System and method for vehicle navigation

Abstract

Systems and methods for sending map data to a vehicle based on a potential driving envelope of the vehicle. The shape of the envelope is determined based on the speed, position, and direction of travel of the vehicle.

Description

[0001] This application is a divisional application of the invention patent application with application number 202080016708.X, application date February 25, 2020, and invention name “System and method for vehicle navigation”.

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS

[0003] This application claims the benefit of priority to U.S. Provisional Application No. 62 / 810,032, filed on February 25, 2019, U.S. Provisional Application No. 62 / 956,993, filed on January 3, 2020, U.S. Provisional Application No. 62 / 956,997, filed on January 3, 2020, U.S. Provisional Application No. 62 / 957,017, filed on January 3, 2020, U.S. Provisional Application No. 62 / 957,019, filed on January 3, 2020, and U.S. Provisional Application No. 62 / 957,028, filed on January 3, 2020. The above applications are incorporated herein by reference in their entirety. Technical Field

[0004] The present disclosure relates generally to vehicle navigation. Background Art

[0005] As technology continues to advance, the goal of fully autonomous vehicles that can navigate on roads is imminent. 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), information from radar or lidar, 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 road (e.g., a specific lane in a multi-lane road), navigate side by side with other vehicles, avoid obstacles and pedestrians, observe traffic signals and signs, and drive from one road to another at an appropriate intersection or junction. Harnessing and interpreting the large amount of information collected by the autonomous vehicle when it travels to its destination creates many design challenges. The massive amount of data (e.g., captured image data, map data, GPS data, sensor data, etc.) that the autonomous vehicle may need to analyze, access and / or store creates challenges that may actually limit or even adversely affect autonomous navigation. Furthermore, if autonomous vehicles rely on traditional mapping technologies to navigate, the massive amounts of data required to store and update maps will pose a huge challenge. Summary of the invention

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

[0007] In one embodiment, a map management system may provide one or more map segments to one or more vehicles. The system may include at least one processor programmed to receive navigation information from a vehicle, the navigation information including an indicator of the vehicle's location, an indicator of the vehicle's speed, and an indicator of the vehicle's direction of travel. The at least one processor may also be programmed to analyze the received navigation information and determine a potential travel envelope for the vehicle. The at least one processor may also be programmed to transmit one or more map segments to the vehicle, the one or more map segments including map information for a geographic area that at least partially overlaps with the potential travel envelope of the vehicle.

[0008] In one embodiment, a computer-implemented method may provide one or more map segments to one or more vehicles. The method may include receiving navigation information from a vehicle, the navigation information including an indicator of a location of the vehicle, an indicator of a speed of the vehicle, and an indicator of a direction of travel of the vehicle. The method may also include analyzing the received navigation information and determining a potential travel envelope of the vehicle. The method may also include transmitting one or more map segments to the vehicle, the one or more map segments including map information of a geographic area that at least partially overlaps with the potential travel envelope of the vehicle.

[0009] In one embodiment, a non-transitory computer-readable medium may store instructions that, when executed by at least one processor, are configured to cause the at least one processor to receive navigation information from a vehicle, the navigation information including an indicator of the vehicle's position, an indicator of the vehicle's speed, and an indicator of the vehicle's direction of travel. The instructions may also cause the at least one processor to analyze the received navigation information and determine a potential travel envelope for the vehicle. The instructions may also cause the at least one processor to send one or more map segments to the vehicle, the one or more map segments including map information for a geographic area that at least partially overlaps with the potential travel envelope of the vehicle.

[0010] In one embodiment, a system can automatically generate a navigation map about one or more road segments. The system may include at least one processor programmed to cause the collection of first navigation information associated with an environment through which a main vehicle passes. The first navigation information may be associated with a first density level. The at least one processor may also be programmed to determine the position of the main vehicle based on an output associated with one or more sensors of the main vehicle. The at least one processor may also be programmed to determine whether the position of the main vehicle is at a geographic area of ​​interest or within a predetermined distance from the geographic area of ​​interest. The at least one processor may also be programmed to cause the collection of second navigation information associated with an environment through which the main vehicle passes based on the determination that the position of the main vehicle is at a geographic area of ​​interest or within a predetermined distance from the geographic area of ​​interest. The second navigation information may be associated with a second density level greater than the first density level. The at least one processor may also be programmed to upload at least one of the collected first navigation information or the collected second navigation information from the main vehicle from the main vehicle, and update the navigation map based on at least one of the uploaded collected first navigation information or the collected second navigation information.

[0011] In one embodiment, a computer-implemented method can automatically generate a navigation map about one or more road segments. The method may include causing the collection of first navigation information associated with an environment traversed by a main vehicle. The first navigation information may be associated with a first density level. The method may also include determining the position of the main vehicle based on an output associated with one or more sensors of the main vehicle. The method may also include determining whether the position of the main vehicle is at a geographic area of ​​interest or within a predetermined distance from the geographic area of ​​interest. Based on the determination that the position of the main vehicle is at a geographic area of ​​interest or within a predetermined distance from the geographic area of ​​interest, cause the collection of second navigation information associated with the environment traversed by the main vehicle. The second navigation information may be associated with a second density level greater than the first density level. The method may also include uploading from the main vehicle at least one of the collected first navigation information or the collected second navigation information from the main vehicle, and updating the navigation map based on at least one of the uploaded collected first navigation information or the collected second navigation information.

[0012] In one embodiment, a non-transitory computer-readable medium may store instructions that, when executed by at least one processor, are configured to cause the at least one processor to cause the collection of first navigation information associated with an environment traversed by a host vehicle. The first navigation information may be associated with a first density level. The instructions may also cause the at least one processor to determine the location of the host vehicle based on outputs associated with one or more sensors of the host vehicle. The instructions may also cause the at least one processor to determine whether the location of the host vehicle is at a geographic area of ​​interest or within a predetermined distance from the geographic area of ​​interest. The instructions may also cause the at least one processor to cause the collection of second navigation information associated with an environment traversed by the host vehicle based on the determination that the location of the host vehicle is at a geographic area of ​​interest or within a predetermined distance from the geographic area of ​​interest. The second navigation information may be associated with a second density level greater than the first density level. The instructions may also cause the at least one processor to upload at least one of the collected first navigation information or the collected second navigation information from the host vehicle from the host vehicle, and update the navigation map based on at least one of the uploaded collected first navigation information or the collected second navigation information.

[0013] Consistent with other disclosed embodiments, a non-transitory computer-readable storage medium may store program instructions that are executed by at least one processing device and may perform any of the methods described herein.

[0014] 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

[0015] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various disclosed embodiments. In the drawings:

[0016] Figure 1 are pictorial representations of exemplary systems consistent with the disclosed embodiments.

[0017] Figure 2A is a diagrammatic side view representation of an exemplary vehicle incorporating systems consistent with the disclosed embodiments.

[0018] Figure 2B yes Figure 2A A diagrammatic top view representation of a vehicle and systems consistent with the disclosed embodiments is shown in FIG.

[0019] Figure 2C is a diagrammatic top view representation of another embodiment of a vehicle incorporating a system consistent with the disclosed embodiments.

[0020] Figure 2D is a diagrammatic top view representation of yet another embodiment of a vehicle incorporating a system consistent with the disclosed embodiments.

[0021] Figure 2E is a diagrammatic top view representation of yet another embodiment of a vehicle incorporating a system consistent with the disclosed embodiments.

[0022] Figure 2F is a pictorial representation of an exemplary vehicle control system consistent with the disclosed embodiments.

[0023] Figure 3A is a pictorial 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.

[0024] 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.

[0025] Figure 3C yes Figure 3B 00140 10 ...

[0026] 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.

[0027] Figure 4 is an exemplary block diagram of a memory configured to store instructions for performing one or more operations consistent with the disclosed embodiments.

[0028] Figure 5A is a flow chart illustrating an exemplary process for eliciting one or more navigation responses based on monocular image analysis consistent with the disclosed embodiments.

[0029] Figure 5B is 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.

[0030] Figure 5C is a flow chart illustrating an exemplary process for detecting road signs and / or lane geometry information in a set of images consistent with the disclosed embodiments.

[0031] Figure 5D is a flow chart illustrating an exemplary process for detecting traffic lights in a set of images consistent with the disclosed embodiments.

[0032] Figure 5E is a flow chart illustrating an exemplary process for inducing one or more navigation responses based on a vehicle path, consistent with the disclosed embodiments.

[0033] Fig. 5F is a flow chart illustrating an exemplary process for determining whether a leading vehicle is changing lanes, consistent with the disclosed embodiments.

[0034] Figure 6 is a flow chart illustrating an exemplary process for eliciting one or more navigation responses based on stereoscopic image analysis consistent with the disclosed embodiments.

[0035] Figure 7 is a flow chart illustrating an exemplary process for eliciting one or more navigation responses based on analysis of three sets of images consistent with the disclosed embodiments.

[0036] Figure 8 A sparse map for providing autonomous vehicle navigation consistent with the disclosed embodiments is shown.

[0037] Fig. 9A A polynomial representation of a portion of a road segment consistent with the disclosed embodiments is shown.

[0038] Fig. 9B A curve representing a target trajectory of a vehicle for a particular road segment contained in a sparse map in three-dimensional space is shown, consistent with the disclosed embodiments.

[0039] Fig.10 Example landmarks that may be included in a sparse map consistent with the disclosed embodiments are shown.

[0040] Fig.11A A polynomial representation of a trajectory consistent with the disclosed embodiments is shown.

[0041] Fig. 11B and Fig. 11C A target trajectory along a multi-lane road is shown consistent with the disclosed embodiments.

[0042] Fig.11D Example road signature profiles consistent with disclosed embodiments are shown.

[0043] Fig.12 is a schematic illustration of a system for autonomous vehicle navigation using crowdsourcing data received from a plurality of vehicles consistent with the disclosed embodiments.

[0044] Fig.13 An example autonomous vehicle road navigation model represented by a plurality of three-dimensional splines is shown consistent with the disclosed embodiments.

[0045] Fig.14A map skeleton generated from combining positioning information from multiple drives consistent with the disclosed embodiments is shown.

[0046] Fig.15 An example of a longitudinal alignment of two vehicles with example markings as landmarks is shown consistent with the disclosed embodiments.

[0047] Fig.16 An example of a longitudinal alignment of multiple vehicles with example markings as landmarks is shown consistent with the disclosed embodiments.

[0048] 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.

[0049] Fig.18 is a schematic illustration of a system for crowdsourcing sparse maps consistent with the disclosed embodiments.

[0050] Fig.19 is 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.

[0051] Fig. 20 A block diagram of a server consistent with the disclosed embodiments is shown.

[0052] Fig.21 A block diagram of a memory consistent with the disclosed embodiments is shown.

[0053] Fig. 22 A process for clustering vehicle trajectories associated with vehicles consistent with the disclosed embodiments is shown.

[0054] Fig.23 A vehicle navigation system that may be used for autonomous navigation consistent with the disclosed embodiments is shown.

[0055] Fig.24A , Fig. 24B , Fig.24C and Fig.24D Exemplary lane markings that may be detected consistent with the disclosed embodiments are shown.

[0056] Fig.24E Exemplary mapped lane markings consistent with the disclosed embodiments are shown.

[0057] Fig.24F Exemplary anomalies associated with detecting lane markings consistent with the disclosed embodiments are shown.

[0058] Fig.25AExemplary images of vehicle surroundings for navigation based on mapped lane markings consistent with the disclosed embodiments are shown.

[0059] Fig.25B Vehicle lateral positioning correction based on mapped lane markings in a road navigation model is shown consistent with the disclosed embodiments.

[0060] Fig.26A is a flow chart illustrating an exemplary process for mapping lane markings for autonomous vehicle navigation consistent with the disclosed embodiments.

[0061] 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.

[0062] Fig. 27 An exemplary system for providing one or more map segments to one or more vehicles consistent with the disclosed embodiments is shown.

[0063] Fig.28A , Fig.28B , Fig.28C and Fig.28D An exemplary potential driving envelope for a vehicle consistent with the disclosed embodiments is shown.

[0064] Fig.28E , Fig.28F , Figure 28G and Fig.28H Exemplary map tiles associated with a potential driving envelope of a vehicle consistent with the disclosed embodiments are shown.

[0065] Fig.29A and Fig.29B Exemplary map tiles consistent with disclosed embodiments are shown.

[0066] Fig.30 An exemplary process for retrieving map tiles consistent with the disclosed embodiments is shown.

[0067] Fig.31A , Fig.31B , Fig.31C and Fig.31D An exemplary process for decoding a map tile consistent with the disclosed embodiments is shown.

[0068] Fig.32 is a flow chart illustrating an exemplary process for providing one or more map segments to one or more vehicles consistent with the disclosed embodiments.

[0069] Fig.33An exemplary system for automatically generating a navigation map for one or more road segments consistent with the disclosed embodiments is shown.

[0070] Fig.34A , Fig.34B and Fig.34C An exemplary process for collecting navigation information consistent with the disclosed embodiments is shown.

[0071] Fig.35 FIG. is a flowchart showing an exemplary process for automatically generating a navigation map for one or more road segments consistent with the disclosed embodiments. DETAILED DESCRIPTION

[0072] The following detailed description refers to the accompanying drawings. Whenever possible, the same reference numbers are used in the drawings and the following description to refer to the same or like parts. Although several illustrative embodiments are described herein, modifications, adaptations, and other embodiments are possible. For example, components shown in the drawings may be replaced, added, or modified, and the illustrative methods described herein may be modified by replacing, reordering, removing, or adding steps of the disclosed methods. Accordingly, the following detailed description is not limited to the disclosed embodiments and examples. Instead, the appropriate scope is defined by the appended claims.

[0073] Autonomous Vehicle Overview

[0074] As used throughout this disclosure, the term "autonomous vehicle" refers to a vehicle capable of achieving at least one navigation change without driver input. "Navigation change" refers to one or more of a vehicle's steering, braking, or acceleration. To be autonomous, a vehicle does not need to be fully automatic (e.g., fully operable without a driver or without driver input). Instead, an autonomous vehicle includes vehicles that are capable of operating under a driver's control during some periods and without driver control during other periods. An autonomous vehicle may also include vehicles that control only some aspects of vehicle navigation, such as steering (e.g., maintaining a vehicle route between vehicle lane limits), but may leave other aspects to the driver (e.g., braking). In some cases, an autonomous vehicle may handle some or all aspects of a vehicle's braking, speed control, and / or steering.

[0075] Since human drivers typically rely on visual cues and observations in order to control vehicles, traffic infrastructure is built accordingly, where lane markings, traffic signs, and traffic lights are all designed to provide visual information to drivers. In view of these design characteristics 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 (e.g., lane markings, traffic signs, traffic lights, etc.) that can be observed by the driver, as well as 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 while the vehicle is traveling, and the vehicle (and other vehicles) may use the information to locate itself on the model.

[0076] In some embodiments of the present disclosure, an autonomous vehicle may use information obtained while navigating (from cameras, GPS devices, accelerometers, velocity sensors, suspension sensors, etc.). In other embodiments, an autonomous vehicle may use information obtained from past navigations of the vehicle (or other vehicles) while navigating. In 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.

[0077] System Overview

[0078] Figure 11 is a block diagram of a representation system 100 consistent with the disclosed exemplary embodiments. The system 100 may include various components depending on the requirements of a particular implementation. In some embodiments, the system 100 may include a processing unit 110, an image acquisition unit 120, a position sensor 130, one or more memory units 140, 150, a map database 160, a user interface 170, and a wireless transceiver 172. The processing unit 110 may include one or more processing devices. In some embodiments, the processing unit 110 may include an application processor 180, an image processor 190, or any other suitable processing device. Similarly, the image acquisition unit 120 may include any number of image acquisition devices and components depending on the requirements of a particular application. In some embodiments, the image acquisition unit 120 may include one or more image capture devices (e.g., cameras), such as image capture device 122, image capture device 124, and image capture device 126. The system 100 may also communicatively connect the processing device 110 to a data interface 128 of the image acquisition device 120. For example, the data interface 128 may include any wired and / or wireless one or more links for transmitting image data acquired by the image acquisition device 120 to the processing unit 110 .

[0079] The wireless transceiver 172 may include one or more devices configured to exchange transmissions over an air interface to one or more networks (e.g., cellular, Internet, etc.) using radio frequencies, infrared frequencies, magnetic fields, or electric fields. The wireless transceiver 172 may transmit and / or receive data using any known standard (e.g., Wi-Fi, Bluetooth Smart, 802.15.4, ZigBee, etc.). Such transmissions may include communications from the host vehicle to one or more remotely located servers. Such transmissions may also include communications (one-way or two-way) between the host vehicle and one or more target vehicles in the host vehicle's environment (e.g., to coordinate navigation of the host vehicle with respect to 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.

[0080] Both application processor 180 and image processor 190 may include various types of hardware-based 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 processor, 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, a processor such as a processor that can be used to process images. processors from manufacturers such as GPUs from manufacturers such as wait).

[0081] In some embodiments, the application processor 180 and / or the image processor 190 may include a processor that can be Any of the EyeQ series processor chips available. These processor designs each contain multiple processing units with local memory and instruction sets. Such processors may contain video inputs for receiving image data from multiple image sensors, and may also contain video output capabilities. In one example, Using 90 nanometer-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 The MIPS34K CPU manages the five VCEs, three VMPs, and a 64-bit mobile DDR controller from Denali, a 128-bit internal Sonics Interconnect, dual 16-bit video input and 18-bit video output controllers, a 16-channel DMA, and several peripherals. TM and DMA, a second MIPS34KCPU and multi-channel DMA and other peripherals. These five VCEs, three and MIPS34K CPUs can perform the intensive visual calculations required for multi-function bundled applications. In another example, a third generation processor and a processor larger than 100nm can be used in the disclosed embodiments. Six times stronger In other examples, the disclosed embodiments may be used and / or Of course, any updated or future EyeQ processing devices may also be used with the disclosed embodiments.

[0082] Any of the processing devices 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 these instructions available to the processing device for execution during operation of the processing device. In some embodiments, configuring the processing device may include programming the processing device directly with 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).

[0083] 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 dedicated hardware-based system that controls multiple hardware-based components of the host vehicle.

[0084] Although Figure 1 Two separate processing devices included in processing unit 110 are depicted, 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. Furthermore, in some embodiments, system 100 may include one or more of processing units 110 without including other components (such as image acquisition unit 120).

[0085] 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 example, the memory may be separate from the processing unit 110. In another example, the memory may be integrated into the processing unit 110.

[0086] Each memory 140, 150 may contain software instructions that, when executed by a processor (e.g., application processor 180 and / or image processor 190), may control the operation of various aspects of system 100. These memory units may contain various databases and image processing software, as well as trained systems, such as neural networks or, for example, deep neural networks. The memory units may contain 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.

[0087] 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 made available to application processor 180 and / or image processor 190.

[0088] 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 .

[0089] The user interface 170 may include any device suitable for providing information to or receiving input from one or more users of the system 100. In some embodiments, the user interface 170 may include a user input device including, for example, a touch screen, a microphone, a keyboard, a pointing device, a tracking wheel, a camera, knobs, buttons, etc. Using such input devices, a user can provide information input or commands to the system 100 by typing instructions or information, providing voice commands, selecting menu options on a screen using buttons, a pointer, or eye tracking capabilities, or by any other suitable technique for communicating information to the system 100.

[0090] The user interface 170 may be equipped with one or more processing devices configured to provide and receive information to and from a user, and to process the information for use by, for example, 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 input or menu selections, etc. In some embodiments, the user interface 170 may include a display, a speaker, a haptic device, and / or any other device for providing output information to a user.

[0091] The map database 160 may comprise any type of database for storing map data useful to the system 100. In some embodiments, the map database 160 may comprise data relating to the location of various items in a reference coordinate system, the various items including roads, water features, geographic features, commercial areas, points of interest, restaurants, gas stations, and the like. The map database 160 may store not only the locations of such items, but may also store descriptors associated with such items, including, for example, names associated with any stored features. In some embodiments, the map database 160 may be physically located with the other components of the system 100. Alternatively or additionally, the map database 160 or a portion thereof may be remotely located relative to the other components of the system 100 (e.g., the processing unit 110). In such embodiments, information from the map database 160 may be downloaded via a wired or wireless data connection to a network (e.g., via a cellular network and / or the Internet, etc.). In some cases, the map database 160 may store a sparse data model that includes a polynomial representation of certain road features (e.g., lane markings) or a target trajectory of the host vehicle. Reference is made below to the description of the map database 160. Figures 8 to 19 Systems and methods for generating such maps are discussed.

[0092] 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. FIG. 2B to FIG. 2E Image capture devices 122, 124, and 126 are further described.

[0093] 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 Figure 1 The processing unit 110 and any other components of the system 100 described above. Although in some embodiments, the vehicle 200 may be equipped with only a single image capture device (e.g., a camera), in other embodiments, such as a combination of Figure 2B-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 the vehicle 200 may be part of an ADAS (Advanced Driver Assistance System) imaging set.

[0094] The image capture device included on the vehicle 200 as part of the image acquisition unit 120 may be positioned in any suitable location. Figure 2A-2E as well as Figure 3A-3C As shown, the image capture device 122 can be located near the rearview mirror. This location can provide a line of sight similar to that of the driver of the vehicle 200, which can help determine what is visible and invisible to the driver. The image capture device 122 can be positioned in any position near the rearview mirror, and placing the image capture device 122 on the driver's side of the mirror can further help obtain an image representing the driver's field of view and / or line of sight.

