System for vehicle navigation

Abstract

A system and method for vehicle navigation are provided. In one embodiment, at least one processor may receive at least one image captured of the vehicle's environment from a camera of the vehicle. The processor may analyze the at least one image to identify road topology features within the vehicle's environment represented by the at least one image and at least one point associated with the at least one image. Based on the identified road topology features, the processor may determine an estimated path for the vehicle within the environment associated with the at least one point. The processor may further cause the vehicle to perform a navigation operation based on the estimated path.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Application No. 62 / 853,300, filed May 28, 2019, U.S. Provisional Application No. 62 / 853,305, filed May 28, 2019, U.S. Provisional Application No. 62 / 853,832, filed May 29, 2019, U.S. Provisional Application No. 62 / 875,226, filed July 17, 2019, U.S. Provisional Application No. 63 / 005,736, filed April 6, 2020, and U.S. Provisional Application No. 63 / 006,340, filed April 7, 2020. The above applications are incorporated herein by reference in their entireties.

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

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

[0004] Embodiments according to the present disclosure provide systems and methods for autonomous vehicle navigation. The disclosed embodiments may use cameras to provide autonomous vehicle navigation features. For example, according to the disclosed embodiments, the disclosed system may include one, two, or more cameras that monitor the vehicle's environment. The disclosed system may provide a navigation response, for example, based on an analysis of images captured by one or more of the cameras.

[0005] In one embodiment, a navigation system for a host vehicle may include at least one processor. The processor may be programmed to receive at least one image captured of the vehicle's environment from a camera of the vehicle and to analyze the at least one image to identify road topology features within the vehicle's environment represented by the at least one image and at least one point associated with the at least one image. The processor may be further programmed to determine an estimated path within the vehicle's environment associated with the at least one point based on the identified road topology features and cause the vehicle to perform a navigation operation based on the estimated path.

[0006] In one embodiment, a navigation system for a host vehicle may include at least one processor. The processor may be programmed to receive at least one image captured from a vehicle's environment from a vehicle camera and to analyze the at least one image to identify one or more road topology features within the vehicle's environment represented in the at least one image. The processor may be further programmed to determine, for each of a plurality of segments of the at least one image, an estimated route at a location within the vehicle's environment corresponding to one of the plurality of segments based on the identified one or more road topology features. The processor may further cause the vehicle to perform a navigation operation based on at least one of the predicted routes associated with the plurality of segments of the at least one image.

[0007] In one embodiment, a navigation system for a host vehicle may include at least one processor. The processor may be programmed to receive at least one image captured of an environment of the host vehicle from a camera of the host vehicle and to analyze the at least one image to identify a representation of a vehicle's travel lane along a road segment and a representation of at least one additional travel lane along the road segment. The processor may be further programmed to analyze the at least one image to identify attributes associated with the at least one additional travel lane and to determine information indicative of a characterization of the at least one additional travel lane based on the attributes. The processor may be programmed to transmit the information indicative of the characterization of the at least one additional travel lane to a server for use in updating the road navigation model.

[0008] According to other disclosed embodiments, a non-transitory computer-readable storage medium may store program instructions that are executed by at least one processing device and that perform any of the methods described herein.

[0009] The foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the scope of the claims. [Brief description of the drawings]

[0010] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various disclosed embodiments.

[0011] [Figure 1] 1 is a diagrammatic representation of an exemplary system according to a disclosed embodiment.

[0012] [Figure 2A] 1 is a side view representation of an exemplary vehicle including a system according to disclosed embodiments.

[0013] [Figure 2B] 2B is a top view representation of the vehicle and system shown in FIG. 2A according to a disclosed embodiment.

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

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

[0016] [Figure 2E] 1 is a top view representation of yet another embodiment of a vehicle including a system according to disclosed embodiments.

[0017] [Figure 2F] 1 is a diagrammatic representation of an exemplary vehicle control system in accordance with a disclosed embodiment.

[0018] [Figure 3A] 1 is a diagrammatic representation of the interior of a vehicle including a rearview mirror and a user interface of a vehicle imaging system according to disclosed embodiments.

[0019] [Figure 3B] 1 is a diagram of an example camera mount configured to be positioned behind a rearview mirror, facing a vehicle windshield, in accordance with a disclosed embodiment.

