Network of traffic light orientations

Abstract

The invention relates to a traffic light oriented network. A navigation system is provided for a host vehicle to receive a captured image representing an environment of the host vehicle from an image capture device associated with the host vehicle, a first trained network configured to generate a first output indicative of a state of a traffic light, and a second trained network configured to generate a second output indicative of a state of the traffic light. A second trained network configured to generate a second output indicative of a proposed navigation action for the host vehicle relative to the traffic light; determining a planned navigation action for the host vehicle based on both the first output from the first trained network and the second output from the second trained network; and causing the host vehicle to take the planned navigation action.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of priority to U.S. Provisional Application No. 63 / 296,275, filed on January 4, 2022, and U.S. Provisional Application No. 63 / 305,840, filed on February 2, 2022. Both applications are incorporated herein by reference in their entirety. Background Art Technical Field

[0003] The present disclosure relates generally to vehicle navigation, and more particularly to systems and methods for detecting objects in a vehicle's environment.

[0004] Background Information

[0005] As technology continues to advance, the goal of fully autonomous vehicles that can navigate on roadways is coming. Autonomous vehicles may need to consider a variety of factors and make appropriate decisions based on those factors to safely and accurately reach the intended destination. For example, autonomous vehicles may need to process and interpret visual information (e.g., information captured from a camera), and may also use information obtained from other sources (e.g., from a GPS device, a speed sensor, an accelerometer, a suspension sensor, etc.). At the same time, in order to navigate to the destination, the autonomous vehicle may also need to identify its location within a specific roadway (e.g., a specific lane in a multi-lane road), navigate side by side with other vehicles, avoid obstacles and pedestrians, obey traffic signals and signs, and travel from one road to another at an appropriate intersection or junction. Utilizing and interpreting the large amount of information collected by the autonomous vehicle when the vehicle travels to its destination brings numerous design challenges. The sheer amount of data that the autonomous vehicle may need to analyze, access and / or store (e.g., captured image data, map data, GPS data, sensor data, etc.) brings challenges that can actually limit or even adversely affect autonomous navigation. Furthermore, if autonomous vehicles rely on traditional mapping technologies for navigation, the sheer volume of data required to store and update maps presents daunting challenges. Summary of the invention

[0006] Embodiments consistent with the present disclosure provide systems and methods for vehicle navigation.

[0007] In one embodiment, a navigation system for a host vehicle may include at least one processor including circuitry and memory. The memory may include instructions that, when executed by the circuitry, cause the at least one processor to: receive a captured image representing an environment of the host vehicle from an image capture device associated with the host vehicle; identify a first segment of the captured image associated with a traffic light; provide the first segment of the captured image to a first trained network, the first trained network configured to generate a first output indicating a state of the traffic light; identify a second segment of the captured image, the second segment including contextual information associated with the traffic light; provide the second segment to a second trained network, the second trained network configured to generate a second output indicating a proposed navigation action for the host vehicle relative to the traffic light; determine a planned navigation action for the host vehicle based on both the first output from the first trained network and the second output from the second trained network; and cause the host vehicle to take the planned navigation action.

[0008] In one embodiment, a method for navigating a host vehicle may include receiving a captured image representing an environment of the host vehicle from an image capture device associated with the host vehicle; identifying a first segment of the captured image associated with a traffic light; providing the first segment of the captured image to a first trained network, the first trained network configured to generate a first output indicating a state of the traffic light; identifying a second segment of the captured image, the second segment including contextual information associated with the traffic light; providing the second segment to a second trained network, the second trained network configured to generate a second output indicating a proposed navigation action for the host vehicle relative to the traffic light; determining a planned navigation action for the host vehicle based on both the first output from the first trained network and the second output from the second trained network; and causing the host vehicle to take the planned navigation action.

[0009] Consistent with other disclosed embodiments, a non-transitory 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 exemplary and explanatory only 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 disclosure, illustrate various disclosed embodiments. In the drawings:

[0012] Figure 1are schematic representations of exemplary systems consistent with the disclosed embodiments.

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

[0014] Figure 2B In accordance with the disclosed embodiments Figure 2A Schematic top view representation of the vehicle and systems shown in .

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

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

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

[0018] Figure 2F is a schematic representation of an exemplary vehicle control system consistent with the disclosed embodiments.

[0019] Figure 3A is a schematic representation of the interior of a vehicle including a rearview mirror and a user interface for a vehicle imaging system consistent with the disclosed embodiments.

[0020] Figure 3B is an illustration of an example of a camera mount configured to be positioned behind a rearview mirror and against a vehicle windshield, consistent with the disclosed embodiments.

[0021] Figure 3C From different perspectives consistent with the disclosed embodiments Figure 3B Illustration of the camera mount shown in .

[0022] Figure 3D is an illustration of an example of a camera mount configured to be positioned behind a rearview mirror and against a vehicle windshield, consistent with the disclosed embodiments.

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

[0024] Figure 5A A flow chart is shown that illustrates an exemplary process for eliciting one or more navigation responses based on monocular image analysis consistent with the disclosed embodiments.

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

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

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

[0028] Figure 5E A flow chart is shown that illustrates an exemplary process for inducing one or more navigation responses based on a vehicle path consistent with the disclosed embodiments.

[0029] Fig. 5F is a flow chart illustrating an example process for determining whether a leading vehicle is changing lanes, consistent with the disclosed embodiments.

[0030] Figure 6 A flow chart is shown that illustrates an exemplary process for eliciting one or more navigation responses based on stereoscopic image analysis consistent with the disclosed embodiments.

[0031] Figure 7 A flow chart is shown that illustrates an exemplary process for inducing one or more navigation responses based on analysis of three sets of images consistent with the disclosed embodiments.

[0032] Figure 8 A sparse map for providing autonomous vehicle navigation consistent with the disclosed embodiments is shown.

[0033] Fig. 9A A polynomial representation of a portion of a road segment consistent with disclosed embodiments is shown.

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

[0035] Fig.10 Example landmarks that may be included in a sparse map consistent with the disclosed embodiments are shown.

[0036] Fig.11A A polynomial representation of a trajectory consistent with the disclosed embodiments is shown.

[0037] Fig. 11B and Fig. 11C A target trajectory along a multi-lane road is shown consistent with the disclosed embodiments.

[0038] Fig.11D An example road signature profile curve consistent with disclosed embodiments is shown.

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

[0040] Fig.13 An example autonomous vehicle road navigation model represented by a plurality of three-dimensional splines consistent with the disclosed embodiments is presented.

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

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

[0043] Fig.16 Examples of longitudinal alignments of a number of drives are shown with example signs serving as landmarks consistent with the disclosed embodiments.

[0044] Fig.17 is a schematic illustration of a system for generating driving data using a camera, a vehicle, and a server consistent with the disclosed embodiments.

[0045] Fig.18 is a schematic illustration of a system for crowdsourcing sparse maps consistent with the disclosed embodiments.

[0046] Fig.19 A flow chart illustrating an exemplary process for generating a sparse map for autonomous vehicle navigation along a road segment consistent with the disclosed embodiments.

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

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

[0049] Fig. 22 A process for clustering vehicle trajectories associated with vehicles consistent with the disclosed embodiments is presented.

[0050] Fig.23 A navigation system for a vehicle consistent with the disclosed embodiments is presented that can be used for autonomous navigation.

[0051] Fig.24A , Fig. 24B , Fig.24C and Fig.24D Exemplary lane markings that may be detected consistent with the disclosed embodiments are shown.

[0052] Fig.24E Exemplary mapped lane markings consistent with the disclosed embodiments are shown.

[0053] Fig.24F Exemplary anomalies associated with detecting lane markings consistent with the disclosed embodiments are shown.

[0054] Fig.25A Exemplary images of vehicle surroundings for navigation based on mapped lane markings consistent with the disclosed embodiments are shown.

[0055] Fig.25B Lateral positioning correction of a vehicle based on mapped lane markings in a road navigation model consistent with the disclosed embodiments is presented.

[0056] Fig.25C and Fig.25D A conceptual representation of a localization technique for localizing a host vehicle along a target track using mapped features included in a sparse map is provided.

[0057] Fig.26A A flow chart illustrating an exemplary process for mapping lane markings for use in autonomous vehicle navigation, consistent with the disclosed embodiments.

[0058] Fig.26B is a flow chart illustrating an exemplary process for autonomously navigating a host vehicle along a road segment using mapped lane markings consistent with the disclosed embodiments.

[0059] Fig.27A A scene showing a fully occluded traffic light.

[0060] Fig.27B A scenario is shown where environmental conditions partially obstruct the view of a traffic light.

[0061] Fig.28 is a flow chart illustrating an exemplary process for implementing a network for traffic light orientation.

[0062] Fig.29 A schematic representation of a network of traffic light orientations according to an exemplary disclosed embodiment is provided.

[0063] Fig.30 The architecture of a network for capturing images and uploading them to the cloud for training the network to implement traffic light orientation is presented.

[0064] Fig.31A , Fig.31B and Fig.31C A captured image is shown, which shows a bus crossing an intersection and blocking a traffic light signal. DETAILED DESCRIPTION

[0065] 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. Although several illustrative embodiments are described herein, modifications, adaptations, and other implementations are possible. For example, the components shown in the drawings may be replaced, added, or modified, and the illustrative methods described herein may be modified by replacing, reordering, removing, or adding steps to the disclosed methods. Therefore, the following detailed description is not limited to the disclosed embodiments and examples. Instead, the appropriate scope is defined by the appended claims.

[0066] Autonomous Vehicle (AV) Overview

[0067] As used throughout this disclosure, the term "autonomous vehicle" refers to a vehicle that is capable of implementing at least one navigation change without driver input. A "navigation change" refers to a change in one or more of the steering, braking, or acceleration of the vehicle. To achieve autonomy, the vehicle need not be fully automatic (e.g., fully operational without a driver or without driver input). Instead, an autonomous vehicle includes a vehicle that can operate under driver control during certain time periods and without driver control during other time periods. An autonomous vehicle may also include a vehicle that controls only some aspects of vehicle navigation, such as steering (e.g., to maintain the vehicle's route between vehicle lane constraints), but may leave other aspects to the driver (e.g., braking). In some cases, an autonomous vehicle may handle some or all aspects of the vehicle's braking, speed control, and / or steering.

[0068] Since human drivers typically rely on visual cues and observation to control vehicles, the traffic infrastructure is also built accordingly, with lane markings, traffic signs, and traffic lights all designed to provide visual information to drivers. In view of these design features of the traffic infrastructure, an autonomous vehicle may include a camera and a processing unit that analyzes visual information captured from the vehicle's environment. The visual information may include, for example, components of the traffic infrastructure that are observable by the driver (e.g., lane markings, traffic signs, traffic lights, etc.) and other obstacles (e.g., other vehicles, pedestrians, debris, etc.). In addition, the autonomous vehicle may also use stored information, such as information that provides a model of the vehicle's environment when navigating. For example, a vehicle may use GPS data, sensor data (e.g., from accelerometers, speed sensors, suspension sensors, etc.), and / or other map data to provide information related to its environment as the vehicle is traveling, and the vehicle (and other vehicles) may use this information to position itself on the model.

[0069] In some embodiments of the present disclosure, an autonomous vehicle may use information obtained while navigating (e.g., from a camera, a GPS device, an accelerometer, a velocity sensor, a suspension sensor, etc.). In other embodiments, an autonomous vehicle may use information obtained from past navigations by the vehicle (or by other vehicles) while navigating. In still other embodiments, an autonomous vehicle may use a combination of information obtained while navigating and information obtained from past navigations. The following sections provide an overview of systems consistent with the disclosed embodiments, followed by an overview of forward imaging systems and methods consistent with the systems. The following sections disclose systems and methods for building, using, and updating sparse maps for autonomous vehicle navigation.

[0070] System Overview

[0071] Figure 11 is a block diagram representation of a system 100 consistent with exemplary disclosed embodiments. System 100 may include various components as required by a particular implementation. In some embodiments, system 100 may include a processing unit 110, an image acquisition unit 120, a position sensor 130, one or more memory units 140, 150, a map database 160, a user interface 170, and a wireless transceiver 172. Processing unit 110 may include one or more processing devices. In some embodiments, processing unit 110 may include an application processor 180, an image processor 190, or any other suitable processing device. Similarly, image acquisition unit 120 may include any number of image acquisition devices and components as required by a particular application. In some embodiments, image acquisition unit 120 may include one or more image capture devices (e.g., cameras), such as image capture device 122, image capture device 124, and image capture device 126. System 100 may also include a data interface 128 that communicatively connects processing device 110 to image acquisition device 120. For example, the data interface 128 may include any one or more wired and / or wireless links for transmitting image data acquired by the image acquisition device 120 to the processing unit 110 .

[0072] The wireless transceiver 172 may include one or more devices configured to exchange transmissions with one or more networks (e.g., cellular networks, the Internet, etc.) over an air interface using radio frequencies, infrared frequencies, magnetic fields, or electric fields. The wireless transceiver 172 may transmit and / or receive data using any known standard (e.g., Wi-Fi, Such transmissions may include communications from the host vehicle to one or more remotely located servers. Such transmissions may also include communications (one-way or two-way) between the host vehicle and one or more target vehicles in the host vehicle's environment (e.g., to facilitate coordination of the host vehicle's navigation with or in conjunction with target vehicles in the host vehicle's environment), or even broadcast transmissions to unspecified recipients in the vicinity of the transmitting vehicle.

[0073] Both application processor 180 and image processor 190 may include various types of processing devices. For example, either or both of application processor 180 and image processor 190 may include a microprocessor, a preprocessor (such as an image preprocessor), a graphics processing unit (GPU), a central processing unit (CPU), support circuits, a digital signal processor, an integrated circuit, a memory, or any other type of device suitable for running applications and suitable for image processing and analysis. In some embodiments, application processor 180 and / or image processor 190 may include any type of single-core or multi-core processor, a mobile device microcontroller, a central processing unit, etc. Various processing devices may be used, including, for example, those purchased from manufacturers such as processors such as and the like, and may include various architectures (e.g., x86 processors, wait).

[0074] In some embodiments, the application processor 180 and / or the image processor 190 may include a processor available from Any of the EyeQ series of processor chips. These processor designs each include multiple processing units with local memory and instruction sets. Such processors may include video inputs for receiving image data from multiple image sensors, and may also include video output capabilities. In one example, Using 90nm micron technology operating at 332Mhz. The architecture consists of two floating point hyperthreaded 32-bit RISC CPUs ( cores), five visual computing engines (VCEs), and three vector microcode processors Denali 64-bit mobile DDR controller, 128-bit internal Sonics interconnect, dual 16-bit video input and 18-bit video output controllers, 16-channel DMA and several peripherals. MIPS34K CPU manages five VCEs, three VMPs TM and DMA, a second MIPS34K CPU and multi-channel DMA, and other peripherals. Five VCEs, three and MIPS34K CPUs for intensive visual computing required by multi-function bundled applications. In another example, (It is a third generation processor and its performance is Six times of) can be used in the disclosed embodiments. In other examples, and / or Of course, any newer or future EyeQ processing device can also be used with the disclosed embodiments.

[0075] Any processing device disclosed herein may be configured to perform certain functions. Configuring a processing device (such as any described EyeQ processor or other controller or microprocessor) to perform certain functions may include programming computer-executable instructions and making those instructions available to the processing device for execution during operation of the processing device. In some embodiments, configuring the processing device may include directly programming the processing device using architectural instructions. For example, a processing device such as a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), etc. may be configured using, for example, one or more hardware description languages ​​(HDLs).

[0076] In other embodiments, configuring the processing device may include storing executable instructions on a memory accessible to the processing device during operation. For example, the processing device may access the memory during operation to obtain and execute the stored instructions. In either case, the processing device configured to perform the sensing, image analysis, and / or navigation functions disclosed herein represents a specialized hardware-based system that controls multiple hardware-based components of the host vehicle.

[0077] Although Figure 1 Two separate processing devices are depicted as 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 complete the tasks of application processor 180 and image processor 190. In other embodiments, these tasks may be performed by more than two processing devices. In addition, in some embodiments, system 100 may include one or more processing units 110 without including other components, such as image acquisition unit 120.

[0078] 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, a memory, or any other type of device for image processing and analysis. The image preprocessor may include a video processor for capturing, digitizing, and processing images from an image sensor. The CPU may include any number of microcontrollers or microprocessors. The GPU may also include any number of microcontrollers or microprocessors. The support circuit may be any number of circuits known in the art, including caches, power supplies, clocks, and input-output circuits. The memory may store software that controls the operation of the system when executed by the processor. The memory may include a database and image processing software. The memory may include any number of random access memories, read-only memories, flash memories, disk drives, optical storage devices, tape storage devices, removable storage devices, and other types of storage devices. In one case, the memory may be separate from the processing unit 110. In another case, the memory may be integrated into the processing unit 110.

[0079] Each memory 140, 150 may include software instructions that, when executed by a processor (e.g., application processor 180 and / or image processor 190), may control the operation of various aspects of system 100. For example, these memory units may include various databases and image processing software, as well as trained systems such as neural networks or deep neural networks. The memory units may include random access memory (RAM), read-only memory (ROM), flash memory, disk drives, optical storage devices, tape storage devices, removable storage devices, and / or any other type of storage device. In some embodiments, the memory units 140, 150 may be separate from the application processor 180 and / or image processor 190. In other embodiments, these memory units may be integrated into the application processor 180 and / or image processor 190.

[0080] Position sensor 130 may include any type of device suitable for determining a location associated with at least one component of system 100. In some embodiments, position sensor 130 may include a GPS receiver. Such a receiver may determine user location and velocity by processing signals broadcast by global positioning system satellites. Position information from position sensor 130 may be used by application processor 180 and / or image processor 190.

[0081] In some embodiments, system 100 may include components such as a speed sensor (eg, tachometer, speedometer) for measuring the speed of vehicle 200 and / or an accelerometer (single-axis or multi-axis) for measuring the acceleration of vehicle 200 .

[0082] User interface 170 may include any device suitable for providing information to or receiving input from one or more users of system 100. In some embodiments, user interface 170 may include user input devices including, for example, a touch screen, microphone, keyboard, pointing device, scroll wheel, camera, knobs, buttons, etc. Using such input devices, a user may be able to provide information input or commands to system 100 by typing instructions or information, providing voice commands, selecting menu options on a screen (using buttons, pointers, or eye tracking capabilities), or via any other suitable technique for communicating information to system 100.

[0083] The user interface 170 may be equipped with one or more processing devices configured to provide information to and receive information from a user and process the information for use, for example, by the application processor 180. In some embodiments, such processing devices may execute instructions for recognizing and tracking eye movements, receiving and interpreting voice commands, recognizing and interpreting touches and / or gestures made on a touch screen, responding to keyboard inputs or menu selections, etc. In some embodiments, the user interface 170 may include a display, a speaker, a haptic device, and / or any other device for providing output information to a user.

[0084] The map database 160 may include any type of database for storing map data useful to the system 100. In some embodiments, the map database 160 may include data related to the location of various items (including roads, water features, geographic features, businesses, points of interest, restaurants, gas stations, etc.) in a reference coordinate system. The map database 160 may store not only the locations of such items, but also descriptors related to those items, including, for example, names associated with any stored features. In some embodiments, the map database 160 may be physically located with other components of the system 100. Alternatively or additionally, the map database 160 or a portion thereof may be remotely located relative to other components of the system 100 (e.g., the processing unit 110). In such embodiments, information from the map database 160 may be downloaded via a wired or wireless data connection to a network (e.g., via a cellular network and / or the Internet, etc.). In some cases, the map database 160 may store sparse data models, including polynomial representations of certain road features (e.g., lane markings) or target trajectories for the host vehicle. Systems and methods for generating such maps are described below with reference to Figures 8 to 19 Have a discussion.

[0085] Image capture devices 122, 124, and 126 may each include any type of device suitable for capturing at least one image from an environment. Furthermore, any number of image capture devices may be used to acquire images for input to an image processor. Some embodiments may include only a single image capture device, while other embodiments may include two, three, or even four or more image capture devices. Image capture devices 122, 124, and 126 will be referred to hereinafter. FIG. 2B to FIG. 2E Further description is given.

[0086] The system 100 or its various components may be incorporated into a variety of different platforms. In some embodiments, the system 100 may be included in a vehicle 200, such as Figure 2A For example, vehicle 200 may be equipped with processing unit 110 and any other components of system 100, as described above with respect to Figure 1While in some embodiments, vehicle 200 may be equipped with only a single image capture device (e.g., a camera), in other embodiments, such as in combination with FIG. 2B to FIG. 2E As discussed above, multiple image capture devices may be used. For example, Figure 2A As shown, either of the image capture devices 122 and 124 of vehicle 200 may be part of an ADAS (Advanced Driver Assistance System) imaging set.

[0087] The image capture device included on the vehicle 200 as part of the image acquisition unit 120 may be positioned at any suitable location. FIG. 2A to FIG. 2E and FIG. 3A to FIG. 3C As shown, the image capture device 122 may be located in the vicinity of the rearview mirror. This location may provide a line of sight similar to the line of sight of the driver of the vehicle 200, which may assist in determining what is and is not visible to the driver. The image capture device 122 may be positioned anywhere near the rearview mirror, but placing the image capture device 122 on the driver's side of the mirror may further assist in obtaining an image representative of the driver's field of view and / or line of sight.

[0088] Other locations for the image capture device for the image acquisition unit 120 may also be used. For example, the image capture device 124 may be located on or in the bumper of the vehicle 200. Such a location may be particularly suitable for an image capture device with a wide field of view. The line of sight of the image capture device located on the bumper may be different from the line of sight of the driver, and thus the bumper image capture device and the driver may not always see the same object. The image capture devices (e.g., image capture devices 122, 124, and 126) may also be located in other locations. For example, the image capture device may be located on or in one or both of the side mirrors of the vehicle 200, located on the roof of the vehicle 200, located on the hood of the vehicle 200, located on the trunk of the vehicle 200, located on the side of the vehicle 200, mounted on any of the windows of the vehicle 200, positioned behind it, or positioned in front of it, and mounted in or near the light graphics at the front and / or rear of the vehicle 200, etc.

[0089] In addition to the image capture device, the vehicle 200 may also include various other components of the system 100. For example, the processing unit 110 may be included on the vehicle 200, or integrated or separate from the vehicle's engine control unit (ECU). The vehicle 200 may also be equipped with a position sensor 130 such as a GPS receiver, and may also include a map database 160 and memory units 140 and 150.

[0090] As previously discussed, the wireless transceiver 172 may communicate and / or receive 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 the one or more servers. Via the wireless transceiver 172, the system 100 may receive, for example, periodic or on-demand updates to data stored in the map database 160, the memory 140, and / or the storage 150. Similarly, the wireless transceiver 172 may upload any data from the system 100 (e.g., images captured by the image acquisition unit 120, data received by the position sensor 130 or other sensors, the vehicle control system, etc.) and / or any data processed by the processing unit 110 to one or more servers.

[0091] The system 100 may upload data to a server (e.g., to the cloud) based on the privacy level settings. For example, the system 100 may implement privacy level settings to regulate or limit the type of data (including metadata) sent to a server that may uniquely identify a vehicle and / or a driver / owner of the vehicle. Such settings may be set by a user via, for example, the wireless transceiver 172, by factory default settings, or by data received by the wireless transceiver 172.

[0092] In some embodiments, the system 100 may upload data according to a "high" privacy level, and under the set setting, the system 100 may transmit data (e.g., location information related to routes, captured images, etc.) without any details about the specific vehicle and / or driver / owner. For example, when uploading data according to the "high" privacy setting, the system 100 may not include the vehicle identification number (VIN) or the name of the driver or owner of the vehicle, and may instead transmit data such as captured images and / or restricted location information related to the route.

[0093] Other privacy levels are contemplated. For example, the system 100 may transmit data to a server according to a "medium" privacy level and include additional information not included under a "high" privacy level, such as the make and / or model of the vehicle and / or the type of vehicle (e.g., a passenger vehicle, a sport utility vehicle, a truck, etc.). In some embodiments, the system 100 may upload data according to a "low" privacy level. Under the "low" privacy level setting, the system 100 may upload data and include information sufficient to uniquely identify a particular vehicle, owner / driver, and / or part or all of a route traveled by the vehicle. Such "low" privacy level data may include, for example, one or more of the VIN, the driver / owner's name, the vehicle's starting point prior to departure, the vehicle's intended destination, the vehicle's make and / or model, the vehicle's type, etc.

[0094] Figure 2Ais a schematic side view representation of an exemplary vehicle imaging system consistent with the disclosed embodiments. Figure 2B for Figure 2A A schematic top view of the embodiment shown is shown. Figure 2B As shown, the disclosed embodiments may include a vehicle 200 that includes a system 100 in its body having a first image capture device 122 positioned in an adjacent area of ​​a rearview mirror and / or near a driver of the vehicle 200, a second image capture device 124 positioned on or in a bumper area (e.g., one of the bumper areas 210) of the vehicle 200, and a processing unit 110.

[0095] like Figure 2C As shown, both image capture devices 122 and 124 may be positioned in the vicinity of a rearview mirror and / or near the driver of vehicle 200. Additionally, although both image capture devices 122 and 124 are shown in Figure 2B and Figure 2C However, it should be understood that other embodiments may include more than two image capture devices. Figure 2D and Figure 2E In the illustrated embodiment, first, second, and third image capture devices 122 , 124 , and 126 are included in the system 100 of a vehicle 200 .

[0096] like Figure 2D As shown, image capture device 122 may be positioned in the vicinity of a rearview mirror and / or near the driver of vehicle 200, and image capture devices 124 and 126 may be positioned on or in a bumper area (e.g., one of bumper areas 210) of vehicle 200. Figure 2E As shown, image capture devices 122, 124, and 126 may be positioned in the vicinity of a rearview mirror and / or near a driver's seat of vehicle 200. The disclosed embodiments are not limited to any particular number and configuration of image capture devices, and the image capture devices may be positioned in any suitable location within and / or on vehicle 200.

[0097] It should be understood that the disclosed embodiments are not limited to vehicles and can be applied in other contexts. It should also be understood that the disclosed embodiments are not limited to a specific type of vehicle 200 and can be applied to all types of vehicles, including cars, trucks, trailers, and other types of vehicles.