[0095] Other positioning can also be used for the image capture device of the image acquisition unit 120. For example, the image capture device 124 can be located on or in the bumper of the vehicle 200. Such positioning can be particularly suitable for image capture devices with a wide field of view. The line of sight of the image capture device located in the bumper can be different from the line of sight of the driver, and therefore, the bumper image capture device and the driver may not always see the same object. The image capture device (e.g., image capture devices 122, 124, and 126) can also be located in other positioning. For example, the image capture device can be located on or in one or both of the side mirrors of the vehicle 200, on the roof of the vehicle 200, on the hood of the vehicle 200, on the trunk of the vehicle 200, on the side of the vehicle 200, installed on any window of the vehicle 200, positioned behind any window of the vehicle 200, or positioned in front of any window of the vehicle 200, and installed in or near the lamps on the front and / or rear of the vehicle 200, etc.

[0096] 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, either integrated with or separate from the vehicle's engine control unit (ECU). The vehicle 200 may also be equipped with a location sensor 130, such as a GPS receiver, and may also include a map database 160 and memory units 140 and 150.

[0097] As discussed earlier, the wireless transceiver 172 can transmit and / or receive data over one or more networks (e.g., a cellular network, the Internet, etc.). For example, the wireless transceiver 172 can upload data collected by the system 100 to one or more servers and download data from one or more servers. For example, via the wireless transceiver 172, the system 100 can receive 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 can 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.

[0098] The system 100 can upload data to a server (e.g., to a cloud) based on a privacy level setting. For example, the system 100 can implement a privacy level setting to specify or limit the type of data (including metadata) that is transmitted to the server that can uniquely identify the vehicle and / or the driver / owner of the vehicle. Such settings can be set by a user via, for example, the wireless transceiver 172, can be initialized by a factory default setting, or by data received by the wireless transceiver 172.

[0099] In some embodiments, the system 100 may upload data according to a "high" privacy level, and, if set, the system 100 may transmit data (e.g., location information associated with the route, captured images, etc.) without any details about a specific vehicle and / or driver / owner. For example, when uploading data according to a "high" privacy setting, 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 limited location information associated with the route.

[0100] Other privacy levels are contemplated. For example, the system 100 may transmit data to a server according to an "intermediate" privacy level and may include additional information not included at a "high" privacy level, such as the make and / or model of the vehicle and / or the type of vehicle (e.g., a passenger car, a sport utility vehicle, a truck, etc.). In some embodiments, the system 100 may upload data according to a "low" privacy level. With a "low" privacy level setting, the system 100 may upload data and include information sufficient to uniquely identify a specific vehicle, the owner / driver, and / or a portion or the entire route traveled by the vehicle. Such "low" privacy level data may include one or more of the following: for example, the VIN, the driver / owner's name, the vehicle's point of origin prior to departure, the vehicle's intended destination, the vehicle's make and / or model, the vehicle type, etc.

[0101] Figure 2A is a diagrammatic side view representation of an exemplary vehicle imaging system consistent with the disclosed embodiments. Figure 2B yes Figure 2A A pictorial top view of the embodiment shown in FIG. Figure 2B As shown, the disclosed embodiments may include a vehicle 200 including a system 100 in its body having a first image capture device 122 positioned near a rearview mirror of the vehicle 200 and / or near a driver, 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.

[0102] like Figure 2C As shown, both image capture devices 122 and 124 may be positioned near a rearview mirror of vehicle 200 and / or near the driver. Figure 2B and Figure 2C Two image capture devices 122 and 124 are shown, and it should be understood that other embodiments may include more than two image capture devices. Figure 2D and 2E In the illustrated embodiment, a first image capture device 122 , a second image capture device 124 , and a third image capture device 126 are included in the system 100 of a vehicle 200 .

[0103] like Figure 2D As shown, image capture device 122 may be positioned near a rearview mirror of vehicle 200 and / or near the driver, and image capture devices 124 and 126 may be positioned on or in a bumper region (e.g., one of bumper regions 210) of vehicle 200. Figure 2E As shown, image capture devices 122, 124, and 126 may be positioned near rearview mirrors and / or near the driver's seat of vehicle 200. The disclosed embodiments are not limited to any particular number and configuration of image capture devices, and the image capture devices may be positioned in any suitable location within or on vehicle 200.

[0104] It should be understood that the disclosed embodiments are not limited to vehicles and can be applied in other scenarios. It should also be understood that the disclosed embodiments are not limited to a specific type of vehicle 200 and can be applicable to all types of vehicles, including cars, trucks, trailers and other types of vehicles.

[0105] 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 example, 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 1280×960 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 the image capture device with a desired focal length and field of view. In some embodiments, the image capture device 122 may be associated with a 6 mm lens or a 12 mm lens. In some embodiments, as Figure 2DAs shown, the image capture device 122 can be configured to capture an image with a desired field of view (FOV) 202. For example, the image capture device 122 can be configured to have a conventional FOV, such as in the range of 40 degrees to 56 degrees, including a 46 degree FOV, a 50 degree FOV, a 52 degree FOV, or a larger FOV. Alternatively, the image capture device 122 can 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 can 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=3800×1900 pixels) with a horizontal FOV of approximately 100 degrees. Such an image capture device may be used in place of a three image capture device configuration. Due to significant lens distortion, in embodiments 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 with a 100 degree horizontal FOV.

[0106] The first image capture device 122 may acquire a plurality of first images of 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 contain a plurality of pixels.

[0107] 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 contained in a particular scan line.

[0108] Image capture devices 122, 124, and 126 may contain any suitable type and number of image sensors, including, for example, CCD sensors or CMOS sensors, etc. 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 scanning of the rows is performed 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.

[0109] 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 more.

[0110] The use of a rolling shutter may cause pixels in different rows to be exposed and captured at different times, which may cause skew and other image artifacts in the captured image frame. On the other hand, when the image capture device 122 is configured to operate with a global or synchronized shutter, all pixels may be exposed for the same amount of time and during a common exposure period. As a result, the image data in a frame collected from a system employing a global shutter represents a snapshot of the entire FOV (such as FOV 202) at a particular time. In contrast, in a rolling shutter application, each row in the frame is exposed and data is captured at a different time. As a result, moving objects may appear distorted in an image capture device with a rolling shutter. This phenomenon will be described in more detail below.

[0111] 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 equal to 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.

[0112] Image capture devices 124 and 126 may acquire a plurality of second and third images of 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 image scan line included in the second and third series.

[0113] Each image capture device 122, 124, and 126 may be positioned at any suitable location and orientation relative to vehicle 200. The relative locations of image capture devices 122, 124, and 126 may be selected to help fuse together information obtained 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 (e.g., FOV 202) and a FOV associated with image capture device 126 (e.g., FOV 206).

[0114] Image capture devices 122, 124, and 126 may be located at any suitable relative heights on vehicle 200. In one example, there may be height differences between image capture devices 122, 124, and 126 that 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 a lateral displacement difference between the image capture devices 122, 124, and 126 to provide additional parallax information for the stereo analysis of the processing unit 110. The difference in lateral displacement can be represented by dx, as 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 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 (one or more) image capture devices.

[0115] 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(s) associated with image capture device 122 may be higher, lower, or the same as the resolution of the image sensor(s) associated with image capture devices 124 and 126. In some embodiments, the image sensor(s) associated with image capture device 122 and / or image capture devices 124 and 126 may have a resolution of 640×480, 1024×768, 1280×960, or any other suitable resolution.

[0116] 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 various 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 that is applied before or after acquiring image data associated with one or more pixels of an image sensor in image capture devices 122, 124, and / or 126. Typically, image data corresponding to each pixel may be acquired based on a clock rate for the device (e.g., one pixel per clock cycle). Furthermore, in embodiments that include a rolling shutter, one or more of the image capture devices 122, 124, and 126 may include a selectable horizontal blanking period that is applied before or after acquiring image data associated with a row of pixels of an image sensor in the image capture devices 122, 124, and / or 126. Furthermore, one or more of the image capture devices 122, 124, and / or 126 may include a selectable vertical blanking period that is applied before or after acquiring image data associated with an image frame of the image capture devices 122, 124, and 126.

[0117] These timing controls may enable synchronization of frame rates associated with image capture devices 122, 124, and 126, even if the line scan rates of each are different. Furthermore, as will be discussed in more detail below, these selectable timing controls, as well as 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 if the field of view of image capture device 122 is different than the FOVs of image capture devices 124 and 126.

[0118] 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 640x480 and the other device includes an image sensor with a resolution of 1280x960, more time will be required to acquire a frame of image data from the sensor with the higher resolution.

[0119] Another factor that can affect the timing of image data acquisition in image capture devices 122, 124, and 126 is the maximum line scan rate. For example, it will take a certain minimum amount of time to acquire a line 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 acquiring a line of image data will be related to the maximum line scan rate for the particular device. Devices that provide higher maximum line scan rates have the potential to provide higher frame rates than devices with lower maximum line scan rates. In some embodiments, one or 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, 1.5, 1.75, or 2 times or more the maximum line scan rate of image capture device 122.

[0120] In another embodiment, image capture devices 122, 124, and 126 may have the same maximum line scan rate, but image capture device 122 may operate at a scan rate that is less than or equal to its maximum scan scan rate. The system may be configured such that one or more of image capture devices 124 and 126 operate at a line scan rate that is equal to the line scan rate of image capture device 122. In other examples, the system may be configured such that the line scan rate of image capture device 124 and / or image capture device 126 may be 1.25, 1.5, 1.75, or 2 or more times the line scan rate of image capture device 122.

[0121] In some embodiments, image capture devices 122, 124, and 126 may be asymmetric. In other words, 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 of ​​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.

[0122] 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 acquires 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 acquire images of objects that are within a few meters of the vehicle. The image capture devices 122, 124, and 126 may also be configured to acquire images of objects at a greater range from the vehicle (e.g., 25 meters, 50 meters, 100 meters, 150 meters, or more). In addition, the focal lengths of image capture devices 122, 124, and 126 may be selected so 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 meters or within 20 meters), while other image capture devices (e.g., image capture devices 124 and 126) may capture images of objects that are farther away from the vehicle 200 (e.g., greater than 20 meters, 50 meters, 100 meters, 150 meters, etc.).

[0123] According to some embodiments, the FOV of one or more image capture devices 122, 124, and 126 may have a wide angle. For example, having a FOV of 140 degrees may be advantageous, especially for image capture devices 122, 124, and 126 that may be used to capture images of areas near vehicle 200. For example, image capture device 122 may be used to capture images of areas to the right or left of vehicle 200, and in such embodiments, it may be desirable for image capture device 122 to have a wide FOV (e.g., at least 140 degrees).

[0124] The field of view associated with each of the image capture devices 122, 124, and 126 may depend on the corresponding focal length. For example, as the focal length increases, the corresponding field of view decreases.

[0125] 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 one particular example, 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.

[0126] The system 100 may be configured such that the field of view of the image capture device 122 at least partially or completely overlaps the field of view of the image capture device 124 and / or the image capture device 126. In some embodiments, the system 100 may be configured such that the fields of view of the image capture devices 124 and 126, for example, fall within (e.g., are narrower than) the field of view of the image capture device 122 and share a common center with the field of view of the image capture device 122. In other embodiments, the 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 the 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.

[0127] Figure 2F is a pictorial representation of an exemplary vehicle control system consistent with the disclosed embodiments. Figure 2F As indicated, the vehicle 200 may include a throttle adjustment system 220, a braking system 230, and a steering system 240. The system 100 may provide input (e.g., control signals) to one or more of the throttle adjustment system 220, the braking system 230, and the steering system 240 via one or more data links (e.g., any wired and / or wireless links for transmitting data). For example, based on an analysis of images acquired by the image capture devices 122, 124, and / or 126, the system 100 may provide control signals to one or more of the throttle adjustment system 220, the braking system 230, and the steering system 240 to navigate the vehicle 200 (e.g., by causing acceleration, steering, lane shifting, etc.). In addition, the system 100 may receive input from one or more of the throttle adjustment system 220, the braking system 230, and the steering system 240 that indicates an operating condition of the vehicle 200 (e.g., speed, whether the vehicle 200 is braking and / or turning, etc.). The following is combined with Figures 4 to 7 Provide further details.

[0128] 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 the 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 interact with the system 100 using 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. 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.

[0129] FIG. 3B to FIG. 3D 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 can include image capture devices 122, 124, and 126. Image capture devices 124 and 126 can be positioned behind visor 380, which can be flush with the vehicle windshield and include a film and / or a composite of anti-reflective materials. For example, visor 380 can be positioned so that the shield is aligned relative to the vehicle windshield with a matching slope. In some embodiments, each of image capture devices 122, 124, and 126 can be positioned behind visor 380, such as at Figure 3D The disclosed embodiments are not limited to any particular configuration of image capture devices 122 , 124 , and 126 , camera mount 370 , and visor 380 . Figure 3C yes Figure 3B The camera mount 370 is shown as an illustration from a front perspective.

[0130] As will be appreciated by those skilled in the art having the benefit of this disclosure, many 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 portion of the system 100 and the components may be rearranged into various configurations while providing the functionality of the disclosed embodiments. Therefore, the foregoing configurations are exemplary, 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.

[0131] As discussed in more detail below and in accordance with various disclosed embodiments, the system 100 can provide various features related to autonomous driving and / or driver assistance technology. For example, the system 100 can analyze image data, location data (e.g., GPS location information), map data, speed data, and / or data from sensors included in the vehicle 200. The system 100 can collect data for analysis from, for example, the image acquisition unit 120, the location sensor 130, and other sensors. In addition, the system 100 can analyze the collected data to determine whether the vehicle 200 should take a certain 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 can automatically control the braking, acceleration, and / or steering of the vehicle 200 (e.g., by transmitting control signals to one or more of the throttle adjustment system 220, the braking system 230, and the steering system 240). In addition, the system 100 can analyze the collected data and issue warnings and / or alarms to the vehicle occupants based on the analysis of the collected data. Additional details of various embodiments provided by the system 100 are provided below.

[0132] Forward multi-imaging system

[0133] 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 front of the vehicle. In other embodiments, the multi-camera system can 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 in front of and / or on the side of the vehicle (e.g., vehicle 200). The first camera can 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 can be connected to a first image processor to perform a monocular image analysis of the image provided by the first camera, and the second camera can be connected to a second image processor to perform a monocular image analysis of the image provided by the second camera. The outputs of the first and second image processors (e.g., processed information) can be combined. In some embodiments, the second image processor can receive images from both the first camera and the second camera to perform stereo analysis. In another embodiment, the system 100 can use a three-camera imaging system, wherein each camera has a different field of view. Thus, such a system may make decisions based on information derived from objects located at different distances in front of and to the side of the vehicle. References to monocular image analysis may refer to instances of image analysis based on images captured from a single viewpoint (e.g., from a single camera). Stereoscopic image analysis may refer to instances of image analysis based on two or more images captured using one or more changes in image capture parameters. For example, captured images suitable for stereoscopic image analysis may include images captured from two or more different positions, from different fields of view, using different focal lengths, and together with parallax information, etc.

[0134] 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 serve as a primary camera or base camera. Image capture devices 122, 124, and 126 may be positioned behind rearview mirror 310 and positioned substantially side by side (e.g., 6 centimeters 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 sun visor 380 flush with the windshield of vehicle 200. Such shielding may be used to minimize the effect of any reflections from the interior of the vehicle on image capture devices 122 , 124 , and 126 .

[0135] In another embodiment, as above combined 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 camera and the main field of view camera (e.g., image capture devices 122 and 126 in the above example). Such a configuration can provide a free line of sight from the wide field of view camera. To reduce reflections, the camera can be mounted closer to the windshield of the vehicle 200, and a polarizer can be included on the camera to dampen reflected light.

[0136] The three-camera system may provide certain performance characteristics. For example, some embodiments may include the ability to verify the detection of an object by one camera based on the detection results from another camera. In the three-camera configuration discussed above, the 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 the image capture devices 122, 124, and 126.

[0137] In a three-camera system, the first processing device can receive images from both the main camera and the narrow field of view camera and perform visual processing for the narrow FOV camera, for example to detect other vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. In addition, the first processing device can calculate the disparity of pixels 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 can then combine the 3D reconstruction with 3D map data, or combine the 3D reconstruction with 3D information calculated based on information from another camera.

[0138] The second processing device can receive images from the main 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 can calculate the camera displacement, and based on the displacement, calculate the disparity of pixels between consecutive images and create a 3D reconstruction of the scene (e.g., structure from motion). The second processing device can transmit the structure from motion based on the 3D reconstruction to the first processing device to be combined with the stereoscopic 3D image.

[0139] The third processing device can 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 can further execute additional processing instructions to analyze the images to identify moving objects in the images, such as vehicles changing lanes, pedestrians, etc.

[0140] In some embodiments, having the streams of image-based information captured and processed independently can provide an opportunity to provide redundancy in the system. Such redundancy can include, for example, using a first image capture device and images processed from that device to verify and / or supplement information obtained by capturing and processing image information from at least a second image capture device.

[0141] In some embodiments, system 100 may use two image capture devices (e.g., image capture devices 122 and 124) to provide navigation assistance for vehicle 200, and use a third image capture device (e.g., image capture device 126) to provide redundancy and verify the analysis of data received from the other two image capture devices. For example, in such a configuration, image capture devices 122 and 124 may provide images for 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. In other words, image capture device 126 (and corresponding processing device) may be considered to provide a redundant subsystem for providing a check on the analysis derived from image capture devices 122 and 124 (e.g., to provide an automatic emergency braking (AEB) system). Additionally, in some embodiments, redundancy and verification of received data may be supplemented based on information received from one or more sensors (e.g., radar, lidar, acoustic sensors, information received from one or more transceivers outside the vehicle, etc.).

[0142] Those skilled in the art will recognize that the above-described camera configurations, camera placements, number of cameras, camera positioning, 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.

[0143] Figure 4 1 is an exemplary functional block diagram of memory 140 and / or memory 150, which may be stored / programmed with instructions for performing one or more operations consistent with the embodiments of the present disclosure. 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.

[0144] like Figure 4 As shown, the memory 140 may store a monocular image analysis module 402, a stereo image analysis module 404, a velocity and acceleration module 406, and a navigation response module 408. The disclosed embodiments are not limited to any particular configuration of 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 contained in the memory 140. Those skilled in the art will appreciate that references to the processing unit 110 in the following discussion may refer to the application processor 180 and the image processor 190 separately or collectively. Accordingly, the steps of any of the following processes may be performed by one or more processing devices.

[0145] 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. FIG. 5A to FIG. 5D As described, the monocular image analysis module 402 may include instructions for detecting a set of features within a 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, the system 100 (e.g., via the processing unit 110) may cause one or more navigation responses in the vehicle 200, such as steering, lane changes, changes in acceleration, etc., as discussed below in conjunction with the navigation response module 408.

[0146] 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 on a first set of second sets 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 second sets of images with additional sensory information (e.g., information from radar) to perform stereo image analysis. For example, stereo image analysis module 404 may include instructions 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 within the first and second sets of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazardous objects, etc. Based on the analysis, the processing unit 110 may cause one or more navigation responses in the vehicle 200, such as steering, changing lanes, 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.

[0147] 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 executing 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 of the vehicle 200 relative to lane markings of the road, etc. In addition, 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 adjustment system 220, the braking system 230, and / or the steering system 240. Based on the calculated target speed, the processing unit 110 can transmit an electrical signal to the throttle regulation system 220, the braking system 230 and / or the steering system 240 of the vehicle 200 to trigger a change in speed and / or acceleration by, for example, physically pressing the brake or releasing the accelerator of the vehicle 200.

[0148] In one embodiment, the navigation response module 408 may store software that is 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 be configured to 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 adjustment 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 electrical signals to the throttle adjustment system 220, the braking system 230, and the steering system 240 of the vehicle 200 to achieve a predetermined angle of rotation by, for example, turning the steering wheel of the vehicle 200, thereby triggering the desired navigation response. In some embodiments, the processing unit 110 may use the output of the navigation response module 408 (e.g., the desired navigation response) as an input to execute the speed and acceleration module 406 for calculating the change in the speed of the vehicle 200.

[0149] 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.

[0150] Figure 5A is a flow chart illustrating an exemplary process 500A for causing one or more navigation responses based on monocular image analysis consistent with the disclosed embodiments. At step 510, the processing unit 110 may receive a plurality of images via a data interface 128 between the processing unit 110 and the image acquisition unit 120. For example, a camera (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 (e.g., or 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.). At step 520, the processing unit 110 may execute the monocular image analysis module 402 to analyze the plurality of images, as described below in conjunction with the processing unit 110. 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.

[0151] At step 520, processing unit 110 may also execute monocular image analysis module 402 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 difficult. 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 the parallax in pixels between frames to build a 3D map of the road. Processing unit 110 may then use the 3D map to detect the road surface and the hazards present on the road surface.