[0020] [Figure 3C] 3C is a diagram of the camera mount shown in FIG. 3B from a different perspective, according to a disclosed embodiment.

[0021] [Figure 3D] 1 is a diagram of an example camera mount configured to be positioned behind a rearview mirror, facing a vehicle windshield, in accordance with a disclosed embodiment.

[0022] [Figure 4]FIG. 1 illustrates an example block diagram of a memory configured to store instructions for performing one or more operations in accordance with the disclosed embodiments.

[0023] [Figure 5A] 1 is a flowchart illustrating an example process for generating one or more navigational responses based on monocular image analysis, according to disclosed embodiments.

[0024] [Figure 5B] 1 is a flowchart illustrating an example process for detecting one or more vehicles and / or pedestrians in a set of images, according to disclosed embodiments.

[0025] [Figure 5C] 1 is a flowchart illustrating an example process for detecting road markings and / or lane geometry information in a set of images, according to disclosed embodiments.

[0026] [Figure 5D] 1 is a flowchart illustrating an example process for detecting traffic lights in a set of images, according to disclosed embodiments.

[0027] [Figure 5E] 1 is a flowchart illustrating an example process for generating one or more navigational responses based on a vehicle path, according to disclosed embodiments.

[0028] [Figure 5F] 4 is a flowchart illustrating an example process for determining whether a leading vehicle is changing lanes, according to disclosed embodiments.

[0029] [Figure 6] 1 is a flowchart illustrating an example process for generating one or more navigational responses based on stereo image analysis, according to disclosed embodiments.

[0030] [Figure 7] 1 is a flowchart illustrating an example process for generating one or more navigational responses based on an analysis of three sets of images, according to disclosed embodiments.

[0031] [Figure 8] 1 illustrates a sparse map for providing autonomous vehicle navigation according to a disclosed embodiment.

[0032] [Figure 9A] 1 illustrates a polynomial representation of a portion of a road segment according to a disclosed embodiment.

[0033] [Figure 9B] 1 illustrates a curve in three-dimensional space representing a target trajectory of a vehicle for a particular road segment contained in a sparse map according to a disclosed embodiment.

[0034] [Figure 10] 1 illustrates examples of landmarks that may be included in a sparse map, according to disclosed embodiments.

[0035] [Figure 11A] 1 illustrates a polynomial representation of a trajectory according to a disclosed embodiment.

[0036] [Figure 11B] 1 illustrates a target trajectory along a multi-lane road according to a disclosed embodiment. [Figure 11C] 1 illustrates a target trajectory along a multi-lane road according to a disclosed embodiment.

[0037] [Figure 11D] 4 illustrates an example road signature profile, according to a disclosed embodiment.

[0038] [Figure 12] FIG. 1 is a schematic diagram of a system that uses crowdsourced data received from multiple vehicles for autonomous vehicle navigation, according to disclosed embodiments.

[0039] [Figure 13] 1 illustrates an example autonomous vehicle road navigation model represented by a number of 3D splines, according to the disclosed embodiments.

[0040] [Figure 14] 1 illustrates a map skeleton generated from combining location information from many runs, according to disclosed embodiments.

[0041] [Figure 15] 13 illustrates an example of longitudinal alignment of two runs with exemplary signs as landmarks, according to disclosed embodiments.

[0042] [Figure 16] 13 illustrates an example of longitudinal alignment of multiple runs with exemplary signs as landmarks, according to disclosed embodiments.

[0043] [Figure 17] FIG. 1 is a schematic diagram of a system for generating travel data using a camera, a vehicle, and a server, according to a disclosed embodiment.

[0044] [Figure 18] FIG. 1 is a schematic diagram of a system for crowdsourcing sparse maps, according to disclosed embodiments.

[0045] [Figure 19] 1 is a flowchart illustrating an example process for generating a sparse map for autonomous vehicle navigation along a road segment, according to a disclosed embodiment.

[0046] [Figure 20] FIG. 2 illustrates a block diagram of a server according to the disclosed embodiments.

[0047] [Figure 21] FIG. 1 illustrates a block diagram of a memory in accordance with disclosed embodiments.

[0048] [Figure 22] 1 illustrates a process for clustering vehicle trajectories associated with a vehicle according to a disclosed embodiment.