[0098] The first image capture device 122 may include any suitable type of image capture device. The image capture device 122 may include an optical axis. In one instance, the image capture device 122 may include an Aptina M9V024WVGA sensor with a global shutter. In other embodiments, the image capture device 122 may provide a resolution of 1280x960 pixels and may include a rolling shutter. The image capture device 122 may include various optical elements. In some embodiments, one or more lenses may be included, for example, to provide a desired focal length and field of view for the image capture device. In some embodiments, the image capture device 122 may be associated with a 6 mm lens or a 12 mm lens. In some embodiments, the image capture device 122 may be configured to capture an image having a desired field of view (FOV) 202, such as Figure 2D As shown. For example, the image capture device 122 may be configured to have a regular FOV, such as in the range of 40 degrees to 56 degrees, including a 46 degree FOV, a 50 degree FOV, a 52 degree FOV, or greater. Alternatively, the image capture device 122 may be configured to have a narrow FOV in the range of 23 degrees to 40 degrees, such as a 28 degree FOV or a 36 degree FOV. In addition, the image capture device 122 may be configured to have a wide FOV in the range of 100 degrees to 180 degrees. In some embodiments, the image capture device 122 may include a wide-angle bumper camera or a camera with up to 180 degrees FOV. In some embodiments, the image capture device 122 may be a 7.2M pixel image capture device with an aspect ratio of approximately 2:1 (e.g., HxV=3800x1900 pixels) with a horizontal FOV of approximately 100 degrees. Such an image capture device can be used to replace a three-image capture device configuration. Due to significant lens distortion, in implementations where the image capture device uses a radially symmetric lens, the vertical FOV of such an image capture device may be significantly less than 50 degrees. For example, such a lens may not be radially symmetric, which would allow a vertical FOV greater than 50 degrees and a 100 degree horizontal FOV.

[0099] The first image capture device 122 may acquire a plurality of first images relative to a scene associated with the vehicle 200. Each of the plurality of first images may be acquired as a series of image scan lines, which may be captured using a rolling shutter. Each scan line may include a plurality of pixels.

[0100] The first image capture device 122 may have a scan rate associated with the acquisition of each of the first series of image scan lines. The scan rate may refer to the rate at which the image sensor may acquire image data associated with each pixel included in a particular scan line.

[0101] For example, image capture devices 122, 124, and 126 may include any suitable type and number of image sensors, including CCD sensors or CMOS sensors. In one embodiment, a CMOS image sensor may be employed with a rolling shutter so that each pixel in a row is read one at a time, and the rows are scanned on a row-by-row basis until the entire image frame has been captured. In some embodiments, the rows may be captured sequentially from top to bottom with respect to the frame.

[0102] In some embodiments, one or more of the image capture devices disclosed herein (e.g., image capture devices 122, 124, and 126) may constitute a high-resolution imager and may have a resolution greater than 5M pixels, 7M pixels, 10M pixels, or greater.

[0103] The use of a rolling shutter may cause pixels in different rows to be exposed and captured at different times, which may result in skew and other image artifacts in the captured image frame. On the other hand, when image capture device 122 is configured to operate with a global or synchronized shutter, all pixels may be exposed for the same amount of time and during a common exposure period. Therefore, the image data in a frame collected from a system employing a global shutter represents a snapshot of the entire FOV (such as FOV 202) at a particular time. In contrast, in a rolling shutter application, each row in the frame is exposed and the data is captured at a different time. Therefore, moving objects may appear distorted in an image capture device with a rolling shutter. This phenomenon will be described in more detail below.

[0104] The second image capture device 124 and the third image capture device 126 may be any type of image capture device. Similar to the first image capture device 122, each of the image capture devices 124 and 126 may include an optical axis. In one embodiment, each of the image capture devices 124 and 126 may include an Aptina M9V024 WVGA sensor with a global shutter. Alternatively, each of the image capture devices 124 and 126 may include a rolling shutter. Similar to the image capture device 122, the image capture devices 124 and 126 may be configured to include various lenses and optical elements. In some embodiments, the lenses associated with the image capture devices 124 and 126 may provide a FOV (such as FOV 204 and 206) that is the same as or narrower than the FOV (such as FOV 202) associated with the image capture device 122. For example, the image capture devices 124 and 126 may have a FOV of 40 degrees, 30 degrees, 26 degrees, 23 degrees, 20 degrees, or less.

[0105] Image capture devices 124 and 126 may acquire a plurality of second and third images relative to a scene associated with vehicle 200. Each of the plurality of second and third images may be acquired as a second and third series of image scan lines, which may be captured using a rolling shutter. Each scan line or row may have a plurality of pixels. Image capture devices 124 and 126 may have second and third scan rates associated with the acquisition of each of the image scan lines included in the second and third series.

[0106] Each image capture device 122, 124, and 126 may be positioned at any suitable location and orientation relative to vehicle 200. The relative positioning of image capture devices 122, 124, and 126 may be selected to assist in fusing together information acquired from the image capture devices. For example, in some embodiments, a FOV associated with image capture device 124 (such as FOV 204) may partially or completely overlap with a FOV associated with image capture device 122 (such as FOV 202) and a FOV associated with image capture device 126 (such as FOV 206).

[0107] Image capture devices 122, 124, and 126 may be located at any suitable relative heights on vehicle 200. In one case, there may be height differences between image capture devices 122, 124, and 126, which may provide sufficient parallax information to enable stereo analysis. Figure 2A As shown, the two image capture devices 122 and 124 are at different heights. For example, there may also be lateral displacement differences between the image capture devices 122, 124, and 126, thereby providing additional parallax information for stereo analysis by the processing unit 110. The lateral displacement differences may be represented by d x Indicates that Figure 2C and Figure 2D In some embodiments, there may be a forward or backward displacement (e.g., a range displacement) between image capture devices 122, 124, and 126. For example, image capture device 122 may be located 0.5 meters to 2 meters or more behind image capture device 124 and / or image capture device 126. This type of displacement may enable one of the image capture devices to cover a potential blind spot of the other image capture devices.

[0108] Image capture device 122 may have any suitable resolution capability (e.g., the number of pixels associated with the image sensor), and the resolution of the image sensor associated with image capture device 122 may be higher, lower, or the same as the resolution of the image sensors associated with image capture devices 124 and 126. In some embodiments, the image sensors associated with image capture device 122 and / or image capture devices 124 and 126 may have a resolution of 640 x 480, 1024 x 768, 1280 x 960, or any other suitable resolution.

[0109] The frame rate (e.g., the rate at which an image capture device acquires a set of pixel data for one image frame before continuing to capture pixel data associated with the next image frame) may be controllable. The frame rate associated with image capture device 122 may be higher, lower, or the same as the frame rate associated with image capture devices 124 and 126. The frame rate associated with image capture devices 122, 124, and 126 may depend on a variety of factors that may affect the timing of the frame rate. For example, one or more of image capture devices 122, 124, and 126 may include a selectable pixel delay period applied before or after the acquisition of image data associated with one or more pixels of an image sensor in image capture devices 122, 124, and / or 126. In general, image data corresponding to each pixel may be acquired according to a clock rate for the device (e.g., one pixel per clock cycle). Additionally, in embodiments including a rolling shutter, one or more of image capture devices 122, 124, and 126 may include a selectable horizontal blanking period applied before or after acquisition of image data associated with rows of pixels of an image sensor in image capture devices 122, 124, and / or 126. Additionally, one or more of image capture devices 122, 124, and / or 126 may include a selectable vertical blanking period applied before or after acquisition of image data associated with image frames of image capture devices 122, 124, and 126.

[0110] These timing controls may enable synchronization of frame rates associated with image capture devices 122, 124, and 126, even when the line scan rate of each image capture device is different. Additionally, as will be discussed in greater detail below, these selectable timing controls, among other factors (e.g., image sensor resolution, maximum line scan rate, etc.), may enable synchronization of image capture from areas where the FOV of image capture device 122 overlaps with one or more FOVs of image capture devices 124 and 126, even when the field of view of image capture device 122 is different than the FOVs of image capture devices 124 and 126.

[0111] The frame rate timing in image capture devices 122, 124, and 126 may depend on the resolution of the associated image sensors. For example, assuming similar line scan rates for both devices, if one device includes an image sensor with a resolution of 640 x 480 and the other device includes an image sensor with a resolution of 1280 x 960, it will take more time to acquire a frame of image data from the sensor with the higher resolution.

[0112] Another factor that may affect the timing of image data acquisition in image capture devices 122, 124, and 126 is the maximum line scan rate. For example, it will take some minimum amount of time to acquire a row of image data from the image sensors included in image capture devices 122, 124, and 126. Assuming that no pixel delay period is added, this minimum amount of time for the acquisition of a row of image data will be related to the maximum line scan rate for the particular device. Devices that give higher maximum line scan rates are likely to provide higher frame rates than devices with lower maximum line scan rates. In some embodiments, one or more of image capture devices 124 and 126 may have a maximum line scan rate that is higher than the maximum line scan rate associated with image capture device 122. In some embodiments, the maximum line scan rate of image capture devices 124 and / or 126 may be 1.25 times, 1.5 times, 1.75 times, or 2 times or more of the maximum line scan rate of image capture device 122.

[0113] In another embodiment, image capture devices 122, 124, and 126 may have the same maximum line scan rate, but image capture device 122 may be operated at a scan rate less than or equal to its maximum scan rate. The system may be configured such that one or more of image capture devices 124 and 126 operate at a line scan rate equal to the line scan rate of image capture device 122. In other cases, the system may be configured such that the line scan rate of image capture device 124 and / or image capture device 126 may be 1.25 times, 1.5 times, 1.75 times, or 2 times or more the line scan rate of image capture device 122.

[0114] In some embodiments, image capture devices 122, 124, and 126 may be asymmetric. That is, they may include cameras with different fields of view (FOV) and focal lengths. For example, the fields of view of image capture devices 122, 124, and 126 may include any desired area relative to the environment of vehicle 200. In some embodiments, one or more of image capture devices 122, 124, and 126 may be configured to acquire image data from the environment in front of vehicle 200, behind vehicle 200, to the side of vehicle 200, or a combination thereof.

[0115] In addition, the focal length associated with each image capture device 122, 124, and / or 126 may be selectable (e.g., by including an appropriate lens, etc.) so that each device captures images of objects at a desired range of distances relative to the vehicle 200. For example, in some embodiments, the image capture devices 122, 124, and 126 may capture images of close-up objects within a few meters from the vehicle. The image capture devices 122, 124, and 126 may also be configured to capture images of objects at a greater range from the vehicle (e.g., 25m, 50m, 100m, 150m, or more). In addition, the focal lengths of image capture devices 122, 124, and 126 may be selected such that one image capture device (e.g., image capture device 122) may capture images of objects that are relatively close to the vehicle (e.g., within 10 m or within 20 m), while other image capture devices (e.g., image capture devices 124 and 126) may capture images of objects that are farther from vehicle 200 (e.g., greater than 20 m, 50 m, 100 m, 150 m, etc.).

[0116] According to some embodiments, the FOV of one or more of the image capture devices 122, 124, and 126 may have a wide angle. For example, having a FOV of 140 degrees may be advantageous, particularly for image capture devices 122, 124, and 126 that may be used to capture images of areas in the vicinity of the vehicle 200. For example, the image capture device 122 may be used to capture images of areas to the right or left of the vehicle 200, and in such embodiments, it may be desirable for the image capture device 122 to have a wide FOV (e.g., at least 140 degrees).

[0117] The field of view associated with each of the image capture devices 122, 124, and 126 may depend on the respective focal length. For example, as the focal length increases, the corresponding field of view decreases.

[0118] Image capture devices 122, 124, and 126 may be configured to have any suitable field of view. In one particular example, image capture device 122 may have a horizontal FOV of 46 degrees, image capture device 124 may have a horizontal FOV of 23 degrees, and image capture device 126 may have a horizontal FOV between 23 degrees and 46 degrees. In another case, image capture device 122 may have a horizontal FOV of 52 degrees, image capture device 124 may have a horizontal FOV of 26 degrees, and image capture device 126 may have a horizontal FOV between 26 degrees and 52 degrees. In some embodiments, the ratio of the FOV of image capture device 122 to the FOV of image capture device 124 and / or image capture device 126 may vary from 1.5 to 2.0. In other embodiments, the ratio may vary between 1.25 and 2.25.

[0119] System 100 may be configured such that the field of view of image capture device 122 at least partially or completely overlaps the field of view of image capture device 124 and / or image capture device 126. In some embodiments, system 100 may be configured such that the fields of view of image capture devices 124 and 126, for example, fall within (e.g., are narrower than) the field of view of image capture device 122 and share a common center with the field of view. In other embodiments, image capture devices 122, 124, and 126 may capture adjacent FOVs or may have partial overlap in their FOVs. In some embodiments, the fields of view of image capture devices 122, 124, and 126 may be aligned such that the center of the narrower FOV image capture device 124 and / or 126 may be located in the lower half of the field of view of the wider FOV device 122.

[0120] Figure 2F is a schematic representation of an exemplary vehicle control system consistent with the disclosed embodiments. Figure 2F As indicated, vehicle 200 may include throttle system 220, brake system 230, and steering system 240. System 100 may provide input (e.g., control signals) to one or more of throttle system 220, brake system 230, and steering system 240 via one or more data links (e.g., any wired and / or wireless links or links for transmitting data). For example, based on analysis of images acquired by image capture devices 122, 124, and / or 126, system 100 may provide control signals to one or more of throttle system 220, brake system 230, and steering system 240 to navigate vehicle 200 (e.g., by causing acceleration, turning, lane changes, etc.). In addition, system 100 may receive input from one or more of throttle system 220, brake system 230, and steering system 24 indicating operating conditions of vehicle 200 (e.g., speed, whether vehicle 200 is braking and / or turning, etc.). Further details are provided below in conjunction with Figures 4 to 7 supply.

[0121] like Figure 3AAs shown, the vehicle 200 may also include a user interface 170 for interacting with a driver or passenger of the vehicle 200. For example, the user interface 170 in a vehicle application may include a touch screen 320, a knob 330, a button 340, and a microphone 350. The driver or passenger of the vehicle 200 may also use a handle (e.g., located on or near the steering column of the vehicle 200, including, for example, a turn signal handle), a button (e.g., located on the steering wheel of the vehicle 200), etc. to interact with the system 100. In some embodiments, the microphone 350 may be positioned adjacent to the rearview mirror 310. Similarly, in some embodiments, the image capture device 122 may be located near the rearview mirror 310. In some embodiments, the user interface 170 may also include one or more speakers 360 (e.g., speakers of a vehicle audio system). For example, the system 100 may provide various notifications (e.g., alarms) via the speaker 360.

[0122] FIG. 3B to FIG. 3D 3 is an illustration of an exemplary camera mount 370 configured to be positioned behind a rearview mirror (e.g., rearview mirror 310) and against a vehicle windshield consistent with the disclosed embodiments. Figure 3B As shown, camera mount 370 may include image capture devices 122, 124, and 126. Image capture devices 124 and 126 may be positioned behind glare shield 380, which may be flush against the vehicle windshield and include a combination of film and / or anti-reflective material. For example, glare shield 380 may be positioned so that the shield is aligned against the vehicle windshield having a matching slope. In some embodiments, each of image capture devices 122, 124, and 126 may be positioned behind glare shield 380, such as, for example Figure 3D The disclosed embodiments are not limited to any particular configuration of the image capture devices 122 , 124 , and 126 , the camera mount 370 , and the glare shield 380 . Figure 3C For the front view Figure 3B An illustration of a camera mount 370 is shown.

[0123] As will be appreciated by those skilled in the art having the benefit of this disclosure, various changes and / or modifications may be made to the foregoing disclosed embodiments. For example, not all components are necessary for the operation of the system 100. In addition, any component may be located in any appropriate part of the system 100, and these components may be rearranged into various configurations while providing the functionality of the disclosed embodiments. Therefore, the foregoing configurations are examples, and regardless of the configurations discussed above, the system 100 may provide a wide range of functionality to analyze the surroundings of the vehicle 200 and navigate the vehicle 200 in response to the analysis.

[0124] As discussed in further detail below and consistent with various disclosed embodiments, the system 100 may provide various features related to autonomous driving and / or driver assistance technology. For example, the system 100 may analyze image data, location data (e.g., GPS location information), map data, speed data, and / or data from sensors included in the vehicle 200. The system 100 may collect data for analysis from, for example, the image acquisition unit 120, the location sensor 130, and other sensors. In addition, the system 100 may analyze the collected data to determine whether the vehicle 200 should take a specific action, and then automatically take the determined action without human intervention. For example, when the vehicle 200 is navigating without human intervention, the system 100 may automatically control the braking, acceleration, and / or steering of the vehicle 200 (e.g., by sending control signals to one or more of the throttle system 220, the braking system 230, and the steering system 240). In addition, the system 100 may analyze the collected data and issue warnings and / or alarms to the vehicle occupants based on the analysis of the collected data. Additional details regarding various embodiments provided by system 100 are provided below.

[0125] Forward multiple imaging system

[0126] As discussed above, the system 100 can provide a driving assistance function using a multi-camera system. The multi-camera system can use one or more cameras facing the forward direction of the vehicle. In other embodiments, the multi-camera system may include one or more cameras facing the side of the vehicle or facing the rear of the vehicle. In one embodiment, for example, the system 100 can use a dual-camera imaging system, wherein the first camera and the second camera (e.g., image capture devices 122 and 124) can 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 the field of view of the second camera. In addition, the first camera may be connected to a first image processor to perform monocular image analysis on the image provided by the first camera, and the second camera may be connected to a second image processor to perform monocular image analysis on the image provided by the second camera. The outputs (e.g., processed information) of the first image processor and the second image processor may be combined. In some embodiments, the second image processor may receive images from both the first camera and the second camera for stereo analysis. In another embodiment, the system 100 may use a three-camera imaging system, wherein each of the cameras has a different field of view. Thus, such systems may make decisions based on information derived from objects located at varying distances both in front of and to the sides of the vehicle. References to monocular image analysis may refer to situations where image analysis is performed based on images captured from a single viewpoint (e.g., from a single camera). Stereoscopic image analysis may refer to situations where image analysis is performed based on two or more images captured using one or more variations of image capture parameters. For example, captured images suitable for stereoscopic image analysis may include: images captured from two or more different positions, images captured from different fields of view, images captured using different focal lengths and parallax information, etc.

[0127] For example, in one embodiment, the system 100 may implement a three-camera configuration using image capture devices 122, 124, and 126. In such a configuration, image capture device 122 may provide a narrow field of view (e.g., 34 degrees, or other values ​​selected from a range of about 20 degrees to 45 degrees, etc.), image capture device 124 may provide a wide field of view (e.g., 150 degrees or other values ​​selected from a range of about 100 degrees to about 180 degrees), and image capture device 126 may provide an intermediate field of view (e.g., 46 degrees or other values ​​selected from a range of about 35 degrees to about 60 degrees). In some embodiments, image capture device 126 may act as a primary or main camera. Image capture devices 122, 124, and 126 may be positioned behind rearview mirror 310 and positioned substantially side by side (e.g., 6 cm apart). In addition, in some embodiments, as discussed above, one or more of image capture devices 122, 124, and 126 may be mounted behind a glare shield 380 flush with the windshield of vehicle 200. Such shielding may serve to minimize the effect of any reflections from the interior of the sedan on image capture devices 122 , 124 , and 126 .

[0128] In another embodiment, as described above in combination with Figure 3B and Figure 3C As discussed, the wide field of view camera (e.g., image capture device 124 in the above example) can be mounted lower than the narrow field of view and main field of view cameras (e.g., image devices 122 and 126 in the above example). This configuration can provide a free line of sight from the wide field of view camera. To reduce reflections, the camera can be mounted close to the windshield of the vehicle 200, and a polarizer on the camera can be included to reduce reflected light.

[0129] A three-camera system may provide certain performance features. For example, some embodiments may include the ability to verify detection of an object by one camera based on detection results from another camera. In the three-camera configuration discussed above, processing unit 110 may include, for example, three processing devices (e.g., three EyeQ series processor chips, as discussed above), where each processing device is dedicated to processing images captured by one or more of image capture devices 122, 124, and 126.

[0130] In a three-camera system, the first processing device may receive images from both the main camera and the narrow field of view camera and perform visual processing for the narrow FOV camera to, for example, detect other vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. In addition, the first processing device may calculate the pixel disparity between the images from the main camera and the narrow camera and create a 3D reconstruction of the environment of the vehicle 200. The first processing device may then combine the 3D reconstruction with 3D map data or with 3D information calculated based on information from another camera.

[0131] The second processing device may receive images from the primary camera and perform visual processing to detect other vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. In addition, the second processing device may calculate camera displacement, and based on the displacement, calculate pixel disparity between consecutive images and create a 3D reconstruction of the scene (e.g., structure from motion). The second processing device may send the structure from motion-based 3D reconstruction to the first processing device to be combined with the stereoscopic 3D image.

[0132] The third processing device may receive images from the wide FOV camera and process the images to detect vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. The third processing device may further execute additional processing instructions to analyze the images to identify moving objects in the images, such as vehicles changing lanes, pedestrians, etc.

[0133] In some embodiments, having image-based information streams captured and processed independently may provide an opportunity to provide redundancy in the system. Such redundancy may include, for example, using a first image capture device and images processed from the device to verify and / or supplement information obtained by capturing and processing image information from at least a second image capture device.

[0134] In some embodiments, system 100 may use two image capture devices (e.g., image capture devices 122 and 124) to provide navigation assistance for vehicle 200, and use a third image capture device (e.g., image capture device 126) to provide redundancy and verify analysis of data received from the other two image capture devices. For example, in such a configuration, image capture devices 122 and 124 may provide images for stereo analysis by system 100 for navigating vehicle 200, while image capture device 126 may provide images for monocular analysis by system 100 to provide redundancy and verification of information obtained based on images captured from image capture device 122 and / or image capture device 124. That is, image capture device 126 (and corresponding processing device) may be considered to provide a redundant subsystem for providing a check on analysis derived from image capture devices 122 and 124 (e.g., to provide an automatic emergency braking (AEB) system). In addition, in some embodiments, 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, etc.).

[0135] Those skilled in the art will recognize that the above camera configurations, camera placements, number of cameras, camera locations, etc. are merely examples. These components and other components described with respect to the overall system may be assembled and used in a variety of different configurations without departing from the scope of the disclosed embodiments. Further details regarding the use of a multi-camera system to provide driver assistance and / or autonomous vehicle functionality are as follows.

[0136] Figure 4 An exemplary functional block diagram of memory 140 and / or 150 that may be stored / programmed with instructions for performing one or more operations consistent with the disclosed embodiments. Although the following refers to memory 140, those skilled in the art will recognize that instructions may be stored in memory 140 and / or 150.

[0137] like Figure 4 As shown, the memory 140 may store a monocular image analysis module 402, a stereo image analysis module 404, a rate and acceleration module 406, and a navigation response module 408. The disclosed embodiments are not limited to any particular configuration of the memory 140. In addition, the application processor 180 and / or the image processor 190 may execute instructions stored in any of the modules 402, 404, 406, and 408 included in the memory 140. Those skilled in the art will understand that references to the processing unit 110 in the following discussion may refer individually or collectively to the application processor 180 and the image processor 190. Therefore, the steps of any of the following processes may be performed by one or more processing devices.

[0138] In one embodiment, monocular image analysis module 402 may store instructions (such as computer vision software) that, when executed by processing unit 110, perform monocular image analysis on a set of images acquired by one of image capture devices 122, 124, and 126. In some embodiments, processing unit 110 may combine information from a set of images with additional sensory information (e.g., information from radar, lidar, etc.) to perform monocular image analysis. As described below in conjunction with FIG. 5A to FIG. 5D As described, monocular image analysis module 402 may include instructions for detecting a set of features within the set of images (such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazardous objects, and any other features associated with the vehicle's environment. Based on the analysis, system 100 (e.g., via processing unit 110) may cause one or more navigation responses in vehicle 200, such as turns, lane changes, changes in acceleration, etc., as discussed below in conjunction with navigation response module 408.

[0139] In one embodiment, stereo image analysis module 404 may store instructions (such as computer vision software) that, when executed by processing unit 110, perform stereo image analysis of a first set of images and a second set of images acquired by a combination of image capture devices selected from any of image capture devices 122, 124, and 126. In some embodiments, processing unit 110 may combine information from the first set of images and the second set of images with additional sensor information (e.g., information from radar) to perform stereo image analysis. For example, stereo image analysis module 404 may include instructions for performing stereo image analysis based on a first set of images acquired by image capture device 124 and a second set of images acquired by image capture device 126. As described below in conjunction with Figure 6 As described, the stereo image analysis module 404 may include instructions for detecting a set of features (such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazardous objects, etc.) within the first set of images and the second set of images. Based on the analysis, the processing unit 110 may cause one or more navigation responses in the vehicle 200, such as turns, lane changes, changes in acceleration, etc., as discussed below in conjunction with the navigation response module 408. In addition, in some embodiments, the stereo image analysis module 404 may implement techniques associated with a trained system (such as a neural network or a deep neural network) or an untrained system (such as a system that may be configured to use a computer vision algorithm to detect and / or mark objects in an environment from which sensory information is captured and processed). In one embodiment, the stereo image analysis module 404 and / or other image processing modules may be configured to use a combination of trained and untrained systems.

[0140] In one embodiment, the speed and acceleration module 406 may store software configured to analyze data received from one or more computing and electromechanical devices in the vehicle 200 that are configured to cause changes in the speed and / or acceleration of the vehicle 200. For example, the processing unit 110 may execute instructions associated with the speed and acceleration module 406 to calculate a target speed for the vehicle 200 based on data derived from the execution of the monocular image analysis module 402 and / or the stereo image analysis module 404. Such data may include, for example, target position, speed and / or acceleration, the position and / or speed of the vehicle 200 relative to nearby vehicles, pedestrians, or road objects, position information for the vehicle 200 relative to lane markings of the road, etc. Among other things, the processing unit 110 may calculate the target speed for the vehicle 200 based on sensory input (e.g., information from a radar) and input from other systems of the vehicle 200, such as the throttle system 220, the braking system 230, and / or the steering system 240 of the vehicle 200. Based on the calculated target speed, processing unit 110 may transmit electronic signals to throttle system 220, braking system 230 and / or steering system 240 of vehicle 200 to trigger a change in speed and / or acceleration by, for example, physically depressing a brake or releasing an accelerator of vehicle 200.

[0141] 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 stereo image analysis module 404. Such data may include position and velocity information associated with nearby vehicles, pedestrians, and road objects, target position information for the vehicle 200, etc. In addition, in some embodiments, the navigation response may be based (partially or entirely) on map data, a predetermined position of the vehicle 200, and / or a 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 stereo image analysis module 404. The navigation response module 408 may also determine the desired navigation response based on sensory input (e.g., information from a radar) and input from other systems of the vehicle 200, such as the throttle system 220, the braking system 230, and the steering system 240 of the vehicle 200. Based on the desired navigation response, the processing unit 110 may transmit electronic signals to the throttle system 220, the brake system 230, and the steering system 240 of the vehicle 200 to trigger the desired navigation response to achieve a predetermined angle of rotation by, for example, turning the steering wheel of the vehicle 200. In some embodiments, the processing unit 110 may use the output of the navigation response module 408 (e.g., the desired navigation response) as an input to the execution of the velocity and acceleration module 406 for calculating the change in the velocity of the vehicle 200.

[0142] Furthermore, any modules disclosed herein (eg, modules 402, 404, and 406) may implement techniques associated with a trained system (such as a neural network or a deep neural network) or an untrained system.