[0152] At step 530, the processing unit 110 may execute the navigation response module 408 to perform the navigation response based on the analysis performed at step 520 and the above combination. Figure 4The described techniques are used to cause one or more navigation responses. The navigation response may include, for example, steering, changing lanes, braking, changes in acceleration, etc. In some embodiments, the processing unit 110 may use data derived from the execution speed and acceleration module 406 to cause the 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 transmit control signals to the steering system 240 and the throttle adjustment system 220 of the vehicle 200, for example, in sequence, so that the vehicle 200 changes a lane and then accelerates. Alternatively, the processing unit 110 may transmit control signals to the braking system 230 and the steering system 240 of the vehicle 200, for example, at the same time, so that the vehicle 200 brakes while changing lanes.

[0153] Figure 5B is a flow chart showing an exemplary process 500B for detecting one or more vehicles and / or pedestrians 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 500B. At step 540, the processing unit 110 may determine a set of candidate objects representing possible vehicles and / or pedestrians. For example, the 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 such a way as to achieve a high "false hit" rate and a low "miss" rate. For example, the processing unit 110 may use a low similarity threshold on the predetermined pattern to identify the candidate object as a possible vehicle or pedestrian. Doing so may allow the processing unit 110 to reduce the likelihood of missing (e.g., not identifying) a candidate object representing a vehicle or pedestrian.

[0154] At step 542, processing unit 110 may filter the set of candidate objects based on classification criteria to exclude certain candidates (e.g., irrelevant or less relevant objects). Such criteria may be derived from various attributes associated with object type categories 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 pseudo candidates from the set of candidate objects.

[0155] At step 544, processing unit 110 may analyze multiple frames of images to determine whether an object in the 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 of the detected object and compare the frame-by-frame position data of the object with the predicted position.

[0156] At step 546, processing unit 110 may construct a set of measurements for the detected objects. Such measurements may include, for example, position, velocity, and acceleration values ​​associated with the detected objects (relative to vehicle 200). In some embodiments, processing unit 110 may construct the measurements based on estimation techniques such as a Kalman filter or linear quadratic estimation (LQE) using a series of time-based observations and / or based on modeling data available for different object categories (e.g., cars, trucks, pedestrians, bicycles, road signs, etc.). The Kalman filter may be based on a measure of the scale of the object, where the scale measure is proportional to the time to collision (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 the set of captured images and derive information associated with the vehicles and pedestrians (e.g., position, velocity, size). Based on the identification and the derived information, processing unit 110 may cause one or more navigation responses in vehicle 200, such as those described above in conjunction with Figure 5A as described.

[0157] At step 548, the processing unit 110 may perform an optical flow analysis of the one or more images to reduce the likelihood 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, associated with other vehicles and pedestrians, and distinct from the motion of the road surface in one or more images. The processing unit 110 may calculate the motion of the candidate object by observing different positions of the object across multiple image frames captured at different times. The processing unit 110 may use the position and time values ​​as input to a mathematical model for calculating the motion of the candidate object. 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 improve the reliability of the system 100.

[0158] Figure 5Cis a flow chart illustrating an exemplary process 500C for detecting road signs and / or lane geometry information in a set of images consistent with the disclosed embodiments. Processing unit 110 may execute monocular image analysis module 402 to implement process 500C. At step 550, processing unit 110 may detect a set of objects by scanning one or more images. In order to detect lane signs, lane geometry information, and other relevant road sign segments, processing unit 110 may filter the set of objects to exclude objects determined to be irrelevant (e.g., small potholes, small rocks, etc.). At step 552, processing unit 110 may group together the road segments that belong to the same road sign or lane sign detected in step 550. Based on the grouping, processing unit 110 may generate a model, such as a mathematical model, representing the detected road segments.

[0159] At step 554, the processing unit 110 may construct a set of measurements associated with the detected road segment. In some embodiments, the processing unit 110 may create a projection of the detected road segment from the image plane onto the real world plane. The projection may be characterized using a cubic polynomial with coefficients corresponding to physical properties such as the position, slope, curvature, and curvature derivative of the detected road. When generating the projection, the processing unit 110 may take into account changes in the road surface, as well as the pitch 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 and roll rate associated with the vehicle 200 by tracking a set of feature points in one or more images.

[0160] At step 556, processing unit 110 may perform a multi-frame analysis by, for example, tracking the detected road segments across consecutive image frames and accumulating frame-by-frame data associated with the detected road 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 signs that appear in the set of captured images and derive lane geometry information. Based on the identification and derived information, processing unit 110 may cause one or more navigation responses in vehicle 200, such as those described above in conjunction with Figure 5A As described.

[0161] At step 558, processing unit 110 may consider additional information sources to further generate a safety model of vehicle 200 in its surrounding environment. Processing unit 110 may use the safety model to define a context in which system 100 may perform autonomous control of vehicle 200 in a safe manner. To generate the safety model, in some embodiments, processing unit 110 may consider the positions and motions of other vehicles, detected curbs and guardrails, 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 signs and lane geometry and increase the reliability of system 100.

[0162] Figure 5D 5 is a flow chart 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 in the image at locations that may contain traffic lights. For example, the processing unit 110 may filter the identified objects to construct a set of candidate objects, while 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 the set of candidate objects reflecting possible traffic lights. For example, the processing unit 110 may track candidate objects across consecutive image frames, estimate the real-world position of the candidate objects, and filter out those moving objects (which are unlikely to be traffic lights). In some embodiments, processing unit 110 may perform color analysis on the candidate objects and identify relative locations of detected colors that appear within possible traffic lights.

[0163] At step 562, processing unit 110 may analyze the geometry of the intersection. The analysis may be based on any combination of (i) the number of lanes detected on either side of vehicle 200, (ii) signs detected on the road (e.g., arrow signs), and (iii) a description of the intersection extracted from map data (e.g., data from map database 160). Processing unit 110 may use information derived from performing monocular analysis module 402 to perform the analysis. In addition, processing unit 110 may determine the correspondence between the traffic lights detected at step 560 and the lanes present near vehicle 200.

[0164] At step 564, as vehicle 200 approaches the intersection, 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 the intersection compared to the number of traffic lights 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 this identification and analysis, processing unit 110 may cause one or more navigation responses in vehicle 200, such as those described above in conjunction with Figure 5A As described.

[0165] Figure 5E 5 is a flow chart 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 may be i Can fall within the range of 1 to 5 meters. In one embodiment, the processing unit 110 can use two polynomials, such as a left road polynomial and a right road polynomial, to construct an initial vehicle path. The processing unit 110 can calculate the geometric midpoint between the two polynomials and offset each point included in the resulting vehicle path by a predetermined offset (e.g., a smart lane offset), if any (a zero offset can correspond to driving in the middle of the lane). The offset can be in a direction perpendicular to the road segment between any two points in the vehicle path. In another embodiment, the processing unit 110 can 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).

[0166] 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 dk between two points in the set of points representing the vehicle path is less than the distance d as described above. i For example, the distance dk may fall within the range of 0.1 to 0.3 meters. The processing unit 110 may reconstruct the vehicle path using a parabolic spline algorithm, which may produce a cumulative distance vector S corresponding to the total length of the vehicle path (ie, based on the set of points representing the vehicle path).

[0167] At step 574, the processing unit 110 may determine a look-ahead point (expressed in coordinates (x 1 , z 1 )). The processing unit 110 can extract a foresight point from the accumulated distance vector S, and the foresight point can be associated with a foresight distance and a foresight time. The foresight distance can have a lower limit ranging from 10 meters to 20 meters and can be calculated as the product of the speed of the vehicle 200 and the foresight time. For example, as the speed of the vehicle 200 decreases, the foresight distance can also decrease (e.g., until it reaches the lower limit). The foresight time can range from 0.5 to 1.5 seconds and can be inversely proportional to the gain of one or more control loops such as a heading error tracking control loop associated with causing a navigation response in the vehicle 200. For example, the gain of the heading error tracking control loop can depend on the bandwidth of a yaw rate loop, a steering actuator loop, vehicle lateral dynamics, etc. Therefore, the higher the gain of the heading error tracking control loop, the shorter the foresight time.

[0168] At step 576, the processing unit 110 may determine a heading error and a yaw rate command based on the foresight point determined at step 574. The processing unit 110 may determine the heading error by calculating the arctangent of the foresight point, e.g., arctan(x1 / z1). The processing unit 110 may determine the yaw rate command as the product of the heading error and a high level control gain. If the foresight distance is not at the lower limit, the high level control gain may be equal to: (2 / foresight time). Otherwise, the high level control gain may be equal to: (2×velocity of vehicle 200 / foresight distance).

[0169] Fig. 5F is a flow chart illustrating an exemplary process 500F for determining whether a leading vehicle is changing lanes consistent with the disclosed embodiments. At step 580, 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 combined Figure 5A and Figure 5B The described techniques determine the position, velocity (e.g., direction and speed), and / or acceleration of the vehicle ahead. The processing unit 110 may also be configured to use the above combined Figure 5E The described techniques determine one or more road polynomials, forward view points (associated with vehicle 200 ), and / or tracking trajectories (eg, a set of points describing a path taken by a leading vehicle).

[0170] At step 582, processing unit 110 may analyze the navigation information determined in step 580. In one embodiment, processing unit 110 may calculate the distance between the tracking trajectory and the road polynomial (e.g., along the trajectory). If the change in this distance along the trajectory exceeds a predetermined threshold (e.g., 0.1 to 0.2 meters on a straight road, 0.3 to 0.4 meters on a moderately curved road, and 0.5 to 0.6 meters on a sharp curve road), processing unit 110 may determine that the leading vehicle is likely to be changing lanes. In the case where multiple vehicles are detected traveling in front of vehicle 200, processing unit 110 may compare the tracking trajectories associated with each vehicle. Based on the comparison, processing unit 110 may determine that a vehicle whose tracking trajectory does not match the tracking trajectories of other vehicles is likely to be changing lanes. Processing unit 110 may additionally compare the curvature of the tracking trajectory (associated with the leading vehicle) with the expected curvature of the road segment in 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 tracking trajectories of other vehicles, from prior knowledge about the road, etc. If the difference between the curvature of the tracking trajectory and the expected curvature of the road segment exceeds a predetermined threshold, processing unit 110 may determine that the leading vehicle is likely to be changing lanes.

[0171] In another embodiment, processing unit 110 may compare the instantaneous position of the leading vehicle to a forward view point (associated with vehicle 200) over a specific time period (e.g., 0.5 to 1.5 seconds). If the distance between the instantaneous position of the leading vehicle and the forward view point changes during the specific time period, and the cumulative sum of the changes exceeds a predetermined threshold (e.g., 0.3 to 0.4 meters on a straight road, 0.7 to 0.8 meters on a moderately curved road, and 1.3 to 1.7 meters on a sharp curve road), processing unit 110 may determine that the leading vehicle is likely changing lanes. In another embodiment, processing unit 110 may analyze the geometry of the tracking trajectory by comparing the lateral distance traveled along the tracking trajectory with the expected curvature of the tracking path. The expected radius of curvature may be determined based on the calculation: (δ z 2 +δ x 2 ) / 2 / (δ x ), where δ x represents the lateral travel distance and δ zThe longitudinal distance traveled is represented by the curvature. If the difference between the lateral distance traveled and the expected curvature exceeds a predetermined threshold (e.g., 500 to 700 meters), the processing unit 110 can determine that the leading vehicle is likely to be changing lanes. In another embodiment, the processing unit 110 can analyze the position of the leading vehicle. If the position of the leading vehicle obscures the road polynomial (e.g., the leading vehicle covers the top of the road polynomial), the processing unit 110 can determine that the leading vehicle is likely to 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 tracking trajectories of the two vehicles are not parallel, the processing unit 110 can determine that the (closer) leading vehicle is likely to be changing lanes.

[0172] 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 this determination based on a weighted average of the various analyses performed at step 582. Under such a scheme, for example, a determination made by processing unit 110 that a leading vehicle is likely to be changing lanes based on a particular type of analysis may be assigned a value of "1" (and "0" used to represent 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.

[0173] Figure 6 6 is a flow chart illustrating 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 and second pluralities 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 and second pluralities 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 and second pluralities of images via two or more data interfaces. The disclosed embodiments are not limited to any particular data interface configuration or protocol.

[0174] At step 620, the processing unit 110 may execute the stereo image analysis module 404 to perform stereo image analysis on the first and second plurality of images to create a 3D map of the road ahead 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. Figures 5A to 5DStereoscopic image analysis may be performed in a manner similar to the steps described above. For example, the processing unit 110 may execute the stereoscopic image analysis module 404 to detect candidate objects (e.g., vehicles, pedestrians, road signs, traffic lights, road hazards, etc.) within the first and second multiple images, filter out a subset of candidate objects based on various criteria, and perform multi-frame analysis, build measurements, 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 and second multiple images, rather than only considering information from one set of images. For example, the processing unit 110 may analyze the differences in pixel-level data (or other subsets of data from two streams of captured images) of candidate objects that appear in both the first and second multiple images. As another example, the processing unit 110 may estimate the position and / or velocity of the candidate object (e.g., relative to the vehicle 200) by observing that the object appears in one of the multiple images but not in the other image, or other differences that may exist relative to the objects that appear in the two image streams. For example, the position, velocity, and / or acceleration relative to the vehicle 200 may be determined based on features such as trajectory, position, movement characteristics, etc. associated with the object that appears in one or both of the image streams.

[0175] At step 630, the processing unit 110 may execute the navigation response module 408 to perform the navigation response based on the analysis performed at step 620 and the above combination. Figure 4 The described techniques may cause one or more navigation responses in the vehicle 200. The navigation responses may include, for example, steering, lane changes, changes in acceleration, changes in speed, braking, etc. In some embodiments, the processing unit 110 may cause the one or more navigation responses using data derived from executing the speed and acceleration module 406. Furthermore, multiple navigation responses may occur simultaneously, sequentially, or any combination thereof.

[0176] Figure 77 is a flow chart 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 the first, second, and 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 the first, second, and third plurality of images of the area in front of and / or to the side 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, second, and third plurality of images via three or more data interfaces. For example, each of the image capture devices 122, 124, 126 may have an associated data interface for communicating data to the processing unit 110. The disclosed embodiments are not limited to any particular data interface configuration or protocol.

[0177] At step 720, processing unit 110 may analyze the first, second, and third plurality of images to detect features within the images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, road hazards, etc. This analysis may be performed in a manner similar to the above combined Figure 5A-5D and Figure 6 For example, the processing unit 110 may perform a monocular image analysis on each of the first, second, and third plurality of images (e.g., via execution of the monocular image analysis module 402 and based on the above combination). FIG. 5A to FIG. 5D Alternatively, the processing unit 110 may perform stereoscopic image analysis on the first and second plurality of images, the second and third plurality of images, and / or the first and third plurality of images (e.g., via execution of the stereoscopic image analysis module 404 and based on the above combined Figure 6 4 and 5. The processing unit 110 may perform a combination of monocular and stereo image analysis. For example, the processing unit 110 may perform monocular image analysis on the first plurality of images (e.g., via execution by the monocular image analysis module 402) and stereo image analysis on the second and third 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 positioning 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.

[0178] 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 certain configurations of image acquisition 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."

[0179] At step 730, the processing unit 110 may cause one or more navigation responses in the vehicle 200 based on information derived from two of the first, second, and third plurality of images. The selection of two of the first, second, and 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 make a selection 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.

[0180] In some embodiments, processing unit 110 may select information derived from two of the first, second, and third pluralities of images by determining the degree 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 between each captured image from image capture devices 122, 124, and 126. Processing unit 110 may also exclude information that is inconsistent between captured images (e.g., vehicles changing lanes, lane models indicating that vehicles are too close to vehicle 200, etc.). Thus, processing unit 110 may select information derived from two of the first, second, and third pluralities of images based on determining consistent and inconsistent information.

[0181] The navigation response may include, for example, steering, lane changing, braking, acceleration change, etc. The processing unit 110 may make a decision based on the analysis performed in step 720 and the above combination. Figure 4The 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 executing the speed 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, second, and third pluralities of images. The multiple navigation responses may occur simultaneously, sequentially, or any combination thereof.

[0182] Sparse road models for autonomous vehicle navigation

[0183] In some embodiments, the disclosed systems and methods may use sparse maps for autonomous vehicle navigation. In particular, sparse maps may be used for autonomous vehicle navigation along road segments. For example, sparse maps may provide sufficient information for navigating an autonomous vehicle without storing and / or updating a large amount of data. As discussed in more detail below, an autonomous vehicle may use a sparse map to navigate one or more roads based on one or more stored trajectories.

[0184] Sparse maps for autonomous vehicle navigation

[0185] In some embodiments, the disclosed systems and methods can generate sparse maps for autonomous vehicle navigation. For example, sparse maps can provide sufficient information for navigation without requiring excessive data storage or data transfer rates. As discussed in more detail below, a vehicle (which can be an autonomous vehicle) can use a sparse map to navigate one or more roads. For example, in some embodiments, a sparse map can contain data related to roads and potential landmarks along the roads that may be sufficient for vehicle navigation, but also exhibit 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 containing detailed map information (such as image data collected along roads).

[0186] For example, a sparse data map can store a three-dimensional polynomial representation of a preferred vehicle path along a road, rather than storing a detailed representation of a road segment. These paths may require very little data storage space. In addition, in the described sparse data map, landmarks can be identified and included in a sparse map road model to help navigation. These landmarks can be located at any spacing suitable for enabling vehicle navigation, but in some cases, these landmarks do not need to be identified and included in the model with high density and short spacing. On the contrary, in some cases, navigation based on landmarks with a spacing of at least 50 meters, at least 100 meters, at least 500 meters, at least 1 kilometer or at least 2 kilometers is possible. As will be discussed in more detail in other chapters, a sparse map can be generated based on data collected or measured by a vehicle equipped with various sensors and devices (such as image capture devices, global positioning system sensors, motion sensors, etc.) when the vehicle travels along a road. In some cases, a sparse map can be generated based on data collected during multiple driving of one or more vehicles along a specific road. Using multiple driving of one or more vehicles to generate a sparse map can be referred to as a "crowdsourcing" sparse map.

[0187] Consistent with the disclosed embodiments, an autonomous vehicle system may use a sparse map for navigation. For example, the disclosed systems and methods may allocate a sparse map for generating a road navigation model for an autonomous vehicle, and may use the sparse map and / or the generated road navigation model to navigate the autonomous vehicle along a road segment. A sparse map consistent with the present disclosure may include one or more three-dimensional contours that may represent predetermined trajectories that autonomous vehicles may traverse as they move along an associated road segment.

[0188] Sparse maps consistent with the present disclosure may also include data representing one or more road features. Such road features may include recognizable landmarks, road signature profiles, and any other road-related features useful in vehicle navigation. Sparse maps consistent with the present disclosure may enable autonomous navigation of a vehicle based on a relatively small amount of data contained in the sparse map. For example, the disclosed embodiments of the sparse map may require a relatively small storage space (and a relatively small bandwidth when portions of the sparse map are transmitted to the vehicle), but may still adequately provide autonomous vehicle navigation, rather than including detailed representations of roads, such as road edges, road curvature, images associated with road segments, or data detailing other physical features associated with road segments. In some embodiments, the small data footprint of the disclosed sparse map may be achieved by storing representations of road-related elements that require a small amount of data but still enable autonomous navigation, which will be discussed in further detail below.

[0189] For example, the disclosed sparse map may store a polynomial representation of one or more trajectories that a vehicle may follow along a road, rather than storing a detailed representation of various aspects of a road. Thus, using the disclosed sparse map, a vehicle may be navigated along a particular road segment, rather than storing (or having to transmit) details about the physical properties of the road to enable navigation along the road, in some cases without having to account for the physical aspects of the road, but rather by aligning the path it travels with a trajectory (e.g., a polynomial spline) along a particular road segment. In this way, a vehicle may be navigated primarily based on a stored trajectory (e.g., a polynomial spline), which may require significantly less storage space than methods involving storage of road images, road parameters, road layouts, etc.

[0190] In addition to the stored polynomial representations of trajectories along road segments, the disclosed sparse maps may also contain small data objects that may represent road features. In some embodiments, the small data objects may contain digital signatures derived from digital images (or digital signals) obtained by sensors (e.g., cameras or other sensors, such as suspension sensors) on vehicles traveling along the road segment. The digital signatures may have a reduced size relative to the signals acquired by the sensors. In some embodiments, the digital signatures may be created to be compatible with a classifier function that is configured to detect and identify road features from signals acquired by the sensors, for example, during subsequent driving. In some embodiments, digital signatures may be created so that the digital signatures have as small a footprint as possible while retaining the ability to associate or match road features with stored signatures based on images of road features (or digital signals generated by sensors if the stored signatures are not image-based and / or contain other data) captured by cameras on subsequent vehicles traveling along the same road segment.

[0191] 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 that is detectable by a camera on a vehicle, and where the camera system on the vehicle is coupled to a classifier that is 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 where such a 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.

[0192] 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 with relatively few bytes while providing enough information for identifying and navigating using such features. In one example, road signs can be identified as recognizable landmarks on which 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 a data-light representation of landmarks (e.g., using a representation 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 a condensed representation of landmarks (and other road features) can utilize sensors and processors configured to detect, identify, and / or classify specific road features contained on such vehicles.

[0193] For example, when a landmark or even a particular type of landmark in a given area is locally unique (e.g., when there are no other landmarks or no other landmarks of the same type), the sparse map can use data indicating the type of landmark (the landmark or the particular type of landmark), and during navigation (e.g., autonomous navigation) when a camera on an autonomous vehicle captures an image of an area containing the landmark (or the particular type of landmark), the processor can process the image, detect the landmark (if indeed present in the image), classify the image as the landmark (or the particular type of landmark), and associate the location of the image with the location of the landmark stored in the sparse map.