[0049] [Diagram 23] 1 illustrates a navigation system for a vehicle that may be used for autonomous navigation, according to disclosed embodiments.

[0050] [Figure 24A] 1 illustrates exemplary lane markings that may be detected according to disclosed embodiments. [Figure 24B] 1 illustrates exemplary lane markings that may be detected according to disclosed embodiments. [Figure 24C] 1 illustrates exemplary lane markings that may be detected according to disclosed embodiments. [Figure 24D] 1 illustrates exemplary lane markings that may be detected according to disclosed embodiments.

[0051] [Figure 24E] 1 illustrates an example mapped lane marking according to a disclosed embodiment.

[0052] [Figure 24F] 1 illustrates an example anomaly associated with detection of lane markings in accordance with a disclosed embodiment.

[0053] [Figure 25A] 1 illustrates an example image of a vehicle's surroundings for navigation based on mapped lane markings, according to a disclosed embodiment.

[0054] [Figure 25B] 1 illustrates a vehicle lateral localization correction based on mapped lane marks in a road navigation model according to a disclosed embodiment.

[0055] [Figure 26A] 1 is a flowchart illustrating an example process for mapping lane markings for use in autonomous vehicle navigation, according to disclosed embodiments.

[0056] [Figure 26B] 4 is a flowchart illustrating an example process for autonomously navigating a host vehicle along a road segment using mapped lane markings, according to disclosed embodiments.

[0057] [Figure 27] FIG. 1 is an exemplary image captured by a host vehicle in accordance with disclosed embodiments.

[0058] [Figure 28] FIG. 13 is another example image having additional example road topology features that may be detected by a host vehicle in accordance with the disclosed embodiments.

[0059] [Figure 29] 1 is a diagram of an exemplary vector field overlaid on an image, in accordance with disclosed embodiments;

[0060] [Diagram 30] 1 illustrates an example predicted path that may be determined according to disclosed embodiments.

[0061] [Diagram 31] 1 is a flowchart illustrating an exemplary process for navigating a host vehicle based on a vector field, according to disclosed embodiments.

[0062] [Diagram 32] 1 is a flowchart illustrating an exemplary process for navigating a host vehicle based on a vector field, according to disclosed embodiments.

[0063] [Diagram 33]FIG. 1 illustrates an example image that may be captured by a host vehicle for lane analysis in accordance with the disclosed embodiments.

[0064] [Diagram 34] 1 illustrates example attributes that may be identified by a host vehicle to determine a lane characterization in accordance with disclosed embodiments.

[0065] [Diagram 35] FIG. 2 is a diagram of an example road segment on which lane characterization may be performed in accordance with the disclosed embodiments.

[0066] [Diagram 36] 1 is a flowchart illustrating an example process for navigating a host vehicle based on semantic lane features, according to a disclosed embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0067] 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 similar parts. Although several exemplary embodiments are described herein, modifications, adaptations and other implementations are possible. For example, substitutions, additions or modifications may be made to the components shown in the drawings, and the exemplary methods described herein may be modified by substituting, reordering, deleting or adding steps of the disclosed methods. Thus, the following detailed description is not limited to the disclosed embodiments and examples. Instead, the appropriate scope is defined by the appended claims.

[0068] Autonomous Vehicle Overview

[0069] As used throughout this disclosure, the term "autonomous vehicle" refers to a vehicle that can implement at least one navigation change without driver input. A "navigation change" refers to a change in one or more of steering, braking, or acceleration of the vehicle. To be autonomous, a vehicle need not be fully automatic (e.g., operating completely without a driver or driver input). Rather, an autonomous vehicle includes a vehicle that can operate under driver control during certain periods of time and without driver control during other periods of time. An autonomous vehicle can also include a vehicle that controls only some aspects of vehicle navigation, such as steering (e.g., to maintain a vehicle course between vehicle lane constraints), but leaves other aspects (e.g., braking) to the driver. In some cases, an autonomous vehicle may handle some or all aspects of braking, speed control, and / or steering of the vehicle.