[0143] Figure 5A Flowchart showing an exemplary process 500A for causing one or more navigation responses based on monocular image analysis consistent with the disclosed embodiments. At step 510, the processing unit 110 may receive a plurality of images via the data interface 128 between the processing unit 110 and the image acquisition unit 120. For example, a camera (such as an image capture device 122 having a field of view 202) included in the image acquisition unit 120 may capture a plurality of images of an area in front of the vehicle 200 (or, for example, to the side or rear of the vehicle) and transmit them to the processing unit 110 via a data connection (e.g., digital, wired, USB, wireless, Bluetooth, etc.). The processing unit 110 may execute the monocular image analysis module 402 at step 520 to analyze the plurality of images, as described below in conjunction with FIG. 5B to FIG. 5D By performing this analysis, processing unit 110 may detect a set of features within the set of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, etc.

[0144] Processing unit 110 may also execute monocular image analysis module 402 at step 520 to detect various road hazards, such as, for example, parts of truck tires, fallen road signs, loose cargo, small animals, etc. Road hazards may vary in structure, shape, size, and color, which may make the detection of such hazards more challenging. In some embodiments, processing unit 110 may execute monocular image analysis module 402 to perform multi-frame analysis on multiple images to detect road hazards. For example, processing unit 110 may estimate camera motion between consecutive image frames and calculate pixel disparity between frames to build a 3D map of the road. Processing unit 110 may then use the 3D map to detect hazards on the road surface and above the road surface.

[0145] At step 530, processing unit 110 may execute navigation response module 408 to perform navigation based on the analysis performed at step 520 and the above combined Figure 4The described techniques are used to cause one or more navigation responses in the vehicle 200. The navigation responses may include, for example, turns, lane changes, changes in acceleration, etc. In some embodiments, the processing unit 110 may use data derived from the execution of the rate and acceleration module 406 to cause one or more navigation responses. In addition, multiple navigation responses may occur simultaneously, sequentially, or in any combination thereof. For example, the processing unit 110 may cause the vehicle 200 to change out of a lane and then accelerate by, for example, sequentially transmitting control signals to the steering system 240 and the throttle system 220 of the vehicle 200. Alternatively, the processing unit 110 may cause the vehicle 200 to brake while changing lanes by, for example, simultaneously transmitting control signals to the braking system 230 and the steering system 240 of the vehicle 200.

[0146] Figure 5B Flowchart showing an exemplary process 500B for detecting one or more vehicles and / or pedestrians in a set of images consistent with the disclosed embodiments. Processing unit 110 may execute monocular image analysis module 402 to implement process 500B. At step 540, processing unit 110 may determine a set of candidate objects representing possible vehicles and / or pedestrians. For example, processing unit 110 may scan one or more images, compare the images to one or more predetermined patterns, and identify possible locations within each image that may contain objects of interest (e.g., vehicles, pedestrians, or parts thereof). The predetermined patterns may be designed in a manner that achieves a high "false hit" rate and a low "miss" rate. For example, processing unit 110 may use a low threshold for similarity to a predetermined pattern to identify a candidate object as a possible vehicle or pedestrian. Doing so may allow processing unit 110 to reduce the probability of missing (e.g., not identifying) a candidate object representing a vehicle or pedestrian.

[0147] At step 542, processing unit 110 may filter a set of candidate objects based on classification criteria to exclude certain candidates (e.g., irrelevant or less relevant objects). Such criteria may be derived from various attributes associated with object types stored in a database (e.g., a database stored in memory 140). Attributes may include object shape, size, texture, location (e.g., relative to vehicle 200), etc. Thus, processing unit 110 may use one or more sets of criteria to reject erroneous candidates from a set of candidate objects.

[0148] At step 544, 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, processing unit 110 may track detected candidate objects across consecutive frames and accumulate frame-by-frame data associated with the detected objects (e.g., size, position relative to vehicle 200, etc.). In addition, processing unit 110 may estimate parameters for the detected objects and compare frame-by-frame position data of the objects with the predicted positions.

[0149] At step 546, processing unit 110 may construct a set of measurements for the detected objects. Such measurements may include, for example, position, velocity, and acceleration values ​​(relative to vehicle 200) associated with the detected objects. In some embodiments, processing unit 110 may construct the measurements based on an estimation technique such as a Kalman filter or linear quadratic estimation (LQE) using a series of time-based observations and / or based on available modeling data for different object types (e.g., cars, trucks, pedestrians, bicycles, road signs, etc.). The Kalman filter may be based on measurements of the scale of the object, where the scale measurement is proportional to the collision time (e.g., the amount of time it takes vehicle 200 to reach the object). Thus, by performing steps 540 to 546, processing unit 110 may identify vehicles and pedestrians that appear within a set of captured images and derive information related to the vehicles and pedestrians (e.g., position, velocity, size). Based on this identification and derived information, processing unit 110 may cause one or more navigation responses in vehicle 200, as described above in conjunction with Figure 5A As described.

[0150] At step 548, the processing unit 110 may perform an optical flow analysis of the one or more images to reduce the probability of detecting "false hits" and missing candidate objects representing vehicles or pedestrians. Optical flow analysis may refer to, for example, analyzing motion patterns relative to the vehicle 200 in one or more images associated with other vehicles and pedestrians, and the motion patterns are different from the road surface motion. The processing unit 110 may calculate the motion of the candidate objects by observing the different positions of the objects across multiple image frames captured at different times. The processing unit 110 may use the position and time values ​​as inputs into a mathematical model to calculate the motion of the candidate objects. Therefore, optical flow analysis may provide another method for detecting vehicles and pedestrians near the vehicle 200. The processing unit 110 may perform optical flow analysis in conjunction with steps 540 to 546 to provide redundancy for detecting vehicles and pedestrians and increase the reliability of the system 100.

[0151] Figure 5CFlowchart of an exemplary process 500C for detecting road markings and / or lane geometry information in a set of images consistent with the disclosed embodiments is shown. Processing unit 110 may execute monocular image analysis module 402 to implement process 500C. At step 550, processing unit 110 may detect a set of objects by scanning one or more images. In order to detect segments of lane markings, lane geometry information, and other relevant road markings, processing unit 110 may filter the set of objects to exclude those objects determined to be irrelevant (e.g., small potholes, small rocks, etc.). At step 552, processing unit 110 may group together segments belonging to the same road marking or lane marking detected in step 550. Based on the grouping, processing unit 110 may develop a model, such as a mathematical model, representing the detected segments.

[0152] At 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 to the real world plane. The projection may be characterized using a 3rd order polynomial with coefficients corresponding to physical properties such as the position, slope, curvature, and curvature derivatives of the detected road. In generating the projection, the processing unit 110 may take into account changes in the road surface and the pitch rate and roll rate associated with the vehicle 200. In addition, the processing unit 110 may model the road elevation by analyzing the position and motion cues present on the road surface. In addition, the processing unit 110 may estimate the pitch rate and roll rate associated with the vehicle 200 by tracking a set of feature points in one or more images.

[0153] At step 556, processing unit 110 may perform a multi-frame analysis by, for example, tracking the detected segments across consecutive image frames and accumulating frame-by-frame data associated with the detected segments. As processing unit 110 performs a multi-frame analysis, the set of measurements constructed at step 554 may become more reliable and associated with increasingly higher confidence levels. Thus, by performing steps 550, 552, 554, and 556, processing unit 110 may identify road markings that appear within a set of captured images and derive lane geometry information. Based on this identified and derived information, processing unit 110 may cause one or more navigation responses in vehicle 200, as described above in conjunction with Figure 5A As described.

[0154] At step 558, processing unit 110 may consider additional information sources to further develop a safety model for vehicle 200 in the context of its surroundings. Processing unit 110 may use the safety model to define scenarios in which system 100 may perform autonomous control of vehicle 200 in a safe manner. To develop the safety model, in some embodiments, processing unit 110 may consider the positions and motions of other vehicles, detected road edges and obstacles, and / or general road shape descriptions extracted from map data (such as data from map database 160). By considering additional information sources, processing unit 110 may provide redundancy for detecting road markings and lane geometry and increase the reliability of system 100.

[0155] Figure 5D Flowchart showing an exemplary process 500D for detecting a traffic light in a set of images consistent with the disclosed embodiments. The processing unit 110 may execute the monocular image analysis module 402 to implement the process 500D. At step 560, the processing unit 110 may scan the set of images and identify objects that appear at locations in the images that may contain traffic lights. For example, the processing unit 110 may filter the identified objects to construct a set of candidate objects, excluding those objects that are unlikely to correspond to traffic lights. The filtering may be done based on various attributes associated with traffic lights, such as shape, size, texture, position (e.g., relative to the vehicle 200), etc. Such attributes may be based on multiple examples of traffic lights and traffic control signals and stored in a database. In some embodiments, the processing unit 110 may perform a multi-frame analysis on a set of candidate objects that reflect possible traffic lights. For example, the processing unit 110 may track the candidate objects across consecutive image frames, estimate the real-world position of the candidate objects, and filter out those objects that are moving (which are unlikely to be traffic lights). In some embodiments, the processing unit 110 may perform a color analysis on the candidate objects and identify the relative position of the detected color that appears inside the possible traffic light.

[0156] At step 562, processing unit 110 may analyze the geometric features of the intersection. The analysis may be based on any combination of: (i) the number of lanes detected on either side of vehicle 200, (ii) markings detected on the road (such as arrow markings), and (iii) a description of the intersection extracted from map data (such as data from map database 160). Processing unit 110 may use information derived from execution of monocular analysis module 402 to perform the analysis. In addition, processing unit 110 may determine a correspondence between the traffic lights detected at step 560 and the lanes present near vehicle 200.

[0157] As vehicle 200 approaches an intersection, at step 564, processing unit 110 may update a confidence level associated with the analyzed intersection geometry and detected traffic lights. For example, the number of traffic lights estimated to be present at an intersection compared to the number actually present at the intersection may affect the confidence level. Therefore, based on the confidence level, processing unit 110 may delegate control to the driver of vehicle 200 in order to improve safety conditions. By performing steps 560, 562, and 564, processing unit 110 may identify traffic lights that appear within the set of captured images and analyze intersection geometry information. Based on the identification and analysis, processing unit 110 may cause one or more navigation responses in vehicle 200, as described above in conjunction with Figure 5A as described.

[0158] Figure 5E Flowchart showing an exemplary process 500E for causing one or more navigation responses in vehicle 200 based on a vehicle path consistent with the disclosed embodiments. At step 570, processing unit 110 may construct an initial vehicle path associated with vehicle 200. The vehicle path may be represented by a set of points expressed in coordinates (x, z), and the distance d between two points in the set of points is i May fall within the range of 1 meter to 5 meters. In one embodiment, the processing unit 110 may use two polynomials (such as a left road polynomial and a right road polynomial) to construct an initial vehicle path. The processing unit 110 may calculate the geometric midpoint between the two polynomials and, if any, offset each point included in the resulting vehicle path by a predetermined offset (e.g., a smart lane offset) (a zero offset may correspond to driving in the middle of the lane). The offset may be in a direction perpendicular to the segment between any two points in the vehicle path. In another embodiment, the processing unit 110 may use a polynomial and an estimated lane width to offset each point of the vehicle path by half the estimated lane width plus a predetermined offset (e.g., a smart lane offset).

[0159] At step 572, the processing unit 110 may update the vehicle path constructed at step 570. The processing unit 110 may reconstruct the vehicle path constructed at step 570 using a higher resolution so that the distance d between two points in a set of points representing the vehicle path is k Smaller than the distance d mentioned above i For example, the distance d k It may fall within the range of 0.1 m to 0.3 m. The processing unit 110 may reconstruct the vehicle path using a parabolic spline algorithm, which may generate a cumulative distance vector S corresponding to the total length of the vehicle path (ie, based on a set of points representing the vehicle path).

[0160] At step 574, the processing unit 110 may determine a look-ahead point (expressed as coordinates (x l ,z l )). The processing unit 110 may extract a look-ahead point from the accumulated distance vector S, and the look-ahead point may be associated with a look-ahead distance and a look-ahead time. The look-ahead distance (which may have a lower limit in the range from 10 meters to 20 meters) may be calculated as the product of the speed of the vehicle 200 and the look-ahead time. For example, as the speed of the vehicle 200 decreases, the look-ahead distance may also decrease (e.g., until it reaches the lower limit). The look-ahead time (which may be in the range from 0.5 seconds to 1.5 seconds) may be inversely proportional to the gain of one or more control loops associated with causing a navigation response in the vehicle 200, such as a heading error tracking control loop. For example, the gain of the heading error tracking control loop may depend on the bandwidth of the yaw rate loop, the steering actuator loop, the lateral dynamics of the car, etc. Therefore, the higher the gain of the heading error tracking control loop, the shorter the look-ahead time.

[0161] At step 576, processing unit 110 may determine a heading error and a yaw rate command based on the look-ahead point determined at step 574. Processing unit 110 may calculate the heading error and the yaw rate command by calculating the inverse tangent of the look-ahead point, e.g., arctan(x l / z l ) to determine the heading error. The processing unit 110 may determine the yaw rate command as the product of the heading error and the high level control gain. If the look-ahead distance is not at the lower limit, the high level control gain may be equal to: (2 / look-ahead time). Otherwise, the high level control gain may be equal to: (2*speed of the vehicle 200 / look-ahead distance).

[0162] Fig. 5F FIG. 5 is a flow chart showing an exemplary process 500F for determining whether a leading vehicle is changing lanes consistent with the disclosed embodiments. At step 580, processing unit 110 may determine navigation information associated with a leading vehicle (e.g., a vehicle traveling in front of vehicle 200). For example, processing unit 110 may use the above-described method in conjunction with Figure 5A and Figure 5B The described techniques can be used to determine the position, velocity (e.g., direction and speed), and / or acceleration of the vehicle ahead. The processing unit 110 can also use the above combined Figure 5E The described techniques determine one or more road polynomials, look-ahead points (associated with vehicle 200 ), and / or snail trajectories (eg, a set of points describing a path taken by a leading vehicle).

[0163] At step 582, processing unit 110 may analyze the navigation information determined at step 580. In one embodiment, processing unit 110 may calculate the distance between the snail trajectory and the road polynomial (e.g., along the trajectory). If the variance of this distance along the trajectory exceeds a predetermined threshold (e.g., 0.1 meters to 0.2 meters on a straight road, 0.3 meters to 0.4 meters on a moderately curved road, and 0.5 meters to 0.6 meters on a road with a sharp curve), processing unit 110 may determine that the leading vehicle may be changing lanes. In the case where multiple vehicles are detected traveling in front of vehicle 200, processing unit 110 may compare the snail trajectories associated with each vehicle. Based on the comparison, processing unit 110 may determine that a vehicle whose snail trajectory does not match the snail trajectories of other vehicles may be changing lanes. Processing unit 110 may additionally compare the curvature of the snail trajectory (associated with the leading vehicle) with the expected curvature of the road segment on which the leading vehicle is traveling. The expected curvature may be extracted from map data (e.g., data from map database 160), from a road polynomial, from snail trajectories of other vehicles, from prior knowledge about the road, etc. If the curvature of the snail trajectory differs from the expected curvature of the road segment by more than a predetermined threshold, processing unit 110 may determine that the leading vehicle may be changing lanes.

[0164] In another embodiment, processing unit 110 may compare the instantaneous position of the leading vehicle with a look-ahead point (associated with vehicle 200) over a specific time period (e.g., 0.5 seconds to 1.5 seconds). If the distance between the instantaneous position of the leading vehicle and the look-ahead point changes over the specific time period, and the cumulative sum of the changes exceeds a predetermined threshold (e.g., 0.3 meters to 0.4 meters on a straight road, 0.7 meters to 0.8 meters on a moderately curved road, and 1.3 meters to 1.7 meters on a road with a sharp curve), processing unit 110 may determine that the leading vehicle may be changing lanes. In another embodiment, processing unit 110 may analyze the geometric characteristics of the snail trajectory by comparing the lateral distance traveled along the snail trajectory with the expected curvature of the snail trajectory. The expected radius of curvature may be determined according to the following calculation: (δ z 2 +δ x 2 ) / 2 / (δ x ), where δ x represents the lateral distance traveled, and δ zRepresents the longitudinal distance traveled. If the difference between the lateral distance traveled and the expected curvature exceeds a predetermined threshold (e.g., 500 meters to 700 meters), the processing unit 110 may determine that the leading vehicle may be changing lanes. In another embodiment, the processing unit 110 may analyze the position of the leading vehicle. If the position of the leading vehicle occludes the road polynomial (e.g., the leading vehicle is covered on top of the road polynomial), the processing unit 110 may determine that the leading vehicle may be changing lanes. In the case where the position of the leading vehicle is such that another vehicle is detected in front of the leading vehicle and the snail trajectories of the two vehicles are not parallel, the processing unit 110 may determine that the (closer) leading vehicle may be changing lanes.

[0165] At step 584, processing unit 110 may determine whether leading vehicle 200 is changing lanes based on the analysis performed at step 582. For example, processing unit 110 may make a determination based on a weighted average of the various analyses performed at step 582. Under such an approach, for example, a determination made by processing unit 110 that a leading vehicle is likely changing lanes based on a particular type of analysis may be assigned a value of "1" (and "0" to indicate a determination that a leading vehicle is unlikely to be changing lanes). Different analyses performed at step 582 may be assigned different weights, and the disclosed embodiments are not limited to any particular combination of analyses and weights.

[0166] Figure 6 Flowchart showing an exemplary process 600 for causing one or more navigation responses based on stereo image analysis consistent with the disclosed embodiments. At step 610, the processing unit 110 may receive the first plurality of images and the second plurality of images via the data interface 128. For example, a camera included in the image acquisition unit 120 (such as the image capture devices 122 and 124 having the fields of view 202 and 204) may capture the first plurality of images and the second plurality of images of the area in front of the vehicle 200 and transmit them to the processing unit 110 via a digital connection (e.g., USB, wireless, Bluetooth, etc.). In some embodiments, the processing unit 110 may receive the first plurality of images and the second plurality of images via two or more data interfaces. The disclosed embodiments are not limited to any particular data interface configuration or protocol.

[0167] At step 620, the processing unit 110 may execute the stereo image analysis module 404 to perform stereo image analysis on the first plurality of images and the second plurality of images to create a 3D map of the road in front of the vehicle and detect features within the image, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, road hazards, etc. The stereo image analysis may be combined with the above FIG. 5A to FIG. 5DThe steps described above are performed in a similar manner. For example, the processing unit 110 may execute the stereo image analysis module 404 to detect candidate objects (e.g., vehicles, pedestrians, road markings, traffic lights, road hazards, etc.) within the first plurality of images and the second plurality of images, filter out subsets of candidate objects based on the respective objects, and perform multi-frame analysis, construct measurement results, and determine confidence levels for the remaining candidate objects. In performing the above steps, the processing unit 110 may consider information from both the first plurality of images and the second plurality of images, rather than information from a single set of images. For example, the processing unit 110 may analyze differences in pixel level data (or other subsets of data from the two streams of captured images) for candidate objects that appear in both the first plurality of images and the second plurality of images. As another example, the processing unit 110 may estimate the position and / or velocity (e.g., relative to the vehicle 200) of the candidate object by observing that the candidate object appears in one of the multiple images but not in the other, or relative to other differences that may exist relative to the object if it appears in two image streams. For example, position, velocity, and / or acceleration relative to vehicle 200 may be determined based on the trajectory, position, movement characteristics, etc. of features associated with objects appearing in one or both of the image streams.

[0168] At step 630, processing unit 110 may execute navigation response module 408 to perform navigation based on the analysis performed at step 620 and the above combined Figure 4 The described techniques may be used to cause one or more navigation responses in the vehicle 200. The navigation responses may include, for example, turns, lane changes, changes in acceleration, changes in velocity, braking, etc. In some embodiments, the processing unit 110 may cause one or more navigation responses using data derived from execution of the velocity and acceleration module 406. Additionally, multiple navigation responses may occur simultaneously, sequentially, or any combination thereof.

[0169] Figure 7Flowchart showing an exemplary process 700 for causing one or more navigation responses based on analysis of three sets of images consistent with the disclosed embodiments. At step 710, the processing unit 110 may receive a first plurality of images, a second plurality of images, and a third plurality of images via the data interface 128. For example, a camera included in the image acquisition unit 120 (such as the image capture devices 122, 124, and 126 having the fields of view 202, 204, and 206) may capture a first plurality of images, a second plurality of images, and a third plurality of images of areas in front of and / or to the sides of 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 the first plurality of images, the second plurality of images, and the third plurality of images via three or more data interfaces. For example, each of the image capture devices 122, 124, 126 may have an associated data interface for transmitting data to the processing unit 110. The disclosed embodiments are not limited to any particular data interface configuration or protocol.

[0170] At step 720, processing unit 110 may analyze the first plurality of images, the second plurality of images, and the third plurality of images to detect features within the images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, road hazards, etc. This analysis may be combined with the above FIG. 5A to FIG. 5D and Figure 6 For example, the processing unit 110 may perform a monocular image analysis on each of the first plurality of images, the second plurality of images, and the third plurality of images (e.g., via the execution of the monocular image analysis module 402 and based on the above combined FIG. 5A to FIG. 5D Alternatively, the processing unit 110 may perform stereoscopic image analysis on the first plurality of images and the second plurality of images, the second plurality of images and the third plurality of images, and / or the first plurality of images and the third plurality of images (e.g., via execution of the stereoscopic image analysis module 404 and based on the above combined with Figure 64. The processing unit 110 may perform a combination of monocular image analysis and stereo image analysis. For example, the processing unit 110 may perform a monocular image analysis on the first plurality of images (e.g., via execution by the monocular image analysis module 402) and a new stereo image analysis on the second and third plurality of images (e.g., via execution by the stereo image analysis module 404). The configuration of the image capture devices 122, 124, and 126—including their respective locations and fields of view 202, 204, and 206—may affect the type of analysis performed on the first, second, and third plurality of images. The disclosed embodiments are not limited to a particular configuration of the image capture devices 122, 124, and 126, or the type of analysis performed on the first, second, and third plurality of images.

[0171] In some embodiments, processing unit 110 may test system 100 based on the images acquired and analyzed at steps 710 and 720. Such testing may provide an indicator of the overall performance of system 100 for a particular configuration of image capture devices 122, 124, and 126. For example, processing unit 110 may determine the proportion of "false hits" (e.g., instances where system 100 incorrectly determines the presence of a vehicle or pedestrian) and "misses."

[0172] At step 730, the processing unit 110 may cause one or more navigation responses in the vehicle 200 based on information derived from both of the first plurality of images, the second plurality of images, and the third plurality of images. The selection of both of the first plurality of images, the second plurality of images, and the third plurality of images may depend on various factors, such as, for example, the number, type, and size of objects detected in each of the plurality of images. The processing unit 110 may also select the object of interest based on image quality and resolution, the effective field of view reflected in the image, the number of frames captured, the degree to which one or more objects of interest actually appear in the frame (e.g., the percentage of frames in which the object appears, the proportion of the object appearing in each such frame, etc.), etc.

[0173] In some embodiments, processing unit 110 may select information derived from both of the first plurality of images, the second plurality of images, and the third plurality of images by determining the extent to which information derived from one image source is consistent with information derived from other image sources. For example, processing unit 110 may combine processed information derived from each of image capture devices 122, 124, and 126 (whether through monocular analysis, stereo analysis, or any combination of both) and determine visual indicators (e.g., lane markings, detected vehicles and their locations and / or paths, detected traffic lights, etc.) that are consistent across images captured from each of image capture devices 122, 124, and 126. Processing unit 110 may also exclude information that is inconsistent across captured images (e.g., vehicles changing lanes, lane models indicating vehicles that are too close to vehicle 200, etc.). Thus, processing unit 110 may select information derived from both of the first plurality of images, the second plurality of images, and the third plurality of images based on the determination of consistent and inconsistent information.

[0174] The navigation response may include, for example, a turn, a lane change, a change in acceleration, etc. The processing unit 110 may, based on the analysis performed at step 720 and the above combined Figure 4 The processing unit 110 may also cause one or more navigation responses using the techniques described herein. The processing unit 110 may also cause one or more navigation responses using data derived from execution of the velocity and acceleration module 406. In some embodiments, the processing unit 110 may cause one or more navigation responses based on the relative position, relative velocity, and / or relative acceleration between the vehicle 200 and an object detected within any of the first plurality of images, the second plurality of images, and the third plurality of images. The multiple navigation responses may occur simultaneously, sequentially, or any combination thereof.

[0175] Sparse road models for autonomous vehicle navigation

[0176] In some embodiments, the disclosed systems and methods may use sparse maps for autonomous vehicle navigation. Specifically, the sparse maps may be used for autonomous vehicle navigation along road segments. For example, the sparse maps may provide sufficient information for navigating the autonomous vehicle without having to store and / or update large amounts of data. As discussed in further detail below, the autonomous vehicle may use the sparse maps to navigate one or more roads based on one or more stored trajectories.

[0177] Sparse maps for autonomous vehicle navigation

[0178] In some embodiments, the disclosed systems and methods may generate a sparse map for autonomous vehicle navigation. For example, a sparse map may provide enough information for navigation without requiring excessive data storage or data transfer rates. As discussed in further detail below, a vehicle (which may be an autonomous vehicle) may use a sparse map to navigate one or more roads. For example, in some embodiments, a sparse map may include data related to roads and potential landmarks along the roads sufficient for vehicle navigation, but the data also exhibits a small data footprint. For example, the sparse data maps described in detail below may require significantly less storage space and data transfer bandwidth than digital maps that include detailed map information (such as image data collected along roads).

[0179] For example, a sparse data map may store a three-dimensional polynomial representation of a preferred vehicle path along a road, rather than storing a detailed representation of a road segment. These paths may require very little data storage space. In addition, in the described sparse data map, landmarks may be identified and included in a sparse map road model to help navigation. These landmarks may be located at any spacing suitable for achieving vehicle navigation, but in some cases, it is not necessary to identify such landmarks with high density and short spacing and include them in the model. On the contrary, in some cases, it is possible to navigate based on landmarks spaced at least 50 meters, at least 100 meters, at least 500 meters, at least 1 kilometer, or at least 2 kilometers apart. As will be discussed in more detail in other chapters, a sparse map may be generated based on data collected or measured by a vehicle equipped with various sensors and devices (such as image capture devices, global positioning system sensors, motion sensors, etc.) when the vehicle is traveling along a roadway. In some cases, a sparse map may be generated based on data collected during multiple driving of one or more vehicles along a specific roadway. Using multiple driving of one or more vehicles to generate a sparse map may be referred to as a "crowdsourcing" sparse map.

[0180] Consistent with the disclosed embodiments, an autonomous vehicle system may use a sparse map for navigation. For example, the disclosed systems and methods may distribute a sparse map for use in generating a road navigation model for an autonomous vehicle, and the autonomous vehicle may be navigated along a road segment using the sparse map and / or the generated road navigation model. A sparse map consistent with the present disclosure may include one or more three-dimensional shapes that may represent predetermined trajectories that autonomous vehicles may traverse as they move along an associated road segment.