[0194] Generating sparse maps

[0195] 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", e.g., by performing image analysis on a plurality of images acquired as one or more vehicles traverse the road segment.

[0196] Figure 8A sparse map 800 is shown that one or more vehicles (e.g., vehicle 200 (which may be an autonomous vehicle)) can access for providing autonomous vehicle navigation. The sparse map 800 may be stored in a memory, such as memory 140 or 150. Such a memory device may include any type of non-transitory storage device or computer-readable medium. For example, in some embodiments, memory 140 or 150 may include a hard drive, an optical disk, 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 memory 140 or 150 or other types of storage devices.

[0197] In some embodiments, sparse map 800 may be stored on a storage device or non-transitory computer-readable medium provided on vehicle 200 (e.g., a storage device included in a navigation system on vehicle 200). A processor provided on vehicle 200 (e.g., processing unit 110) may access sparse map 800 stored in a storage device or computer-readable medium provided on vehicle 200 to generate navigation instructions for guiding autonomous vehicle 200 when the vehicle traverses a road segment.

[0198] However, the sparse map 800 does not need to be stored locally relative to the vehicle. In some embodiments, the sparse map 800 may be stored on a storage device or computer-readable medium that is provided on a remote server that communicates with the vehicle 200 or a device associated with the vehicle 200. A processor (e.g., processing unit 110) provided on the vehicle 200 may receive data contained in the sparse map 800 from the remote server and may execute data for guiding autonomous driving of the vehicle 200. In such an embodiment, the remote server may store all or only a portion of the sparse map 800. Accordingly, a storage device or computer-readable medium provided on the vehicle 200 and / or on one or more additional vehicles may store the remaining (multiple) portions of the sparse map 800.

[0199] In addition, in such an embodiment, the sparse map 800 can be accessed by multiple vehicles (e.g., tens, hundreds, thousands, or millions of vehicles, etc.) passing through different road sections. It should also be noted that the sparse map 800 can contain multiple sub-maps. For example, in some embodiments, the sparse map 800 can contain hundreds, tens, millions, or more sub-maps that can be used to navigate the vehicle. Such sub-maps can be referred to as local maps, and vehicles traveling along the road can access any number of local maps related to the location where the vehicle is traveling. The local map portion of the sparse map 800 can 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 to retrieve relevant local maps.

[0200] In general, the sparse map 800 can be generated based on data collected from one or more vehicles as they travel along a road. For example, using sensors on one or more vehicles (e.g., cameras, speedometers, GPS, accelerometers, etc.), the trajectories of one or more vehicles traveling along a road can be recorded, and a polynomial representation of the preferred trajectory of the vehicle for subsequent travel along the road can be determined based on the collected trajectory of the one or more vehicles. Similarly, the data collected by one or more vehicles can help identify potential landmarks along a particular road. The data collected from the 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 navigating one or more autonomous vehicles. However, in some embodiments, map generation may not end at the initial generation of the map. As will be discussed in more detail below, as the vehicle continues to pass through the roads contained in the sparse map 800, the sparse map 800 can be continuously or periodically updated based on the data collected from the vehicle.

[0201] 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 location, road contour location, etc. The location of the map elements included in the sparse map 800 may be obtained using GPS data collected from vehicles passing through the road. For example, a vehicle passing through an identified landmark may determine the location of the identified landmark using GPS location information associated with the vehicle and a determination of the location of the identified landmark relative to the vehicle (e.g., based on image analysis of data collected from one or more cameras on the vehicle). When additional vehicles pass through the location of the identified landmark, this location determination of the identified landmark (or any other feature included in the sparse map 800) may be repeated. Some or all of the additional location determinations may be used to refine the location information stored in the sparse map 800 relative to the identified landmark. For example, in some embodiments, multiple location measurements relative to a specific feature stored in the sparse map 800 may be averaged together. However, any other mathematical operation may also be used to refine the stored location of the map element based on multiple determined locations of the map element.

[0202] The sparse map of the disclosed embodiment can use a relatively small amount of storage data to enable the autonomous navigation of the vehicle. In certain embodiments, the sparse map 800 can have a data density (e.g., comprising data representing target track, landmarks and any other stored road features) less than 2MB per kilometer of road, less than 1MB per kilometer of road, less than 500kB per kilometer of road or less than 100kB per kilometer of road. In certain embodiments, the data density of the sparse map 800 can be less than 10kB per kilometer of road, or even less than 2kB per kilometer of road (e.g., 1.6kB per kilometer), or no more than 10kB per kilometer of road, or no more than 20kB per kilometer of road. In certain embodiments, the sparse map with 4GB or less data in total can also be used to autonomously navigate most of the roads (if not all) of the United States. These data density values ​​can represent the average on the specific road sections in the entire sparse map 800, the local map in the sparse map 800 and / or the sparse map 800.

[0203] As described above, the sparse map 800 may include a representation 810 of multiple target trajectories for guiding autonomous driving or navigation along a road segment. Such a target trajectory may be stored as a three-dimensional spline. For example, the target trajectory stored in the sparse map 800 may be determined based on two or more reconstructed trajectories of a vehicle previously traversing along a particular road segment. A road segment may be associated with a single target trajectory or multiple target trajectories. For example, on a two-lane road, a first target trajectory may be stored to represent an intended path along the road in a first direction, and a second target trajectory may be stored to represent an intended path along the road in another direction (e.g., opposite to the first direction). Additional target trajectories regarding a particular road segment may be stored. For example, on a multi-lane road, one or more target trajectories may be stored, which represent the intended path of travel of a vehicle in one or more lanes associated with the multi-lane road. In some embodiments, each lane of a multi-lane road may be associated with its own target trajectory. In other embodiments, the stored target trajectory may be less than the lanes present on the multi-lane road. In this case, a vehicle navigating on a multi-lane road can use any stored target trajectory to guide its navigation by taking into account lane offsets from the lane in which the target trajectory is stored (e.g., if a vehicle is traveling in the leftmost lane of a three-lane highway and target trajectories are stored only for the 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).

[0204] In some embodiments, the target track can represent the ideal path that the vehicle should take when traveling. The target track can be located at the approximate center of the lane of travel, for example. In other cases, the target track can be located at other places relative to the road section. For example, the target track can approximately coincide with the center of the road, the edge of the road, or the edge of the lane, etc. In this case, the navigation based on the target track can include the determined offset maintained relative to the positioning of the target track. In addition, in some embodiments, the determined offset maintained relative to the positioning of the target track can be different based on the type of vehicle (for example, along at least a portion of the target track, a passenger car comprising two axles can have different offsets with a truck comprising more than two axles).

[0205] The sparse map 800 may also include data associated with a plurality of predetermined landmarks 820 associated with specific road segments, local maps, and the like. 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 adjust the heading direction to match the direction of the target track at the determined location.

[0206] A plurality of landmarks 820 can be identified and stored in the sparse map 800 at any suitable spacing. In some embodiments, landmarks can be stored at a relatively high density (e.g., every several meters or more). However, in some embodiments, significantly larger landmark spacing values ​​can be adopted. For example, in the sparse map 800, the identified (or recognized) landmarks can be spaced 10 meters, 20 meters, 50 meters, 100 meters, 1 kilometer, or 2 kilometers apart. In some cases, the identified landmarks can be located at a distance of even more than 2 kilometers apart.

[0207] Between landmarks, and therefore between determinations of the vehicle's position relative to the target track, the vehicle can navigate based on dead reckoning, in which the vehicle uses sensors to determine its ego motion and estimate its position relative to the target track. Since errors can accumulate during navigation by dead reckoning, the position determination relative to the target track may become increasingly inaccurate over time. The vehicle can use landmarks (and their known locations) that appear in the sparse map 800 to eliminate errors caused by dead reckoning in position determination. In this way, the identified landmarks contained in the sparse map 800 can be used as navigation anchors from which the accurate position of the vehicle relative to the target track can be determined. Because a certain amount of error in position fixes may be acceptable, the identified landmarks do not need to always be available to the autonomous vehicle. Instead, suitable navigation may even be based on landmark spacings of 10 meters, 20 meters, 50 meters, 100 meters, 500 meters, 1 kilometer, 2 kilometers, or more as described above. In some embodiments, a density of 1 identified landmark per 1 kilometer of road is sufficient to maintain longitudinal position determination accuracy within 1 meter. Therefore, it is not necessary to store every potential landmark that occurs along a road segment in the sparse map 800 .

[0208] Additionally, in some embodiments, lane markings may be used for positioning of the vehicle during landmark intervals. By using lane markings during landmark intervals, accumulation during navigation by dead reckoning may be minimized.

[0209] In addition to target tracks and identified landmarks, the sparse map 800 may contain information related to various other road features. For example, Fig. 9A A representation of a curve along a particular road segment is shown 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. Fig. 9A Such polynomials for the left and right sides of a single lane are shown in . Regardless of how many lanes a road may have, a road may be represented by a polynomial similar to Fig. 9A For example, the left and right sides of a multi-lane road can be represented by polynomials similar to Fig. 9A The polynomial shown in FIG. 1 and the intermediate lane markings (e.g., dashed line markings indicating lane boundaries, solid yellow lines indicating boundaries between lanes traveling in different directions, etc.) included on multi-lane roads may also be represented by a polynomial such as Fig. 9A The polynomial shown is used to represent .

[0210] like Fig. 9A As shown, a polynomial (e.g., a first order, second order, third order, or any suitable order polynomial) may be used to represent lane 900. For illustration, lane 900 is shown as a two-dimensional lane, and the polynomial is shown as a two-dimensional polynomial. Fig. 9A As shown, lane 900 includes a left side 910 and a right side 920. In some embodiments, more than one polynomial may be used to represent the location of 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 about 100 m, although other lengths greater than or less than 100 m may also be used. In addition, the polynomials may overlap with each other so as to facilitate seamless transitions in navigation based on subsequent polynomials encountered as the host vehicle travels along the road. For example, each of the left side 910 and the right side 920 may be represented by a plurality of third-order polynomials that are divided into segments of about 100 meters in length (an example of a first predetermined range) and overlap each other by about 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.

[0211] exist Fig. 9A In the example shown, the left side 910 of lane 900 is represented by two sets of third-order polynomials. The first set contains polynomial segments 911, 912, and 913. The second set contains polynomial segments 914, 915, and 916. The two sets, while being substantially parallel to each other, follow the positioning of their respective road sides. 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 described, 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 0 to 50m, 50m to 100m, or more 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., containing a height component) to represent elevation changes in a road segment in addition to XY curvature. Fig. 9AIn the example shown, the right side 920 of lane 900 is further represented by a first group having polynomial segments 921 , 922 , and 923 and a second group having polynomial segments 924 , 925 , and 926 .

[0212] Return to the target track of 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 while 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 shown in . The sparse map 800 may contain a plurality of trajectories (e.g., millions or billions or more to represent the trajectories of vehicles along various road segments along roads around the world). In some embodiments, each target trajectory may correspond to a spline connecting the three-dimensional polynomial segments.

[0213] 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 using a cubic polynomial requiring approximately 192 bytes of data per 100 m. For a host vehicle traveling approximately 100 km / hr, this can translate into a data usage / transmission requirement of approximately 200 kB per hour.

[0214] The sparse map 800 may describe the lane network using a combination of geometry descriptors and metadata. The geometry may be described using polynomials or splines as described above. The metadata may describe the number of lanes, special features (such as shared lanes), and possibly other sparse labels. The total footprint of such indicators may be negligible.

[0215] Accordingly, a sparse map according to an embodiment of the present disclosure may include at least one line representation of a road surface feature extending along a road segment, each line representation representing a path along the road segment that substantially corresponds to the road surface feature. In some embodiments, as described above, at least one line representation of a road surface feature may include a spline, a polynomial representation, or a curve. Furthermore, in some embodiments, the road surface feature may include at least one of a road edge or a lane marking. Furthermore, as discussed below with respect to "crowdsourcing," road surface features may be identified by image analysis of multiple images acquired as one or more vehicles traverse a road segment.

[0216] As previously described, the sparse map 800 can include multiple predetermined landmarks associated with road segments. Each landmark in the sparse map 800 can be represented and identified using less data than the actual image stored, rather than storing the actual image of the landmark and relying on image recognition analysis based on, for example, captured images and stored images. The data representing the landmark can still include enough information to describe or identify the landmark along the road. Storing data describing the characteristics of the landmark, rather than the actual image of the landmark, can reduce the size of the sparse map 800.

[0217] Fig.10 Examples of the types of landmarks that can be represented in the sparse map 800 are shown. Landmarks can include any visible and recognizable objects along a road segment. Landmarks can be selected so that they are fixed and their positioning and / or content do not change frequently. The landmarks included in the sparse map 800 are useful for determining the positioning of the vehicle 200 relative to the target trajectory when the vehicle passes through a particular road segment. Examples of landmarks can 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.

[0218] Fig.10 Examples of landmarks shown in 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 for directing vehicles off a road, and the like. Accordingly, at least one of the plurality of landmarks may include a road sign.

[0219] General signs may have nothing to do with transportation. For example, general signs may include billboards used for advertising, or welcome boards near the 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, but generally the logo 1040 can have other shapes, such as square, circle, triangle, etc.

[0220] Landmarks may also include roadside fixtures. Roadside fixtures may not be signs and may not be related to traffic or directions. For example, roadside fixtures may include lamp posts (e.g., lamp posts 1035), power line posts, traffic light posts, etc.

[0221] 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 (e.g., icons, badges, bar codes, etc.) added to existing road signs, which may be recognized or identified 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 may use when determining its position along the target trajectory.

[0222] In some embodiments, the landmarks contained in the sparse map 800 can be represented by a data object of a predetermined size. The data representing the landmark can include any suitable parameters for identifying a specific landmark. For example, in some embodiments, the landmarks stored in the sparse map 800 can include parameters such as the physical size of the landmark (e.g., to support an estimate 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 can be associated with a data size. For example, 8 bytes of data can be used to store the landmark size. 12 bytes of data can be used to specify the distance to the previous landmark, the lateral offset and the height. The type code associated with a landmark such as a direction sign or a traffic sign can require approximately 2 bytes of data. For general signs, a 50-byte data storage device can be used to store an image signature that enables identification of general signs. The landmark GPS location can be associated with a 16-byte data storage device. These data sizes for each parameter are just examples, and other data sizes can also be used.

[0223] Representing landmarks in the sparse map 800 in this way can provide a streamlined solution for effectively representing landmarks in a database. In some embodiments, signs can be referred to as semantic signs and non-semantic signs. Semantic signs can include signs of any category with standardized meanings (e.g., speed limit signs, warning signs, direction signs, etc.). Non-semantic signs can include any signs that are not associated with standardized meanings (e.g., general advertising signs, signs identifying commercial establishments, etc.). For example, each semantic sign can be represented by 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 codes; and 16 bytes for GPS coordinates). Sparse map 800 can use a tag system to represent landmark types. In some cases, each traffic sign or direction sign can be associated with its own tag, which can be stored in the database as part of the landmark identification. For example, the database can contain approximately 1,000 different tags to represent various traffic signs, and approximately 10,000 different tags to represent direction signs. Of course, any suitable number of tags can be used, and additional tags can be created as needed. In some embodiments, a general purpose marker may use less than about 100 bytes (e.g., about 86 bytes, including 8 bytes for size; 12 bytes for distance, lateral offset, and altitude to a previous landmark; 50 bytes for an image signature; and 16 bytes for GPS coordinates).

[0224] Thus, for semantic road signs that do not require image signatures, even at a relatively high landmark density of approximately 1 per 50 meters, the data density impact on the sparse map 800 can be approximately 760 bytes per kilometer (e.g., 20 landmarks per kilometer x 38 bytes per landmark = 760 bytes). Even for general purpose signs that include an image signature component, the data density impact is approximately 1.72 kilobytes per kilometer (e.g., 20 landmarks per kilometer x 86 bytes per landmark = 1,720 bytes). For semantic road signs, this equates to approximately 76kB of data usage per hour for a vehicle traveling 100kmm / hr. For general purpose signs, this equates to approximately 170kB of data usage per hour for a vehicle traveling 100kmm / hr.

[0225] In some embodiments, a general rectangular object such as a rectangular sign can be represented in the sparse map 800 by no more than 100 bytes of data. The representation of a general rectangular object (e.g., general sign 1040) in the sparse map 800 can include a compressed (condensed) image signature (e.g., compressed image signature 1045) associated with the general rectangular object. Such a compressed image signature can be used, for example, to assist in the identification of the general sign, such as as a recognizable landmark. Such a compressed image signature (e.g., image information derived from actual image data representing the object) can avoid the need to store the actual image of the object, or avoid the need to perform comparative image analysis on the actual image to identify the landmark.

[0226] 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 the image, which is on the host vehicle 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 the image of the generic sign 1040 to describe the generic sign 1040.

[0227] For example, in Fig.10 , the circles, triangles, and stars shown in the compressed image signature 1045 can represent areas of different colors. The patterns represented by the circles, triangles, and stars can be stored in the sparse map 800, for example, within the 50 bytes designated as containing the image signature. It is worth noting that the circles, triangles, and stars are not necessarily intended to indicate that these shapes are stored as part of the image signature. Instead, these shapes are intended to conceptually represent recognizable areas with discernible color differences, text areas, graphic shapes, or other changes in characteristics that can be associated with general signs. 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 the same or different analysis based on a comparison of the stored compressed image signature with image data captured, for example, using a camera on an autonomous vehicle.

[0228] Accordingly, multiple landmarks may be identified by performing image analysis on 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 do 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 do appear exceeds a threshold.

[0229] Returning to the target trajectory that the host vehicle can use to navigate a specific road segment, Fig.11A A polynomial representation of a trajectory captured during the process of building or maintaining a sparse map 800 is shown. The polynomial representation of the target trajectory contained in the sparse map 800 can be determined based on two or more reconstructed trajectories that the vehicle previously traversed along the same road segment. In some embodiments, the polynomial representation of the target trajectory contained in the sparse map 800 can be an aggregation of two or more reconstructed trajectories that the vehicle previously traversed along the same road segment. In some embodiments, the polynomial representation of the target trajectory contained in the sparse map 800 can be an average of two or more reconstructed trajectories that the vehicle previously traversed along the same road segment. Other mathematical operations can also be used to construct a target trajectory along a road path based on reconstructed trajectories collected from vehicles traversing along a road segment.

[0230] like Fig.11A As shown, a road segment 1100 can be driven by multiple vehicles 200 at different times. Each vehicle 200 can collect data related to the path taken by the vehicle along the road segment. The path driven by a particular vehicle can be determined based on camera data, accelerometer information, speed sensor information and / or GPS information and other potential sources. Such data can 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) can be determined for a particular road segment. Such a target trajectory can represent the preferred path of the vehicle when the host vehicle is traveling along the road segment (e.g., guided by an autonomous navigation system).

[0231] exist Fig.11AIn the example shown, a first reconstructed trajectory 1101 may be determined based on data received from a first vehicle that traverses the road segment 1100 in a first time period (e.g., day 1), a second reconstructed trajectory 1102 may be obtained from a second vehicle that traverses the road segment 1100 in a second time period (e.g., day 2), and a third reconstructed trajectory 1103 may be obtained from a third vehicle that traverses the road segment 1100 in a third time period (e.g., day 3). Each trajectory 1101, 1102, and 1103 may be represented by a polynomial, such as a three-dimensional polynomial. It should be noted that in some embodiments, any reconstructed trajectory may be assembled on a vehicle that traverses the road segment 1100.

[0232] Additionally or alternatively, such a reconstructed trajectory 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 the trajectory of 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 traversal of a road segment, in some embodiments, each target trajectory contained in the sparse map 800 may be determined based on two or more reconstructed trajectories of vehicles traversing the same road segment. 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.

[0233] Fig. 11B and Fig. 11C The concept of target trajectories associated with road segments existing 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. Geographic area 1111 may also include a branch road segment 1130 intersecting road segment 1120. Road segment 1130 may include a two-lane road, each lane being designated for a different direction of travel. Geographic area 1111 may also include other road features, such as stop lines 1132, stop signs 1134, speed limit signs 1136, and hazard signs 1138.

[0234] like Fig. 11C As shown, sparse map 800 may include local map 1140, which includes a road model for assisting autonomous navigation of vehicles within geographic area 1111. For example, local map 1140 may include target tracks 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 tracks 1141 and / or 1142 that the autonomous vehicle may access or rely on when crossing lane 1122. Similarly, local map 1140 may include target tracks 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 tracks 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 (specifically, relative to target trajectory 1141 associated with the rightmost lane of lane 1120) to road segment 1130 (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 (specifically, relative to target trajectory 1146) to a portion of road segment 1124 (specifically, as shown, relative to target trajectory 1143 associated with the left lane of lane 1124).

[0235] 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. These landmarks may include a first landmark 1150 associated with stop line 1132, a second landmark 1152 associated with stop sign 1134, a third landmark associated with speed limit sign 1154, and a fourth landmark 1156 associated with hazard sign 1138. Such landmarks may be used, for example, to help an autonomous vehicle determine its current location relative to any shown target track so that the vehicle can adjust its heading to match the direction of the target track at the determined location.