[0070] Because human drivers typically rely on visual cues and observations to control the vehicle, the transportation infrastructure is built accordingly, with lane markings, traffic signs, and traffic lights designed to provide visual information to the driver. In light of these design characteristics of the transportation 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 transportation infrastructure observable by the driver (e.g., lane markings, traffic signs, traffic lights, etc.) and other obstacles (e.g., other vehicles, pedestrians, debris, etc.). Additionally, the autonomous vehicle may use stored information when navigating, such as information that provides a model of the vehicle's environment. For example, the vehicle may use GPS data, sensor data (e.g., from accelerometers, speed sensors, suspension sensors, etc.), and / or other map data to provide information related to the vehicle's environment while the vehicle is traveling, and the vehicle (and other vehicles) may use the information to locate itself in the model.

[0071] In some embodiments of the present disclosure, an autonomous vehicle may use information obtained while navigating (e.g., from cameras, GPS devices, accelerometers, speed sensors, suspension sensors, etc.). In other embodiments, an autonomous vehicle may use information obtained from past navigation by the vehicle (or other vehicles) while navigating. In still other embodiments, an autonomous vehicle may use a combination of information obtained while navigating and information obtained from past navigation. The following sections provide an overview of a system according to disclosed embodiments, followed by an overview of a forward-looking imaging system and method according to the system. In the following sections, systems and methods for building, using, and updating sparse maps for autonomous vehicle navigation are disclosed.

[0072] System Overview

[0073] FIG. 1 is a block diagram representation of a system 100 according to an exemplary embodiment disclosed. 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 imaging devices (e.g., cameras), such as an imaging device 122, an imaging device 124, an imaging device 126, etc. The system 100 may also include a data interface 128 communicatively connecting the processing device 110 to the image acquisition device 120. For example, the data interface 128 may include any one or more wired and / or wireless links for transmitting image data acquired by the image acquisition device 120 to the processing unit 110 .

[0074] Wireless transceiver 172 may include one or more devices configured to exchange transmissions with one or more networks (e.g., cellular, Internet, etc.) over a wireless interface through the use of radio frequencies, infrared frequencies, magnetic fields, or electric fields. Wireless transceiver 172 may transmit and / or receive data using any known standard (e.g., Wi-Fi, Bluetooth, Bluetooth Smart, 802.15.4, ZigBee, etc.). Such transmissions may include communications from the host vehicle to one or more remotely located servers. Such transmissions may include communications (one-way or two-way) between the host vehicle and one or more target vehicles in the host vehicle's environment (e.g., to facilitate coordinating the host vehicle's navigation in light of or with target vehicles in the host vehicle's environment), as well as broadcast transmissions to unspecified recipients in the vicinity of the transmitting vehicle.

[0075] Both application processor 180 and image processor 190 may include various types of processing devices. For example, either or both of application processor 180 and image processor 190 may include a microprocessor, a preprocessor (such as an image preprocessor), a graphics processing unit (GPU), a central processing unit (CPU), support circuitry, a digital signal processor, an integrated circuit, memory, or any other type of device suitable for executing applications and processing and analyzing images. In some embodiments, application processor 180 and / or image processor 190 may include any type of single-core or multi-core processor, mobile device microcontroller, central processing unit, etc. A variety of processing devices are available and may include a variety of architectures (e.g., x86 processor, ARM, etc.), including, for example, processors available from manufacturers such as Intel®, AMD®, or GPUs available from manufacturers such as NVIDIA®, ATI®, etc.

[0076] In some embodiments, application processor 180 and / or image processor 190 may include any of the EyeQ series of processors available from Mobileye®. These processor designs include multiple processing units, each with a local memory and instruction set. Such processors may include video inputs that receive image data from multiple image sensors, and may also include video output capabilities. In one example, the EyeQ2® uses 90 nm-micron technology operating at 332 MHz. The EyeQ2® architecture consists of two floating-point hyper-threaded 32-bit RISC CPUs (MIPS32® 34K® cores), five Vision Computation Engines (VCEs), three Vector Microcode Processors (VMPs®), a Denali 64-bit mobile DDR controller, a 128-bit internal acoustic interconnect, dual 16-bit video input and 18-bit video output controllers, a 16-channel DMA, and several peripherals. The MIPS34K CPU manages the 5 VCEs, 3 VMPs™ and DMA, the second MIPS34K CPU and multi-channel DMA, and other peripherals. The 5 VCEs, 3 VMPs, and the MIPS34K CPU can perform intensive vision calculations required by multi-function bundle applications. In another example, the disclosed embodiment may use the EyeQ3, which is a third generation processor and is six times more powerful than the EyeQ2. In another example, the EyeQ4 and / or EyeQ5 may be used with the disclosed embodiment. Of course, newer or future EyeQ processing devices may be used with the disclosed embodiment.