[0181] Sparse maps consistent with the present disclosure may also include data representing one or more road features. Such road features may include recognized landmarks, road signature profiles, and any other road-related features useful in navigating a vehicle. Sparse maps consistent with the present disclosure may enable autonomous navigation of a vehicle based on a relatively small amount of data included in the sparse map. For example, rather than including a detailed representation of a road, such as road edges, road curvature, images associated with road segments, or data detailing other physical features associated with road segments, the disclosed embodiments of the sparse map may require relatively small storage space (and relatively small bandwidth when portions of the sparse map are transferred to the vehicle), but may still adequately provide autonomous vehicle navigation. The small data footprint of the disclosed sparse map, discussed in further detail below, may be achieved in some embodiments by storing representations of road-related elements that require a small amount of data but still enable autonomous navigation.

[0182] For example, rather than storing detailed representations of various aspects of a road, the disclosed sparse map may store a polynomial representation of one or more trajectories that a vehicle may follow along a road. Thus, rather than storing (or having to transmit) details about the physical properties of a road to enable navigation along the road, using the disclosed sparse map, a vehicle may navigate along a particular road segment without having to interpret the physical aspects of the road in some cases, but rather by aligning its travel path with a trajectory (e.g., a polynomial spline) along the particular road segment. In this way, a vehicle may be navigated primarily based on stored trajectories (e.g., polynomial splines), which may require significantly less storage space than methods involving storage of roadway images, road parameters, road layouts, etc.

[0183] In addition to the stored polynomial representation of the trajectory along the road segment, the disclosed sparse map may also include small data objects that can represent road features. In some embodiments, the small data object may include a digital signature derived from a digital image (or digital signal) obtained by a sensor (e.g., a camera or other sensor, such as a suspension sensor) mounted on a vehicle traveling along the road segment. The digital signature may have a reduced size relative to the signal acquired by the sensor. In some embodiments, the digital signature may be created to be compatible with a classifier function that is configured to detect and identify road features from a signal acquired by a sensor, for example, during subsequent driving. In some embodiments, a digital signature may be created so that the digital signature has as small a footprint as possible while maintaining the ability to correlate or match road features with stored signatures based on an image of the road feature (or if the stored signature is not based on an image and / or includes other data, based on a digital signal generated by a sensor) that is captured by a camera mounted on a vehicle traveling along the same road segment at a subsequent time.

[0184] In some embodiments, the size of the data object may be further associated with the uniqueness of the road feature. For example, for a road feature detectable by a camera mounted on a vehicle, and when the camera system mounted on the vehicle is coupled to a classifier capable of distinguishing image data corresponding to the road feature as being associated with a particular type of road feature (e.g., a road sign), and when such road sign is locally unique in the area (e.g., there are no identical road signs or road signs of the same type nearby), it may be sufficient to store data indicating the type of road feature and its location.

[0185] As will be discussed in further detail below, road features (e.g., landmarks along road segments) can be stored as small data objects that can represent road features in relatively few bytes, while providing sufficient information for identifying such features and using such features for navigation. In one example, road signs can be identified as landmarks that can be identified based on which the navigation of a vehicle can be based. Representations of road signs can be stored in sparse maps to include, for example, a few bytes of data indicating the type of landmark (e.g., a stop sign) and a few bytes of data indicating the location of the landmark (e.g., coordinates). Navigation based on such data-based representations of landmarks (e.g., using representations sufficient to locate, identify, and navigate based on landmarks) can provide the desired level of navigation functionality associated with sparse maps without significantly increasing the data overhead associated with sparse maps. Such condensed representations of landmarks (and other road features) can utilize sensors and processors configured to detect, identify, and / or classify specific road features, including those mounted on such vehicles.

[0186] A sparse map can use data indicating a class of landmarks (the sign or the specific type of sign) when, for example, a sign or even a specific type of sign is locally unique in a given area (e.g., when there are no other signs, nor any other signs of the same type), and during navigation (e.g., autonomous navigation), when a camera mounted on an autonomous vehicle captures an image of an area that includes the sign (or the specific type of sign), a processor can process the image, detect the sign (if indeed present in the image), classify the image as a sign (or the specific type of sign), and associate the location of the image with the location of the sign as stored in the sparse map.

[0187] A sparse map may include any suitable representation of objects identified along a road segment. In some cases, objects may be referred to as semantic objects or non-semantic objects. Semantic objects may include, for example, objects associated with a predetermined type classification. Such type classifications may be useful in reducing the amount of data required to describe semantic objects identified in the environment, which is beneficial both during the acquisition phase (e.g., reducing costs associated with bandwidth usage for transmitting driving information from multiple driving acquisition vehicles to a server) and during the navigation phase (e.g., reduction in map data may speed up the transmission of map tiles from a server to a navigation vehicle, and may also reduce costs associated with bandwidth usage for such transmissions). A semantic object classification type may be assigned to any type of object or feature expected to be encountered along a roadway.

[0188] Semantic objects can be further divided into two or more logical groups. For example, in some cases, the semantic object type of a group can be associated with a predetermined dimension. Such semantic objects may include specific speed limit signs, yield signs, merge signs, stop signs, traffic lights, directional arrows on the roadway, manhole covers, or any other type of objects that can be associated with standardized sizes. A benefit given by such semantic objects is that very little data may be required to represent / completely define the object. For example, if the standardized size of the speed limit size is known, the acquisition vehicle may only need to identify (by analyzing the captured image) the existence of the speed limit sign (identified type) together with an indication of the position of the detected speed limit sign (e.g., the 2D position in the captured image of the center or a corner of the sign (or, alternatively, the 3D position in the real world coordinates)) to provide enough information for map generation on the server side. In the case of transmitting the 2D image position to the server, the position associated with the captured image of the detected sign can also be transmitted, so the server can determine the real world position of the sign (e.g., by using the structure in motion technology of multiple captured images from one or more acquisition vehicles). Even in the case where information is limited (only a few bytes are needed to define each detected object), the server can build a map including fully represented speed limit signs based on the type classifications (representing speed limit signs) received from one or more collecting vehicles together with the location information for the detected signs.

[0189] Semantic objects may also include other recognized object or feature types that are not associated with specific standardized characteristics. Such objects or features may include potholes, tar seams, light poles, non-standardized signs, curbs, trees, branches, or any other type of recognized object type with one or more variable characteristics (e.g., variable size). In such cases, in addition to transmitting to the server an indication of the type of object or feature detected (e.g., pothole, pole, etc.) and location information for the detected object or feature, the collection vehicle may also transmit an indication of the size of the object or feature. The size may be expressed in terms of 2D image size (e.g., using a bounding box or one or more size values) or real-world size (determined by structure-in-motion computation, based on LIDAR or RADAR system output, based on trained neural network output, etc.).

[0190] Non-semantic objects or features may include any detectable object or feature that falls outside of the identified category or type, but still may provide valuable information in map generation. In some cases, such non-semantic features may include detected corners of buildings or corners of detected windows of buildings, unique stones or objects near roadways, concrete splashes in roadway shoulders, or any other detectable objects or features. When such objects or features are detected, one or more acquisition vehicles may transmit the location of one or more points (2D image points or 3D real-world points) associated with the detected object / feature to a map generation server. In addition, compressed or simplified image segments (e.g., image hashes) may be generated for regions of captured images including detected objects or features. The image hash may be calculated based on a predetermined image processing algorithm, and may form a valid signature for detected non-semantic objects or features. Such signatures may be useful for navigation relative to sparse maps including non-semantic features or objects, because vehicles crossing roadways may apply algorithms similar to those used to generate image hashes in order to confirm / verify the presence of mapped non-semantic features or objects in captured images. Using this technique, non-semantic features can be added to the richness of a sparse map (e.g., to enhance its usefulness in navigation) without adding significant data overhead.

[0191] As noted, target trajectories may be stored in a sparse map. These target trajectories (e.g., 3D splines) may represent preferred or recommended paths for each available lane of a roadway, each valid way through an intersection, for merges and exits, etc. In addition to target trajectories, other road features may also be detected, collected, and incorporated into the sparse map in the form of representative splines. Such features may include, for example, road edges, lane markings, curbs, guardrails, or any other objects or features extending along a roadway or road segment.

[0192] Generating sparse maps

[0193] In some embodiments, the sparse map may include at least one line representation of road surface features extending along a road segment and a plurality of landmarks associated with the road segment. In some aspects, the sparse map may be generated via "crowd sourcing", such as by image analysis of a plurality of images acquired while one or more vehicles traverse the road segment.

[0194] Figure 8 A sparse map 800 is shown that one or more vehicles (e.g., vehicle 200 (which may be an autonomous vehicle)) may access to provide autonomous vehicle navigation. The sparse map 800 may be stored in a memory, such as memory 140 or 150. Such a memory device may include any type of non-transitory storage device or computer-readable medium. For example, in some embodiments, the memory 140 or 150 may include a hard drive, an optical disk, a flash memory, a magnetic-based storage device, an optical-based storage device, etc. In some embodiments, the sparse map 800 may be stored in a database (e.g., map database 160), which may be stored in the memory 140 or 150 or other types of storage devices.

[0195] In some embodiments, the sparse map 800 may be stored on a storage device or a non-transitory computer-readable medium that is configured to be loaded on the vehicle 200 (e.g., a storage device included in a navigation system that is loaded on the vehicle 200). A processor provided on the vehicle 200 (e.g., the processing unit 110) may access the sparse map 800 stored in the storage device or the computer-readable medium that is configured to be loaded on the vehicle 200 to generate navigation instructions for guiding the autonomous vehicle 200 when the vehicle passes through the road segment.

[0196] However, the sparse map 800 need not be stored locally relative to the vehicle. In some embodiments, the sparse map 800 may be stored on a storage device or computer-readable medium disposed on a remote server that communicates with the vehicle 200 or a device associated with the vehicle 200. A processor disposed on the vehicle 200 (e.g., the processing unit 110) may receive data included in the sparse map 800 from the remote server and may execute the data for guiding the autonomous driving of the vehicle 200. In such embodiments, the remote server may store all or only a portion of the sparse map 800. Therefore, a storage device or computer-readable medium disposed to be loaded on the vehicle 200 and / or loaded on one or more additional vehicles may store the remainder of the sparse map 800.

[0197] In addition, in such embodiments, the sparse map 800 can be made accessible to multiple vehicles (e.g., tens, hundreds, thousands, or millions of vehicles, etc.) passing through various road segments. It should also be noted that the sparse map 800 may include multiple sub-maps. For example, in some embodiments, the sparse map 800 may include hundreds, thousands, millions, or more sub-maps (e.g., map tiles) that can be used to navigate the vehicle. Such sub-maps may be referred to as local maps or map tiles, and vehicles traveling along the roadway may access any number of local maps related to the location where the vehicle is traveling. The local map segments of the sparse map 800 may be stored together with a global navigation satellite system (GNSS) key as an index to the database of the sparse map 800. Therefore, although the calculation of the steering angle for navigating the main vehicle in the present system can be performed without relying on the GNSS position of the main vehicle, road features, or landmarks, such GNSS information can be used for retrieval of relevant local maps.

[0198] Generally speaking, the sparse map 800 can be generated based on data (e.g., driving information) collected from one or more vehicles while traveling along a roadway. For example, using sensors (e.g., cameras, speedometers, GPS, accelerometers, etc.) mounted on one or more vehicles, the trajectories of one or more vehicles traveling along the roadway can be recorded, and a polynomial representation of the preferred trajectory of the vehicle for subsequent travel along the roadway 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 roadway. Data collected from passing vehicles can also be used to identify road profile information, such as road width profiles, road roughness profiles, traffic line spacing profiles, road conditions, etc. Using the collected information, the sparse map 800 can be generated and distributed (e.g., for local storage or via in-flight data transmission) for use in navigating one or more autonomous vehicles. However, in some embodiments, map generation may not end at the initial generation of the map. As will be discussed in greater detail below, the sparse map 800 may be continuously or periodically updated based on data collected from vehicles as those vehicles continue to traverse roadways included in the sparse map 800 .

[0199] The data recorded in the sparse map 800 may include location information based on global positioning system (GPS) data. For example, location information may be included in the sparse map 800 for various map elements (including, for example, landmark locations, road contour locations, etc.). The locations of the map elements included in the sparse map 800 can be obtained using GPS data collected from vehicles crossing the roadway. For example, a vehicle passing an identified landmark can determine the location of the identified landmark using GPS location information associated with the vehicle and the determination of the location of the identified landmark relative to the vehicle (e.g., based on image analysis of data collected from one or more cameras mounted on the vehicle). When additional vehicles pass through the location of the identified landmark, such location determination of the identified landmark (or any other feature included in the sparse map 800) can be repeated. Some or all of the additional location determinations can be used to refine the location information relative to the identified landmark stored in the sparse map 800. For example, in some embodiments, multiple location measurements relative to a particular feature stored in the sparse map 800 can be averaged together. However, any other mathematical operation may be used to refine the stored location of a map element based on a plurality of determined locations for the map element.

[0200] In one specific example, a collection vehicle may pass through a specific road segment. Each collection vehicle captures images of their respective environments. Images may be collected at any suitable frame capture 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 indications of the detection of semantic and / or non-semantic objects / features to a mapping server together with the locations associated with those objects / features. In more detail, type indicators, size indicators, etc. may be transmitted together with the location information. The location information may include any suitable information for enabling a mapping server to aggregate detected objects / features into a sparse map useful in navigation. In some cases, the location information may include one or more 2D image locations (e.g., XY pixel locations) in a captured image where semantic or non-semantic features / objects are detected. Such image locations may correspond to the center, corner, etc. of a feature / object. In this scenario, in order to help the mapping server reconstruct driving information and align driving information from multiple collection vehicles, each collection vehicle may also provide the server with the location (e.g., GPS location) where each image was captured.

[0201] In other cases, the acquisition vehicle may provide one or more 3D real-world points associated with the detected objects / features to the server. Such 3D points may be relative to a predetermined origin (such as the origin of the driving segment) and may be determined by any suitable technique. In some cases, structure-in-motion techniques may be used to determine the 3D real-world position of the detected objects / features. For example, a specific object such as a specific speed limit sign may be detected in two or more captured images. Using information such as the known ego motion (speed, trajectory, GPS location, etc.) of the acquisition vehicle between captured images, together with observed changes (changes in XY pixel location, changes in size, etc.) of the speed limit sign in the captured images, the real-world position of one or more points associated with the speed limit sign may be determined and passed to the mapping server. Such an approach is optional because it requires more computation on parts of the acquisition vehicle system. The sparse maps of the disclosed embodiments may enable autonomous navigation of the vehicle using a relatively small amount of stored data. In some embodiments, sparse map 800 may have a data density (e.g., including data representing target tracks, landmarks, and any other stored road features) of less than 2MB per kilometer of road, less than 1MB per kilometer of road, less than 500kB per kilometer of road, or less than 100kB per kilometer of road. In some embodiments, the data density of sparse map 800 may be less than 10kB per kilometer of road or even less than 2kB per kilometer of road (e.g., 1.6kB per kilometer), or no more than 10kB per kilometer of road, or no more than 20kB per kilometer of road. In some embodiments, most (if not all) of the roadways in the United States may be autonomously navigated using a sparse map with a total of 4GB or less data. These data density values ​​may represent an average value on the entire sparse map 800, on a local map within the sparse map 800, and / or on a specific road segment within the sparse map 800.

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

[0203] In some embodiments, the target trajectory may represent the ideal path that the vehicle should take when the vehicle is traveling. The target trajectory may be located, for example, at the approximate center of the driving lane. In other cases, the target trajectory may be located at other locations relative to the road segment. For example, the target trajectory may approximately coincide with the center of the road, the edge of the road, or the edge of the lane, etc. In such cases, navigation based on the target trajectory may include a determined offset to be maintained relative to the location of the target trajectory. In addition, in some embodiments, the determined offset to be maintained relative to the location of the target trajectory may be different based on the type of vehicle (for example, a passenger vehicle including two axles may have an offset along at least a portion of the target trajectory that is different from a truck including more than two axles).

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

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

[0206] Between landmarks, and therefore between determinations of the vehicle's position relative to the target track, the vehicle may navigate based on dead reckoning, where the vehicle uses sensors to determine its ego motion and estimate its position relative to the target track. Because errors may accumulate during navigation by dead reckoning, position determination relative to the target track may become less and less accurate over time. The vehicle may use landmarks (and their known locations) that appear in the sparse map 800 to remove dead reckoning-induced errors in position determination. In this way, the identified landmarks included in the sparse map 800 may serve as navigation anchors from which the vehicle's accurate position relative to the target track may be determined. Since a certain amount of error in the location location is acceptable, the identified landmarks do not need to always be available to the autonomous vehicle. Instead, suitable navigation may even be based on landmark spacing of 10 meters, 20 meters, 50 meters, 100 meters, 500 meters, 1 kilometer, 2 kilometers, or more meters as noted above. In some embodiments, a density of 1 identified landmark per 1 km of road may be sufficient to maintain longitudinal position determination accuracy within 1 m. Therefore, not every potential landmark occurring along a road segment needs to be stored in the sparse map 800 .

[0207] Additionally, in some embodiments, lane markings may be used to localize the vehicle during landmark intervals. By using lane markings during landmark intervals, the accumulation of errors during navigation by dead reckoning may be minimized.

[0208] In addition to target tracks and identified landmarks, the sparse map 800 may also include information related to various other road features. For example, Fig. 9A 800 shows a representation of a curve along a particular road segment that may be stored in the sparse map 800. In some embodiments, a single lane of a road may be modeled by a three-dimensional polynomial description of the left and right sides of the road. Such a polynomial representing the left and right sides of a single lane is in Fig. 9A Regardless of how many lanes a road may have, it can be similar to Fig. 9A The method shown in represents the road using polynomials. For example, the left and right sides of a multi-lane road can be represented by something like Fig. 9A, and including the middle lane markings on multi-lane roads (e.g., dashed dashed markings indicating lane boundaries, solid yellow lines indicating boundaries between lanes traveling in different directions, etc.) may also be represented using polynomials such as Fig. 9A It is represented by the polynomials shown.

[0209] like Fig. 9A As shown, lane 900 may be represented using a polynomial (e.g., a first order, second order, third order, or any suitable order polynomial). For purposes of illustration, lane 900 is shown as a two-dimensional lane and the polynomial is shown as a two-dimensional polynomial. Fig. 9A As depicted, lane 900 includes a left side 910 and a right side 920. In some embodiments, more than one polynomial may be used to represent a location on each side of a road or lane boundary. For example, each of the left side 910 and the right side 920 may be represented by a plurality of polynomials of any suitable length. In some cases, the polynomial may have a length of approximately 100 m, but other lengths greater than or less than 100 m may also be used. In addition, the polynomials may overlap with each other to facilitate seamless transitions when navigating based on subsequently encountered polynomials as the host vehicle travels along the roadway. For example, each of the left side 910 and the right side 920 may be represented by a plurality of third-order polynomials divided into segments of approximately 100 meters in length (an example of a first predetermined range) and overlapping each other by approximately 50 meters. The polynomials representing the left side 910 and the right side 920 may or may not have the same order. For example, in some embodiments, some polynomials may be second-order polynomials, some may be third-order polynomials, and some may be fourth-order polynomials.

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

[0211] Returning to the target track of the sparse map 800, Fig. 9B 3D polynomials representing target trajectories for a vehicle traveling along a particular road segment are shown. The target trajectory represents not only the XY path that the host vehicle should travel along the particular road segment, but also the elevation changes that the host vehicle will experience when traveling along the road segment. Thus, each target trajectory in the sparse map 800 may be represented by one or more 3D polynomials, such as Fig. 9B The three-dimensional polynomial 950 is shown. The sparse map 800 may include a plurality of trajectories (e.g., millions or billions or more to represent the trajectories of vehicles along various road segments of the world's roadways). In some embodiments, each target trajectory may correspond to a spline connecting the three-dimensional polynomial segments.

[0212] Regarding the data footprint of the polynomial curves stored in the sparse map 800, in some embodiments, each cubic polynomial can be represented by four parameters, each requiring four bytes of data. A suitable representation can be obtained with a cubic polynomial, requiring approximately 192 bytes of data per 100m. This can translate into approximately 200kB per hour in data usage / transmission requirements for a host vehicle traveling approximately 100km / hr.

[0213] Sparse map 800 can use a combination of geometric feature descriptors and metadata to describe the lane network. The geometric features can be described by polynomials or splines as described above. The metadata can describe the number of lanes, special features (such as carpool lanes), and possible other sparse labels. The total footprint of such indicators may be negligible.

[0214] Thus, a sparse map according to an embodiment of the present disclosure may include at least one line representation of a road surface feature extending along a road segment, each line representation representing a path along a road segment that substantially corresponds to the road surface feature. In some embodiments, as discussed above, at least one line representation of a road surface feature may include a spline, a polynomial representation, or a curve. Further, in some embodiments, the road surface feature may include at least one of a road edge or a lane marking. Further, as discussed below with respect to "crowdsourcing," road surface features may be identified by image analysis of multiple images acquired as one or more vehicles traverse a road segment.

[0215] As previously indicated, the sparse map 800 may include a plurality of predetermined landmarks associated with road segments. Each landmark in the sparse map 800 may be represented and identified using less data than the actual image stored, rather than storing the actual image of the landmark and relying on, for example, image recognition analysis based on the captured image and the stored image. The data representing the landmark may still include sufficient information for describing or identifying the landmark along the road. Storing data describing the characteristics of the landmark rather than the actual image of the landmark may reduce the size of the sparse map 800.

[0216] Fig.10 Examples of the types of landmarks that can be represented in the sparse map 800 are shown. Landmarks may include any visible and identifiable objects along a road segment. Landmarks may be selected so that they are fixed and do not change frequently with respect to their location and / or content. When a vehicle passes through a particular road segment, the landmarks included in the sparse map 800 may be useful in determining the location of the vehicle 200 relative to the target trajectory. Examples of landmarks may include traffic signs, directional signs, general signs (e.g., rectangular signs), roadside fixtures (e.g., lamp posts, reflectors, etc.), and any other suitable categories. In some embodiments, lane markings on the road may also be included in the sparse map 800 as landmarks.

[0217] Fig.10 Examples of landmarks shown include traffic signs, directional signs, roadside fixtures, and general signs. Traffic signs may include, for example, speed limit signs (e.g., speed limit sign 1000), yield signs (e.g., yield sign 1005), route number signs (e.g., route number sign 1010), traffic light signs (e.g., traffic light sign 1015), 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 places. For example, directional signs may include highway signs 1025 with arrows for directing vehicles to different roads or places, exit signs 1030 with arrows directing vehicles off a road, and the like. Thus, at least one of the plurality of landmarks may include a road sign.

[0218] General signs may not be related to traffic. For example, general signs may include billboards used for advertising, or welcome signs adjacent to a border between two countries, states, counties, cities, or towns. Fig.10 A general sign 1040 ("Joe's Restaurant") is shown. Although the general sign 1040 may have a rectangular shape, such as Fig.10 As shown, however, generally the logo 1040 may have other shapes, such as square, circle, triangle, etc.

[0219] Landmarks may also include roadside fixtures. Roadside fixtures may be objects that are not signs and may not be related to traffic or directions. For example, roadside fixtures may include lamp posts (e.g., lamp posts 1035), power poles, traffic light poles, etc.

[0220] Landmarks may also include beacons specifically designed for autonomous vehicle navigation systems. For example, such beacons may include independent structures placed at predetermined intervals to help navigate the host vehicle. Such beacons may also include visual / graphic information added to existing road signs (e.g., icons, symbols, bar codes, etc.) that can be identified or recognized by vehicles traveling along the road segment. Such beacons may also include electronic components. In such embodiments, electronic beacons (e.g., RFID tags, etc.) may be used to transmit non-visual information to the host vehicle. Such information may include, for example, landmark identification and / or landmark location information that the host vehicle can use to determine its position along the target trajectory.

[0221] In some embodiments, the landmark included in the sparse map 800 can be represented by a data object of a predetermined size. The data representing the landmark may include any suitable parameters for identifying a specific landmark. For example, in some embodiments, the landmark stored in the sparse map 800 may include parameters such as the physical size of the landmark (e.g., to support the estimation of the distance to the landmark based on a known size / scale), the distance to the previous landmark, the lateral offset, the height, the type code (e.g., the landmark type-what type of direction sign, traffic sign, etc.), the GPS coordinates (e.g., to support global positioning) and any other suitable parameters. Each parameter may be associated with the data size. For example, 8 bytes of data may be used to store the landmark size. The distance to the previous landmark, the lateral offset and the height may be specified using 12 bytes of data. The type code associated with a landmark such as a direction sign or a traffic sign may require about 2 bytes of data. For general signs, 50 bytes of data storage may be used to store image signatures that can identify general signs. The landmark GPS position may be associated with 16 bytes of data storage. These data sizes for each parameter are only examples, and other data sizes may also be used. Representing landmarks in the sparse map 800 in this manner can provide a lean solution for efficiently representing landmarks in a database. In some embodiments, objects may be referred to as standard semantic objects or non-standard semantic objects. Standard semantic objects may include any kind of object for which there is a standardized set of characteristics (e.g., speed limit signs, warning signs, direction signs, traffic lights, etc. with known sizes or other characteristics). Non-standard semantic objects may include any object that is not associated with a standardized set of characteristics (e.g., general advertising signs, signs identifying commercial establishments, potholes, trees, etc., which may have variable sizes). Each non-standard semantic object can be represented using 38 bytes of data (e.g., 8 bytes for size; 12 bytes for distance, lateral offset, and height to previous landmarks; 2 bytes for type code; and 16 bytes for location coordinates). Standard semantic objects can be represented using even less data because a mapping server may not need size information to fully represent objects in a sparse map.

[0222] Sparse map 800 can use a label system to represent landmark types. In some cases, each traffic sign or direction sign can be associated with its own label, which is stored in the database as part of the landmark identification. For example, the database may include nearly 1,000 different labels to represent various traffic signs and nearly about 10,000 different labels to represent direction signs. Of course, any suitable number of labels can be used, and additional labels can be created as needed. In some embodiments, a universal sign can be represented using less than about 100 bytes (e.g., about 86 bytes, including size of 8 bytes; distance to previous landmarks, lateral offset and altitude of 12 bytes; image signature of 50 bytes; and GPS coordinates of 16 bytes).

