, and 126. Furthermore, the host vehicle 2710 may be equipped with one or more processing devices, such as the processing device 110 described above.

[0368] The host vehicle 2710 may process images captured by the camera 2712 (for example, via the processing device 110) to identify one or more features for generating a road navigation model along the road section 2720. These features may be identified according to various image analysis or processing techniques, as described throughout this disclosure, and may be tracked across multiple image frames. For example, features may correspond to road signs 2724 and lane marks 2726, as shown in Figure 27. Features may correspond to various other objects or surface features along the road section 2720. Using the driving information collected by the host vehicle 2710 (which may include feature points of road signs 2724 and lane marks 2726), a mapped target trajectory 2722, which may be represented as a three-dimensional spline, may be generated. Additional details regarding the mapping of the target trajectory are provided above. Although the road section 2720 is shown to represent a single driving lane for illustrative purposes, the road section as used herein may refer to multiple lanes traveling in the same direction, lanes traveling in multiple directions, or various other configurations. Furthermore, road divisions may be of predetermined lengths (e.g., 10m, 30m, 100m, etc.) or of various lengths based on road characteristics (e.g., landmark density, landmark location, intersections with other roads, traffic volume, or similar).

[0369] The overpass 2700 may further include a road passing through road section 2720, which may include road section 2730. Driving information captured by a vehicle crossing road section 2730 may be used to generate a navigation map for road section 2730, which may include a mapped target trajectory 2732. For example, a vehicle may capture images of its environment while crossing road section 2730 and identify features such as road signs 2734 to generate a navigation map. As discussed above, a road navigation map may be generated based on correlated information from multiple vehicles (and / or multiple crossings of the same road section by the same vehicle) within the same geographical location. Even though road sections 2720 and 2730 are close to each other (in this case, overlapping), the road sections may be relatively far apart in terms of drivable distance. For example, to reach road section 2730, the host vehicle 2710 may need to travel several kilometers along road section 2720, follow other roads, cross multiple intersections, etc. Therefore, despite their relative physical proximity, road sections 2720 and 2730 may be far apart in terms of drivable distance. Consequently, attempting to align driving information or road model sections for road sections 2720 and 2730 may result in inaccurate road navigation models.

[0370] In some embodiments, as part of a decision on whether to aggregate and align specific points from different routes and use them to create a road surface mesh, the disclosed systems and methods can determine the proximity of two or more points in terms of their drivable distance from each other. For example, if two points, features, etc. are separated by a relatively long drivable distance, the system may not aggregate, align, or use both of these points when generating a mesh representing a drivable road surface. On the other hand, if two points, features, etc. are separated by a shorter drivable distance, the system may appropriately determine, for example, that these points are part of the same road section and that a vehicle can travel directly from one to the other. Therefore, in such situations, the disclosed systems may aggregate or align such directly navigable points, or use both when generating a mesh representing a drivable road surface. As a result of the disclosed systems and methods, the generated map may have higher accuracy and may enable more accurate and effective route decisions and driving operations.

[0371] Figure 28 is an overhead view of an overpass 2700 according to a disclosed embodiment. As described above with respect to Figure 27, the overpass 2700 may include overlapping first road sections 2720 and second road sections 2730. A host vehicle 2710 may collect travel information to create a first road model section associated with road section 2720, and a second host vehicle 2820 may collect travel information to create a second road model section associated with road section 2730. Each of the first and second road model sections may be generated by correlating travel information collected along the corresponding road sections. For example, a host vehicle 2710 may transmit collected travel information to a server 2810. The server 2810 may be configured to receive information from one or more additional host vehicles traveling along road section 2720 and generate a road model (e.g., a sparse map, etc.) based on the received data. Server 2810 may also transmit road models (or road model update data) to one or more autonomous or semi-autonomous vehicles that can be used for navigation. In some embodiments, Server 2810 may correspond to Server 1230 as described above. Therefore, any description or disclosure made herein with respect to Server 1230 may also apply to Server 2810, and vice versa. Furthermore, although Host Vehicle 2710 is shown to communicate with Server 2810, it should be understood that Server 2810 may communicate with Vehicles 2820 and 2822 as well.

[0372] In some embodiments, the server 2810 may correlate data received from a host vehicle traversing the road section 2720 in order to generate a road model having road model sections associated with the road section 2720. Correlation may include determining the refined location of an object or feature based on the determined location index. For example, the server 2810 may correlate a first location index of an identified object relative to the road section 2720 (e.g., a road sign 2724) based on first travel information with a second location index of the same object relative to the road section determined based on second travel information. The server 2810 may also determine the final location index (or refined location) of this object based on the first and second location indexes. In some embodiments, correlation may include aggregating the objects identified in the travel information. For example, server 2810 may aggregate one or more objects belonging to a first category of objects identified in first driving information (e.g., first image) and one or more objects belonging to the same category of objects identified in second driving information (e.g., second image). Server 2810 may also remove one or more duplicate objects from the aggregated objects.