[0077] Any of the processing devices disclosed herein can be configured to perform a particular function. Configuring a processing device, such as any of the described EyeQ processors or other controllers or microprocessors, to perform a particular function may include programming computer-executable instructions and providing the processing device with those instructions for execution during operation of the processing device. In some embodiments, configuring a processing device may include directly programming architectural instructions into the processing device. Processing devices, such as field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), and the like, may be configured, for example, using one or more hardware description languages ​​(HDLs).

[0078] In other embodiments, configuring the processing device may include storing executable instructions on a memory accessible by the processing device during operation. For example, the processing device may access the memory during operation to retrieve and execute the stored instructions. In any event, a processing device configured to perform the sensing, image analysis, and / or navigation functions disclosed herein represents a dedicated hardware-based system that controls multiple hardware-based components of a host vehicle.

[0079] 1 shows two separate processing devices included in processing unit 110, more or fewer processing devices may be used. For example, in some embodiments, a single processing device may be used to accomplish the tasks of application processor 180 and image processor 190. In other embodiments, these tasks may be performed by three or more processing devices. Furthermore, in some embodiments, system 100 may include one or more of processing units 110 without including other components, such as image acquisition unit 120.

[0080] 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 that processes and analyzes images. The image preprocessor may include a video processor that captures, digitizes, and processes images from an image sensor. The CPU may include any number of microcontrollers or microprocessors. The GPU may also include any number of microcontrollers or microprocessors. The support circuits may be any number of circuits commonly known in the art, including cache, power, clock, and input / output circuits. The memory may store software that, when executed by the processor, controls the operation of the system. The memory may include databases and image processing software. The memory may include any number of random access memories, read-only memories, flash memories, disk drives, optical storage devices, tape storage devices, removable storage devices, and other types of storage devices. In one example, the memory may be separate from the processing unit 110. In another example, the memory may be integrated into the processing unit 110.

[0081] Each memory 140, 150 may contain software instructions that, when executed by a processor (e.g., application processor 180 and / or image processor 190), may control the operation of various aspects of system 100. These memory units may include various databases and image processing software as well as trained systems such as neural networks or deep neural networks. The memory units may include random access memory (RAM), read only memory (ROM), flash memory, disk drives, optical storage devices, tape storage devices, removable storage devices, and / or any other type of storage device. In some embodiments, memory units 140, 150 may be separate from application processor 180 and / or image processor 190. In other embodiments, these memory units may be integrated into application processor 180 and / or image processor 190.

[0082] Position sensor 130 may include any type of device suitable for determining a location associated with at least one component of system 100. In some embodiments, position sensor 130 may include a GPS receiver. Such a receiver may determine the location and velocity of a user by processing signals broadcast by Global Positioning System satellites. Position information from position sensor 130 may be provided to application processor 180 and / or image processor 190.

[0083] In some embodiments, system 100 may include components such as a speed sensor (e.g., a tachometer, speedometer) for measuring the speed of vehicle 200 and / or an accelerometer (either single-axis or multi-axis) for measuring the acceleration of vehicle 200.

[0084] User interface 170 may include any device suitable for providing information or receiving input from one or more users of system 100. In some embodiments, user interface 170 may include user input devices including, for example, a touch screen, a microphone, a keyboard, a pointer device, a track wheel, a camera, a knob, buttons, etc. Using such input devices, a user may be able to provide information input or commands to system 100 by typing instructions or information, providing voice commands, selecting on-screen menu options using buttons, pointer or eye tracking functions, or through any other suitable technique for communicating information to system 100.

[0085] User interface 170 may comprise one or more processing devices configured to provide information to or receive information from a user and process that information, e.g., for use by application processor 180. In some embodiments, such processing devices may execute instructions to recognize and track eye movements, receive and interpret voice commands, recognize and interpret touches and / or gestures made on a touchscreen, respond to keyboard entries or menu selections, etc. In some embodiments, user interface 170 may include a display, a speaker, a haptic device, and / or any other device that provides output information to a user.