[0223] Thus, for semantic road signs that do not require image signatures, even at a relatively high landmark density of approximately 1 per 50m, the data density impact on the sparse map 800 may be close to 760 bytes per kilometer (e.g., 20 landmarks per km x 38 bytes per landmark = 760 bytes). Even for generic signs that include image signature components, the data density impact is approximately 1.72kB per km (e.g., 20 landmarks per km x 86 bytes per landmark = 1,720 bytes). For semantic road signs, this is equivalent to approximately 76kB of data usage per hour for a vehicle traveling at 100km / hr. For generic signs, this is equivalent to approximately 170kB per hour for a vehicle traveling at 100km / hr. It should be noted that in some environments (e.g., urban environments), the density of detected objects available for inclusion in a sparse map may be much higher (perhaps more than one per meter). In some embodiments, a roughly rectangular object (such as a rectangular sign) may be represented in the sparse map 800 by no more than 100 bytes of data. The representation of a generally rectangular object (e.g., generic sign 1040) in the sparse map 800 may include a compressed image signature or image hash (e.g., compressed image signature 1045) associated with the generally rectangular object. The compressed image signature / image hash may be determined using any suitable image hashing algorithm and may be used, for example, to help identify the generic sign as a recognized landmark. Such a compressed image signature (e.g., image information derived from actual image data representing the object) may avoid the need for storage of actual images of the object or the need for comparative image analysis of the actual images in order to identify the landmark.

[0224] refer to Fig.10, the sparse map 800 may include or store a compressed image signature 1045 associated with the generic sign 1040, rather than an actual image of the generic sign 1040. For example, after an image capture device (e.g., image capture device 122, 124, or 126) captures an image of the generic sign 1040, a processor (e.g., image processor 190 or any other processor that can process images mounted on or remotely located relative to the host vehicle) may perform image analysis to extract / create a compressed image signature 1045 that includes a unique signature or pattern associated with the generic sign 1040. In one embodiment, the compressed image signature 1045 may include a shape, a color pattern, a brightness pattern, or any other feature that can be extracted from an image of the generic sign 1040 to describe the generic sign 1040.

[0225] For example, in Fig.10 , the circles, triangles, and stars shown in the compressed image signature 1045 may represent areas of different colors. The patterns represented by the circles, triangles, and stars may be stored in the sparse map 800, for example, within the 50 bytes designated as comprising the image signature. It is worth noting that the circles, triangles, and stars are not necessarily meant to indicate that such shapes are stored as part of the image signature. Instead, these shapes are meant to conceptually represent recognizable areas with discernible color differences, text areas, graphic shapes, or other changes in characteristics that may be associated with a general sign. Such compressed image signatures can be used to identify landmarks in the form of general signs. For example, the compressed image signature can be used to perform a same-and-different analysis based on a comparison of the stored compressed image signature with image data captured, for example, using a camera mounted on an autonomous vehicle.

[0226] Thus, multiple landmarks may be identified by image analysis of multiple images acquired as one or more vehicles traverse a road segment. As explained below with respect to "crowdsourcing," in some embodiments, image analysis to identify multiple landmarks may include accepting potential landmarks when a ratio of images in which the landmarks appear to images in which the landmarks do not appear exceeds a threshold. Additionally, in some embodiments, image analysis to identify multiple landmarks may include rejecting potential landmarks when a ratio of images in which the landmarks do not appear to images in which the landmarks appear exceeds a threshold.

[0227] Returning to a target trajectory that the host vehicle can use to navigate a particular road segment, Fig.11AA polynomial representation of a trajectory captured during the process of establishing or maintaining a sparse map 800 is shown. The polynomial representation of a target trajectory included in the sparse map 800 may be determined based on two or more reconstructed trajectories of a vehicle's previous travel along the same road segment. In some embodiments, the polynomial representation of a target trajectory included in the sparse map 800 may be an aggregation of two or more reconstructed trajectories of a vehicle's previous travel along the same road segment. In some embodiments, the polynomial representation of a target trajectory included in the sparse map 800 may be an average of two or more reconstructed trajectories of a vehicle's previous travel along the same road segment. Other mathematical operations may also be used to construct a target trajectory along a road path based on reconstructed trajectories collected from vehicles traveling along a road segment.

[0228] like Fig.11A As shown, a road segment 1100 may be driven by multiple vehicles 200 at different times. Each vehicle 200 may collect data related to the path that the vehicle is traveling along the road segment. The path traveled by a particular vehicle may be determined based on camera data, accelerometer information, speed sensor information, and / or GPS information, as well as other potential sources. Such data may be used to reconstruct the trajectory of the vehicle traveling along the road segment, and based on these reconstructed trajectories, a target trajectory (or multiple target trajectories) may be determined for a particular road segment. Such a target trajectory may represent a preferred path for the host vehicle when the vehicle is traveling along the road segment (e.g., guided by an autonomous navigation system).

[0229] exist Fig.11A In the example shown, a first reconstructed trajectory 1101 may be determined based on data received from a first vehicle traversing the road segment 1100 at a first time period (e.g., day 1), a second reconstructed trajectory 1102 may be obtained from a second vehicle traversing the road segment 1100 at a second time period (e.g., day 2), and a third reconstructed trajectory 1103 may be obtained from a third vehicle traversing the road segment 1100 at a third time period (e.g., day 3). Each trajectory 1101, 1102, and 1103 may be represented by a polynomial trace, such as a three-dimensional polynomial trace. It should be noted that in some embodiments, any of the reconstructed trajectories may be assembled and loaded on a vehicle traversing the road segment 1100.

[0230] Additionally or alternatively, such reconstructed trajectories may be determined on the server side based on information received from vehicles traversing the road segment 1100. For example, in some embodiments, the vehicles 200 may transmit data related to their movement along the road segment 1100 (e.g., steering angle, heading, time, location, speed, sensed road geometry and / or sensed landmarks, etc.) to one or more servers. The server may reconstruct a trajectory for the vehicle 200 based on the received data. The server may also generate a target trajectory for guiding navigation of an autonomous vehicle that will travel along the same road segment 1100 at a later time based on the first trajectory 1101, the second trajectory 1102, and the third trajectory 1103. Although a target trajectory may be associated with a single previous travel of a road segment, in some embodiments, each target trajectory included in the sparse map 800 may be determined based on two or more reconstructed trajectories of vehicles traversing the same road segment. In Fig.11A , the target track is represented by 1110. In some embodiments, the target track 1110 may be generated based on an average of the first track 1101, the second track 1102, and the third track 1103. In some embodiments, the target track 1110 included in the sparse map 800 may be an aggregation (e.g., a weighted combination) of two or more reconstructed tracks.

[0231] At a mapping server, the server may receive actual trajectories for a particular road segment from multiple collection vehicles that traverse the road segment. In order to generate a target trajectory for each valid path along the road segment (e.g., each lane, each driving direction, each path through an intersection, etc.), the received actual trajectories may be aligned. The alignment process may include correlating the actual, collected trajectories with each other using the detected objects / features identified along the road segment and the collected locations of those detected objects / features. Once aligned, an average or "best fit" target trajectory, etc., may be determined for each available lane based on the aggregated, correlated / aligned actual trajectories.

[0232] Fig. 11B and Fig. 11C The concept of target trajectories associated with road segments present within the geographic area 1111 is further illustrated. Fig. 11BAs shown, a first road segment 1120 within a geographic area 1111 may include a multi-lane road including two lanes 1122 designated for vehicles traveling in a first direction and two additional lanes 1124 designated for vehicles traveling in a second direction opposite to the first direction. Lanes 1122 and lanes 1124 may be separated by a double yellow line 1123. The geographic area 1111 may also include a branch road segment 1130 intersecting the road segment 1120. The road segment 1130 may include a two-lane road, each lane being designated for a different direction of travel. The geographic area 1111 may also include other road features, such as a stop line 1132, a stop sign 1134, a speed limit sign 1136, and a hazard sign 1138.

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

[0234] Sparse map 800 may also include representations of other road-related features associated with geographic area 1111. For example, sparse map 800 may also include representations of one or more landmarks identified in geographic area 1111. Such landmarks may include a first landmark 1150 associated with stop line 1132, a second landmark 1152 associated with stop sign 1134, a third landmark 1154 associated with a speed limit sign, and a fourth landmark 1156 associated with hazard sign 1138. Such landmarks may be used, for example, to assist an autonomous vehicle in determining its current position relative to any of the illustrated target tracks so that the vehicle may adjust its heading to match the direction of the target track at the determined location.

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

[0236] Alternatively or simultaneously, profile 1160 may represent changes in road width, as determined based on image data obtained via a camera mounted on a vehicle traveling on a particular road segment. For example, such profiles may be used to determine a particular location of an autonomous vehicle relative to a particular target trajectory. That is, as the autonomous vehicle traverses a road segment, it may measure a profile associated with one or more parameters associated with the road segment. If the measured profile can be correlated / matched with a predetermined profile that plots changes in parameters relative to position along the road segment, the measured profile and the predetermined profile may be used (e.g., by superimposing on corresponding segments of the measured profile and the predetermined profile) in order to determine a current position along the road segment, and therefore a current position relative to a target trajectory for the road segment.

[0237] In some embodiments, the sparse map 800 may include different tracks based on different characteristics associated with users of the autonomous vehicle, environmental conditions, and / or other parameters related to driving. For example, in some embodiments, different tracks may be generated based on different user preferences and / or profiles. A sparse map 800 including such different tracks may be provided to different autonomous vehicles of different users. For example, some users may prefer to avoid toll roads, while other users may prefer to take the shortest or fastest route, regardless of whether there are toll roads on the route. The disclosed system may generate different sparse maps with different tracks based on such different user preferences or profiles. As another example, some users may prefer to drive in a fast-moving lane, while other users may prefer to always maintain a position in the center lane.

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

[0239] Other different parameters related to driving may also be used as a basis for generating and providing different sparse maps to different autonomous vehicles. For example, when an autonomous vehicle is traveling at high speeds, turns may be tighter. Tracks associated with specific lanes rather than roads may be included in sparse map 800 so that the autonomous vehicle can maintain itself within a specific lane as the vehicle follows a specific track. When images captured by a camera mounted on the autonomous vehicle indicate that the vehicle has deviated from the lane (e.g., crossed a lane marking), an action may be triggered within the vehicle to bring the vehicle back to the designated lane according to the specific track.

[0240] Crowdsourcing sparse maps

[0241] 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 typically included on today's vehicles as OEM equipment) and an appropriate image analysis processor can be used as a collection vehicle. No special equipment (e.g., high-resolution imaging and / or positioning systems) is required. Due to the disclosed crowdsourcing techniques, the generated sparse maps can be extremely accurate and can include extremely detailed location information (achieving navigation error limits of 10 cm or less) without requiring any specialized imaging or sensing equipment as input to the map generation process. Crowdsourcing also enables faster (and inexpensive) updates to the generated maps, as new driving information is continuously available to the mapping server system from any road traversed by a private or commercial vehicle that is minimally equipped to also serve as a collection vehicle. There is no need for a designated vehicle equipped with high-resolution imaging and mapping sensors. Therefore, the costs associated with building such specialized vehicles can be avoided. Furthermore, updates to the currently disclosed sparse maps can be made much faster than systems that rely on dedicated, specialized mapping vehicles (which, due to their expense and specialized equipment, are typically limited to fleets of specialized vehicles that are far less in number than the number of private or commercial vehicles already available for the disclosed acquisition techniques).

[0242] The disclosed sparse maps generated by crowdsourcing may be extremely accurate because they can be generated based on many inputs from multiple (tens, hundreds, millions, etc.) collection vehicles that have collected driving information along a particular road segment. For example, each collection vehicle driving along a particular road segment can record its actual trajectory and can determine position information relative to detected objects / features along the road segment. This information is transmitted from multiple collection vehicles to a server. The actual trajectories are aggregated to generate a refined target trajectory for each valid driving path along the road segment. In addition, the position information for each detected object / feature (semantic or non-semantic) along the road segment collected from multiple collection vehicles can also be aggregated. Therefore, the mapped position of each detected object / feature can constitute an average of hundreds, thousands, or millions of individually determined positions for each detected object / feature. Such techniques can produce extremely accurate mapped positions for detected objects / features.

[0243] In some embodiments, the disclosed system and method may generate a sparse map for autonomous vehicle navigation. For example, the disclosed system and method may use crowdsourced data for the generation of a sparse map, which one or more autonomous vehicles may use to navigate along the system of a road. As used herein, "crowdsourcing" means receiving data from various vehicles (e.g., autonomous vehicles) traveling on a road segment at different times, and such data is used to generate and / or update a road model, including sparse map tiles. The model or any of its sparse map tiles may then be transmitted to a vehicle or other vehicle traveling along the road segment later to assist autonomous vehicle navigation. The road model may include multiple target trajectories representing a preferred trajectory that an autonomous vehicle should follow when passing through a road segment. The target trajectory may be the same as the reconstructed actual trajectory collected from a vehicle passing through a road segment, which may be transmitted from a vehicle to a server. In some embodiments, the target trajectory may be different from the actual trajectory previously taken by one or more vehicles when passing through a road segment. The target trajectory may be generated based on the actual trajectory (e.g., by averaging or any other suitable operation).

[0244] The vehicle trajectory data that the vehicle may upload to the server may correspond to an actual reconstructed trajectory for the vehicle or may correspond to a recommended trajectory that may be based on or related to the actual reconstructed trajectory of the vehicle, but may be different from the actual reconstructed trajectory. For example, the vehicle may modify its actual, reconstructed trajectory and submit (e.g., recommend) the modified actual trajectory to the server. The road model may use the recommended, modified trajectory as a target trajectory for autonomous navigation of other vehicles.

[0245] In addition to trajectory information, other information that may be used in building the sparse data map 800 may include information related to potential landmark candidates. For example, through crowdsourcing of information, the disclosed systems and methods may identify potential landmarks in the environment and refine landmark locations. Landmarks may be used by the navigation system of an autonomous vehicle to determine and / or adjust the vehicle's position along a target trajectory.

[0246] The reconstructed trajectory that a vehicle may generate as the vehicle travels along a road may be obtained by any suitable method. In some embodiments, a reconstructed trajectory may be developed by piecing together segments of the motion of the vehicle using, for example, ego-motion estimates (e.g., three-dimensional translation and three-dimensional rotation of the camera, and therefore the body of the vehicle). The rotation and translation estimates may be determined based on analysis of images captured by one or more image capture devices together with information from other sensors or devices such as inertial sensors and velocity sensors. For example, an inertial sensor may include an accelerometer or other suitable sensor configured to measure changes in translation and / or rotation of the body of the vehicle. The vehicle may include a velocity sensor that measures the velocity of the vehicle.

[0247] In some embodiments, the ego-motion of the camera (and thus the vehicle body) may be estimated based on an optical flow analysis of the captured images. Optical flow analysis of an image sequence identifies the movement of pixels from the image sequence, and determines the motion of the vehicle based on the identified movement. The ego-motion may be integrated over time and along road segments to reconstruct a trajectory associated with a road segment that the vehicle has followed.

[0248] Data collected by multiple vehicles at different times along multiple drives along a road segment (e.g., reconstructed trajectories) can be used to construct a road model (e.g., including target trajectories, etc.) included in the sparse data map 800. The data collected by multiple vehicles at different times along multiple drives of a road segment can also be averaged to improve the accuracy of the model. In some embodiments, data about road geometry and / or landmarks can be received from multiple vehicles traveling through a public road segment at different times. Such data received from different vehicles can be combined to generate a road model and / or to update a road model.

[0249] The geometric features of the reconstructed trajectory (and also the target trajectory) along the road segment can be represented by a curve in three-dimensional space, which can be a spline connecting three-dimensional polynomials. The reconstructed trajectory curve can be determined from the analysis of a video stream or multiple images captured by a camera mounted on the vehicle. In some embodiments, a location a few meters ahead of the current position of the vehicle is identified in each frame or image. The location is the location where the vehicle is expected to travel within a predetermined time period. This operation can be repeated frame by frame, and the vehicle can calculate the camera's self-motion (rotation and translation) at the same time. At each frame or image, a short-range model for the desired path is generated by the vehicle in a reference system attached to the camera. The short-range model can be spliced ​​together to obtain a three-dimensional model of the road in a certain coordinate system, which can be an arbitrary or predetermined coordinate system. The three-dimensional model of the road can then be fitted by a spline, which can include or connect one or more polynomials of suitable order.

[0250] To derive a short-range road model for each frame, one or more detection modules may be used. For example, a bottom-up lane detection module may be used. A bottom-up lane detection module may be useful when drawing lane markings on a road. This module may find edges in the image and assemble them together to form lane markings. A second module may be used in conjunction with the bottom-up lane detection module. The second module is an end-to-end deep neural network that may be trained to predict the correct short-range path from an input image. In both modules, the road model may be detected in an image coordinate system and converted into a three-dimensional space that may be virtually attached to a camera.

[0251] Although the reconstructed trajectory modeling method may introduce an accumulation of errors due to the integration of ego motion over long periods of time, which may include a noise component, such errors may be insignificant because the generated model can provide sufficient accuracy for navigation on a local scale. In addition, the integrated errors may be eliminated by using external information sources (such as satellite images or geodetic results). For example, the disclosed system and method may use a GNSS receiver to eliminate accumulated errors. However, GNSS positioning signals may not always be available and accurate. The disclosed system and method can implement steering applications that are weakly dependent on the availability and accuracy of GNSS positioning. In such systems, the use of GNSS signals may be limited. For example, in some embodiments, the disclosed system may use GNSS signals only for database indexing purposes.

[0252] In some embodiments, the distance scale (e.g., local scale) associated with autonomous vehicle navigation steering applications can be approximately 50 meters, 100 meters, 200 meters, 300 meters, etc. Such distances can be used because the geometric road model is mainly used for two purposes: planning the front trajectory and positioning the vehicle on the road model. In some embodiments, when the control algorithm turns the vehicle based on a target point located 1.3 seconds ahead (or any other time, such as 1.5 seconds, 1.7 seconds, 2 seconds, etc.), the planning task can use a model within a typical range of 40 meters ahead (or any other suitable distance ahead, such as 20 meters, 30 meters, 50 meters). According to a method called "tail alignment" described in more detail in another section, the positioning task uses a road model within a typical range of 60 meters behind the car (or any other suitable distance, such as 50 meters, 100 meters, 150 meters, etc.). The disclosed system and method can generate a geometric model with sufficient accuracy within a specific range (such as 100 meters) so that the planned trajectory will not deviate from the center of the lane by more than, for example, 30 cm.

[0253] As explained above, a three-dimensional road model can be constructed from detecting short-range road segments and stitching them together. Stitching can be achieved by computing a six-degree ego-motion model using video and / or images captured by a camera, data from an inertial sensor reflecting the motion of the vehicle, and a host vehicle velocity signal. The accumulated error may be small enough on some local range scale, such as about 100 meters. All of this can be done in a single drive on a particular road segment.

[0254] In some embodiments, multiple drives can be used to average the resulting model and further improve its accuracy. The same car may travel the same route multiple times, or multiple cars may send their collected model data to a central server. In any case, a matching process can be performed to identify overlapping models and average them to generate a target trajectory. Once the convergence criteria are met, the constructed model (e.g., including the target trajectory) can be used for steering. Subsequent drives can be used for further model improvements and to adapt to infrastructure changes.

[0255] If multiple cars are connected to a central server, it becomes feasible to share the driving experience (such as sensed data) between multiple cars. Each vehicle client can store a partial copy of the general road model, which may be related to its current location. A two-way update process between the vehicle and the server can be performed by the vehicle and the server. The small footprint concept discussed above enables the disclosed system and method to use very small bandwidth for two-way updates.

[0256] Information related to potential landmarks may also be determined and forwarded to the central server. For example, the disclosed systems and methods may determine one or more physical attributes of potential landmarks based on one or more images that include the landmarks. The physical attributes may include the physical size of the landmark (e.g., height, width), the distance from the vehicle to the landmark, the distance between the landmark and the previous landmark, the lateral position of the landmark (e.g., the position of the landmark relative to the lane of travel), the GPS coordinates of the landmark, the type of landmark, the identification of text on the landmark, etc. For example, the vehicle may analyze one or more images captured by a camera to detect potential landmarks, such as speed limit signs.

[0257] The vehicle may determine the distance from the vehicle to a landmark or a location associated with a landmark (e.g., any semantic or non-semantic object or feature along a road segment) based on an analysis of one or more images. In some embodiments, the distance may be determined based on an analysis of an image of the landmark using a suitable image analysis method (such as a scaling method and / or an optical flow method). As previously indicated, the location of the object / feature may include a 2D image location of one or more points associated with the object / feature (e.g., an XY pixel location in one or more captured images), or may include a 3D real-world location of one or more points (e.g., determined by structure techniques in motion / optical flow, lidar or radar information, etc.). In some embodiments, the disclosed systems and methods may be configured to determine the type or classification of a potential landmark. In the event that the vehicle determines that a potential landmark corresponds to a predetermined type or classification stored in a sparse map, it may be sufficient for the vehicle to transmit an indication of the type or classification of the landmark to a server along with the location of the landmark. The server may store such indications. At a later time, during navigation, the navigation vehicle may capture an image including representations of landmarks, process the image (e.g., using a classifier) ​​and compare the resulting landmarks to confirm detection of the mapped landmarks and to use the mapped landmarks to locate the navigation vehicle relative to the sparse map.

[0258] In some embodiments, multiple autonomous vehicles driving on a road segment can communicate with a server. The vehicles (or clients) can generate curves describing their driving in an arbitrary coordinate system (e.g., by integrating ego motion). The vehicles can detect landmarks and locate them in the same frame. The vehicles can upload the curves and landmarks to the server. The server can collect data from the vehicles over multiple drives and generate a unified road model. For example, as described below with respect to Fig.19 As discussed, the server can use the uploaded curves and landmarks to generate a sparse map with a unified road model.

[0259] The server may also distribute the model to clients (e.g., vehicles). For example, the server may distribute the sparse map to one or more vehicles. The server may continuously or periodically update the model as new data is received from the vehicles. For example, the server may process the new data to evaluate whether the data includes information that should trigger an update or creation of new data on the server. The server may distribute the updated model or the update to the vehicles for use in providing autonomous vehicle navigation.

[0260] The server may use one or more criteria to determine whether new data received from a vehicle should trigger an update to the model or trigger the creation of new data. For example, the server may determine that the new data should trigger an update to the model when the new data indicates that a previously identified landmark at a particular location no longer exists or has been replaced by another landmark. As another example, the server may determine that the new data should trigger an update to the model when the new data indicates that a road segment has been closed, and when this has been confirmed by data received from other vehicles.

[0261] The server may distribute the updated model (or updated portion of the model) to one or more vehicles traveling on the road segment associated with the update to the model. The server may also distribute the updated model to vehicles that are about to travel on the road segment, or vehicles whose planned trips include the road segment associated with the update to the model. For example, when an autonomous vehicle travels along another road segment before reaching the road segment associated with the update, the server may distribute the update or updated model to the autonomous vehicle before the vehicle reaches the road segment.

[0262] In some embodiments, a remote server may collect trajectories and landmarks from multiple clients (e.g., vehicles traveling along a public road segment). The server may use landmarks to match curves and create an average road model based on the trajectories collected from multiple vehicles. The server may also calculate a road map and the most likely path for each node or junction of the road segment. For example, the remote server may align the trajectories to generate a crowdsourced sparse map from the collected trajectories.

[0263] The server may average landmark attributes received from multiple vehicles traveling along a public road segment (such as the distance between one landmark to another landmark (e.g., the previous landmark along the road segment) measured by multiple vehicles) to determine arc length parameters and support positioning and speed calibration for each client vehicle along the path. The server may average the physical dimensions of landmarks measured by multiple vehicles traveling along the public road segment and identifying the same landmark. The averaged physical dimensions can be used to support distance estimation, such as the distance from the vehicle to the landmark. The server may average the lateral positions of landmarks measured by multiple vehicles traveling along the public road segment and identifying the same landmark (e.g., from the lane in which the vehicle is traveling to the position in the landmark). The averaged lateral portion can be used to support lane allocation. The server may average the GPS coordinates of landmarks measured by multiple vehicles traveling along the same road segment and identifying the same landmark. The averaged GPS coordinates of the landmarks can be used to support global localization or positioning of the landmarks in the road model.

[0264] In some embodiments, the server may identify model changes, such as construction, detours, new signs, removal of signs, etc., based on data received from the vehicle. The server may update the model continuously, periodically, or on the fly as new data is received from the vehicle. The server may distribute updates to the model or the updated model to the vehicle for use in providing autonomous navigation. For example, as discussed further below, the server may use crowdsourced data to filter out "ghost" landmarks detected by the vehicle.

[0265] In some embodiments, the server may analyze driver interventions during automated driving. The server may analyze data received from the vehicle at the time and location where the intervention occurred and / or data received before the time the intervention occurred. The server may identify certain portions of the data that caused the intervention or were closely related to the intervention, such as data indicating a temporary lane closure setting, data indicating pedestrians in the road. The server may update the model based on the identified data. For example, the server may modify one or more trajectories stored in the model.

[0266] Fig.12 Schematic illustration of a system for generating sparse maps using crowdsourcing (and for distributing and navigating using the crowdsourced sparse maps). Fig.12 A road segment 1200 including one or more lanes is shown. Multiple vehicles 1205, 1210, 1215, 1220, and 1225 may travel on the road segment 1200 at the same time or at different times (although Fig.12 1200 at the same time). At least one of vehicles 1205, 1210, 1215, 1220, and 1225 may be an autonomous vehicle. To simplify this example, all vehicles 1205, 1210, 1215, 1220, and 1225 are assumed to be autonomous vehicles.

[0267] Each vehicle may be similar to the vehicles disclosed in other embodiments (e.g., vehicle 200), and may include components or devices included in or associated with the vehicles disclosed in other embodiments. Each vehicle may be equipped with an image capture device or camera (e.g., image capture device 122 or camera 122). Each vehicle may communicate with a remote server 1230 via one or more networks (e.g., through a cellular network and / or the Internet, etc.) through a wireless communication path 1235 as indicated by a dashed line. 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 the road segment 1200 at different times, and may process the collected data to generate an autonomous vehicle road navigation model or an update to the model. The server 1230 may transmit the autonomous vehicle road navigation model or an update to the model to the vehicle that transmits the data to the server 1230. The server 1230 may transmit the autonomous vehicle road navigation model or an update to the model to other vehicles traveling on the road segment 1200 at a later time.

[0268] When vehicles 1205, 1210, 1215, 1220, and 1225 travel on road segment 1200, navigation information collected (e.g., detected, sensed, or measured) by vehicles 1205, 1210, 1215, 1220, and 1225 may be transmitted to server 1230. In some embodiments, navigation information may be associated with public road segment 1200. Navigation information may include a trajectory associated with each of vehicles 1205, 1210, 1215, 1220, and 1225 when each vehicle travels on road segment 1200. In some embodiments, the trajectory may be reconstructed based on data sensed by various sensors and devices provided on vehicle 1205. For example, the trajectory may be reconstructed based on at least one of accelerometer data, speed data, landmark data, road geometry or contour data, vehicle positioning data, and self-motion data. In some embodiments, the trajectory may be reconstructed based on data from an inertial sensor such as an accelerometer and the velocity of vehicle 1205 sensed by a speed sensor. In addition, in some embodiments, the trajectory may be determined based on the camera's sensed ego motion (e.g., by a processor on each of the vehicles 1205, 1210, 1215, 1220, and 1225), which sensed ego motion may indicate three-dimensional translation and / or three-dimensional rotation (or rotational motion). The camera's (and therefore the vehicle body's) ego motion may be determined from analysis of one or more images captured by the camera.