[0236] In some embodiments, the sparse map 800 may also include road signature profiles. Such road signature profiles may be associated with any discernible / measurable change in at least one parameter associated with a road. For example, in some cases, such profiles may be associated with changes in road surface information, such as changes in surface roughness of a particular road segment, changes in road width on a particular road segment, changes in distance between dashed lines 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 above parameters or other parameters, in one example, profile 1160 may represent a measurement of road surface roughness, e.g., as obtained by monitoring one or more sensors that provide outputs indicative of an amount of suspension displacement as a vehicle travels on a particular road segment.

[0237] Alternatively or simultaneously, profile 1160 can represent changes in road width, as determined based on image data obtained via a camera on a vehicle traveling on a particular road segment. For example, such a profile is useful in determining a particular positioning of an autonomous vehicle relative to a particular target track. That is, as it traverses a road segment, the autonomous vehicle can 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 maps parameter changes relative to position along the road segment, the measured and predetermined profiles can be used (e.g., by overlaying corresponding portions of the measured and predetermined profiles) to determine a current position along the road segment, and therefore determine a current position of the target track relative to the road segment.

[0238] 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 containing 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.

[0239] Based on different environmental conditions, such as day and night, snow, rain, fog, etc., different trajectories can be generated and included in the sparse map 800. Autonomous vehicles traveling under different environmental conditions can be provided with sparse maps 800 generated based on such different environmental conditions. In some embodiments, a camera provided on an autonomous vehicle can detect environmental conditions, and such information can be provided back to a server that generates and provides sparse maps. For example, a server can generate or update an already generated sparse map 800 to include trajectories that may be more suitable or safer for autonomous driving under detected environmental conditions. When an autonomous vehicle travels along a road, updates to the sparse map 800 based on environmental conditions can be performed dynamically.

[0240] Other different parameters related to driving can also be used as a basis for generating different sparse maps 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 can be included in the sparse map 800 so that the autonomous vehicle can remain within a specific lane when the vehicle follows a specific track. When images captured by a camera on the autonomous vehicle indicate that the vehicle has drifted outside of the lane (e.g., crossed a lane marking), an action can be triggered within the vehicle to bring the vehicle back to the designated lane according to the specific track.

[0241] Crowdsourcing sparse maps

[0242] In some embodiments, the disclosed system and method can generate a sparse map for autonomous vehicle navigation. For example, the disclosed system and method can use crowdsourced data to generate a sparse map, which one or more autonomous vehicles can use to navigate along the system of roads. As used herein, "crowdsourcing" means receiving data from various vehicles (e.g., autonomous vehicles) traveling on road segments at different times, and these data are used to generate and / or update road models. The model can be transmitted to vehicles or other vehicles traveling along the road segment later to assist autonomous vehicle navigation. The road model can include multiple target trajectories, which represent the preferred trajectories that the autonomous vehicle should follow when passing through the road segment. The target trajectory can be the same as the actual trajectory reconstructed from the vehicle collected through the road segment, which can be transmitted from the vehicle to the server. In some embodiments, the target trajectory can be different from the actual trajectory previously adopted by one or more vehicles when passing through the road segment. The target trajectory can be generated based on the actual trajectory (e.g., by averaging or any other suitable operation).

[0243] The vehicle trajectory data that the vehicle can upload to the server can correspond to the actual reconstructed trajectory of the vehicle, or can correspond to a recommended trajectory that can be based on or related to the actual reconstructed trajectory of the vehicle, but can be different from the actual reconstructed trajectory. For example, the vehicles can modify their actual reconstructed trajectories and submit (e.g., recommend) the modified actual trajectories to the server. The road model can use the recommended, modified trajectory as a target trajectory for autonomous navigation of other vehicles.

[0244] In addition to trajectory information, other information potentially 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 can identify potential landmarks in the environment and refine landmark locations. The navigation system of the autonomous vehicle can use these landmarks to determine and / or adjust the vehicle's position along the target trajectory.

[0245] The reconstructed trajectory that can be generated as the vehicle travels along the road can be obtained by any suitable method. In some embodiments, the reconstructed trajectory can be generated by stitching together segments of the vehicle's movement using, for example, ego-motion estimates (e.g., three-dimensional translation and three-dimensional rotation of the camera (and thus the vehicle body)). The rotation and translation estimates can be determined based on analysis of images captured by one or more image capture devices and information from other sensors or devices (such as inertial sensors and speed sensors). For example, the inertial sensor can include an accelerometer or other suitable sensor that is configured to measure changes in vehicle body translation and / or rotation. The vehicle can include a speed sensor that measures the speed of the vehicle.

[0246] 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 based on the identified motion, the motion of the vehicle is determined. The ego-motion may be integrated over time and along a road segment to reconstruct a trajectory associated with a road segment that the vehicle has followed.

[0247] Data collected by multiple vehicles in multiple driving at different times along the road segment (e.g., reconstructed trajectories) can be used to build a road model (e.g., including target trajectories, etc.) included in the sparse data map 800. Data collected by multiple vehicles in multiple driving at different times along the 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 passing through a public road segment at different times. Such data received from different vehicles can be combined to generate a road model and / or update a road model.

[0248] The geometry of the reconstructed trajectory (and 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 by analyzing a video stream or multiple images captured by a camera mounted on the vehicle. In some embodiments, a positioning is identified in each frame or image a few meters ahead of the current position of the vehicle. The positioning is the position where the vehicle is expected to travel within a predetermined time period. This operation can be repeated frame by frame, and at the same time, the vehicle can calculate the camera's self-motion (rotation and translation). In each frame or each image, the vehicle generates a short-range model of the expected path in a reference frame attached to the camera. The short-range models 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. Then, the three-dimensional model of the road can be fitted by a spline, which can contain or connect one or more polynomials of suitable order.

[0249] In order to summarize the short-range road model at each frame, one or more detection modules can be used. For example, a bottom-up lane detection module can be used. The bottom-up lane detection module can be useful when mapping lane markings on the road. This module can find edges in the image and assemble them together to form lane markings. The second module can be used with the bottom-up lane detection module. The second module is an end-to-end deep neural network that can be trained to predict the correct short-range path from the input image. In both modules, the road model can be detected in the image coordinate system and transformed into a three-dimensional space that can be virtually attached to the camera.

[0250] Although the reconstructed trajectory modeling method may introduce an accumulation of errors due to the integration of self-motion over long periods of time, which may contain noise components, such errors may be insignificant because the generated model can provide sufficient accuracy for navigation on a local scale. In addition, the integration error can also be eliminated by using external information sources such as satellite imagery or geodetic measurements. For example, the disclosed system and method can use a GNSS receiver to eliminate cumulative errors. However, GNSS positioning signals may not always be available and accurate. The disclosed system and method can enable steering applications that are weakly dependent on the availability and accuracy of GNSS positioning. In such a system, 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.

[0251] In some embodiments, the range 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 used primarily for two purposes: planning the forward trajectory and locating the vehicle on the road model. In some embodiments, when the control algorithm steers the vehicle based on a target point located 1.3 seconds ahead (or any other time, such as 1.5 seconds, 1.7 seconds, 2 seconds, etc.), the planning task can use a model within a typical range of 40 meters ahead (or any other suitable distance ahead, such as 20 meters, 30 meters, 50 meters). 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.). According to a method called "tail alignment" described in more detail in another section. The disclosed system and method can generate a geometric model with sufficient accuracy over a specific range (such as 100 meters) so that the planned trajectory does not deviate from the center of the lane by more than, for example, 30 centimeters.

[0252] As described above, a three-dimensional road model can be constructed from detecting short-range segments and stitching them together. Stitching can be enabled 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 speed signal. The accumulated error may be small enough over some local range scale, such as about 100 meters. All of this can be done in a single drive on a particular road segment.

[0253] In some embodiments, multiple drives can be used to average the resulting model and further improve its accuracy. The same car can drive the same route multiple times, or multiple cars can transmit their collected model data to a central server. In any case, a matching process can be performed to identify overlapping models and enable averaging to generate a target trajectory. Once the convergence criteria are met, the constructed model (e.g., containing the target trajectory) can be used for steering. Subsequent drives can be used for further model improvements and adaptation to infrastructure changes.

[0254] If multiple cars are connected to a central server, sharing driving experience (such as sensed data) between them becomes feasible. Each vehicle client can store a partial copy of the general road model, which can 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 perform two-way updates using very small bandwidth.

[0255] 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 containing 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, text identification 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.

[0256] The vehicle can determine the distance from the vehicle to the landmark based on the analysis of one or more images. In some embodiments, the distance can be determined based on the analysis of the image of the landmark using a suitable image analysis method, such as a scaling method and / or an optical flow method. In some embodiments, the disclosed system and method can be configured to determine the type or classification of the potential landmark. In the case where the vehicle determines that a potential landmark corresponds to a predetermined type or classification stored in a sparse map, it is sufficient for the vehicle to communicate an indication of the type or classification of the landmark to the server together with its location. The server can store such an indication. At a later time, other vehicles can capture an image of the landmark, process the image (e.g., using a classifier), and compare the result of processing the image with the indication of the type of landmark stored in the server. There can be various types of landmarks, and different types of landmarks can be associated with different types of data uploaded to and stored in the server, different processing on the vehicle can detect the landmark and communicate information about the landmark to the server, and the system on the vehicle can receive the landmark data from the server and use the landmark data for identifying the landmark in autonomous navigation.

[0257] In some embodiments, multiple autonomous vehicles driving on a road segment can communicate with a server. The vehicle (or client) can generate a curve describing its driving in an arbitrary coordinate system (e.g., by integrating the ego motion). The vehicle can detect landmarks and locate them in the same frame. The vehicle can upload the curve and landmarks to the server. The server can collect data from the vehicles through multiple driving and generate a unified road model. Or, for example, as shown below with reference to Fig.19 As discussed, the server can generate a sparse map with a unified road model using the uploaded curves and landmarks.

[0258] 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 contains information that should trigger an update or creation of new data on the server. The server may distribute the updated model or updates to the vehicles for use in providing autonomous vehicle navigation.

[0259] 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.

[0260] 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 model update. The server may also distribute the updated model to vehicles that are about to travel on the road segment, or whose planned trips include the road segment associated with the model update. 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.

[0261] 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 match curves using landmarks and create an average road model based on the trajectories collected from multiple vehicles. The server may also compute a graph of the road and the most likely path at each node or connection of the road segment. For example, the remote server may align the trajectories to generate a crowdsourced sparse map from the collected trajectories.

[0262] The server may average landmark attributes received from multiple vehicles traveling along a public road segment, such as the distance between one landmark and another landmark (e.g., the previous landmark along the road segment) measured by multiple vehicles to determine arc length parameters and support positioning and speed calibration of 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 average physical dimensions may be used to support distance estimation, such as the distance from the vehicle to the landmark. The server may average the lateral positions of 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 location of the landmark). The average lateral portion may 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 average GPS coordinates of the landmarks may be used to support global positioning (localization) or positioning (positioning) of the landmarks in the road model.

[0263] In some embodiments, based on data received from the vehicle, the server can identify model changes, such as construction, detours, new signs, removal of signs, etc. The server can continuously or periodically or instantaneously update the model when new data is received from the vehicle. The server can 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 can use crowdsourced data to filter out "ghost" landmarks detected by the vehicle.

[0264] In some embodiments, the server may analyze driver interventions during autonomous driving. The server may analyze data received from the vehicle at the time and location of the intervention, and / or data received before the time the intervention occurred. The server may identify certain portions of the data that caused the intervention or were closely related to the intervention, e.g., data indicating a temporary lane closure setting, data indicating a pedestrian in the road. The server may update the model based on the identified data. For example, the server may modify one or more trajectories stored in the model.

[0265] Fig.12 is a schematic diagram of a system for generating sparse maps using crowdsourcing (and for sparse map distribution and navigation using crowdsourcing). Fig.12 A road segment 1200 is shown that includes one or more lanes. Multiple vehicles 1205, 1210, 1215, 1220, and 1225 may travel on the road segment 1200 at the same time or at different times (although Fig.121200 at the same time). At least one of vehicles 1205, 1210, 1215, 1220, and 1225 may be an autonomous vehicle. To simplify this example, it is assumed that all vehicles 1205, 1210, 1215, 1220, and 1225 are autonomous vehicles.

[0266] Each vehicle may be similar to the vehicle disclosed in other embodiments (e.g., vehicle 200), and may include components or devices included in the vehicle disclosed in other embodiments or associated with the vehicle disclosed in other embodiments. Each vehicle may be equipped with an image capture device or camera (e.g., image capture device 122 or camera 122). Each vehicle may communicate with a remote server 1230 via one or more networks (e.g., via a cellular network and / or the Internet, etc.) through a wireless communication path 1235 as shown by a dotted line. Each vehicle may transmit data to the server 1230 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 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 later.

[0267] 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 vehicle 1205, 1210, 1215, 1220, and 1225 as each vehicle travels on road segment 1200. In some embodiments, the trajectory may be reconstructed based on data sensed by various sensors and devices provided on vehicle 1205. For example, the trajectory may be reconstructed based on at least one of accelerometer data, speed data, landmark data, road geometry or contour data, vehicle position 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 speed of vehicle 1205 sensed by a speed sensor. Additionally, in some embodiments, the trajectory may be determined (e.g., by a processor on each of the vehicles 1205, 1210, 1215, 1220, and 1225) based on sensed camera ego motion, which may indicate three-dimensional translation and / or three-dimensional rotation (or rotational motion). The camera (and thus the vehicle body) ego motion may be determined by analysis of one or more images captured by the camera.

[0268] In some embodiments, the trajectory of the vehicle 1205 may be determined by a processor provided 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 provided in the vehicle 1205 and determine the trajectory based on the data received from the vehicle 1205.

[0269] 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 a lane structure and / or a landmark. The lane structure may include the total number of lanes of the road segment 1200, the lane type (e.g., single lane, double lane, driving lane, overtaking lane, etc.), the signs on the lanes, the lane width, etc. In some embodiments, the navigation information may include a lane assignment, for example, which lane of the plurality of lanes the vehicle is traveling in. For example, the lane assignment may be associated with a numeric value "3", which indicates that the vehicle is traveling in the third lane from the left or right. As another example, the lane assignment may be associated with a text value "center lane", which indicates that the vehicle is traveling in the center lane.

[0270] The server 1230 may store 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 the lanes of the road segment at different times. The server 1230 may generate an autonomous vehicle road navigation model or a portion (e.g., an updated portion) of the model based on a plurality of tracks determined from 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 for autonomous navigation along public road segment 1200.

[0271] As mentioned above, the autonomous vehicle road navigation model can be included in the sparse map (e.g., Figure 8 800). The sparse map 800 may contain a sparse record of data related to road geometry and / or landmarks along the road, which may provide sufficient information for guiding the autonomous navigation of the autonomous vehicle, but without requiring excessive data storage. In some embodiments, the autonomous vehicle road navigation model may be stored separately from the sparse map 800, and when the model is executed for navigation, map data from the sparse map 800 may be used. In some embodiments, the autonomous vehicle road navigation model may use the map data contained in the sparse map 800 to determine a target trajectory along the road segment 1200 for guiding the autonomous navigation of autonomous vehicles 1205, 1210, 1215, 1220, and 1225 or other vehicles traveling later along the road segment 1200. For example, when the autonomous vehicle road navigation model is executed by a processor included in the navigation system of the vehicle 1205, the model may enable the processor to compare the trajectory determined based on the navigation information received from the vehicle 1205 with the predetermined trajectory contained in the sparse map 800 to verify and / or correct the current driving route of the vehicle 1205.

[0272] In the autonomous vehicle road navigation model, the geometry 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 understood by those skilled in the art, a spline can be a numerical function for fitting data defined piece by piece by a series of polynomials. The spline for fitting the three-dimensional geometry data of the road can 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 can include one or more three-dimensional polynomials of different orders of the data points of the three-dimensional geometry data of the road that are connected (e.g., fitted). In some embodiments, the autonomous vehicle road navigation model can include a three-dimensional spline corresponding to the target track along a public road segment (e.g., road segment 1200) or a lane of the road segment 1200.

[0273] As described above, the autonomous vehicle road navigation model included in the sparse map may include other information, such as the identification of at least one landmark along 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) provided on the vehicle 1205 may process the image of the landmark to extract identification information of the landmark. The landmark identification information, rather than the actual image of the landmark, may be stored in the sparse map 800. The landmark identification information may require much less storage space than the actual image. Other sensors or systems (e.g., a GPS system) may also provide certain identification information of the landmark (e.g., the location of the landmark). The landmark may include at least one of a traffic sign, an arrow sign, a lane sign, a dashed lane sign, a traffic light, a stop line, a directional sign (e.g., a highway exit sign with an arrow indicating a direction, a highway sign with an arrow pointing to a different direction or place), a landmark beacon, or a lamp post. A landmark beacon refers to a device (e.g., an RFID device) installed along a road segment that transmits or reflects a signal to a receiver mounted on a vehicle such that when a vehicle passes by the device, the beacon received by the vehicle and the location of the device (e.g., determined from the device's GPS location) can be used as a landmark to be included in the autonomous vehicle road navigation model and / or sparse map 800.

[0274] The identification of at least one landmark may include the location of at least one landmark. The location of the landmark may be determined based on location measurements performed using sensor systems (e.g., global positioning systems, inertial-based 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 measurements to 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.

[0275] The identification of the landmark may include the size of the landmark. A processor provided on a vehicle (e.g., 1205) may estimate the physical size of the landmark based on analysis of the image. Server 1230 may receive multiple estimates of the physical size of the same landmark from different vehicles through different drives. Server 1230 may average the different estimates to derive the physical size of the landmark and store the landmark size in the road model. The physical size estimate may be used to further determine or estimate the distance from the vehicle to the landmark. The distance to the landmark may be estimated based on the current speed of the vehicle and based on the extended ratio of the position of the landmark appearing in the image relative to the extended focus of the camera. For example, the distance to the landmark may be estimated by Z=V*dt*R / D, where V is the 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 from t1 to t2 in the image. dt represents (t2-t1). For example, the distance to a 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 extended focus in the image, dt is the time interval, and D is the image displacement of the landmark along the epipolar line. Other equations equivalent to the above equation, such as Z = V*ωω / Δω, can be used to estimate the distance to the landmark. Here, V is the vehicle speed, ω is the image length (similar to the object width), and Δω is the change in the image length per unit time.

[0276] 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 (such as height or width), and ω is the number of pixels when the landmark is out of the image. Based on the above equation, ΔZ = f*W*Δω / ω can be used 2+f*ΔW / ω to calculate the change in distance Z, 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 on the server side. The final error in the distance estimate can be very small. When using the above equation, two error sources may appear, namely ΔW and Δω. Their contribution to the distance error is given by ΔZ=f*W*Δω / ω 2 +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).

[0277] 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 frequently occurring distance 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.

[0278] 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 The curves 1301, 1302, and 1303 shown in are 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 of 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, while other data points may be associated with data associated with a road signature profile.

[0279] Fig.14The raw positioning data 1410 (e.g., GPS data) received from five separate drivings is shown. If a driving is simultaneously separated by a vehicle at the same time, by the same vehicle at a separated time, or by a separated vehicle at a separated time, the driving can be separated from another driving. In order to take into account the errors in the positioning data 1410 and the different positioning of vehicles in the same lane (e.g., one vehicle may be 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 positioning 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 instead of from driving 1. The skeleton 1420 may not be detailed enough for navigating the vehicle (e.g., because unlike the above-mentioned spline, the skeleton 1420 combines the driving of multiple lanes on the same road), but it can provide useful topological information and can be used to define intersections.

[0280] Fig.15 An example is shown by which additional detail can be generated for a sparse map within a segment of a map skeleton (e.g., segment A to segment B within skeleton 1420). Fig.15 As shown, data (e.g., ego-motion data, road sign data, etc.) can be shown as a driving position S (or S 1 or S 2 ). Server 1230 can identify landmarks of the sparse map by identifying unique matches between landmarks 1501, 1503, and 1505 of driving 1510 and landmarks 1507 and 1509 of driving 1520. This matching algorithm can obtain the identification of landmarks 1511, 1513, and 1515. However, those skilled in the art will recognize that other matching algorithms can be used. For example, probabilistic optimization can be used instead of unique matching or in combination with unique matching. Server 1230 can align driving longitudinally to align matching landmarks. For example, server 1230 can select one driving (e.g., driving 1520) as a reference driving, and then shift and / or elastically stretch (multiple) other driving (e.g., driving 1510) for alignment.

[0281] Fig.16 An example of aligned landmark data used 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.16In the example of , data from drive 1613 includes a "ghost" landmark, and server 1230 may identify it as such because drives 1601, 1603, 1605, 1607, 1609, and 1611 do not include identification of landmarks near the landmark identified in drive 1613. Accordingly, server 1230 may accept a potential landmark when the ratio of images in which the landmark does appear 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 does appear exceeds a threshold.

[0282] Fig.17 A system 1700 for generating driving data is depicted, which can be used to crowdsource sparse maps. Fig.17 As shown, 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 multiple types of data, such as self-motion data, traffic sign data, road data, etc. Camera data and positioning data may be segmented into driving segments 1705. For example, driving segments 1705 may each have camera data and positioning data from less than 1 kilometer of driving.