[0373] Server 2810 can determine whether the data associated with section 2720 and the data associated with section 2730 should be aggregated, aligned, and used to create the same road surface mesh, or whether they should be correlated based on the drivable distance between sections. As shown in Figure 28, road section 2720 may be associated with point 2802. Point 2820 can be associated with road section 2720 in various ways. In some embodiments, point 2802 may represent an object or feature detected along road section 2720. As another example, point 2802 may be the centroid, a corner point, an endpoint, or any other point geometrically related to road section 2720. Similarly, road section 2730 may be associated with point 2804. In model space (e.g., the XY plane), points 2802 and 2804 may be separated by a distance d1, which can be relatively short. In other words, because points 2802 and 2804 are close together, the drivable information associated with these points can usually be correlated within the model. However, according to the disclosed embodiments, the drivable distance d2 between point 2802 and point 2804 may be determined to evaluate whether the travel information associated with these points should be correlated within the model. The drivable distance d2 may represent the minimum distance a virtual vehicle located at point 2802 must travel to reach point 2804 (or vice versa). An example of a dashed line representing the distance d2 is shown in Figure 28, but it should be understood that the distance d2 can be any shape or length required to reach point 2804. The drivable distance d2 may be determined by assuming that the virtual vehicle follows one or more traffic laws, regulations, guidelines, and / or best practices to reach point 2804. In some embodiments, the distance d2 may be determined based on a target trajectory included in the road model, which may be generated by server 2810 based on travel information collected by the vehicle. Alternatively or additionally, the distance d2 may be determined based on an external map database...

Claims

1. A system for generating a road surface model, wherein the system is A processor having a circuit and memory, wherein the memory, when executed by the circuit, provides to the at least one processor Allowing access to multiple points associated with one or more drivable roads; The road surface model is generated based on the aforementioned multiple points, and the road surface model includes a mesh representing the surface of one or more drivable roads, wherein generating the road surface model is performed by Determining the drivable distance between the first point among the plurality of points and the second point among the plurality of points; and Based on the determination that the drivable distance between the first point and the second point is shorter than or equal to a predetermined distance threshold, the first point is meshed together with the second point. Including; and The road surface model is stored for use in vehicle navigation along one or more of the aforementioned drivable roads. Includes orders A system equipped with these features.

2. The system according to claim 1, wherein the aforementioned multiple points are included in driving information collected by multiple vehicles crossing the one or more drivable roads.

3. The system according to claim 1 or 2, wherein the drivable distance is determined based on at least one target trajectory associated with the one or more drivable roads.

4. The system according to claim 3, wherein the at least one target trajectory is represented as a 3D spline.

5. The system according to any one of claims 1 to 4, wherein the predetermined distance threshold is 10 meters or less.

6. The system according to any one of claims 1 to 5, wherein the predetermined distance threshold is 20 meters or less.

7. The system according to any one of claims 1 to 6, wherein the predetermined distance threshold is 30 meters or less.

8. The system according to any one of claims 1 to 7, wherein the predetermined distance threshold is 50 meters or less.

9. The system according to any one of claims 1 to 8, wherein the predetermined distance threshold is 100 meters or less.

10. The system according to any one of claims 1 to 9, wherein the predetermined distance threshold is 110% or less of the physical distance separating the first point and the second point.

11. The system according to any one of claims 1 to 10, wherein the predetermined distance threshold is equal to the physical distance separating the first point and the second point.

12. The system according to any one of claims 1 to 11, wherein the meshing includes the application of a triangulation algorithm.

13. A method for generating a road surface model, wherein the method is The stage of accessing multiple points associated with one or more drivable roads; The step of generating the road surface model based on the aforementioned plurality of points, wherein the road surface model includes a mesh representing the surface of one or more drivable roads, and the step of generating the road surface model is: A step of determining the drivable distance between a first point among the plurality of points and a second point among the plurality of points; and Steps to mesh the first point together with the second point, based on the determination that the drivable distance between the first point and the second point is shorter than or equal to a predetermined distance threshold. Including; and Steps include storing the road surface model for use in vehicle navigation along one or more drivable roads. A method for providing this.

14. A computer program, when executed by at least one processor, includes instructions causing the at least one processor to execute a method for generating a road surface model, wherein the method is The stage of accessing multiple points associated with one or more drivable roads; The step of generating the road surface model based on the aforementioned plurality of points, wherein the road surface model includes a mesh representing the surface of one or more drivable roads, and the step of generating the road surface model is: A step of determining the drivable distance between a first point among the plurality of points and a second point among the plurality of points; and Steps to mesh the first point together with the second point, based on the determination that the drivable distance between the first point and the second point is shorter than or equal to a predetermined distance threshold. Including; and Steps include storing the road surface model for use in vehicle navigation along one or more drivable roads. A computer program that includes the following features.

Images