[0269] In some embodiments, the trajectory of the vehicle 1205 may be determined by a processor disposed on the vehicle 1205 and transmitted to the server 1230. In other embodiments, the server 1230 may receive data sensed by various sensors and devices disposed in the vehicle 1205, and determine the trajectory based on the data received from the vehicle 1205.

[0270] In some embodiments, the navigation information transmitted from the vehicles 1205, 1210, 1215, 1220, and 1225 to the server 1230 may include data about the road surface, road geometry, or road profile. The geometry of the road segment 1200 may include lane structures and / or landmarks. The lane structure may include the total number of lanes of the road segment 1200, the lane type (e.g., one-way lane, two-way lane, driving lane, overtaking lane, etc.), markings on the lanes, lane width, etc. In some embodiments, the navigation information may include lane allocations, such as which lane of a plurality of lanes the vehicle is traveling in. For example, the lane allocation may be associated with a numerical value "3" indicating that the vehicle is traveling in the third lane from the left or right. As another example, the lane allocation may be associated with a text value "center lane" indicating that the vehicle is traveling in the center lane.

[0271] The server 1230 may store the navigation information on a non-transitory computer-readable medium such as a hard drive, an optical disk, a tape, a memory, etc. The server 1230 may generate (e.g., by a processor included in the server 1230) at least a portion of an autonomous vehicle road navigation model for the public road segment 1200 based on the navigation information received from the plurality of vehicles 1205, 1210, 1215, 1220, and 1225, and may store the model as part of a sparse map. The server 1230 may determine a track associated with each lane based on crowdsourced data (e.g., navigation information) received from a plurality of vehicles (e.g., 1205, 1210, 1215, 1220, and 1225) traveling on lanes of the road segment at different times. The server 1230 may generate an autonomous vehicle road navigation model or a portion of the model (e.g., an updated portion) based on a plurality of tracks determined based on the crowdsourced navigation data. Server 1230 can transmit the model or updated portion of the model to one or more of autonomous vehicles 1205, 1210, 1215, 1220, and 1225 traveling on road segment 1200, or any other autonomous vehicle traveling on the road segment at a later time, for use in updating an existing autonomous vehicle road navigation model provided in the vehicle's navigation system. The autonomous vehicle road navigation model can be used by the autonomous vehicle when autonomously navigating along public road segment 1200.

[0272] As explained above, the autonomous vehicle road navigation model may include a sparse map (e.g., Figure 8 800). Sparse map 800 may include a sparse record of data related to road geometry and / or landmarks along the road, which may provide sufficient information for guiding autonomous navigation of an autonomous vehicle without requiring excessive data storage. In some embodiments, an autonomous vehicle road navigation model may be stored separately from sparse map 800, and map data from sparse map 800 may be used when the model is executed for navigation. In some embodiments, the autonomous vehicle road navigation model may use map data included in sparse map 800 to determine a target trajectory along road segment 1200 for guiding autonomous navigation of autonomous vehicles 1205, 1210, 1215, 1220, and 1225 or other vehicles traveling later along road segment 1200. For example, when the autonomous vehicle road navigation model is executed by a processor included in a navigation system of vehicle 1205, the model may enable the processor to compare a trajectory determined based on navigation information received from vehicle 1205 with a predetermined trajectory included in the sparse map 800 to verify and / or correct the current driving route of vehicle 1205.

[0273] In the autonomous vehicle road navigation model, the geometric features of road features or target tracks can be encoded by curves in three-dimensional space. In one embodiment, the curve can be a three-dimensional spline including one or more connected three-dimensional polynomials. As will be understood by those skilled in the art, a spline can be a numerical function defined by a series of polynomials for fitting data. The spline for fitting the three-dimensional geometric feature data of the road may include a linear spline (first order), a quadratic spline (second order), a cubic spline (third order), or any other spline (other orders), or a combination thereof. The spline may include one or more three-dimensional polynomials of different orders of the data points of the three-dimensional geometric feature data of the road that are connected (e.g., fitting). In some embodiments, the autonomous vehicle road navigation model may include a three-dimensional spline corresponding to the target track of the lane along a public road segment (e.g., road segment 1200) or road segment 1200.

[0274] As explained above, the autonomous vehicle road navigation model included in the sparse map may include other information, such as the identification of at least one landmark along the road segment 1200. The landmark may be visible within the field of view of a camera (e.g., camera 122) mounted on each of the vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, the camera 122 may capture an image of the landmark. A processor (e.g., processor 180, 190, or processing unit 110) disposed on the vehicle 1205 may process the image of the landmark to extract identification information for the landmark. The landmark identification information, rather than the actual image of the landmark, may be stored in the sparse map 800. The landmark identification information may require much less storage space than the actual image. Other sensors or systems (e.g., GPS systems) may also provide certain identification information of the landmark (e.g., the location of the landmark). Landmarks may include at least one of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign (e.g., a highway exit sign with an arrow indicating a direction, a highway sign with arrows pointing to different directions or places), a landmark beacon, or a lamppost. A landmark beacon refers to a device (e.g., an RFID device) installed along a road segment that transmits or reflects a signal to a receiver installed on a vehicle, so that when the vehicle passes by the device, the beacon received by the vehicle and the location of the device (e.g., determined from the GPS location of the device) can be used as a landmark to be included in the autonomous vehicle road navigation model and / or the sparse map 800.

[0275] The identification of at least one landmark may include the location of the at least one landmark. The location of the landmark may be determined based on location measurements made using sensor systems (e.g., global positioning systems, inertial positioning systems, landmark beacons, etc.) associated with multiple vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, the location of the landmark may be determined by averaging location measurements detected, collected, or received by sensor systems on different vehicles 1205, 1210, 1215, 1220, and 1225 over multiple drives. For example, vehicles 1205, 1210, 1215, 1220, and 1225 may transmit location measurement data to a server 1230, which may average the location measurements and use the averaged location measurements as the location of the landmark. The location of the landmark may be continuously refined by measurements received from vehicles in subsequent drives.

[0276] The identification of the landmark may include the size of the landmark. A processor disposed on the vehicle (e.g., 1205) may estimate the physical size of the landmark based on an analysis of the image. The server 1230 may receive multiple estimates of the physical size of the same landmark from different vehicles passing through different drives. The server 1230 may average the different estimates to arrive at a physical size for the landmark and store the landmark size in the road model. The physical size estimate may be used to further determine or estimate the distance from the vehicle to the landmark. The distance to the landmark may be estimated based on the current speed of the vehicle and based on the extended scale of the location of the landmark appearing in the image relative to the extended focus of the camera. For example, the distance to the landmark may be estimated by Z=V*dt*R / D, where V is the speed of the vehicle, R is the distance from the landmark at time t1 to the extended focus in the image, and D is the change in the distance of the landmark in the image from t1 to t2. dt represents (t2-t1). For example, the distance to the landmark can be estimated by Z = V*dt*R / D, where V is the speed of the vehicle, R is the distance between the landmark and the expanded focus in the image, dt is the time interval, and D is the image displacement of the landmark along the epipolar line. Other equations equivalent to the above equation, such as Z = V*ω / Δω, can be used to estimate the distance to the landmark. Here, V is the vehicle speed, ω is the image length (such as the object width), and Δω is the change in the image length per unit time.

[0277] When the physical size of the landmark is known, the distance to the landmark can also be determined based on the following equation: Z = f*W / ω, where f is the focal length, W is the size of the landmark (e.g., height or width), and ω is the number of pixels when the landmark leaves the image. According to the above equation, the change in distance Z can be calculated using ΔZ = f*W*Δω / ω2+f*ΔW / ω, where ΔW decays to zero by averaging, and where Δω is the number of pixels representing the accuracy of the bounding box in the image. The value of the estimated physical size of the landmark can be calculated by averaging multiple observations at the server side. The error in the resulting distance estimate may be very small. There are two sources of error that may occur when using the above formula, namely ΔW and Δω. Their contribution to the distance error is given by ΔZ = f*W*Δω / ω2+f*ΔW / ω. However, ΔW decays to zero by averaging; therefore, ΔZ is determined by Δω (e.g., the inaccuracy of the bounding box in the image).

[0278] For landmarks of unknown size, the distance to the landmark can be estimated by tracking feature points on the landmark between consecutive frames. For example, certain features that appear on a speed limit sign can be tracked between two or more image frames. Based on these tracked features, a distance distribution for each feature point can be generated. The distance estimate can be extracted from the distance distribution. For example, the most frequent distance that appears in the distance distribution can be used as the distance estimate. As another example, the average of the distance distribution can be used as the distance estimate.

[0279] Fig.13 An example autonomous vehicle road navigation model represented by a plurality of three-dimensional splines 1301 , 1302 , and 1303 is shown. Fig.13 Curves 1301, 1302, and 1303 are shown for illustration purposes only. Each spline may include one or more three-dimensional polynomials connecting a plurality of data points 1310. Each polynomial may be a first order polynomial, a second order polynomial, a third order polynomial, or a combination of any suitable polynomials having different orders. Each data point 1310 may be associated with navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, each data point 1310 may be associated with data associated with a landmark (e.g., size, location, and identification information of the landmark) and / or a road signature profile (e.g., road geometry, road roughness profile, road curvature profile, road width profile). In some embodiments, some data points 1310 may be associated with data associated with a landmark, and other data points may be associated with data associated with a road signature profile.

[0280] Fig.14 The raw location data 1410 (e.g., GPS data) received from five separate drivings is shown. If a driving is traversed by separate vehicles at the same time, by the same vehicle at separate times, or by separate vehicles at separate times, then a driving can be separated from another driving. In order to take into account the errors in the location data 1410 and the different locations of vehicles in the same lane (e.g., one vehicle may drive closer to the left side of the lane than another vehicle), the server 1230 can use one or more statistical techniques to generate a map skeleton 1420 to determine whether the changes in the raw location data 1410 represent actual deviations or statistical errors. Each path in the skeleton 1420 can be linked back to the raw data 1410 that formed the path. For example, the path between A and B in the skeleton 1420 is linked to the raw data 1410 from driving 2, 3, 4, and 5 but not from driving 1. The skeleton 1420 may not be detailed enough to be used for navigating the vehicle (e.g., because it combines driving from multiple lanes on the same road, unlike the spline described above), but it can provide useful topological information and can be used to define intersections.

[0281] Fig.15 An example of how additional detail can be generated for a sparse map within a segment of a map skeleton (e.g., segment A to B within skeleton 1420) is shown. Fig.15As depicted, data (e.g., ego-motion data, road marking data, etc.) may be shown as a function of location S (or S1 or S2) along the driving. Server 1230 may identify landmarks for a sparse map by identifying unique matches between landmarks 1501, 1503, and 1505 of driving 1510 and landmarks 1507 and 1509 of driving 1520. Such matching algorithms may result in 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, probabilistic optimization may be used in place of or in conjunction with unique matching. Server 1230 may align driving longitudinally to align matching landmarks. For example, server 1230 may select a driving (e.g., driving 1520) as a reference driving, and then transform and / or elastically stretch other driving (e.g., driving 1510) for alignment.

[0282] Fig.16 An example of aligned landmark data for use in a sparse map is shown. Fig.16 In the example of FIG. 1 , landmark 1610 includes a road sign. Fig.16 The example further depicts data from multiple drivers 1601, 1603, 1605, 1607, 1609, 1611, and 1613. Fig.16 In the example of , the data from drive 1613 consists of a "ghost" landmark, and server 1230 may identify the landmark as such because none of drives 1601, 1603, 1605, 1607, 1609, and 1611 include identifications of landmarks in the vicinity of the identified landmark in drive 1613. Thus, server 1230 may accept a potential landmark when the ratio of images in which the landmark appears to images in which the landmark does not appear exceeds a threshold, and / or may reject a potential landmark when the ratio of images in which the landmark does not appear to images in which the landmark appears exceeds a threshold.

[0283] Fig.17 A system 1700 is depicted for generating driving data that can be used to crowdsource sparse maps. Fig.17 As depicted, system 1700 may include camera 1701 and positioning device 1703 (e.g., GPS locator). Camera 1701 and positioning device 1703 may be mounted on a vehicle (e.g., one of vehicles 1205, 1210, 1215, 1220, and 1225). Camera 1701 may generate a plurality of various types of data, such as ego-motion data, traffic sign data, road data, etc. The camera data and location data may be segmented into driving segments 1705. For example, driving segments 1705 may each have camera data and location data from less than 1 km of driving.

[0284] In some embodiments, system 1700 may remove redundancy in driving segment 1705. For example, if a landmark appears in multiple images from camera 1701, system 1700 may remove redundant data such that driving segment 1705 contains only one copy of the location of the landmark and any metadata associated with the landmark. By way of further example, if lane markings appear in multiple images from camera 1701, system 1700 may remove redundant data such that driving segment 1705 contains only one copy of the location of the lane markings and any metadata associated with the lane markings.

[0285] The system 1700 also includes a server (e.g., server 1230). The server 1230 can receive driving segments 1705 from the vehicle and reassemble the driving segments 1705 into a single driving 1707. Such an arrangement can allow for reduced bandwidth requirements when transmitting data between the vehicle and the server, while also allowing the server to store data related to the entire driving.

[0286] Fig.18 Describes a method further configured to crowdsource sparse maps Fig.17 system 1700. As in Fig.17 In , system 1700 includes a vehicle 1810 and a positioning device (e.g., a GPS locator), the vehicle using, for example, a camera (which generates, for example, self-motion data, traffic sign data, road data, etc.) to capture driving data. Fig.17 In the example, the vehicle 1810 divides the collected data into driving segments (in Fig.18 Server 1230 then receives the driving segments and reconstructs the driving from the received segments (depicted as "DS1 1", "DS21", "DSN 1" in FIG. 1 ). Fig.18 1).

[0287] As in Fig.18 As further depicted, 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, ego-motion data, traffic sign data, road data, etc.) and a positioning device (e.g., a GPS locator). Similar to vehicle 1810, vehicle 1820 segments the collected data into driving segments (in Fig.18 2", "DS2 2", "DSN 2"). The server 1230 then receives the driving segments and reconstructs the driving from the received segments (in Fig.18 Any number of additional vehicles may be used. For example, Fig.18 Also included is a "Sedan N" which captures driving data, segments it into driving segments (in Fig.18and sends it to the server 1230 for reconstruction into a driving (in Fig.18 depicted as "Drive N" in Figure 1).

[0288] like Fig.18 As depicted, server 1230 may construct a sparse map (depicted as “Map”) using reconstructed drives (e.g., “Drive 1,” “Drive 2,” and “Drive N”) collected from multiple vehicles (e.g., “Car 1” (also labeled as vehicle 1810), “Car 2” (also labeled as vehicle 1820), and “Car N”).

[0289] Fig.19 19 is a flow chart illustrating an example process 1900 for generating a sparse map for autonomous vehicle navigation along a road segment. Process 1900 may be performed by one or more processing devices included in server 1230.

[0290] Process 1900 may include receiving a plurality of images acquired as one or more vehicles traverse a road segment (step 1905). Server 1230 may receive images from cameras included in one or more of vehicles 1205, 1210, 1215, 1220, and 1225. For example, as vehicle 1205 travels along road segment 1200, camera 122 may capture one or more images of the environment surrounding vehicle 1205. In some embodiments, server 1230 may also receive condensed image data from which redundancy has been removed by a processor on vehicle 1205, as described above with respect to Fig.17 discussed.

[0291] Process 1900 may further include identifying at least one line representation of a road surface feature extending along a road segment based on the plurality of images (step 1910). Each line representation may represent a path along a road segment substantially corresponding to a road surface feature. For example, server 1230 may analyze the environmental image received from camera 122 to identify a road edge or lane marking and determine a driving trajectory along the road segment 1200 associated with the road edge or lane marking. In some embodiments, the trajectory (or line representation) may include a spline, a polynomial representation, or a curve. Server 1230 may determine the driving trajectory of vehicle 1205 based on the camera ego motion (e.g., three-dimensional translation and / or three-dimensional rotation motion) received in step 1905.

[0292] Process 1900 may also include identifying a plurality of landmarks associated with the road segment based on the plurality of images (step 1910). For example, server 1230 may analyze the environmental images received from camera 122 to identify one or more landmarks, such as road signs along road segment 1200. Server 1230 may identify landmarks using analysis of a plurality of images acquired as one or more vehicles traverse the road segment. To enable crowdsourcing, the analysis may include rules for accepting and rejecting possible landmarks associated with the road segment. For example, the analysis may include accepting potential landmarks when the ratio of images in which the landmark appears to images in which the landmark does not appear exceeds a threshold, and / or rejecting potential landmarks when the ratio of images in which the landmark does not appear to images in which the landmark appears exceeds a threshold.

[0293] Process 1900 may include other operations or steps performed by server 1230. For example, navigation information may include a target trajectory for a vehicle traveling along a road segment, and process 1900 may include clustering vehicle trajectories associated with a plurality of vehicles traveling on the road segment by server 1230, and determining the target trajectory based on the clustered vehicle trajectories, as discussed in further detail below. Clustering vehicle trajectories may include clustering a plurality of trajectories associated with vehicles traveling on the road segment into a plurality of clusters by server 1230 based on at least one of the absolute heading of the vehicle or the lane assignment of the vehicle. Generating the target trajectory may include averaging the clustered trajectories by server 1230. By way of another example, process 1900 may include aligning the data received in step 1905. As described above, other processes or steps performed by server 1230 may also be included in process 1900.

[0294] The disclosed systems and methods may include other features. For example, the disclosed systems may use local coordinates instead of global coordinates. For autonomous driving, some systems may present data in world coordinates. For example, longitude and latitude coordinates on the surface of the earth may be used. In order to use a map for steering, the host vehicle may determine its position and orientation relative to the map. It seems natural to use an onboard GPS device in order to locate the vehicle on the map and in order to find a rotational transformation between the subject reference system and the world reference system (e.g., north, east, and down). Once the subject reference system is aligned with the map reference system, the desired route can be expressed in the subject reference system and steering commands can be calculated or generated.

[0295] The disclosed systems and methods can enable autonomous vehicle navigation (e.g., steering control) with a low-footprint model that can be collected by the autonomous vehicle itself without the assistance of expensive survey equipment. To support autonomous navigation (e.g., steering applications), a road model can include a sparse map having geometric features of the road, a lane structure of the road, and landmarks that can be used to determine the location or position of the vehicle along a trajectory included in the model. As discussed above, the generation of the sparse map can be performed by a remote server that communicates with a vehicle traveling on the road and receives data from the vehicle. The data can include sensed data, a trajectory reconstructed based on the sensed data, and / or a recommended trajectory that can represent a modified reconstructed trajectory. As discussed below, the server can transmit the model back to the vehicle or other vehicles traveling on the road later to assist in autonomous navigation.

[0296] Fig. 20 A block diagram of server 1230 is shown. Server 1230 may include a communication unit 2005, which may include both hardware components (e.g., communication control circuits, switches, and antennas) and software components (e.g., communication protocols, computer codes). For example, communication unit 2005 may include at least one network interface. Server 1230 may communicate with vehicles 1205, 1210, 1215, 1220, and 1225 through communication unit 2005. For example, server 1230 may receive navigation information transmitted from vehicles 1205, 1210, 1215, 1220, and 1225 through communication unit 2005. Server 1230 may distribute autonomous vehicle road navigation models to one or more autonomous vehicles through communication unit 2005.

[0297] Server 1230 may include at least one non-transitory storage medium 2010, such as a hard drive, an optical disk, a magnetic tape, etc. Storage device 1410 may be configured to store data, such as navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225 and / or autonomous vehicle road navigation models generated by server 1230 based on the navigation information. Storage device 2010 may be configured to store any other information, such as sparse maps (e.g., as described above with respect to Figure 8 The sparse map discussed in 800).

[0298] In addition to or in lieu of storage device 2010, server 1230 may include memory 2015. Memory 2015 may be similar to or different from memory 140 or 150. Memory 2015 may be a non-transitory memory, such as flash memory, random access memory, etc. Memory 2015 may be configured to store data, such as computer code or instructions executable by a processor (e.g., processor 220), map data (e.g., data of sparse map 800), autonomous vehicle road navigation models, and / or navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225.

[0299] Server 1230 may include at least one processing device 2020 configured to execute computer code or instructions stored in memory 2015 to perform various functions. For example, processing device 2020 may analyze navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225, and generate an autonomous vehicle road navigation model based on the analysis. Processing device 2020 may control communication unit 1405 to distribute the autonomous vehicle road navigation model to one or more autonomous vehicles (e.g., one or more of vehicles 1205, 1210, 1215, 1220, and 1225 or any vehicle traveling on road segment 1200 at a later time). Processing device 2020 may be similar to or different from processors 180, 190 or processing unit 110.

[0300] Fig.21 A block diagram of a memory 2015 is shown, which may store computer code or instructions for performing one or more operations for generating a road navigation model for use in autonomous vehicle navigation. Fig.21 As shown, the memory 2015 may store one or more modules for performing operations for processing vehicle navigation information. For example, the memory 2015 may include a model generation module 2105 and a model distribution module 2110. The processor 2020 may execute instructions stored in any one of the modules 2105 and 2110 included in the memory 2015.

[0301] The model generation module 2105 may store instructions that, when executed by the processor 2020, may generate at least a portion of an autonomous vehicle road navigation model for a public road segment (e.g., road segment 1200) based on navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225. For example, when generating the autonomous vehicle road navigation model, the processor 2020 may cluster vehicle trajectories along the public road segment 1200 into different clusters. The processor 2020 may determine a target trajectory along the public road segment 1200 based on clustered vehicle trajectories for each of the different clusters. Such operations may include finding a mean or average trajectory of the vehicle trajectories of the clusters in each cluster (e.g., by averaging data representing the vehicle trajectories of the clusters). In some embodiments, the target trajectory may be associated with a single lane of the public road segment 1200.

[0302] The road model and / or sparse map may store trajectories associated with road segments. These trajectories may be referred to as target trajectories, which are provided to the autonomous vehicle for autonomous navigation. The target trajectories may be received from multiple vehicles, or may be generated based on actual trajectories received from multiple vehicles or recommended trajectories (actual trajectories with some modifications). The target trajectories included in the road model or sparse map may be continuously updated (e.g., averaged) using new trajectories received from other vehicles.

[0303] A vehicle traveling on a road segment may collect data through various sensors. The data may include landmarks, road signature profiles, vehicle motion (e.g., accelerometer data, speed data), vehicle location (e.g., GPS data), and the actual trajectory itself may be reconstructed, or the data may be transmitted to a server that will reconstruct the actual trajectory for the vehicle. In some embodiments, the vehicle may transmit data related to the trajectory (e.g., a curve in an arbitrary reference frame), landmark data, and lane assignments along the driving path to a server 1230. Various vehicles traveling along the same road segment under multiple drives may have different trajectories. The server 1230 may identify the route or trajectory associated with each lane from the trajectories received from the vehicle through a clustering process.

[0304] Fig. 22A process for clustering vehicle trajectories associated with vehicles 1205, 1210, 1215, 1220, and 1225 for determining a target trajectory for a common road segment (e.g., road segment 1200) is shown. The target trajectory or multiple target trajectories determined from the clustering process may be included in the autonomous vehicle road navigation model or sparse map 800. In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 traveling along road segment 1200 may transmit multiple trajectories 2200 to server 1230. In some embodiments, server 1230 may generate trajectories based on landmarks, road geometry, and vehicle motion information received from vehicles 1205, 1210, 1215, 1220, and 1225. In order to generate the autonomous vehicle road navigation model, server 1230 may cluster vehicle trajectories 1600 into multiple clusters 2205, 2210, 2215, 2220, 2225, and 2230, as shown in FIG. Fig. 22 shown.

[0305] Various criteria may be used to cluster. In some embodiments, all of the driving in the cluster may be similar with respect to the absolute heading along the road segment 1200. The absolute heading may be obtained from the GPS signals received by the vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, the absolute heading may be obtained using dead reckoning. As will be appreciated by those skilled in the art, dead reckoning may be used to determine the current position and therefore the heading of the vehicles 1205, 1210, 1215, 1220, and 1225 by using previously determined positions, estimated speeds, and the like. Trajectories clustered by absolute heading may be useful for identifying routes along the roadway.

[0306] In some embodiments, all drives in a cluster may be similar with respect to lane assignment (e.g., in the same lane before and after an intersection) along the drive on road segment 1200. Trajectories clustered by lane assignment may be useful for identifying lanes along a roadway. In some embodiments, two criteria (e.g., absolute heading and lane assignment) may be used for clustering.

[0307] In each cluster 2205, 2210, 2215, 2220, 2225 and 2230, the trajectory can be averaged to obtain a target trajectory associated with a particular cluster. For example, the trajectories from multiple drivings associated with the same lane cluster can be averaged. The averaged trajectory can be a target trajectory associated with a particular lane. In order to average the clustering of trajectories, the server 1230 can select a reference frame for an arbitrary trajectory C0. For all other trajectories (C1, ..., Cn), the server 1230 can find a rigid transformation that maps Ci to C0, where i = 1, 2, ..., n, where n is a positive integer corresponding to the total number of trajectories included in the cluster. The server 1230 can calculate a mean curve or trajectory in the C0 reference frame.

[0308] In some embodiments, landmarks may define arc lengths that match between different drives, which may be used to align the track with the lane. In some embodiments, lane markings before and after the intersection may be used to align the track with the lane.

[0309] 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. Lanes adjacent to each other may be aligned as if they were the same lane, and then they may shift laterally.

[0310] Landmarks identified along a road segment may be mapped to a common reference frame, first at the lane level and then at the intersection level. For example, the same landmark may be identified multiple times by multiple vehicles in multiple drives. The data received about the same landmark in different drives may be slightly different. Such data may be averaged and mapped to the same reference frame, such as the C0 reference frame. Additionally or alternatively, the variance of the data received for the same landmark in multiple drives may be calculated.

[0311] In some embodiments, each lane of road segment 120 may be associated with a target track and certain landmarks. The target track or multiple such target tracks may be included in an autonomous vehicle road navigation model that may later be used by other autonomous vehicles traveling along the same road segment 1200. Landmarks identified by vehicles 1205, 1210, 1215, 1220, and 1225 as they travel along road segment 1200 may be recorded in association with the target track. The data for the target track and landmarks may be continuously or periodically updated with new data received from other vehicles in subsequent drives.

[0312] For the positioning of autonomous vehicles, the disclosed systems and methods may use an extended Kalman filter. The location of the vehicle may be determined based on three-dimensional position data and / or three-dimensional orientation data, by predicting the future location ahead of the current location of the vehicle through the integration of self-motion. The positioning of the vehicle may be corrected or adjusted by image observation of a landmark. For example, when a vehicle detects a landmark within an image captured by a camera, the landmark may be compared with a known landmark stored in a road model or sparse map 800. A known landmark may have a known location (e.g., GPS data) along a target track stored in a road model and / or sparse map 800. Based on the current speed and the image of the landmark, the distance from the vehicle to the landmark may be estimated. The location of the vehicle along the target track may be adjusted based on the distance to the landmark and the known location of the landmark (stored in the road model or sparse map 800). The location / location data of the landmark stored in the road model and / or sparse map 800 (e.g., the average from multiple driving) may be assumed to be accurate.