[0283] In some embodiments, system 1700 can remove redundancy in driving segment 1705. For example, if a landmark appears in multiple images from camera 1701, system 1700 can 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. As a further example, if a lane marker appears in multiple images from camera 1701, system 1700 can remove redundant data such that driving segment 1705 contains only one copy of the location of the lane marker and any metadata associated with the lane marker.

[0284] The system 1700 also includes a server (e.g., server 1230). The server 1230 can receive the driving segments 1705 from the vehicle and reassemble the driving segments 1705 into a single driving 1707. This arrangement can reduce bandwidth requirements when transmitting data between the vehicle and the server, while also allowing the server to store data related to the entire driving.

[0285] Fig.18 Depicts a further configuration for crowdsourcing sparse maps Fig.17 System 1700. Fig.17As shown, system 1700 includes a vehicle 1810 that uses, for example, a camera (which generates, for example, ego-motion data, traffic sign data, road data, etc.) and a positioning device (e.g., a GPS locator) to capture driving data. Fig.17 As shown, the vehicle 1810 segments the collected data into driving segments (in Fig.18 Then, the server 1230 receives the driving segments and reconstructs the driving (in Fig.18 described as “driving 1” in the text).

[0286] like Fig.18 As further depicted in FIG. 1 , 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 The server 1230 then receives the driving segments and reconstructs the driving (in Fig.18 Any number of additional vehicles may be used. For example, Fig.18 Also included is a "Car N" that captures driving data, segments it into driving segments (in Fig.18 , "DS1N", "DS2N", "DSN N"), and transmit it to the server 1230 for reconstruction into a driving (in Fig.18 depicted as "Drive N" in Figure 1).

[0287] like Fig.18 As shown, server 1230 can build 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”).

[0288] 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.

[0289] 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 image data that has been stripped down by a processor on vehicle 1205 to remove redundancy, as described above with reference to FIG. Fig.17 discussed.

[0290] Process 1900 may further include identifying at least one line representation of a road surface feature extending along a road segment based on a 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 trajectory of the vehicle 1205's travel based on the camera ego motion (e.g., three-dimensional translation and / or three-dimensional rotation motion) received at step 1905.

[0291] Process 1900 may also include identifying multiple landmarks associated with the road segment based on multiple images (step 1910). For example, server 1230 may analyze the environmental images received from camera 122 to identify one or more landmarks, such as road signs along road segment 1200. Server 1230 may use analysis of multiple images acquired when one or more vehicles traverse the road segment to identify the landmarks. 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 landmarks do appear to images in which the landmarks do not appear exceeds a threshold, and / or rejecting potential landmarks when the ratio of images in which the landmarks do not appear to images in which the landmarks do appear exceeds a threshold.

[0292] Process 1900 may include other operations or steps performed by server 1230. For example, the navigation information may include a target trajectory for a vehicle traveling along a road segment, and process 1900 may include clustering, by server 1230, vehicle trajectories associated with a plurality of vehicles traveling on the road segment, and determining the target trajectory based on the clustered vehicle trajectories, as discussed in further detail below. Clustering vehicle trajectories may include clustering, by server 1230, a plurality of trajectories associated with vehicles traveling on the road segment into a plurality of clusters based on at least one of an absolute heading of the vehicle or a lane assignment of the vehicle. Generating the target trajectory may include averaging the clustered trajectories by server 1230. As a further example, process 1900 may include aligning the data received in step 1905. As described above, other processes or steps performed by server 1230 may also be included in process 1900.

[0293] 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 a GPS device on the vehicle in order to locate the vehicle on the map and to find a rotational transformation between the body reference frame and the world reference frame (e.g., north, east, and down). Once the body reference frame is aligned with the map reference frame, the desired route can be expressed in the body reference frame and a steering command can be calculated or generated.

[0294] The disclosed systems and methods can enable autonomous vehicle navigation (e.g., steering control) with low-footprint models that can be collected by the autonomous vehicle itself without the assistance of expensive surveying instruments. To support autonomous navigation (e.g., steering applications), a road model can include a sparse map having the geometry of the road, its lane structure, and landmarks that can be used to determine the location or position of the vehicle along a trajectory contained in the model. As described above, the generation of the sparse map can be performed by a remote server that communicates with and receives data from a vehicle traveling on the road. The data can include sensed data, a trajectory reconstructed based on the sensed data, and / or a recommended trajectory that can represent a modified reconstructed trajectory. As described below, the server can transmit the model back to the vehicle or other vehicles traveling on the road later to assist in autonomous navigation.

[0295] Fig. 20A block diagram of a server is shown. Server 1230 may include a communication unit 2005, which may include 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 via communication unit 2005. For example, server 1230 may receive navigation information transmitted from vehicles 1205, 1210, 1215, 1220, and 1225 via communication unit 2005. Server 1230 may assign an autonomous vehicle road navigation model to one or more autonomous vehicles via communication unit 2005.

[0296] Server 1230 may include at least one non-transitory storage medium 2010, such as a hard drive, an optical disk, a tape, etc. Storage device 1410 may be configured to store data, such as navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225 and / or an autonomous vehicle road navigation model generated by server 1230 based on the navigation information. Storage device 2010 may be configured to store any other information, such as a sparse map (e.g., as described above with reference to FIG. 1 ). Figure 8 The sparse map discussed in 800).

[0297] In addition to or in place of storage device 2010, server 1230 may include memory 2015. Memory 2015 may be similar to or different from memory 140 or 150. Memory 2015 may be a non-transitory memory, such as flash memory, random access memory, etc. Memory 2015 may be configured to store data, such as computer code or instructions executable by a processor (e.g., processor 220), map data (e.g., data of sparse map 800), autonomous vehicle road navigation models, and / or navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225.

[0298] 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 later). Processing device 2020 may be similar to or different from processor 180, 190 or processing unit 110.

[0299] Fig.21 2015 can store computer code or instructions for performing one or more operations for generating a road navigation model for 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 allocation module 2110. The processor 2020 may execute instructions stored in any module 2105 and 2110 included in the memory 2015.

[0300] The model generation module 2105 may store instructions that, when executed by the processor 2020, may generate at least a portion of an autonomous vehicle road navigation model for a public road segment (e.g., road segment 1200) based on navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225. For example, when generating the autonomous vehicle road navigation model, the processor 2020 may cluster vehicle trajectories along the public road segment 1200 into different clusters. The processor 2020 may determine a target trajectory along the public road segment 1200 based on the clustered vehicle trajectories for each different cluster. Such an operation may include finding a mean or average trajectory of the clustered vehicle trajectories in each cluster (e.g., by averaging data representing the clustered vehicle trajectories). In some embodiments, the target trajectory may be associated with a single lane of the public road segment 1200.

[0301] 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 or recommended trajectories (actual trajectories with some modifications) received from multiple vehicles. The target trajectories contained in the road model or sparse map may be continuously updated (e.g., averaged) with new trajectories received from other vehicles.

[0302] 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 which will reconstruct the actual trajectory of the vehicle. In some embodiments, the vehicle may transmit data related to the trajectory (e.g., curves in an arbitrary reference frame), landmark data, and lane assignments along the path of travel to the server 1230. Various vehicles traveling along the same road segment in a variety of driving styles 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.

[0303] Fig. 22 A process of 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 can 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 can transmit multiple trajectories 2200 to server 1230. In some embodiments, server 1230 can 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 can cluster vehicle trajectories 1600 into multiple clusters 2205, 2210, 2215, 2220, 2225, and 2230, as shown in FIG. Fig. 22 shown.

[0304] Clustering can be performed using various criteria. In some embodiments, all of the drives in a cluster can be similar in terms of absolute heading along road segment 1200. The absolute heading can be obtained from GPS signals received by vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, dead reckoning can be used to obtain the absolute heading. As will be appreciated by those skilled in the art, dead reckoning can be used to determine the current position and therefore the heading of vehicles 1205, 1210, 1215, 1220, and 1225 by using previously determined positions, estimated speeds, and the like. Trajectories clustered by absolute heading can be helpful in identifying routes along a road.

[0305] In some embodiments, all the drives in a cluster may be similar with respect to lane assignments along the drives on road segment 1200 (e.g., in the same lane before and after an intersection). Trajectories clustered by lane assignment may help identify lanes along the road. In some embodiments, both criteria (e.g., absolute heading and lane assignment) may be used for clustering.

[0306] In each cluster 2205, 2210, 2215, 2220, 2225, and 2230, the trajectories can be averaged to obtain a target trajectory associated with a particular cluster. For example, trajectories from multiple drives associated with the same lane cluster can be averaged. The average trajectory can be a target trajectory associated with a particular lane. In order to average a set 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 contained in the cluster. The server 1230 can calculate a mean curve or trajectory in the C0 reference frame.

[0307] In some embodiments, landmarks can define arc length matching between different drives, which can be used to align the track with the lane. In some embodiments, lane markings before and after the intersection can be used to align the track with the lane.

[0308] To assemble lanes from the trajectory, the server 1230 may select a reference frame for any lane. The server 1230 may map partially overlapping lanes to the selected reference frame. The 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 later they may be laterally displaced.

[0309] 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.

[0310] In some embodiments, each lane of road segment 120 may be associated with a target track and certain landmarks. The target track or multiple such target tracks may be included in an autonomous vehicle road navigation model, which may later be used by other autonomous vehicles traveling along the same road segment 1200. As the vehicle travels along road segment 1200, landmarks identified by vehicles 1205, 1210, 1215, 1220, and 1225 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.

[0311] For the positioning of autonomous vehicles, the disclosed systems and methods can use an extended Kalman filter. The positioning of the vehicle can be determined based on three-dimensional position data and / or three-dimensional orientation data, by predicting the future positioning ahead of the current positioning of the vehicle through the integration of self-motion. The positioning of the vehicle can be corrected or adjusted by image observation of landmarks. For example, when a vehicle detects a landmark in an image captured by a camera, the landmark can be compared with a known landmark stored in a road model or sparse map 800. A known landmark can have a known positioning (e.g., GPS data) along a target trajectory stored in a road model and / or sparse map 800. Based on the current speed and image of the landmark, the distance from the vehicle to the landmark can be estimated. The positioning of the vehicle along the target trajectory can be adjusted based on the distance to the landmark and the known positioning of the landmark (stored in the road model or sparse map 800). The position / positioning data of the landmark stored in the road model and / or sparse map 800 (e.g., the mean from multiple driving) can be assumed to be accurate.

[0312] In some embodiments, the disclosed system can form a closed-loop subsystem in which an estimate of the vehicle's six-degree-of-freedom positioning (e.g., three-dimensional position data plus three-dimensional orientation data) can be used to navigate the autonomous vehicle (e.g., steer the wheels of the autonomous vehicle) to reach a desired point (e.g., stored 1.3 seconds ahead). In turn, data measured from steering and actual navigation can be used to estimate the six-degree-of-freedom positioning.

[0313] In some embodiments, poles along the road, such as lamp posts and power 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 a road segment can also be used as landmarks for locating the vehicle. When poles are used for positioning, the x-view of the pole (i.e., from the perspective of the vehicle) can be used instead of the y-view (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.

[0314] Fig.23 A navigation system for a vehicle is shown that can be used for autonomous navigation using crowdsourced sparse maps. For illustration, the vehicle is referred to as vehicle 1205. Fig.23 The vehicle shown in the figure may be any other vehicle disclosed herein, including, for example, vehicles 1210, 1215, 1220, and 1225, as well as the vehicle 200 shown in other embodiments. Fig.12 As 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, which is configured to provide navigation guidance for the vehicle 1205 traveling on a road (e.g., a 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 the acceleration or deceleration of the vehicle 1205. Fig.23 The vehicle 1205 shown in the figure 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.

[0315] 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 also 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 provided on the vehicle 1205 and / or received from the server 1230), road geometry sensed by the road profile sensor 2330, images captured by the camera 122, and / or 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 that measures the movement of the vehicle's suspension 2305 to derive a road roughness profile. In some embodiments, the road profile sensor 2330 may include a radar sensor to measure the distance from the vehicle 1205 to the side of the road (e.g., 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) can be used to capture a road image showing the curvature of the road. The vehicle 1205 can use such an image to detect the curvature of the road.

[0316] 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 associated with vehicle 1205. The navigation information may include a trajectory associated with vehicle 1205 traveling 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 movement. 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.

[0317] The at least one processor 2315 may also be programmed to transmit navigation information from the vehicle 1205 to the server 1230. In some embodiments, the navigation information may be transmitted to the server 1230 along with 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. The at least one processor 2315 may receive an autonomous vehicle road navigation model or a portion of a model from the server 1230. The autonomous vehicle road navigation model received from the server 1230 may include at least one update based on the navigation information transmitted from the vehicle 1205 to the server 1230. The portion of the model transmitted from the server 1230 to the vehicle 1205 may include an updated portion of the model. The at least one processor 2315 may cause at least one navigation maneuver (e.g., steering, such as turning, braking, accelerating, passing another vehicle, etc.) of the vehicle 1205 based on the received autonomous vehicle road navigation model or the updated portion of the model.

[0318] 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 via the communication unit 2305. Alternatively or additionally, the various sensors or components of the vehicle 1205 may also communicate with the server 1230 and transmit the data or information collected by the sensors or components to the server 1230.

[0319] In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 can communicate with each other and can share navigation information with each other, so that at least one of vehicles 1205, 1210, 1215, 1220, and 1225 can, for example, generate an autonomous vehicle road navigation model using crowdsourcing based on information shared by other vehicles. In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 can share navigation information with each other, and each vehicle can update the autonomous vehicle road navigation model provided in its own vehicle. In some embodiments, at least one of vehicles 1205, 1210, 1215, 1220, and 1225 (e.g., vehicle 1205) can be used as a hub vehicle. At least one processor 2315 of the hub vehicle (e.g., vehicle 1205) can perform some or all of the functions performed by server 1230. For example, at least one processor 2315 of the hub vehicle can communicate with other vehicles and receive navigation information from other vehicles. At least one processor 2315 of the hub vehicle may generate an autonomous vehicle road navigation model or an update to the model based on the shared information received from other vehicles. At least one processor 2315 of the hub vehicle may transmit the autonomous vehicle road navigation model or an update to the model to other vehicles to provide autonomous navigation guidance.

[0320] Mapped lane markings and navigation based on mapped lane markings

[0321] As previously mentioned, the autonomous vehicle road navigation model and / or sparse map 800 may include a plurality of mapped lane markers associated with road segments. As discussed in more detail below, these mapped lane markers may be used when the autonomous vehicle is navigating. For example, in some embodiments, the mapped lane markers may be used to determine the lateral position and / or orientation relative to the planned trajectory. Using this position information, the autonomous vehicle may adjust the heading direction to match the direction of the target trajectory at the determined position.

[0322] Vehicle 200 can be configured to detect lane markings in a given road segment. A road segment can include any sign on the road for guiding vehicle traffic on the road. For example, a lane marker can be a solid or dotted line that distinguishes the edge of a driving lane. Lane markings can also include double lines, such as double solid lines, double dotted lines, or a combination of solid and dotted lines to indicate, for example, whether overtaking is allowed in an adjacent lane. Lane markings can also include highway entrance and exit signs indicating, for example, a deceleration lane of an exit ramp, or a dotted line indicating that the lane is only a turn or that the lane is about to end. Signs can also indicate work zones, temporary lane changes, driving paths through intersections, central dividers, dedicated lanes (e.g., bicycle lanes, HOV lanes, etc.), or other various signs (e.g., pedestrian crossings, speed humps, railway crossings, stop lines, etc.).

[0323] 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 based on features identified in one or more captured images to detect point locations associated with the lane markings. 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 on both sides of the vehicle may be detected simultaneously from a single image. In other embodiments, different cameras may be used to capture images on multiple sides of the vehicle. Instead of uploading actual images of the markings, the lane markings may be stored as splines or a series of points in the sparse map 800, thereby reducing the size of the sparse map 800 and / or the data that must be uploaded remotely by the vehicle.

[0324] Figure 24A-Figure 24D Exemplary point locations that may be detected by vehicle 200 to represent a particular lane marking are shown. Similar to the landmarks described above, vehicle 200 may use various image recognition algorithms or software to identify point locations within a captured image. For example, vehicle 200 may identify a series of edge points, corner points, or various other point locations associated with a particular lane marking. Fig.24A 24 shows a solid lane marker 2410 that can be detected by the vehicle 200. The lane marker 2410 can represent the outside edge of the road, represented by a solid white line. Fig.24AAs shown, the vehicle 200 can be configured to detect multiple edge positioning points 2411 along the lane markings. The positioning points 2411 can be collected to represent the lane markings at any interval sufficient to create mapped lane markings in the sparse map. For example, the lane markings can be represented by one point per meter of the detected edge, one point per five meters of the detected edge, or at other suitable spacings. In some embodiments, the spacing can be determined by other factors rather than set intervals, such as, for example, based on the point at which the vehicle 200 has the highest confidence ranking for the positioning of the detected point. Although Fig.24A Edge positioning points on the inner edge of lane marking 2410 are shown, these points can be collected on the outer edge of the line or along both edges. Fig.24A A single line is shown in FIG, but similar edge points can be detected for double solid lines. For example, point 2411 can be detected along the edge of one or two solid lines.

[0325] 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 that can be detected by the vehicle 200 is shown. The vehicle can detect a series of corner points 2421 representing the corners of the lane dash line to define the complete boundary of the dash line, rather than as Fig.24A Identify edge points as in Fig. 24B Each corner of a given dotted line marker being located is shown, but the vehicle 200 can detect or upload a subset of the points shown in the figure. For example, the vehicle 200 can detect the leading edge or leading corner of a given dotted line marker, or can detect the two corner points closest to the interior of the lane. In addition, not every dotted line marker can be captured, for example, the vehicle 200 can capture and / or record points representing samples of dotted line markers (e.g., every other, every three, every five, etc.), or points representing dotted line markers at predetermined intervals (e.g., every meter, every five meters, every ten meters, etc.). Corner points of similar lane markers (such as signs showing that the lane is for an exit ramp, signs that a particular lane is about to end, or various other lane markers that may have detectable corner points) can also be detected. Corner points of lane markers consisting of double dashed lines or a combination of solid and dashed lines can also be detected.

[0326] 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.24CA series of points that may represent the centerline of a given lane marking are shown. For example, a solid lane 2410 may be represented by a centerline point 2441 along the centerline 2440 of the lane marking. In some embodiments, the vehicle 200 may be configured to detect these centerpoints using various image recognition techniques such as a convolutional neural network (CNN), scale-invariant feature transform (SIFT), histogram of oriented gradient (HOG) features, or other techniques. Alternatively, the vehicle 200 may detect other points such as Fig.24A The edge points 2411 shown, and the centerline points 2441 can be calculated, for example, by detecting points along each edge and determining the midpoint between the edge points. Similarly, the dashed lane marker 2420 can be represented by a centerline point 2451 along the centerline 2450 of the lane marker. The centerline points can be located at the edges of the dashed lines, as shown in Figure 24C, or at various other locations along the centerline. For example, each dashed line can be represented by a single point at the geometric center of the dashed line. The points can also be spaced apart at predetermined intervals along the centerline (e.g., every meter, every 5 meters, every 10 meters, etc.). The centerline points 2451 can be detected directly by the vehicle 200, or can be calculated based on other detected reference points (such as corner points 2421), such as Fig. 24B Using techniques similar to those described above, center lines can also be used to represent other lane marking types, such as double lines.

[0327] In some embodiments, vehicle 200 may identify points representing other features, such as a vertex between two intersecting lane markings. Fig.24D Exemplary points representing an intersection between two lane markers 2460 and 2465 are shown. Vehicle 200 can calculate a vertex 2466 representing an intersection between two lane markers. For example, one of lane markers 2460 or 2465 can represent a train crossing area or other intersection area in a road segment. Although lane markers 2460 and 2465 are shown as crossing each other perpendicularly, various other configurations can be detected. For example, lane markers 2460 and 2465 can cross at other angles, or one or both lane markers 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 to provide further information about the orientation of lane markers 2460 and 2465.

[0328] The vehicle 200 can associate real-world coordinates with each detected point of the lane marker. For example, a positioning identifier (including the coordinates of each point) can be generated to be uploaded to a server for mapping lane markers. The positioning identifier can also include other identification information about the point, including whether the point represents a corner point, an edge point, a center point, etc. Therefore, the vehicle 200 can be configured to determine the real-world position of each point based on the analysis of the image. For example, the vehicle 200 can detect other features in the image (such as various landmarks as described above) to locate the real-world position of the lane marker. This can include determining the location of the lane marker 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 trajectory of the vehicle) to the lane marker. When the landmark is not available, the location of the lane marker point can be determined relative to the vehicle position determined based on dead reckoning. The real-world coordinates included in the positioning identifier can be expressed as absolute coordinates (e.g., latitude / longitude coordinates), or can be relative to other features, such as based on the longitudinal position along the target trajectory and the lateral distance from the target trajectory. The location identifiers may then be uploaded to a server for use in generating mapped lane markings in a navigation model, such as a sparse map 800. In some embodiments, the server may construct a spline representing the lane markings of a road segment. Alternatively, the vehicle 200 may generate the spline and upload it to a server for recording in the navigation model.

[0329] Fig.24E An exemplary navigation model or sparse map of a corresponding road segment including mapped lane markings is shown. The sparse map may include a target trajectory 2475 that the vehicle follows along the road segment. As described above, the target trajectory 2475 may represent an ideal path taken by the vehicle 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 may be calculated using various methods as described above, for example, based on the aggregation (e.g., weighted combination) of two or more reconstructed trajectories of the vehicle passing through the same road segment.