[0086] Map database 160 may include any type of database that stores map data useful to system 100. In some embodiments, map database 160 may include data related to the locations in a reference coordinate system of various items, including roads, water features, geographic features, businesses, points of interest, restaurants, gas stations, etc. Map database 160 may store not only the locations of such items, but also descriptors related to those items, including, for example, names associated with any of the stored features. In some embodiments, map database 160 may be physically located with other components of system 100. Alternatively or additionally, map database 160 or portions thereof may be located remotely with respect to other components of system 100 (e.g., processing unit 110). In such embodiments, information from map database 160 may be downloaded to a network (e.g., via a cellular network and / or the Internet, etc.) via a wired or wireless data connection. In some cases, map database 160 may store a sparse data model that includes polynomial representations of specific road features (e.g., lane marks) or a target trajectory of the host vehicle. Systems and methods for generating such maps are discussed below with reference to FIGS.

[0087] Imaging devices 122, 124, and 126 may each include any type of device suitable for capturing at least one image from an environment. Additionally, any number of imaging devices may be used to obtain images for input to the image processor. Some embodiments may include only a single imaging device, while other embodiments may include two, three, or even four or more imaging devices. Imaging devices 122, 124, and 126 are further described below with reference to Figures 2B-2E.

[0088] System 100 or various components of system 100 may be incorporated into a variety of different platforms. In some embodiments, system 100 may be included in vehicle 200, as shown in FIG. 2A. For example, vehicle 200 may include processing unit 110 and any other components of system 100, as described above with respect to FIG. 1. In some embodiments, vehicle 200 may include only a single imaging device (e.g., a camera), while in other embodiments, such as those discussed in connection with FIGS. 2B-2E, multiple imaging devices may be used. For example, as shown in FIG. 2A, either of imaging devices 122 and 124 of vehicle 200 may be part of an ADAS (advanced driver assistance system) imaging set.

[0089] The imaging device included in vehicle 200 as part of image capture unit 120 may be positioned in any suitable location. In some embodiments, as shown in Figures 2A-2E and 3A-3C, imaging device 122 may be positioned near the rearview mirror. This location may provide a line of sight similar to that of the driver of vehicle 200 and may assist in determining what the driver can and cannot see. Although imaging device 122 may be positioned anywhere near the rearview mirror, placing imaging device 122 on the driver's side of the mirror may further assist in capturing images representative of the driver's field of view and / or line of sight.

[0090] Other locations may also be used for the imaging devices of the image acquisition unit 120. For example, the imaging device 124 may be located on or in the bumper of the vehicle 200. Such a location may be particularly suitable for an imaging device with a wide field of view. The line of sight of an imaging device located in the bumper may be different from the line of sight of the driver, and thus the bumper imaging device and the driver do not always see the same object. The imaging devices (e.g., imaging devices 122, 124, and 126) may also be located in other locations. For example, the imaging devices may be located in or on one or both side mirrors of the vehicle 200, on the roof of the vehicle 200, on the hood of the vehicle 200, on the trunk of the vehicle 200, on the side of the vehicle 200, mounted on, positioned behind, or positioned in front of any window of the vehicle 200, mounted in or near the front and / or rear lights of the vehicle 200, etc.

[0091] In addition to the imaging device, vehicle 200 may include various other components of system 100. For example, processing unit 110 may be integrated into the vehicle's engine control unit (ECU) or included in vehicle 200 separate from the ECU. Vehicle 200 may also be equipped with a position sensor 130, such as a GPS receiver, and vehicle 200 may also include a map database 160 and memory units 140 and 150.

[0092] As described above, the wireless transceiver 172 may transmit and / or receive data via one or more networks (e.g., a cellular network, the Internet, etc.). For example, the wireless transceiver 172 may upload data collected by the system 100 to one or more servers and download data from one or more servers. Through the wireless transceiver 172, the system 100 may receive updates to the data stored in the map database 160, the memory 140, and / or the memory 150, for example, periodically or on demand. Similarly, the wireless transceiver 172 may upload any data from the system 100 (e.g., images captured by the image acquisition unit 120, data received by the position sensor 130, other sensors or vehicle control systems, etc.) and / or any data processed by the processing unit 110 to one or more servers.