[0313] In some embodiments, the disclosed system can form a closed-loop subsystem in which an estimate of the vehicle's six-degree-of-freedom position (e.g., three-dimensional position data plus three-dimensional orientation data) can be used to navigate the autonomous vehicle (e.g., turn its steering wheel) to reach a desired point (e.g., 1.3 seconds ahead in storage). In turn, data from the steering and actual navigation measurements can be used to estimate the six-degree-of-freedom position.

[0314] In some embodiments, poles along the road (such as lamp posts and electric or cable poles) can be used as landmarks for locating the vehicle. Other landmarks such as traffic signs, traffic lights, arrows on the road, stop lines, and static features or signatures of objects along road segments can also be used as landmarks for locating the vehicle. When using poles for positioning, the x-observation of the pole (i.e., the viewing angle from the vehicle) can be used instead of the y-observation (i.e., the distance to the pole) because the base of the pole may be obscured and sometimes they are not on the road plane.

[0315] Fig.23 A navigation system for a vehicle is shown that can be used for autonomous navigation using crowdsourced sparse maps. For demonstration purposes, the vehicle is referred to as vehicle 1205. Fig.23 The vehicle shown may be any other vehicle disclosed herein, including, for example, vehicles 1210, 1215, 1220, and 1225, as well as vehicle 200 shown in other embodiments. Fig.12As shown, the vehicle 1205 can communicate with the server 1230. The vehicle 1205 can include an image capture device 122 (e.g., a camera 122). The vehicle 1205 can include a navigation system 2300 configured to provide navigation guidance for the vehicle 1205 to travel on a road (e.g., road segment 1200). The vehicle 1205 can also include other sensors, such as a speed sensor 2320 and an accelerometer 2325. The speed sensor 2320 can be configured to detect the speed of the vehicle 1205. The accelerometer 2325 can be configured to detect acceleration or deceleration of the vehicle 1205. Fig.23 The vehicle 1205 shown can be an autonomous vehicle, and the navigation system 2300 can be used to provide navigation guidance for autonomous driving. Alternatively, the vehicle 1205 can also be a non-autonomous, human-controlled vehicle, and the navigation system 2300 can still be used to provide navigation guidance.

[0316] 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 the sparse map 800 (which may be stored on a storage device configured to be loaded on the vehicle 1205 and / or received from the server 1230), geometric features sensed by the road profile sensor 2330, images captured by the camera 122, and / or autonomous vehicle road navigation models received from the server 1230. The road profile sensor 2330 may include different types of devices for measuring different types of road profiles (such as road surface roughness, road width, road height, road curvature, etc.). For example, the road profile sensor 2330 may include a device for measuring the movement of the suspension of the vehicle 2305 to obtain a road roughness profile. In some embodiments, the road profile sensor 2330 may include a radar sensor to measure the distance from the vehicle 1205 to the side of the road (e.g., an obstacle on the side of the road), thereby measuring the width of the road. In some embodiments, the road profile sensor 2330 may include a device configured to measure the upper and lower elevations 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 of the road. The vehicle 1205 may use such images to detect the curvature of the road.

[0317] At least one processor 2315 may be programmed to receive at least one environment image associated with vehicle 1205 from camera 122. At least one processor 2315 may analyze at least one environment image to determine navigation information related to vehicle 1205. The navigation information may include a trajectory related to the travel of vehicle 1205 along road segment 1200. At least one processor 2315 may determine the trajectory based on the movement of camera 122 (and thus the vehicle), such as three-dimensional translation and three-dimensional rotation. In some embodiments, at least one processor 2315 may determine the translation and rotation movement of camera 122 based on analysis of multiple images acquired by camera 122. In some embodiments, the navigation information may include lane assignment information (e.g., in which lane vehicle 1205 is traveling along road segment 1200). The navigation information transmitted from vehicle 1205 to server 1230 may be used by server 1230 to generate and / or update an autonomous vehicle road navigation model, which may be transmitted from server 1230 back to vehicle 1205 for providing autonomous navigation guidance for vehicle 1205.

[0318] At least one processor 2315 may also be programmed to transmit navigation information from the vehicle 1205 to the server 1230. In some embodiments, the navigation information may be transmitted to the server 1230 along with the road information. The road location information may include at least one of a GPS signal 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 the model from the server 1230. The autonomous vehicle road navigation model received from the server 1230 may include at least one update based on the navigation information transmitted from the vehicle 1205 to the server 1230. The portion of the model transmitted from the server 1230 to the vehicle 1205 may include an updated portion of the model. At least one processor 2315 may cause at least one navigation maneuver (e.g., steering such as turning, braking, accelerating, overtaking another vehicle, etc.) performed by the vehicle 1205 based on the received autonomous vehicle road navigation model or the updated portion of the model.

[0319] At least one processor 2315 may be configured to communicate with various sensors and components included in the vehicle 1205, including the communication unit 1705, the GPS unit 2315, the camera 122, the speed sensor 2320, the accelerometer 2325, and the road profile sensor 2330. The at least one processor 2315 may collect information or data from the various sensors and components and transmit the information or data to the server 1230 through the communication unit 2305. Alternatively or additionally, the various sensors or components of the vehicle 1205 may also communicate with the server 1230 and transmit the data or information collected by the sensors or components to the server 1230.

[0320] In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 may communicate with each other and may share navigation information with each other, so that at least one of vehicles 1205, 1210, 1215, 1220, and 1225 may generate an autonomous vehicle road navigation model using crowdsourcing, for example, based on information shared by other vehicles. In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 may share navigation information with each other, and each vehicle may update its own autonomous vehicle road navigation model set in the vehicle. In some embodiments, at least one of vehicles 1205, 1210, 1215, 1220, and 1225 (e.g., vehicle 1205) may be used as a hub vehicle. At least one processor 2315 of the hub vehicle (e.g., vehicle 1205) may perform some or all of the functions performed by server 1230. For example, at least one processor 2315 of the hub vehicle may communicate with other vehicles and receive navigation information from other vehicles. At least one processor 2315 of the hub vehicle may generate an autonomous vehicle road navigation model or updates to the model based on shared information received from other vehicles. At least one processor 2315 of the hub vehicle may transmit the autonomous vehicle road navigation model or updates to the model to other vehicles for providing autonomous navigation guidance.

[0321] Navigation based on sparse maps

[0322] As previously discussed, an autonomous vehicle road navigation model including a sparse map 800 may include multiple mapped lane markings and multiple mapped objects / features associated with a road segment. As discussed in more detail below, these mapped lane markings, objects, and features may be used when the autonomous vehicle is navigating. For example, in some embodiments, mapped objects and features may be used to locate the host vehicle relative to a map (e.g., relative to a mapped target track). Mapped lane markings may be used to determine (e.g., as a check) a lateral position and / or orientation relative to a planned or target track. Using this position information, the autonomous vehicle may be able to adjust the heading direction to match the direction of the target track at a determined location.

[0323] The vehicle 200 may be configured to detect lane markings in a given road segment. A road segment may include any markings on a road for directing vehicle traffic on a roadway. For example, lane markings may be continuous lines or dashed lines that delimit the edge of a travel lane. Lane markings may also include double lines, such as double continuous lines, double dashed lines, or a combination of continuous and dashed lines, indicating, for example, whether travel is allowed in an adjacent lane. Lane markings may also include highway entrance and exit markings indicating, for example, a deceleration lane for an exit ramp, or dotted lines indicating that a lane is only turning or that a lane is ending. Markings may further indicate work zones, temporary lane changes, travel paths through intersections, medians, dedicated lanes (e.g., bicycle lanes, HOV lanes, etc.), or other miscellaneous markings (e.g., crosswalks, deceleration humps, railroad crossings, stop lines, etc.).

[0324] The vehicle 200 may use a camera such as the image capture devices 122 and 124 included in the image acquisition unit 120 to capture images of surrounding lane markings. The vehicle 200 may analyze the images to detect point locations associated with the lane markings based on features identified within one or more of the captured images. These point locations may be uploaded to a server to represent the lane markings in the sparse map 800. Depending on the position and field of view of the camera, lane markings may be detected simultaneously for both sides of the vehicle 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 lane markings, the markings may be stored in the sparse map 800 as splines or a series of points, thereby reducing the size of the sparse map 800 and / or the data that must be uploaded remotely by the vehicle.

[0325] FIG. 24A to FIG. 24D Exemplary point locations that can be detected by vehicle 200 to represent a particular lane marking are shown. Similar to the landmarks described above, vehicle 200 can use various image recognition algorithms or software to identify point locations within a captured image. For example, vehicle 200 can identify a series of edge points, corner points, or various other point locations associated with a particular lane marking. Fig.24A Continuous lane markings 2410 are shown that may be detected by vehicle 200. Lane markings 2410 may represent the outer edge of a roadway and are represented by a continuous white line. Fig.24A As shown, vehicle 200 may be configured to detect a plurality of edge location points 2411 along lane markings. Location points 2411 may be collected to represent lane markings at any spacing sufficient to create mapped lane markings in a sparse map. For example, lane markings may be represented by one point per meter of detected edge, one point per five meters of detected edge, or at other suitable spacings. In some embodiments, spacing may be determined by other factors, such as, for example, based on the point at which vehicle 200 has the highest confidence rating for the location of the detected point, rather than at a set interval. Although Fig.24A While edge point locations on the inner edge of lane marking 2410 are shown, points may be collected on the outer edge of the line or along both edges. Fig.24A A single line is shown in , but similar edge points can be detected for dual continuous lines. For example, point 2411 can be detected along the edge of one or more of the continuous lines.

[0326] Vehicle 200 may also represent lane markings differently depending on the type or shape of the lane markings. Fig. 24B An exemplary dashed lane marking 2420 is shown that may be detected by the vehicle 200. Fig.24A By identifying edge points as in , the vehicle can detect a series of corner points 2421 representing the corners of the lane dash to define the complete boundary of the dash. Fig. 24B Each corner of a given dash mark positioned is shown, and the vehicle 200 can detect or upload a subgroup of the points shown in the figure. For example, the vehicle 200 can detect the front edge or front corner of a given dash mark, or can detect two corner points closest to the interior of the lane. In addition, not every dash mark can be captured, for example, the vehicle 200 can capture and / or record points representing samples of dash marks (e.g., every other, every three, every five, etc.) or dash marks at predetermined intervals (e.g., every meter, every five meters, every 10 meters, etc.). For similar lane markings, such as markings showing that the lane is an exit ramp, markings that a specific lane ends, or other various lane signs that may have detectable corner points, corner points can also be detected. Corner points can also be detected for lane markings consisting of a combination of double dash dash lines or continuous lines and dash dash lines.

[0327] In some embodiments, the points uploaded to the server to generate mapped lane markings may represent points other than detected edge points or corner points. Fig.24C A series of points that may represent the centerline of a given lane marking are shown. For example, a continuous lane 2410 may be represented by a centerline point 2441 along the centerline 2440 of the lane marking. In some embodiments, the vehicle 200 may be configured to detect these center points using various image recognition techniques such as convolutional neural networks (CNNs), scale-invariant feature transforms (SIFTs), histogram of oriented gradients (HOG) features, or other techniques. Alternatively, the vehicle 200 may detect other points such as Fig.24A 2411, and centerline points 2441 may be calculated, for example, by detecting points along each edge and determining the midpoint between the edge points. Similarly, dashed lane marking 2420 may be represented by centerline points 2451 along the centerline 2450 of the lane marking. The centerline points may be located at the edges of the dashed lane markings, such as Fig.24C, or at various other locations along the centerline. For example, each dash can be represented by a single point at the geometric center of the dash. These points can also be spaced at predetermined intervals along the centerline (e.g., every meter, 5 meters, 10 meters, etc.). Centerline point 2451 can be detected directly by vehicle 200, or can be based on other detected reference points (such as Fig. 24B The center line can also be used to represent other lane marking types, such as double lines, using similar techniques as described above.

[0328] In some embodiments, vehicle 200 may identify points representing other features, such as a vertex between two intersecting lane markings. Fig.24D An exemplary point representing the intersection between two lane markings 2460 and 2465 is shown. The vehicle 200 can calculate a vertex 2466 representing the intersection between the two lane markings. For example, one of the lane markings 2460 or 2465 can represent other intersections in a train crossing area or a road segment. Although lane markings 2460 and 2465 are shown as crossing each other perpendicularly, various other configurations can be detected. For example, lane markings 2460 and 2465 can cross at other angles, or one or both of the lane markings can terminate at vertex 2466. Similar techniques can also be applied to intersections between dashed lines or other lane marking types. In addition to vertex 2466, various other points 2467 can also be detected, thereby providing further information about the orientation of lane markings 2460 and 2465.

[0329] The vehicle 200 may associate real-world coordinates with each detected point of the lane marking. For example, a location identifier may be generated, including coordinates for each point, to be uploaded to a server for mapping the lane marking. The location identifier may further include other identification information about the point, including whether the point represents a corner point, an edge point, a center point, etc. The vehicle 200 may therefore be configured to determine the real-world position of each point based on the analysis of the image. For example, the vehicle 200 may detect other features in the image, such as the various landmarks described above, to locate the real-world position of the lane marking. This may involve determining the location of the lane marking in the image relative to the detected landmark, or determining the location of the vehicle based on the detected landmark, and then determining the distance from the vehicle (or the target track of the vehicle) to the lane marking. When the landmark is not available, the location of the lane marking point may be determined relative to the location of the vehicle determined based on dead reckoning. The real-world coordinates included in the location identifier may be expressed as absolute coordinates (e.g., latitude / longitude coordinates), or may be relative to other features, such as based on the longitudinal position along the target track and the lateral distance from the target track. The location identifiers may then be uploaded to a server for generation of mapped lane markings in a navigation model, such as sparse map 800. In some embodiments, the server may construct splines representing lane markings for the road segment. Alternatively, vehicle 200 may generate splines and upload them to a server for recording in the navigation model.

[0330] Fig.24E An exemplary navigation model or sparse map for a corresponding road segment including mapped lane markings is shown. The sparse map may include a target trajectory 2475 for the vehicle to travel along the road segment. As described above, the target trajectory 2475 may represent an ideal path for the vehicle to take when traveling on the corresponding road segment, or may be located elsewhere on the road (e.g., the center line of the road, etc.). The target trajectory 2475 can be calculated in various ways as described above, for example, based on the aggregation (e.g., weighted combination) of two or more reconstructed trajectories of vehicles passing through the same road segment.

[0331] In some embodiments, target trajectories can be generated equally for all vehicle types and for all roads, vehicles and / or environmental conditions. However, in other embodiments, various other factors or variables may also be considered when generating target trajectories. Different target trajectories may be generated for different types of vehicles (e.g., private cars, light trucks, and full trailers). For example, a target trajectory with a relatively tighter turning radius may be generated for a small private car compared to a larger 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 conditions or estimated tire conditions, braking conditions or estimated braking conditions, fuel remaining, etc.), or environmental factors (e.g., time of day, visibility, weather, etc.). The target trajectory may also depend on one or more aspects or features of a specific road segment (e.g., speed limit, frequency and size of turns, slope, etc.). In some embodiments, various user settings may also be used to determine the target trajectory, such as a set driving mode (e.g., desired driving aggressiveness, economic mode, etc.).

[0332] The sparse map may also include mapped lane markings 2470 and 2480 representing lane markings along the road segment. The mapped lane markings may be represented by a plurality of location identifiers 2471 and 2481. As described above, the location identifier may include a location in the real world coordinates of a point associated with the detected lane markings. Similar to the target trajectory in the model, the lane markings may also include elevation data and may be represented as a curve in a three-dimensional space. For example, the curve may be a spline connecting a three-dimensional polynomial of a suitable order, which may be calculated based on the location identifier. The mapped lane markings may also include other information or metadata about the lane markings, such as an identifier of the type of lane markings (e.g., between two lanes with the same driving direction, between two lanes with opposite driving directions, the edge of a roadway, etc.) and / or other characteristics of the lane markings (e.g., continuous, dashed, single, double, yellow, white, etc.). In some embodiments, the mapped lane markings may be continuously updated within the model, for example, using crowdsourcing techniques. The same vehicle may upload location identifiers during multiple occasions of traveling the same road segment, or data may be selected from multiple vehicles (such as 1205, 1210, 1215, 1220, and 1225) traveling the road segment at different times. The sparse map 800 may then be updated or refined based on subsequent location identifiers received from the vehicle and stored in the system. As the mapped lane markings are updated and refined, the updated road navigation model and / or sparse map may be distributed to multiple autonomous vehicles.

[0333] Generating mapped lane markings in a sparse map may also include detecting and / or reducing errors based on anomalies in the image or in the actual lane markings themselves. Fig.24F An exemplary anomaly 2495 associated with detecting lane markings 2490 is shown. Anomaly 2495 may appear in an image captured by vehicle 200, for example, from an object that blocks the camera from viewing the lane markings, debris on the lens, etc. In some cases, the anomaly may be caused by the lane markings themselves, which may be damaged or worn, or partially covered by dirt, debris, water, snow or other materials on the road. Anomaly 2495 may cause vehicle 200 to detect an error point 2491. Sparse map 800 can provide correct mapped lane markings and eliminate errors. In some embodiments, vehicle 200 may detect error point 2491, for example, by detecting anomaly 2495 in an image or by identifying errors based on lane marking points detected before and after the anomaly. Based on the detection of the anomaly, the vehicle may ignore point 2491 or may adjust it to be consistent with other detected points. In other embodiments, the error may be corrected after the point has been uploaded, for example, by determining that the point is outside the expected threshold based on other points uploaded during the same trip or based on the aggregation of data from previous trips along the same road segment.

[0334] The mapped lane markings in the navigation model and / or sparse map can also be used for navigation by an autonomous vehicle traversing the corresponding roadway. For example, a vehicle navigating along a target trajectory may periodically use the mapped lane markings in the sparse map to align itself with the target trajectory. As mentioned above, a vehicle may navigate between landmarks based on dead reckoning, where the vehicle uses sensors to determine its ego motion and estimate its position relative to the target trajectory. Errors may accumulate over time, and the position determination of the vehicle relative to the target trajectory may become increasingly inaccurate. Therefore, the vehicle may use the lane markings (and their known locations) that appear in the sparse map 800 to reduce dead reckoning-induced errors in position determination. In this way, the marked lane markings included in the sparse map 800 may act as navigation anchors from which the exact position of the vehicle relative to the target trajectory may be determined.

[0335] Fig.25A An exemplary image 2500 of a vehicle's surroundings that can be used for navigation based on mapped lane markings is shown. Image 2500 may be captured, for example, by vehicle 200 via image capture devices 122 and 124 included in image acquisition unit 120. Image 2500 may include an image of at least one lane marking 2510, such as Fig.25A The image 2500 may also include one or more landmarks 2521, such as road signs, which are used for navigation as described above. Fig.25ASome elements shown in , such as elements 2511 , 2530 , and 2520 , which do not appear in captured image 2500 , but are detected and / or determined by vehicle 200 , are also shown for reference.

[0336] Use the above about FIG. 24A to FIG. 24D and Fig.24F Using various techniques described above, the vehicle may analyze image 2500 to identify lane markings 2510. Various points 2511 may be detected corresponding to features of lane markings in the image. For example, point 2511 may correspond to an edge of a lane marking, a corner of a lane marking, a midpoint of a lane marking, a vertex between two intersecting lane markings, or various other features or locations. Point 2511 may be detected as a location corresponding to a point stored in a navigation model received from a server. For example, if a sparse map containing points representing the centerline of a mapped lane marking is received, point 2511 may also be detected based on the centerline of lane marking 2510.

[0337] The vehicle may also determine a longitudinal position represented by element 2520 and located along the target track. Longitudinal position 2520 may be determined from image 2500, for example, by detecting landmarks 2521 within image 2500 and comparing the measured locations with known landmark locations stored in a road model or sparse map 800. The location of the vehicle along the target track may then be determined based on the distance to the landmarks and the known locations of the landmarks. Longitudinal position 2520 may also be determined from images other than those used to determine the location of lane markings. For example, longitudinal position 2520 may be determined by detecting landmarks in images taken simultaneously or nearly simultaneously with image 2500 from other cameras within image acquisition unit 120. In some cases, the vehicle may not be close to any landmarks or other reference points used to determine longitudinal position 2520. In such cases, the vehicle may navigate based on dead reckoning, and therefore may use sensors to determine its ego motion and estimate longitudinal position 2520 relative to the target track. The vehicle may also determine distance 2530, which represents the actual distance between the vehicle and lane markings 2510 observed in the captured image. The camera angle, the speed of the vehicle, the width of the vehicle, or various other factors may be considered when determining distance 2530.

[0338] Fig.25BA lateral positioning correction of a vehicle based on mapped lane markings in a road navigation model is shown. As described above, vehicle 200 can use one or more images captured by vehicle 200 to determine a distance 2530 between vehicle 200 and lane markings 2510. Vehicle 200 may also have access to a road navigation model, such as a sparse map 800, which may include mapped lane markings 2550 and a target trajectory 2555. Mapped lane markings 2550 may be modeled using the techniques described above (e.g., using crowdsourced location identifiers captured by multiple vehicles). Target trajectory 2555 may also be generated using the various techniques previously described. Vehicle 200 may also determine or estimate a longitudinal position 2520 along target trajectory 2555, as described above with respect to Fig.25A The vehicle 200 may then determine an expected distance 2540 based on the lateral distance between the target trajectory 2555 and the mapped lane markings 2550 corresponding to the longitudinal position 2520. The lateral positioning of the vehicle 200 may be corrected or adjusted by comparing the actual distance 2530 measured using the captured images to the expected distance 2540 from the model.

[0339] Fig.25C and Fig.25D Illustrations associated with another example for locating a host vehicle during navigation based on mapped landmarks / objects / features in a sparse map are provided. Fig.25C A series of images captured from a vehicle navigating along a road segment 2560 is conceptually represented. In this example, the road segment 2560 includes a straight section of a two-lane divided highway marked by road edges 2561 and 2562 and a center lane marker 2563. As shown, the host vehicle is navigating along a lane 2564 associated with a mapped target track 2565. Therefore, in an ideal situation (and without influencing factors such as the presence of a target vehicle or object in the roadway), the host vehicle should closely track the mapped target track 2565 as it navigates along the lane 2564 of the road segment 2560. In practice, the host vehicle may experience drift while navigating along the mapped target track 2565. For efficient and safe navigation, the drift should be maintained within acceptable limits (e.g., + / -10 cm of lateral displacement from the target track 2565 or any other suitable threshold). In order to periodically account for drift and make any required course corrections to ensure that the host vehicle follows the target trajectory 2565, the disclosed navigation system may be able to use one or more mapped features / objects included in the sparse map to locate the host vehicle along the target trajectory 2565 (e.g., determine the lateral and longitudinal position of the host vehicle relative to the target trajectory 2565).

[0340] As a simple example, Fig.25CA speed limit sign 2566 is shown that may appear in five different, sequentially captured images as the host vehicle travels along road segment 2560. For example, at a first time t0, sign 2566 may appear in a captured image near the horizon. As the host vehicle approaches sign 2566, sign 2566 will appear at different 2D X-Y pixel locations in the captured images in subsequent captured images at times t1, t2, t3, and t4. For example, in the captured image space, sign 2566 will move downward and to the right along curve 2567 (e.g., a curve extending through the center of the sign in each of the five captured image frames). As the host vehicle approaches sign 2566, the sign will also appear to increase in size (i.e., it will occupy a larger number of pixels in the subsequently captured images).

[0341] These changes in the image space representation of objects such as signs 2566 can be used to determine the local position of the host vehicle along the target trajectory. For example, as described in the present disclosure, any detectable object or feature (such as a semantic feature such as sign 2566 or a detectable non-semantic feature) can be identified by one or more collection vehicles of a previously traversed road segment (e.g., road segment 2560). The mapping server can collect the collected driving information from multiple vehicles, aggregate and correlate the information, and generate a sparse map that includes, for example, a target trajectory 2565 for a lane 2564 of road segment 2560. The sparse map can also store the location of the sign 2566 (along with type information, etc.). During navigation (e.g., before entering road segment 2560), a map tile including a sparse map for road segment 2560 can be supplied to the host vehicle. In order to navigate in the lane 2564 of road segment 2560, the host vehicle can follow the mapped target trajectory 2565.

[0342] The mapped representation of the landmark 2566 may be used by the host vehicle to locate itself relative to the target track. For example, a camera on the host vehicle will capture an image 2570 of the host vehicle's environment, and the captured image 2570 may include an image representation of the landmark 2566 having a particular size and a particular XY image location, such as Fig.25D2565. For example, based on a sparse map including a representation of marker 2566, the navigation processor of the host vehicle may determine that, in response to the host vehicle traveling along target trajectory 2565, the representation of marker 2566 should appear in the captured image such that the center of marker 2566 will move (in image space) along line 2567. If a captured image (such as image 2570) shows that the center (or other reference point) is displaced from line 2567 (e.g., the expected image space trajectory), the host vehicle navigation system may determine that it was not on target trajectory 2565 at the time of the captured image. However, based on the image, the navigation processor may determine the appropriate navigation correction to return the host vehicle to target trajectory 2565. For example, if analysis shows that the image location of marker 2566 is displaced in the image to the left of the expected image space location on line 2567 by a distance 2572, the navigation processor may cause a heading change of the host vehicle (e.g., change the steering angle of the wheels) to move the host vehicle to the left by a distance 2573. In this manner, each captured image can be used as part of a feedback loop process such that the difference between the observed image position of the landmark 2566 and the expected image trajectory 2567 can be minimized to ensure that the host vehicle continues to travel with little or no deviation along the target trajectory 2565. Of course, the more mapped objects that are available, the more frequently the described positioning techniques can be employed, which can reduce or eliminate drift-induced deviations from the target trajectory 2565.

[0343] The process described above can be used to detect the lateral orientation or displacement of the host vehicle relative to the target track. The positioning of the host vehicle relative to the target track 2565 can also include the determination of the longitudinal position of the target vehicle along the target track. For example, the captured image 2570 includes a representation of the marker 2566 having a particular image size (e.g., a 2D XY pixel area). As the mapped marker 2566 travels through the image space along the line 2567 (e.g., as the size of the marker gradually increases, such as Fig.25C 2565 ), which size can be compared to the expected image size of the mapped sign. Based on the image size of sign 2566 in image 2570, and based on the expected size progression in image space relative to mapped target track 2565, the host vehicle can determine its longitudinal position relative to target track 2565 (at the time when image 2570 was captured). This longitudinal position coupled with any lateral displacement relative to target track 2565 as described above allows for complete positioning of the host vehicle relative to target track 2565 as the host vehicle navigates along road 2560.