[0330] In some embodiments, target tracks 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 can also be considered when generating target tracks. Different target tracks can be generated for different types of vehicles (e.g., private cars, light trucks and full trailers). For example, a target track with a relatively small turning radius can be generated for a small private car compared to a larger semi-trailer truck. In some embodiments, roads, vehicles and environmental conditions can also be considered. For example, different target tracks can 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, remaining fuel, etc.), or environmental factors (e.g., time of day, visibility, weather, etc.). Target tracks can also depend on one or more aspects or features (e.g., speed limit, turning frequency and size, slope, etc.) of a specific road section. In some embodiments, various user settings can also be used to determine target tracks, such as the driving mode set (e.g., desired driving enthusiasm, economic mode, etc.).

[0331] The sparse map may also include mapped lane markers 2470 and 2480 representing lane markers along the road segment. The mapped lane markers may be represented by a plurality of positioning identifiers 2471 and 2481. As described above, the positioning identifier may include the positioning of a point associated with the detected lane marker in real-world coordinates. Similar to the target trajectory in the model, the lane marker may also include elevation data and may be represented as a curve in three-dimensional space. For example, the curve may be a spline connecting three-dimensional polynomials of suitable order. The curve may be calculated based on the positioning identifier. The mapped lane marker may also include other information or metadata about the lane marker, such as an identifier of the type of lane marker (e.g., a lane marker between two lanes with the same driving direction, a lane marker between two lanes with opposite driving directions, a road edge, etc.) and / or other characteristics of the lane marker (e.g., solid line, dashed line, single line, double line, yellow, white, etc.). In some embodiments, for example, crowdsourcing techniques may be used to continuously update the mapped lane marker within the model. The same vehicle may upload a location identifier 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 target vehicles.

[0332] Generating mapped lane markings in a sparse map may also include detecting and / or mitigating 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 from, for example, an object that obstructs the camera's view of the lane marking, debris on the lens, etc. In some cases, the anomaly may be caused by the lane marking itself, which may be damaged or worn, or partially covered by dust, debris, water, snow, or other materials on the road. Anomaly 2495 may cause an error point 2491 detected by vehicle 200. Sparse map 800 can provide correct mapping lane markings and eliminate errors. In some embodiments, vehicle 200 can detect error point 2491, for example, by detecting anomaly 2495 in an image, or by identifying errors based on detected lane marking points before and after the anomaly. Based on detecting the anomaly, the vehicle can omit point 2491, or it can be adjusted to be consistent with other detected points. In other embodiments, the error can 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.

[0333] The mapped lane markers in the navigation model and / or sparse map can also be used for navigation of autonomous vehicles passing through corresponding roads. For example, a vehicle navigating along a target trajectory can periodically use the mapped lane markers in the sparse map to align itself with the target trajectory. As described above, between landmarks, the vehicle can navigate based on dead reckoning, where the vehicle uses sensors to determine its self-motion and estimate its position relative to the target trajectory. Errors may accumulate over time, and the position determination of the vehicle relative to the target trajectory may become increasingly inaccurate. Accordingly, the vehicle can use lane markers (and their known positioning) that appear in the sparse map 800 to reduce errors caused by dead reckoning in position determination. In this way, the identified lane markers included in the sparse map 800 can be used as navigation anchors, from which the accurate position of the vehicle relative to the target trajectory can be determined.

[0334] Fig.25A An exemplary image 2500 of a vehicle surrounding environment that can be used for navigation based on mapped lane markings is shown. Image 2500 can be captured, for example, by vehicle 200 through image capture devices 122 and 124 included in image acquisition unit 120. Image 2500 can 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, for use in navigation as described above. Fig.25ASome elements shown in 2500 that do not appear in captured image 2500 but are detected and / or determined by vehicle 200 (such as elements 2511, 2530, and 2520) are also shown for reference.

[0335] Use the reference above Figure 24A-Figure 24D and Fig.24F Using various techniques described above, the vehicle can analyze image 2500 to identify lane markings 2510. Various points 2511 corresponding to features of lane markings in the image can be detected. For example, point 2511 can correspond to an edge of a lane marking, a corner of a lane marking, a midpoint of a lane marking, a vertex between two intersecting lane markings, or various other features or locations. Point 2511 can be detected as a location corresponding to a point stored in a navigation model received from a server. For example, if a sparse map containing points representing the centerline of a mapped lane marking is received, point 2511 can also be detected based on the centerline of lane marking 2510.

[0336] The vehicle may also be configured to determine a longitudinal position represented by element 2520 and located along the target track. For example, longitudinal position 2520 may be determined from image 2500 by detecting landmark 2521 within image 2500 and comparing the measured location with known landmark locations stored in the 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 landmark and the known location of the landmark. 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 near any landmark or other reference point used to determine longitudinal position 2520. In this case, the vehicle may navigate based on dead reckoning, and thus may use sensors to determine its ego motion and estimate longitudinal position 2520 relative to the target track. The vehicle may also be configured to determine a distance 2530 representing the actual distance between the vehicle and lane marking 2510 as observed in the captured image(s). In determining distance 2530, the camera angle, the speed of the vehicle, the width of the vehicle, or various other factors may be considered.

[0337] Fig.25BA lateral positioning correction of a vehicle based on mapped lane markings in a road navigation model is shown. As described above, the vehicle 200 can use one or more images captured by the vehicle 200 to determine the distance 2530 between the vehicle 200 and the lane marking 2510. The vehicle 200 can also access a road navigation model (such as a sparse map 800), which can include mapped lane markings 2550 and a target trajectory 2555. The mapped lane markings 2550 can be modeled using the techniques described above, such as using crowdsourced positioning identifiers captured by multiple vehicles. The target trajectory 2555 can also be generated using the various techniques previously described. The vehicle 200 can also be configured to determine or estimate the longitudinal position 2520 along the target trajectory 2555, as described above with reference to FIG. Fig.25A The vehicle 200 can then determine an expected distance 2540 based on the lateral distance between the target track 2555 and the mapped lane marker 2550 corresponding to the longitudinal position 2520. The lateral positioning of the vehicle 200 can be corrected or adjusted by comparing the actual distance 2530 measured using the captured image(s) to the expected distance 2540 from the model.

[0338] Fig.26A 26 is a flow chart illustrating an exemplary process 2600A for mapping lane markings for 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 a location in real-world coordinates of a point associated with the detected lane marking, as described above with reference to FIG. Fig.24EAs described. In some embodiments, the positioning identifier may also contain other data, such as additional information about a road segment or lane marking. 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 as described above. The positioning identifier may be generated by a vehicle (such as vehicles 1205, 1210, 1215, 1220, and 1225) based on an image 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 the at least one image to detect lane markings in the environment of the main vehicle, and analyzing the at least one image to determine the position of the detected lane marking relative to the positioning associated with the main vehicle. As described above, lane markings may include a variety of different marking types, and the positioning identifier may correspond to various points relative to the lane marking. For example, in the case where the detected lane marking is part of a dashed line marking a lane boundary, these points may correspond to the detected corners of the lane marking. In the case where the detected lane marking is part of a solid line marking a lane boundary, these points may correspond to the detected edges of the lane marking having various spacings as described above. In some embodiments, these points may correspond to the center lines of detected lane markings, such as Fig.24C As shown, or may correspond to at least one of a vertex between two intersecting lane markers and two other points associated with the intersecting lane markers, such as Fig.24D shown.

[0339] At step 2612, process 2600A may include associating the detected lane marking with a corresponding road segment. 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 positioning information stored in the autonomous vehicle road navigation model. Server 1230 may determine a road segment in the model that corresponds to the real-world road segment at which the lane marking was detected.

[0340] At step 2614, process 2600A may include updating the autonomous vehicle road navigation model with respect to the corresponding road segment based on the two or more positioning identifiers associated with the detected lane markings. For example, the autonomous road navigation model may be the sparse map 800, and server 1230 may update the sparse map to include or adjust the mapped lane markings in the model. Server 1230 may update the sparse map based on the above reference. Fig.24EIn some embodiments, updating the autonomous vehicle road navigation model may include storing one or more position indicators of detected lane markings in real-world coordinates. The autonomous vehicle road navigation model may also include at least one target trajectory followed by the vehicle along the corresponding road segment, such as Fig.24E shown.

[0341] 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, which 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.

[0342] In some embodiments, lane markings may be mapped using data received from multiple vehicles (such as through crowdsourcing techniques), as described above with reference to Fig.24E 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 marker, and receiving a second communication from a second host vehicle including an additional location identifier associated with the detected lane marker. 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 also include refining the determination of at least one location associated with the detected lane marker 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 marker.

[0343] 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 of the host vehicle along the road segment and a positioning identifier associated with one or more lane markings associated with the road segment. For example, vehicle 200 may receive a sparse map 800 or another road navigation model generated using process 2600A. In some embodiments, the target trajectory may be represented as a three-dimensional spline, such as Fig. 9B As shown above. Figure 24A-Figure 24F The positioning identifier may include the positioning of a point associated with a lane marking in real-world coordinates (e.g., a corner point of a dashed lane marking, an edge point of a solid 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.).

[0344] 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.

[0345] 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 of the vehicle between detected landmarks.

[0346] At step 2623, process 2600B may include determining an expected lateral distance to a lane marker based on the determined longitudinal position of the host vehicle along the target trajectory and based on two or more positioning identifiers associated with at least one lane marker. For example, vehicle 200 may use sparse map 800 to determine the expected lateral distance to the lane marker. Fig.25B As shown, the longitudinal position 2520 along the target track 2555 can be determined in step 2622. Using the sparse map 800, the vehicle 200 can determine the expected distance 2540 to the mapped lane marker 2550 corresponding to the longitudinal position 2520.

[0347] 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.

[0348] At step 2625, process 2600B may include determining an actual lateral distance to at least one lane marker 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 marker 2510, such as Fig.25A When determining distance 2530, 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.

[0349] At step 2626, process 2600B may include determining an autonomous steering action for the host vehicle based on a difference between the expected lateral distance to at least one lane marker and the determined actual lateral distance to at least one lane marker. Fig.25B As described above, 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 that the vehicle is to follow. Accordingly, 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 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.

[0350] Map management using electronic horizon

[0351] Despite the increase in processing power and storage capacity and the reduction in cost, it is still desirable to use them more efficiently. The systems and methods disclosed herein may allow a vehicle to dynamically receive and load map data related to its travel route, rather than loading a large set of map data that the vehicle may not use during the trip. In doing so, the systems and methods may reduce the hardware requirements of the vehicle by receiving and processing the map data that the vehicle may need. In addition, the systems and methods may also allow the transmission cost of data exchanged between the vehicle and, for example, a central server that deploys map data to be reduced. In addition, the disclosed systems and methods may allow the vehicle to receive the latest map data that the vehicle may need more frequently. For example, the systems and methods may determine a potential travel area (or potential travel envelope) of the vehicle based on navigation information, such as the vehicle's location, speed, direction of travel, etc. The systems and methods may also be configured to determine one or more road segments associated with a potential travel area from the vehicle, and transmit map data related to the road segments to the vehicle. The vehicle (and / or the driver) may navigate based on the received map data.

[0352] Fig. 27 An exemplary system 2700 for providing one or more map segments to one or more vehicles consistent with the disclosed embodiments is shown. Fig. 27As shown, the system 2700 may include a server 2701, one or more vehicles 2702, and one or more vehicle devices 2703 associated with the vehicle, a database 2704, and a network 2705. The server 2701 may be configured to provide one or more map segments to one or more vehicles based on navigation information received from one or more vehicles (and / or one or more vehicle devices associated with the vehicle). For example, the vehicle 2702 and / or the vehicle device 2703 may be configured to collect navigation information and transmit the navigation information to the server 2701. The server 2701 may transmit one or more map segments to the vehicle 2702 and / or the vehicle device 2703, the one or more map segments including map information for a geographic area based on the received navigation information. The database 2704 may be configured to store information for components of the storage system 2700 (e.g., the server 2701, the vehicle 2702, and / or the vehicle device 2703). The network 2705 may be configured to facilitate communication between components of the system 2700.

[0353] The server 2701 may be configured to receive navigation information from the vehicle 2702 (and / or the vehicle device 2703). In some embodiments, the navigation information may include the location of the vehicle 2702, the speed of the vehicle 2702, and the driving direction of the vehicle 2702. The server 2701 may also be configured to analyze the received navigation information and determine the potential driving envelope of the vehicle 2702. The potential driving envelope of the vehicle may be an area surrounding the vehicle. For example, the potential driving envelope of the vehicle may include an area covering: a first predetermined distance from the vehicle in the driving direction of the vehicle, a second predetermined distance from the vehicle in the direction opposite to the driving direction of the vehicle, a third predetermined distance from the vehicle on the left side of the vehicle, and a fourth predetermined distance from the vehicle on the right side of the vehicle. In some embodiments, the first predetermined distance from the vehicle in the driving direction of the vehicle may include a predetermined distance in front of the vehicle, which may constitute the electronic horizon of the vehicle. In some embodiments, the potential driving envelope of the vehicle may include one or more distances (one, two, three, ..., n) from the vehicle relative to its current position in one or more (or all) possible driving directions of the vehicle. For example, on a road where the vehicle may be able to make a U-turn, the potential driving envelope of the vehicle may include, in addition to at least a predetermined distance in the forward direction, a predetermined distance from the vehicle in the opposite direction, because the vehicle may perform a U-turn and may (usually) navigate in a direction opposite to its current direction of motion. As another example, if driving in the opposite direction is not possible at the current location (e.g., there are physical obstacles) and a U-turn is not possible within a distance ahead of the current location, the potential driving envelope may not include a distance in the opposite direction. Similar to an actual horizon in the real world, the electronic horizon may be associated with the potential driving distance of the vehicle within a certain time window based on the current speed and current driving direction of the host vehicle. The server 2701 may also be configured to send one or more map segments to the vehicle, the one or more map segments including map information of a geographic area that at least partially overlaps with the potential driving envelope of the vehicle 2702.

[0354] In some embodiments, server 2701 can be a cloud server that performs the functions disclosed herein. The term "cloud server" refers to a computer platform that provides services via a network (e.g., the Internet). In this example configuration, server 2701 can use a virtual machine that may not correspond to separate hardware. For example, computing and / or storage capabilities can be achieved by allocating appropriate portions of computing / storage capabilities that meet needs from an extensible resource library (repository) such as a data center or a distributed computing environment. In one example, server 2701 can use customized hardwired logic, one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs), firmware, and / or program logic to implement the methods described herein, and these logics or circuits, etc., together with a computer system, make server 2701 a dedicated machine.

[0355] The vehicle 2702 and / or the vehicle device 2703 may be configured to collect navigation information and transmit the navigation information to the server 2701. For example, the vehicle 2702 and / or the vehicle device 2703 may be configured to receive data from one or more sensors and determine navigation information such as the location, speed, and / or driving direction of the vehicle based on the received data. In some embodiments, the navigation information may include sensor data received from one or more sensors associated with the vehicle 3302 (e.g., from a GPS device, a speed sensor, an accelerometer, a suspension sensor, a camera, a LIDAR device, a visual detection and ranging (VIDAR) device, etc., or a combination thereof). The vehicle 2702 and / or the vehicle device 2703 may also be configured to transmit the navigation information to the server 2701 via, for example, a network 2705. Alternatively or additionally, the vehicle 2702 and / or the vehicle device 2703 may be configured to transmit the sensor data to the server 2701. The vehicle 2702 and / or the vehicle device 2703 may also be configured to receive map information from the server 2701 via, for example, the network 2705. The map information may include data related to the location of various items in a reference coordinate system, including, for example, roads, water features, geographic features, businesses, points of interest, restaurants, gas stations, sparse data models including polynomial representations of certain road features (e.g., lane markings), target trajectories of the host vehicle, etc., or a combination thereof. In some embodiments, the vehicle 2702 and / or the vehicle device 2703 may be configured to plan a route path and / or navigate the vehicle 2702 based on the map information. For example, the vehicle 2702 and / or the vehicle device 2703 may be configured to determine a route to a destination based on the map information. Alternatively or additionally, the vehicle 2702 and / or the vehicle device 2703 may be configured to perform at least one navigation action (e.g., turning, stopping at a certain location, etc.) based on the received map information. In some embodiments, the vehicle 2702 may include a device having a configuration similar to the above-described system 100 and / or performing similar functions. Alternatively or additionally, the vehicle device 2703 may have a similar configuration and / or perform similar functions as the above-described system 100 .

[0356] The database 2704 may include a map database configured to store map data for components of the system 2700 (e.g., the server 2701, the vehicle 2702, and / or the vehicle device 2703). In some embodiments, the server 2701, the vehicle 2702, and / or the vehicle device 2703 may be configured to access the database 2704 via the network 2705, and obtain stored data from the database 2704 and / or upload data to the database 2704. For example, the server 2701 may transmit data related to one or more road navigation models to the database 2704 for storage. The vehicle 2702 and / or the vehicle device 2703 may download the road navigation model from the database 2704. In some embodiments, the database 2704 may include data related to the location of various items in a reference coordinate system, including roads, water features, geographic features, businesses, points of interest, restaurants, gas stations, etc., or combinations thereof. In some embodiments, the database 2704 may include a database similar to the map database 160 described elsewhere in this disclosure.

[0357] The network 2705 may be any type of network (including infrastructure) that provides communication, exchanges information, and / or facilitates information exchange between components of the system 2700. For example, the network 2705 may include or be part of the following network: the Internet, a local area network, a wireless network (e.g., a Wi-Fi / 302.11 network), or other suitable connection. In other embodiments, one or more components of the system 2700 may communicate directly via a dedicated communication link, such as a telephone network, an extranet, an intranet, the Internet, satellite communications, offline communications, wireless communications, repeater communications, a local area network (LAN), a wide area network (WAN), a virtual private network (VPN), etc.

[0358] As described elsewhere in this disclosure, the vehicle 2702 may transmit navigation information to the server 2701 via the network 2705. The server 2701 may analyze the navigation information received from the vehicle 2702 and determine a potential driving envelope of the vehicle 2702 based on the analysis of the navigation information. The potential driving envelope may cover the location of the vehicle 2702. In some embodiments, the potential driving envelope may include a boundary. The boundary of the potential driving envelope may have a shape, including, for example, a triangular shape, a quadrilateral shape, a parallelogram shape, a rectangular shape, a square (or substantially square) shape, a trapezoidal shape, a diamond shape, a hexagonal shape, an octagonal shape, a circular (or substantially circular) shape, an elliptical shape, an egg shape, an irregular shape, the like, or a combination thereof. Figure 28A-Figure 28D An exemplary potential travel envelope of a vehicle in region 2800 is shown consistent with the disclosed embodiments. Fig.28AAs shown, the server 2701 can determine a potential driving envelope having a boundary 2811 for the vehicle 2702, which can include a trapezoidal shape. As another example, Fig.28B As shown, server 2701 may determine a potential driving envelope having a boundary 2812 for vehicle 2702, which may include an elliptical shape. As another example, as shown in FIG. 28, server 2701 may determine a potential driving envelope having a boundary 2813 for vehicle 2702, which may include a triangular shape. As another example, as shown in FIG. 28, server 2701 may determine a potential driving envelope having a boundary 2814 for vehicle 2702, which may include a rectangular shape. Alternatively or additionally, the shape of the potential driving envelope may have boundaries determined by one or more potential paths on which the vehicle may start to travel from the location of the vehicle (e.g., the current location). Those skilled in the art will appreciate that the shape of the potential driving envelope is not limited to the exemplary shapes described in the present disclosure. Other shapes are also possible. For example, the potential driving envelope may include an irregular shape (e.g., determined based on one or more boundaries of a jurisdiction such as a country, state, county, city, and / or road) and / or a portion of any shape described herein.

[0359] As described elsewhere in the present disclosure, the server 2701 may also be configured to transmit one or more map segments to the vehicle 2702, the one or more map segments including map information of a geographic area that at least partially overlaps with the potential driving envelope of the vehicle. In some embodiments, the one or more map segments transmitted to the vehicle 2702 may include one or more tiles representing an area with a predetermined area. The size and / or shape of the tiles may vary. In some embodiments, the area of ​​a tile in an area may be in the range of 0.25 to 100 square kilometers, which may be limited to a sub-range of 0.25 square kilometers to 1 square kilometer, 1 square kilometer to 10 square kilometers, 10 to 25 square kilometers, 25 to 50 square kilometers, and 50 to 100 square kilometers. In some embodiments, the predetermined area of ​​the tile may be less than or equal to ten square kilometers. Alternatively, the predetermined area of ​​the tile may be less than or equal to one square kilometer. Alternatively, the predetermined area of ​​the tile may be less than or equal to ten square kilometers. Alternatively or additionally, the size of the tile may vary based on the type of area in which the tile is located. For example, the tile size in rural areas or areas with fewer roads can be larger than the tile size in urban areas or areas with more roads. In some embodiments, the size of the tile can be determined based on the information density around the current position of the vehicle (or the location where the data is obtained), the possible path of the vehicle, the number of possible routes in the area, the type of route in the area and general navigation mode (e.g., main, urban, rural, dirt, etc.) and / or the trend of the relevant area. For example, if a large percentage of vehicles usually stay on the main road in a specific area or region, map information for a short distance along the auxiliary road can be obtained. If the vehicle actually navigates to an auxiliary road that is usually less traveled, more map information for the auxiliary road (e.g., map information for a longer distance along the branch road) can be obtained and / or transmitted to the vehicle. In some embodiments, the tile can have a rectangular shape, a square shape, a hexagonal shape, etc., or a combination thereof. Those skilled in the art will understand that the shape of the tile is not limited to the shape described in the present disclosure. For example, the tile can include an irregular shape (e.g., determined according to at least one boundary of a jurisdiction (state, county, city or town) or other area (e.g., street, highway)). Alternatively or additionally, tiles may include portions of any shape disclosed herein.

[0360] Figure 28E-28H An exemplary map tile showing a potential driving envelope of a vehicle consistent with the disclosed embodiments is shown. Figure 28E-28H As shown, server 2701 may divide area 2800 (or a smaller or larger area) into a plurality of tiles 2811. Server 2701 may also be configured to determine one or more tiles that at least partially overlap with the potential driving envelope of vehicle 2702. For example, Fig.28EAs shown, server 2701 may determine region 2831 having tiles that intersect or are within boundary 2821 of the potential travel envelope of vehicle 2702. As another example, Fig.28F As shown, server 2701 may determine region 2832 having tiles that intersect or are within boundary 2822 of the potential travel envelope of vehicle 2702. As another example, Figure 28G As shown, server 2701 may determine region 2833 having tiles that intersect or are within boundary 2823 of the potential travel envelope of vehicle 2702. As another example, Fig.28H As shown, server 2701 can determine area 2834, which has tiles that intersect or are within boundary 2824 of the potential travel envelope of vehicle 2702. In some embodiments, server 2701 can transmit map information and / or data related to one or more road segments in the determined area to vehicle 2702.

[0361] Fig.29A and Fig.29B An exemplary map tile consistent with the disclosed embodiments is shown. Fig.29A As shown, an area (or map) may be divided into multiple tiles of different levels. For example, in some embodiments, an area may be divided into multiple tiles of level 1, and each tile of level 1 may be divided into multiple tiles of level 2. Each tile of level 2 may be divided into multiple tiles of level 3, and so on. Fig.29B A plurality of tiles in a region are shown. Alternatively or additionally, a region or a country may be divided into tiles based on jurisdiction (e.g., state, county, city, town) and / or other regions (e.g., streets, highways). In some embodiments, the area of ​​the tiles may vary. For example, Fig.29A and 29B As shown, a region (or map) may be divided into different levels, and tiles of a specific level may have a specific area.

[0362] In some embodiments, tiles may be presented in a data blob that may include a metadata block (e.g., 64 bytes), a signature block (e.g., 256 bytes), and an encoded map data block (e.g., various sizes in MapBox format).

[0363] In some embodiments, server 2701 may retrieve data associated with one or more tiles in an area and transmit the data to vehicle 2702 via, for example, network 2705 .

[0364] Alternatively or additionally, the vehicle 2702 may retrieve data associated with one or more tiles from a storage device. For example, the vehicle 2702 may receive one or more road segments from the server 2701, as described elsewhere in this disclosure. The vehicle 2702 may also store the received one or more road segments in a local storage and load one or more tiles contained in the one or more road segments into a memory for processing. Alternatively, the vehicle 2702 may include a local storage configured to store one or more road segments and retrieve data associated with the one or more road segments from the local storage, rather than receiving the one or more road segments from the server 2701.

[0365] In some embodiments, the vehicle 2702 may obtain data (e.g., map information) related to one or more tiles based on the location of the vehicle. For example, the vehicle 2702 may determine its current location (as described elsewhere in this disclosure) and determine the first tile where the current location is located. The vehicle 2702 may also obtain the first tile and one or more (or all) tiles adjacent to the first tile. Alternatively or additionally, the vehicle 2702 may obtain one or more (or all) tiles within a predetermined distance from the first tile. Alternatively or additionally, the vehicle 2702 may obtain one or more (or all) tiles within a predetermined separation degree from the first tile (e.g., one or more (or all) tiles within a second separation degree; i.e., one or more (or all) tiles adjacent to the first tile or adjacent to a tile adjacent to the first tile). When the vehicle 2702 moves to the second tile, the vehicle 2702 may obtain the second tile. The vehicle 2702 may also obtain the first tile and one or more (or all) tiles adjacent to the second tile. Alternatively or additionally, the vehicle 2702 may obtain one or more (or all) tiles within a predetermined distance from the second tile. Alternatively or additionally, the vehicle 2702 may obtain one or more (or all) tiles within a predetermined separation from the second tile (e.g., one or more (or all) tiles within the second separation; i.e., one or more (or all) tiles adjacent to the second tile or adjacent to a tile adjacent to the second tile). In some embodiments, the vehicle 2702 may also delete (or overwrite) the first tile and / or a previously obtained tile that is not adjacent to the second tile. Alternatively or additionally, the vehicle 2702 may delete (or overwrite) one or more previously obtained tiles that are not within a predetermined distance from the second tile. Alternatively or additionally, the vehicle 2702 may delete (or overwrite) one or more (or all) tiles that were previously obtained that are not within a predetermined separation from the second tile.

[0366] Fig.30 An exemplary process for obtaining one or more tiles is shown. Fig.30As shown, vehicle 2702 (and / or server 2701) may be configured to determine that at time point 1, the position of vehicle 2702 is in tile 5. Vehicle 2702 may also be configured to obtain (or load) adjacent tiles 1-tile 4 and tiles 6-tile 9. At time point 2, vehicle 2702 (and / or server 2701) may be configured to determine that the position of vehicle 2702 moves from tile 5 to tile 3. Vehicle 2702 may be configured to obtain (or load) new tiles 10-14 adjacent to tile 3. Vehicle 2702 may also be configured to retain tiles 2, 3, 5, and 6, and delete tiles 1, 4, and 7-9. Therefore, vehicle 2702 may obtain (or load) a subset of tiles (e.g., 9 tiles) at a time to reduce memory usage and / or computational load. In some embodiments, vehicle 2702 may be configured to decode tiles before loading data into memory.

[0367] Alternatively or additionally, the vehicle 2702 may determine a sub-portion of a tile within which the vehicle's position falls and load tiles adjacent to the sub-portion. Fig.31A As shown, vehicle 2702 may determine that the location of vehicle 2702 is in a sub-tile (a sub-tile with a dot pattern in tile 5), and load map data of tiles adjacent to the sub-tile (i.e., tiles 4, 7, and 9) into memory for processing. As another example, Fig.31B As shown, vehicle 2702 may determine that the location of vehicle 2702 is in the upper left sub-tile of tile 5. Vehicle 2702 may also load tiles adjacent to the upper left sub-tile of tile 5 (i.e., tiles 1, 2, and 4). Thus, vehicle 2702 may obtain (or load) a subset of tiles (e.g., 4 tiles) at a time to reduce memory usage and / or computational load. As another example, if Fig.31C As shown, vehicle 2702 may determine that the location of vehicle 2702 is in the upper right sub-tile, and load map data of tiles adjacent to the sub-tile (i.e., tiles 2, 3, and 6) into memory for processing. As another example, Fig.31D As shown, vehicle 2702 can determine that the position of vehicle 2702 is in the lower right sub-tile, and load the map data of the tiles adjacent to the sub-tile (i.e., tiles 6, 8, and 9) into memory for processing. In some embodiments, vehicle 2702 can be configured to decode the tiles before loading the data into memory.

[0368] Fig.32is a flow chart illustrating a method for providing one or more map segments to one or more vehicles consistent with the disclosed embodiments. One or more steps of process 3200 may be performed by a vehicle (e.g., vehicle 2702), a device associated with a host vehicle (e.g., vehicle device 2703), and / or a server (e.g., server 2701). Although the description of process 3200 provided below uses server 2701 as an example, those skilled in the art will appreciate that one or more steps of process 3200 may be performed by a vehicle (e.g., vehicle 2702) and a vehicle device (e.g., vehicle device 2703). For example, vehicle 2702 may determine a potential driving envelope based on navigation information. In conjunction with or in lieu of receiving map data from server 2701, vehicle 2702 may also retrieve portions of map data associated with the potential driving envelope from a local storage body and load the retrieved data into memory for processing.

[0369] In step 3201, navigation information may be received from a vehicle. For example, server 2701 may receive navigation information from vehicle 2702 via, for example, network 2705. In some embodiments, the navigation information received from the vehicle may include an indicator of the vehicle's position, an indicator of the vehicle's speed, and an indicator of the vehicle's direction of travel. For example, vehicle 2702 may be configured to receive data from one or more sensors, the one or more sensors including, for example, a GPS device, a speed sensor, an accelerometer, a suspension sensor, etc., or a combination thereof. Vehicle 2702 may also be configured to determine navigation information such as the vehicle's position, speed, and / or driving direction based on the received data. Vehicle 2702 may also be configured to transmit navigation information to server 2701 via, for example, network 2705. Alternatively or additionally, vehicle 2702 may be configured to transmit sensor data to server 2701. Server 2701 may be configured to determine navigation information based on the received sensor data, and the navigation information may include the position of vehicle 2702, the speed of vehicle 2702, and / or the direction of travel of vehicle 2702.

[0370] In some embodiments, the vehicle 2702 may continuously transmit navigation information (and / or sensor data) to the server 2701. Alternatively, the vehicle 2702 may intermittently transmit navigation information (and / or sensor data) to the server 2701. For example, the vehicle 2702 may transmit navigation information (and / or sensor data) to the server 2701 multiple times over a period of time. For example, the vehicle 2702 may transmit navigation information (and / or sensor data) to the server 2701 once per minute. Alternatively, the vehicle may transmit navigation information when the vehicle has more reliable and / or faster network access (e.g., has a stronger wireless signal, is connected via WIFI, etc.).

[0371] In step 3202, the received navigation information may be analyzed and a potential driving envelope of the vehicle may be determined. For example, the server 2701 may analyze the position, speed, and / or driving direction of the vehicle 2702 and determine a potential driving envelope (which may include a determined area relative to the vehicle 2702). For example, Fig.28A As shown, the server 2701 can determine an area covering the location of the vehicle 2702 and determine a potential driving envelope having a boundary 2811 based on the determined area.

[0372] In some embodiments, server 2701 may be configured to determine a potential driving envelope extending from and around the location of vehicle 2702. For example, Fig.28A As shown, server 2701 can determine a line 2802 passing through the location of vehicle 2702 (or the centroid of vehicle 2702). Server 2701 can also be configured to determine one side of the boundary of the potential driving envelope in the driving direction of vehicle 2702, and determine the other side of the boundary of the potential driving envelope in the direction opposite to the driving direction. For example, server 2701 can determine the upper boundary of the potential driving envelope in the driving direction of vehicle 2702, and determine the lower boundary of the potential driving envelope in the direction opposite to the driving direction of vehicle 2702. In some embodiments, the potential driving envelope may extend farther along the driving direction of the vehicle than in the direction opposite to the driving direction of the vehicle. For example, as Fig.28A As shown, the upper boundary of the potential travel envelope may have a distance 2803 from line 2802 (or a first distance from the location of vehicle 2702), and the lower boundary of the potential travel envelope may have a distance 2804 from line 2802 (or a second distance from the location of vehicle 2702). Distance 2803 may be greater than distance 2804 (and / or the first distance may be greater than the second distance). In some embodiments, the location of the centroid of the boundary may be offset from the location of vehicle 2702 along the direction of travel of vehicle 2702.

[0373] Alternatively or additionally, when determining the potential driving profile of vehicle 2702, server 2701 may consider the potential driving distance within a period of time (or time window). For example, server 2701 may determine the potential driving distance within a predetermined amount of time and determine the potential driving profile including the potential driving distance. In some embodiments, the potential driving distance within the predetermined amount of time may be determined based on the position of vehicle 2702 and / or the speed of vehicle 2702. In some embodiments, server 2701 may further determine the potential driving profile based on a selected or predetermined time window. The time window may be selected or determined based on an indicator of vehicle speed. The predetermined amount of time (or time window) may be in the range of 0.1 seconds to 24 hours. In some embodiments, the predetermined amount of time (or time window) may be limited to a sub-range of 0.1 seconds to 1 second, 1 second to 5 seconds, 5 to 10 seconds, 10 to 60 seconds, 1 minute to 5 minutes, 5 to 10 minutes, 10 to 60 minutes, 1 hour to 5 hours, 5 to 10 hours, and 10 to 24 hours. In some embodiments, the predetermined amount of time (or time window) may be determined based on the frequency of transmission of navigation information from the vehicle 2702 to the server 2701. For example, the server 2701 may determine the predetermined amount of time (or time window) based on the interval between two transmissions of navigation information from the vehicle 2702. The server 2701 may determine a longer time period for determining the potential driving distance within a longer transmission interval.

[0374] In some embodiments, the potential travel envelope may include a boundary. The boundary of the potential travel envelope may have a shape including a triangle, a quadrilateral, a parallelogram, a rectangle, a square (or substantially square), a trapezoid, a rhombus, a hexagon, an octagon, a circle (or substantially circle), an ellipse, an egg, or any other shape or a combination thereof. Figure 28A-Figure 28D An exemplary potential travel envelope of a vehicle in region 2800 is shown consistent with the disclosed embodiments. Fig.28A As shown, the server 2701 can determine a potential driving envelope having a boundary 2811 for the vehicle 2702, which can include a trapezoidal shape. As another example, Fig.28B As shown, server 2701 may determine a potential driving envelope having boundary 2812 for vehicle 2702, which may include an elliptical shape. As another example, as shown in FIG. 28 , server 2701 may determine a potential driving envelope having boundary 2813 for vehicle 2702, which may include a triangular shape. As another example, as shown in FIG. 28 , server 2701 may determine a potential driving envelope having boundary 2814 for vehicle 2702, which may include a rectangular shape.

[0375] In some embodiments, the vehicle 2702 (and / or vehicle device 2703) may determine a potential driving envelope based on navigation information.

[0376] At step 3203, one or more map segments may be sent to vehicle 2702. In some embodiments, the map segment(s) may include map information for a geographic area that at least partially overlaps with a potential travel envelope of vehicle 2702. For example, server 2701 may transmit, via network 2705, one or more map segments that include map data for a geographic area that at least partially overlaps with a potential travel envelope of vehicle 2702.

[0377] In some embodiments, one or more map segments include one or more tiles representing an area having a predetermined area. Fig.28E As shown, the server 2701 can determine one or more tiles 2831 that at least partially overlap with the potential driving envelope of the vehicle 2702 (i.e., the potential driving envelope having the boundary 2821), and transmit the map data associated with the tiles 2831 to the vehicle 2702 via the network 2705.

[0378] In some embodiments, the area of ​​the tile transmitted to the vehicle 2702 may vary. Fig.29A and Fig.29B As shown, the area (or map) can be divided into different levels, and the tiles of a specific level can have a specific area. In some embodiments, the predetermined area of ​​the tiles sent to the vehicle 2702 can be in the range of 0.25 square kilometers to 100 square kilometers, which can be limited to the sub-range of 0.25 square kilometers to 1 square kilometer, 1 square kilometer to 10 square kilometers, 10 to 25 square kilometers, 25 to 50 square kilometers and 50 to 100 square kilometers. In some embodiments, the predetermined area of ​​the tile can be less than or equal to 10 square kilometers. Alternatively, the predetermined area of ​​the tile can be less than or equal to 1 square kilometer. Alternatively, the predetermined area of ​​the tile can be less than or equal to 10 square meters. In some embodiments, the tile can have a rectangular shape, a square shape, a hexagonal shape, etc., or a combination thereof.

[0379] In some embodiments, the map information sent to the vehicle 2702 may include a polynomial representation of a target trajectory along one or more road segments as described elsewhere in this disclosure. For example, the map information may include a polynomial representation of a target trajectory along one or more road segments. Fig. 9A , Fig. 9B and Fig.11A Polynomial representation of a portion of a road segment consistent with the disclosed embodiments shown. For example, the map information may include a polynomial representation of a target trajectory determined based on two or more reconstructed trajectories previously traversed by the vehicle along one or more road segments.

[0380] In some embodiments, after receiving the one or more road segments, the vehicle...

Claims

1. A vehicle navigation system for obtaining one or more map segments from a map database stored in a local storage of the vehicle, the system include: 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 a plurality of map segments from a server, the plurality of map segments comprising map information for a geographic area in which the vehicle is traveling; receiving sensor data collected from one or more sensors associated with the vehicle; determining navigation information for the vehicle based on the sensor data, the navigation information including an indicator of the location of the vehicle and an indicator of a direction of travel of the vehicle; analyzing the navigation information to determine a potential travel envelope of the vehicle, the potential travel envelope representing a boundary around the vehicle within which the vehicle is expected to travel, wherein determining the potential travel envelope of the vehicle includes determining whether the vehicle is likely to travel in a direction opposite to a direction of travel of the vehicle within a predetermined time window, and wherein if it is determined that the vehicle is unlikely to travel in a direction opposite to a direction of travel of the vehicle within the predetermined time window, then the potential travel envelope does not include a distance opposite to the direction of travel of the vehicle; selecting a subset of the plurality of map segments from the one or more map segments, the subset comprising map information for a geographic area that at least partially overlaps with the potential travel envelope of the vehicle; and A selected subset of the plurality of map segments is loaded for processing.

2. The map management system according to claim 1, in, The potential driving envelope includes a determined area relative to the vehicle.

3. The map management system according to claim 1, in, The potential travel profile includes a potential travel distance within a predetermined amount of time.

4. The map management system according to claim 1, in, The potential driving envelope extends from and surrounds the position of the vehicle.

5. The map management system according to claim 4, in, The potential travel envelope extends further along a travel direction of the vehicle than in a direction opposite to the travel direction of the vehicle.

6. The map management system according to claim 1, in, The potential travel envelope includes a boundary.

7. The map management system according to claim 6, in, The boundary has a substantially circular shape.

8. The map management system according to claim 6, in, The border has an egg shape.

9. The map management system according to claim 6, in, The boundary has a triangular shape.

10. The map management system according to claim 6, in, The boundary has an irregular shape.

11. The map management system according to claim 6, in, The position of the centroid of the boundary is offset from the position of the vehicle along the travel direction of the vehicle.

12. The map management system according to claim 1, in, Determining the potential travel profile is further based on a selected time window.

13. The map management system according to claim 12, in, The navigation information also includes an indicator of a speed of the vehicle, and wherein the time window is selected based on the indicator of the speed of the vehicle.

14. The map management system according to claim 1, in, The one or more map segments include one or more tiles representing an area having a predetermined area.

15. The map management system according to claim 14, in, The predetermined area is less than or equal to one square kilometer.

16. The map management system according to claim 14, in, The predetermined area is less than or equal to ten square kilometers.

17. The map management system according to claim 14, in, The predetermined area is less than or equal to twenty-five square kilometers.

18. The map management system according to claim 1, in, The map information includes a polynomial representation of a target trajectory along one or more road segments.

19. The map management system according to claim 1, in, The navigation information also includes an indicator of the speed of the vehicle.

20. A computer-implemented method for retrieving one or more map segments from a map database stored on a local storage volume of a vehicle, include: receiving a plurality of map segments from a server, the plurality of map segments comprising map information for a geographic area in which the vehicle is traveling; receiving sensor data collected from one or more sensors associated with the vehicle; determining navigation information for the vehicle based on the sensor data, the navigation information comprising an indicator of the location of the vehicle and an indicator of a direction of travel of the vehicle; analyzing the navigation information to determine a potential travel envelope of the vehicle, the potential travel envelope representing a boundary around the vehicle within which the vehicle is expected to travel, wherein determining the potential travel envelope of the vehicle includes determining whether the vehicle is likely to travel in a direction opposite to a direction of travel of the vehicle within a predetermined time window, and wherein if it is determined that the vehicle is unlikely to travel in a direction opposite to a direction of travel of the vehicle within the predetermined time window, then the potential travel envelope does not include a distance opposite to the direction of travel of the vehicle; Selecting a subset of the plurality of map segments from the one or more map segments, the subset including map information for a geographic area at least partially overlapping the potential travel envelope of the vehicle; and loading the selected subset of the plurality of map segments for processing.

21. A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, are configured to cause the at least one processor to: receiving a plurality of map segments from a server, the plurality of map segments comprising map information for a geographic area in which a vehicle is traveling; receiving sensor data collected from one or more sensors associated with the vehicle; determining navigation information for the vehicle based on the sensor data, the navigation information comprising an indicator of the location of the vehicle and an indicator of a direction of travel of the vehicle; analyzing the navigation information to determine a potential travel envelope of the vehicle, the potential travel envelope representing a boundary around the vehicle within which the vehicle is expected to travel, wherein determining the potential travel envelope of the vehicle includes determining whether the vehicle is likely to travel in a direction opposite to a direction of travel of the vehicle within a predetermined time window, and wherein if it is determined that the vehicle is unlikely to travel in a direction opposite to a direction of travel of the vehicle within the predetermined time window, then the potential travel envelope does not include a distance opposite to the direction of travel of the vehicle; Selecting a subset of the plurality of map segments from the one or more map segments, the subset including map information for a geographic area that at least partially overlaps the potential travel envelope of the vehicle; and loading the selected subset of the plurality of map segments for processing.

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