[0344] Fig.25C and Fig.25DOnly one example of the disclosed positioning technique using a single mapped object and a single target track is provided. In other examples, there may be more target tracks (e.g., one target track for each drivable lane of a multi-lane highway, city streets, complex intersections, etc.) and there may be more mappings available for positioning. For example, a sparse map representing an urban environment may include many objects per meter that can be used for positioning.

[0345] Fig.26A Flowchart showing an exemplary process 2600A for mapping lane markings for use in autonomous vehicle navigation consistent with the disclosed embodiments. At step 2610, process 2600A may include receiving two or more location identifiers associated with detected lane markings. For example, step 2610 may be performed by server 1230 or one or more processors associated with the server. The location identifier may include the location of a point associated with the detected lane marking in real-world coordinates, as described above with respect to Fig.24E As described. In some embodiments, the location identifier may also include other data, such as additional information about the road segment or lane markings. Additional data may also be received during step 2610, such as accelerometer data, speed data, landmark data, road geometry or profile data, vehicle positioning data, self-motion data, or various other forms of data described above. The location identifier may be generated by a vehicle such as vehicles 1205, 1210, 1215, 1220, and 1225 based on images captured by the vehicle. For example, the identifier may be determined based on obtaining at least one image representing the environment of the main vehicle from a camera associated with the main vehicle, analyzing at least one image to detect lane markings in the environment of the main vehicle, and analyzing at least one image to determine the position of the detected lane markings relative to the location associated with the main vehicle. As described above, lane markings may include a variety of different marking types, and the location identifier may correspond to a variety of points relative to the lane markings. For example, in the case where the detected lane markings are part of a short dashed line marking a lane boundary, the point may correspond to a detected corner of the lane markings. In the case where the detected lane marking is part of a continuous line marking the lane boundary, the point may correspond to the detected edge of the lane marking, with various spacings as described above. In some embodiments, the point may correspond to the centerline of the detected lane marking, such as Fig.24C As shown, or may correspond to a vertex between two intersecting lane markings and at least two other points associated with the intersecting lane markings, such as Fig.24D shown.

[0346] At step 2612, process 2600A may include associating the detected lane markings with corresponding road segments. For example, server 1230 may analyze the real-world coordinates or other information received during step 2610 and compare the coordinates or other information with location information stored in the autonomous vehicle road navigation model. Server 1230 may determine a road segment in the model that corresponds to a real-world road segment where the lane markings were detected.

[0347] At step 2614, process 2600A may include updating an autonomous vehicle road navigation model relative to the corresponding road segment based on the two or more location identifiers associated with the detected lane markings. For example, the autonomous road navigation model may be sparse map 800, and server 1230 may update the sparse map to include or adjust the mapped lane markings in the model. Server 1230 may update the sparse map based on the above description of the lane markings. Fig.24E In some embodiments, updating the autonomous vehicle road navigation model may include storing one or more indicators of the locations of detected lane markings in real-world coordinates. The autonomous vehicle road navigation model may also include at least one target trajectory for the vehicle to travel along the corresponding road segment, such as Fig.24E shown.

[0348] At step 2616, process 2600A may include distributing the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles. For example, server 1230 may distribute the updated autonomous vehicle road navigation model to vehicles 1205, 1210, 1215, 1220, and 1225 that may use the model for navigation. The autonomous vehicle road navigation model may be distributed via one or more networks (e.g., via a cellular network and / or the Internet, etc.), via wireless communication paths 1235, such as Fig.12 shown.

[0349] In some embodiments, lane markings may be mapped using data received from multiple vehicles, such as through crowdsourcing techniques, as described above with respect to Fig.24EAs described. For example, process 2600A may include receiving a first communication from a first host vehicle including a location identifier associated with a detected lane marking, and receiving a second communication from a second host vehicle including an additional location identifier associated with the detected lane marking. For example, the second communication may be received from a subsequent vehicle traveling on the same road segment, or from the same vehicle on a subsequent trip along the same road segment. Process 2600A may further include refining the determination of at least one location associated with the detected lane marking based on the location identifier received in the first communication and based on the additional location identifier received in the second communication. This may include using an average of multiple location identifiers and / or filtering out "ghost" identifiers that may not reflect the real-world location of the lane marking.

[0350] Fig.26B 26 is a flow chart illustrating an exemplary process 2600B for autonomously navigating a host vehicle along a road segment using mapped lane markings. Process 2600B may be performed, for example, by processing unit 110 of autonomous vehicle 200. At step 2620, process 2600B may include receiving an autonomous vehicle road navigation model from a server-based system. In some embodiments, the autonomous vehicle road navigation model may include a target trajectory for the host vehicle along the road segment and a location identifier associated with one or more lane markings associated with the road segment. For example, vehicle 200 may receive sparse map 800 or another road navigation model developed using process 2600A. In some embodiments, the target trajectory may be represented as a three-dimensional spline, such as Fig. 9B As mentioned above FIG. 24A to FIG. 24F As described, the location identifier may include a location in real-world coordinates of a point associated with a lane marking (e.g., a corner point of a dashed lane marking, an edge point of a continuous lane marking, a vertex between two intersecting lane markings and other points associated with intersecting lane markings, a center line associated with a lane marking, etc.).

[0351] At step 2621, process 2600B may include receiving at least one image representing an environment of the vehicle. The image may be received from an image capture device of the vehicle, such as by image capture devices 122 and 124 included in image acquisition unit 120. The image may include an image of one or more lane markings, similar to image 2500 described above.

[0352] At step 2622, process 2600B may include determining the longitudinal position of the host vehicle along the target trajectory. Fig.25A As described, this may be based on other information in the captured image (eg, landmarks, etc.) or by dead reckoning the vehicle between detected landmarks.

[0353] At step 2623, process 2600B may include determining an expected lateral distance to a lane marking based on the determined longitudinal position of the host vehicle along the target trajectory and based on two or more location identifiers associated with at least one lane marking. For example, vehicle 200 may use sparse map 800 to determine the expected lateral distance to the lane marking. Fig.25B As shown, longitudinal position 2520 along target trajectory 2555 may be determined in step 2622. Using sparse map 800, vehicle 200 may determine expected distance 2540 to mapped lane marking 2550 corresponding to longitudinal position 2520.

[0354] At step 2624, process 2600B may include analyzing at least one image to identify at least one lane marking. For example, vehicle 200 may use various image recognition techniques or algorithms to identify lane markings within the image, as described above. For example, lane marking 2510 may be detected by image analysis of image 2500, such as Fig.25A shown.

[0355] At step 2625, process 2600B may include determining an actual lateral distance to at least one lane marking based on the analysis of at least one image. For example, the vehicle may determine distance 2530 representing the actual distance between the vehicle and lane marking 2510, such as Fig.25A The camera angle, the speed of the vehicle, the width of the vehicle, the position of the camera relative to the vehicle, or various other factors may be considered when determining distance 2530.

[0356] At step 2626, process 2600B may include determining an autonomous steering action for the host vehicle based on a difference between an expected lateral distance to at least one lane marking and a determined actual lateral distance to at least one lane marking. Fig.25B As described, vehicle 200 may compare actual distance 2530 with expected distance 2540. The difference between the actual distance and the expected distance may indicate an error (and its magnitude) between the actual position of the vehicle and the target trajectory to be followed by the vehicle. Therefore, the vehicle may determine an autonomous steering action or other autonomous action based on the difference. For example, if actual distance 2530 is less than expected distance 2540, such as Fig.25B As shown, the vehicle can determine an autonomous steering action to guide the vehicle to the left away from lane marking 2510. Therefore, the position of the vehicle relative to the target trajectory can be corrected. Process 2600B can be used, for example, to improve the navigation of the vehicle between landmarks.

[0357] Processes 2600A and 2600B provide only examples of techniques that may be used to navigate a host vehicle using the disclosed sparse map. In other examples, the same techniques as those described above may be used to navigate a host vehicle using the disclosed sparse map. Fig.25C and Fig.25D Processes consistent with those described.

[0358] Traffic light orientation network

[0359] As described in the above sections, a host vehicle (e.g., an autonomous vehicle or a semi-autonomous vehicle) can navigate along a road segment in an environment using mapped information, such as target trajectories, object locations, road topography, etc. One or more cameras associated with the host vehicle can capture a series of images representing objects and road features present in the host vehicle's environment. Based on analysis of the images, and in some cases, based on comparisons between various aspects of the images and the mapped information, the host vehicle navigation system can generate a planned navigation action for the host vehicle. For example, in some cases, the captured image may include a representation of a traffic light. Comparing the image representation of the traffic light with the mapped traffic light relevance information can, for example, indicate whether the captured traffic light is relevant to the host vehicle (e.g., whether the traffic light is relevant to the driving lane of the road segment on which the host vehicle is traveling). If it is determined that the traffic light is relevant, how the host vehicle navigation system responds to the traffic light (e.g., whether to slow down or stop the vehicle, whether to maintain the current speed, etc.) will depend on the state of the traffic light. For example, if the traffic light is determined to include an illuminated red light, the host vehicle navigation system can cause the vehicle's brakes to be applied. If the traffic light is determined to include an illuminated green light, the host vehicle navigation system may cause the host vehicle to maintain a current speed, initiate forward movement from rest, and the like.

[0360] However, in some cases, detection of the state of a traffic light may be difficult or impossible. For example, in some cases, the relevant traffic light may be blocked or partially blocked from being viewed by a camera mounted on the host vehicle. Such blockage may be caused by other vehicles, pedestrians, etc. In other cases, environmental conditions may increase the difficulty of determining the state of the traffic light represented in the captured image. For example, rain, snow, fog, glare, etc. may all result in increased difficulty in detecting the state of the traffic light.

[0361] The disclosed embodiments are intended to provide a robust traffic light detection system to assist a vehicle navigation system in making navigation decisions related to traffic lights. For example, the disclosed embodiments may employ two traffic light navigation systems. One or more of the systems may include a trained model, as discussed in more detail below, and the systems may operate independently (e.g., each system may generate an output without relying on the output of another system). The first system may be configured to detect or infer the state of a traffic light based on an input image. Based on the detected state and optionally based on mapped traffic light correlation information, the first system may provide a first output indicating a recommended navigation action for a host vehicle. The second system may be configured to generate a second output indicating a recommended navigation action for a host vehicle. Rather than relying solely on the state of the traffic light, the second system may be configured to infer a more comprehensive scenario of the scene represented by the input image as compared to the scenario provided by the first system, and use the more comprehensive scenario to generate a second output indicating a recommended navigation action for the host vehicle.

[0362] The host vehicle navigation system can then use the combination of the first and second system outputs to plan and execute navigation actions for the host vehicle. One potential advantage provided by this approach is redundancy, which can enhance the operation of the navigation system, especially in the case where the operational capability of either of the two traffic light systems is impaired. Another potential advantage is the use of a wider field of view of complex scenes and multiple "hints" from the behavior of different objects in the scene and the layout of objects appearing in the scene to gain insight and understand or enforce their benefits. In addition, the disclosed embodiments can provide the possibility of weighting one output relative to another based on the associated confidence level. For example, if a particular traffic light is blocked or partially blocked and cannot be viewed by a camera mounted on the host vehicle, the confidence of the first system output (e.g., based on the state of the traffic light) may be lower than under normal conditions. In such cases, the host vehicle navigation system may rely more heavily on the output of the second system, especially when the confidence level associated with the second system output is normal or higher than the confidence level in the first output. The following sections discuss various aspects of the disclosed embodiments in more detail.

[0363] The disclosed network of traffic light orientations can assist in generating effective, safe planned navigation actions in response to relevant traffic lights, even in situations where determining the lighting state of the relevant traffic lights is more difficult than normal (e.g., when relying solely on the first system mentioned above). For example, the disclosed system can assist in generating appropriate planned navigation actions where one or more obstructions at least partially block the field of view of the host vehicle's camera, where the traffic lights include malfunctioning lights (or lights at low lighting levels), where weather or other environmental conditions hinder traffic light state determination, etc. In some cases, the disclosed system can assist in generating appropriate navigation actions where the determined / inferred state of the traffic light suggests one action (e.g., a detected "green" state of the traffic light indicating that the host vehicle is free to proceed through the intersection), but a more comprehensive analysis of the scene indicates another action (e.g., another vehicle has entered the intersection at a red light).

[0364] In some embodiments, the disclosed system may use one or more AV maps. An autonomous vehicle (AV) map (or AV map) may include information for supporting and / or implementing one or more autonomous vehicle (AV) functions of a vehicle in a manner that enables the vehicle to operate in a safe manner and / or navigate in an accurate manner. AV functions supported and / or implemented by an autonomous vehicle (or semi-autonomous vehicle) may include one or more autonomously controlled functions (e.g., functions determined, selected, and / or implemented based on instructions executed by at least one processor) such as steering, accelerating, and / or braking the vehicle. AV functions may be part of a driving strategy such as RSS, which is developed and implemented, for example, by Mobileye of Israel. Information used to operate the vehicle in a safe manner may include, but is not limited to, information related to one or more regulations applicable to the location or jurisdiction of the vehicle (e.g., right-hand traffic or left-hand traffic jurisdiction), information related to the environment in which the vehicle is located (e.g., information related to drivable paths, stop signs, traffic lights, speed limits, lane markings, landmarks, free space, virtual or physical stop lines, traffic light correlation information, etc.), and / or information related to adjusting and / or adapting the navigation of the vehicle to take into account one or more objects in the vehicle's environment (e.g., other vehicles, pedestrians, objects, obstacles, obstructions, hazards, road work zones, traffic cones, etc.). The vehicle may use one or more sensors (e.g., cameras, radars, lidars) to sense objects in the vehicle's environment, as discussed herein. The AV map may serve as a redundant information source for the sensed information, and in some cases, may also supplement the sensed information (e.g., providing locations of virtual stop lines where there are no marked stop lines on the road). In some embodiments, operating the vehicle in a safe manner may further include operating the vehicle to maintain a comfort level for one or more passengers in the vehicle. The comfort level may include one or more predetermined criteria (e.g., related to speed, acceleration, and / or turning) selected so that the vehicle operates in a manner that meets or exceeds a specified or selected passenger comfort level. The comfort level may be formalized and expressed using an appropriate mathematical formula. For example, a mathematical formula that limits the degree of jerkiness or acceleration applied to a passenger in different directions may be used. Information used to operate the vehicle in an accurate manner may include, but is not limited to, information related to a planned or specified navigation path (e.g., a trajectory, as discussed herein) or a route (e.g., from a specific location such as a starting location to a destination). In some embodiments, the information included in the AV map may further include information that supports and / or implements one or more AV functions in an effective manner.Information used to operate the vehicle in an efficient manner may include information related to speed, acceleration, lane changes, and / or lane positioning, and / or information about traveling and / or selecting a path or route based on traffic conditions (e.g., traveling a route that is farther than a shorter route that experiences traffic conditions) and / or other factors or attributes related to potential routes (e.g., weather conditions, road conditions, or other characteristics of the route, such as traveling on a highway without traffic lights rather than a road with traffic lights). In some embodiments, the AV map may further include at least some information from a high-resolution (HD) map. In some embodiments, the AV map may be a sparse map, as described above. In some embodiments, the AV map may be crowdsourced, as discussed herein. In this disclosure, the terms "AV map" and "sparse map" are used interchangeably.

[0365] Fig.27A and Fig.27B Two example scenarios are provided to further illustrate the operation of the disclosed traffic light oriented network. Fig.27A 2705. In this example, bus 2705 may obscure a relevant traffic light 2720 (not shown). Although relevant traffic light 2720 may not be detectable in images captured by a camera mounted on the host vehicle, the host vehicle navigation system may be aware of traffic light 2720 based on information (e.g., relevance and / or location information) stored in one or more AV maps. In such cases, the disclosed system may be based on indicating that a representation of traffic light 2720 should be present in the image shown. Fig.27A Based on the mapped information within a particular region or segment of the captured image of the scene (and, for example, the known local position of the host vehicle relative to the map), the particular segment of the captured image is analyzed as possibly including a representation of the traffic light 2720. In such a case, the first system (i.e., the system based on the state of the traffic light) may be unable to determine or infer the state of the traffic light 2720. Therefore, any output generated by the first system may be associated with a low confidence level (which may also be output as a parameter of the first (or second) system).

[0366] To determine an appropriate planned navigation action for the host vehicle, the vehicle navigation system in this case may rely more heavily on the output of the second system of the disclosed network of traffic light orientations. For example, based on the overall view of the scene and contextual information discernible from the overall view, the second system may infer the state of the traffic light 2710 and may generate an output to the vehicle navigation system indicating a recommended navigation action for the host vehicle. Based on the second trained system (which may include a representation of Fig.27A27 (a captured image of most or all of the scene shown), the trained system may rely on one or more indicators represented in the captured image that are obscured from the state of traffic light 2720. For example, the state of traffic light 2710, traffic light 2711, crosswalk sign 2715, and / or the brake lights of bus 2705 may be used to infer the state of the associated traffic light 2720. The illuminated brake lights of bus 2705 indicate that traffic light 2720 is in a red state and / or that the host vehicle should stop or remain stationary. Similarly, an illuminated red light associated with traffic light 2711 indicates that the host vehicle should stop or remain stationary (particularly where the mapped information indicates that traffic light 2711 is also associated with the host vehicle). Additionally, the green illumination light associated with traffic light 2710 and the stop signal associated with crosswalk sign 2715 may also indicate that the host vehicle should stop or remain stationary, particularly where the mapped information confirms that traffic light 2710 is associated with an intersection street 2713 and where the location / orientation of crosswalk sign 2715 indicates that sign 2715 controls pedestrian traffic across intersection street 2713 (which crosses street 2714 on which the host vehicle is traveling). In other words, if traffic light 2710 indicates that cars traveling on street 2713 may freely cross street 2714, the second trained system may use this information as a clue that the host vehicle should not proceed through the intersection with street 2713. Similarly, if crosswalk sign 2715 indicates that pedestrians may not freely cross street 2713, the second trained system may use this information as a clue that the host vehicle should also not proceed through street 2713. Based on the information associated with crosswalk sign 2715, the second trained system may use this information as a clue that the host vehicle should not proceed through street 2713. Fig.27A Based on the comprehensive information associated with the scene of the host vehicle 2710, the second trained system can generate an output indicating a recommendation that the host vehicle should stop, remain stationary, and / or not cross the street ahead 2713. Such output from the second trained system can assist the host vehicle navigation system in generating appropriate planned navigation actions for the host vehicle, even in the case where the state of the traffic light 2720 cannot be determined, for example, by the first trained system.

[0367] Fig.27B Another scenario is shown in which the disclosed system can assist in generating a suitable planned navigation action for the host vehicle. In this case, a traffic light 2725 associated with the host vehicle can be detected in the captured image representing the scene. In this case, Fig.27A In contrast to the scenario, traffic light 2725 is not completely obscured. However, due to environmental conditions (e.g., snow, rain, sleet, etc.), the inference made by the first trained system about the state of traffic light 2725 may be associated with a confidence level that is less than the confidence level associated with the state inference made under clearer / fairer / normal conditions. For example, Fig.27BThe presence of weather in the scene may reduce visibility, which may reduce the image quality of the representation of the traffic light 2725 included in the captured image. Therefore, the traffic light state determination made by the first trained system based on the captured image may provide an insufficient confidence level (e.g., less than a predetermined confidence threshold) in the detected state of the associated traffic light 2725.

[0368] In the Fig. 27 scenario, the output of the second trained system based on the comprehensive view of the scene may also be valuable in determining appropriate navigation actions for the host vehicle. As shown in Fig. 27, a pedestrian 2730 is present at the crosswalk. Fig.27B The presence of the representation of pedestrian 2730 in the captured image of the scene may serve as contextual information from which the second trained system may infer that the host vehicle cannot freely proceed through the intersection. Thus, if, for example, due to weather, the first trained system is unable to provide an output associated with a confidence level above a predetermined threshold, the host vehicle navigation system may still be able to plan an appropriate navigation action for the host vehicle (e.g., stop, remain stationary, etc.) based on the output of the second trained system.

[0369] Fig.28 Flowchart showing an exemplary process 2800 for a navigation system of a host vehicle. Process 2800 shows an exemplary process, wherein the system may include at least one processor, the at least one processor including a circuit system and a memory, wherein the memory includes instructions that, when executed by the circuit system, cause the at least one processor to receive a captured image representing an environment of the host vehicle from an image capture device associated with the host vehicle. The host vehicle may be an autonomous or semi-autonomous vehicle, as described above. The host vehicle may include one or more cameras mounted on the host vehicle. The one or more cameras may be positioned at different locations and / or orientations relative to the host vehicle, so that each of the one or more cameras may provide a different field of view relative to the host vehicle. The host vehicle may further include a processor configured to identify and / or classify one or more objects in the environment of the host vehicle. It should be understood that the disclosed embodiments may be used to identify and classify a wide variety of objects, including traffic lights, pedestrians, vehicles, lane markings, road signs, highway exit ramps, road obstacles or hazards (e.g., road debris, etc.), and any other features associated with the environment of the host vehicle. Such classification may be accomplished using any suitable technology. In some cases, classification may be included as part of an image segmentation process, where one or more regions of a captured image are analyzed to return a prediction or characterization of an object, object type, etc. represented in one or more regions of the captured image. Any of the systems or operations described in the above sections may be incorporated with the disclosed network for traffic light orientation.

[0370] exist Fig.28 In step 2810, one or more processors associated with the disclosed embodiments may receive a captured image representing an environment of the host vehicle from an image capture device associated with the host vehicle. At step 2820, the processor may identify a first segment of the captured image associated with a traffic light (e.g., using an image segmentation process). The first segment may be associated with the traffic light based on a detection of a representation of the traffic light in the first segment. The first seg...

Claims

1. A navigation system for a host vehicle, the system comprising: at least one processor comprising circuitry and memory, wherein the memory comprises instructions that, when executed by the circuitry, cause the at least one processor to: receiving, from an image capture device associated with the host vehicle, a captured image representative of an environment of the host vehicle; identifying a first segment of the captured image associated with a traffic light; providing the first segment of the captured image to a first trained network configured to generate a first output indicative of a state of the traffic light; identifying a second segment of the captured image, the second segment comprising contextual information associated with the traffic light; providing the second segment to a second trained network configured to generate a second output indicative of a proposed navigation action for the host vehicle relative to the traffic light; determining a planned navigation maneuver for the host vehicle based on both the first output from the first trained network and the second output from the second trained network; and The host vehicle is caused to take the planned navigation action. 2 . The system of claim 1 , wherein the first output indicative of a state of the traffic light comprises an indication of an illumination state of one or more lights included on the traffic light. 3 . The system of claim 1 , wherein the first output indicative of a state of the traffic light comprises an indication of a color associated with one or more lights associated with the traffic light. The system of claim 1 , wherein the first output indicative of a state of the traffic light comprises a point detection.

5. The system of claim 4, wherein the point detection comprises at least one of illumination detection, color detection, and shape detection.

6. The system of claim 1, wherein the first output indicative of a state of the traffic light represents at least one of a stop, go, or slow signal.

7. The system of claim 1, wherein the memory includes instructions that, when executed by the circuit system, cause the at least one processor to determine whether the traffic light is associated with the host vehicle. 8 . The system of claim 7 , wherein the determination of whether the traffic light is relevant to the host vehicle is determined based on traffic light relevance information stored in a map. 9 . The system of claim 8 , wherein the traffic light correlation information is stored in the map for each of a plurality of lanes of a road segment. 10 . The system of claim 8 , wherein traffic light relevance is further based on a determined lane of travel for the host vehicle.

11. The system of claim 7, wherein the first segment of the captured image is provided to the first trained network based on whether the traffic light is determined to be relevant to the host vehicle.

12. The system of claim 7, wherein if the traffic light is determined to be not relevant to the host vehicle, then the first segment of the captured image is not provided to the first trained network.

13. The system of claim 1, wherein the first segment of the captured image includes a representation of the traffic light that is at least partially obscured. The system of claim 13 , wherein the traffic light is at least partially obscured by one or more objects.

15. The system of claim 14, wherein the one or more objects include at least one of a truck or a tree branch.

16. The system of claim 13, wherein the traffic light is at least partially obscured by environmental conditions.

17. The system of claim 16, wherein the environmental condition comprises at least one of fog, rain, snow, or glare.

18. The system of claim 1, wherein the first segment of the captured image is determined based on map information storing a location of the traffic light.

19. The system of claim 18, wherein the first segment of the captured image corresponds to an area of ​​the captured image where a representation of the traffic light is expected to appear based on the map information.

20. The system of claim 19 wherein the representation of the traffic light is not visible or not clearly visible in the first segment, the first output indicating a state of the traffic light is associated with a confidence level below a predetermined threshold, and in response, the second output of the second trained network is weighted more heavily than the first output of the first trained network in determining the planned navigation action for the host vehicle.

21. The system of claim 1, wherein the second segment of the captured image comprises the entire captured image.

22. The system of claim 1, wherein the second segment of the captured image includes the first segment of the captured image.

23. The system of claim 1, wherein the contextual information includes a status of a pedestrian crossing signal.

24. The system of claim 1, wherein the contextual information includes a representation of pedestrians present in an intersection associated with the traffic light.

25. The system of claim 1, wherein the contextual information includes one or more indicators of movement of pedestrians.

26. The system of claim 1, wherein the contextual information includes one or more indicators of movement of one or more target vehicles.

27. The system of claim 1, wherein an indicator of a confidence level associated with the first output indicating the state of the traffic light is output from the first trained network.

28. The system of claim 27, wherein the indicator of the confidence level output from the first trained network is used to determine a weight distribution between the first output of the first trained network and the second output of the second trained network, the weight distribution being used to determine the planned navigation action for the host vehicle.

29. The system of claim 1, wherein an indicator is output from the second trained network that is associated with a confidence level of the second output indicative of a proposed navigation action for the host vehicle relative to the traffic light.

30. The system of claim 29, wherein the indicator of the confidence level output from the second trained network is used to determine a weight distribution between the first output of the first trained network and the second output of the second trained network, the weight distribution being used to determine the planned navigation action for the host vehicle.

31. The system of claim 1, wherein the navigation action instructed by the second trained network comprises at least one of stopping, slowing down, maintaining speed, or moving forward.

32. A method for navigating a host vehicle, the method comprising: receiving, from an image capture device associated with the host vehicle, a captured image representative of an environment of the host vehicle; identifying a first segment of the captured image associated with a traffic light; providing the first segment of the captured image to a first trained network configured to generate a first output indicative of a state of the traffic light; identifying a second segment of the captured image, the second segment comprising contextual information associated with the traffic light; providing the second segment to a second trained network configured to generate a second output indicative of a proposed navigation action for the host vehicle relative to the traffic light; determining a planned navigation maneuver for the host vehicle based on both the first output from the first trained network and the second output from the second trained network; and The host vehicle is caused to take the planned navigation action.

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