[0093] System 100 may upload data to a server (e.g., the cloud) based on a privacy level setting. For example, system 100 may implement a privacy level setting that regulates or limits the types of data (including metadata) that may uniquely identify the vehicle and / or the driver / owner of the vehicle that are sent to the server. Such settings may be set by a user via wireless transceiver 172, may be initialized by factory default settings, or may be set by data received by wireless transceiver 172, for example.

[0094] In some embodiments, system 100 may upload data according to a "high" privacy level, where under certain settings system 100 may transmit data (e.g., location information associated with the route, images taken, etc.) without any details about the specific vehicle and / or driver / owner. For example, when uploading data according to a "high" privacy level, system 100 may not include the vehicle identification number (VIN) or the name of the driver or owner of the vehicle, but instead transmit data such as images taken and / or limited location information associated with the route.

[0095] Other privacy levels are contemplated. For example, system 100 may transmit data to the server according to a "medium" privacy level, which may include additional information not included under a "high" privacy level, such as the make and / or model of the vehicle and / or vehicle type (e.g., passenger car, sport utility vehicle, truck, etc.). In some embodiments, system 100 may upload data according to a "low" privacy level. Under a "low" privacy level setting, system 100 may upload and include sufficient data to uniquely identify a particular vehicle, owner / driver, and / or some or all of the route traveled by the vehicle. Such "low" privacy level data may include, for example, one or more of the following: VIN, driver / owner name, starting point of the vehicle prior to departure, intended destination of the vehicle, make and / or model of the vehicle, type of vehicle, etc.

[0096] FIG 2A is a side view representation of an exemplary vehicle imaging system according to a disclosed embodiment. FIG 2B is a top view representation of the embodiment shown in FIG 2A. As shown in FIG 2B, the disclosed embodiment may include a vehicle 200 including a system 100 in its body having a first imaging device 122 positioned near a rearview mirror and / or near a driver of the vehicle 200, a second imaging device 124 positioned on or in a bumper area (e.g., one of the bumper areas 210) of the vehicle 200, and a processing unit 110.

[0097] As shown in Figure 2C, both imaging devices 122 and 124 may be positioned near the rearview mirror and / or near the driver of vehicle 200. Additionally, while two imaging devices 122 and 124 are shown in Figures 2B and 2C, it should be understood that other embodiments may include three or more imaging devices. For example, in the embodiment shown in Figures 2D and 2E, a first imaging device 122, a second imaging device 124, and a third imaging device 126 are included in system 100 of vehicle 200.

[0098] 2D , imaging device 122 may be positioned near a rearview mirror and / or near the driver of vehicle 200, and imaging devices 124 and 126 may be positioned on or in a bumper region (e.g., one of bumper regions 210) of vehicle 200. Also, as shown in FIG 2E , imaging devices 122, 124 and 126 may be positioned near a rearview mirror and / or near the driver's seat of vehicle 200. The disclosed embodiments are not limited to any particular number and configuration of imaging devices, and imaging devices may be positioned in any suitable location in and / or on vehicle 200.

[0099] It should be understood that the disclosed embodiments are not limited to vehicles and may be applicable in other contexts, and it should also be understood that the disclosed embodiments are not limited to a particular type of vehicle 200 and may be applicable to all types of vehicles, including cars, trucks, trailers, and other types of vehicles.

[0100] The first imaging device 122 may include any suitable type of imaging device. The imaging device 122 may include an optical axis. In one example, the imaging device 122 may include an Aptina M9V024 WVGA sensor with a global shutter. In other embodiments, the imaging device 122 may provide a resolution of 1280×960 pixels and may include a rolling shutter. The imaging device 122 may include various optical elements. In some embodiments, one or more lenses may be included to provide, for example, a desired focal length and field of view of the imaging device. In some embodiments, a 6 mm lens or a 12 mm lens may be associated with the imaging device 122. In some embodiments, the imaging device 122 may be configured to capture an image having a desired field of view (FOV) 202, as shown in FIG. 2D. For example, the imaging device 122 may be configured to have a typical FOV, such as within the range of 40 degrees to 56 degrees, including a 46 degree FOV, a 50 degree FOV, a 52 degree FOV, or degrees greater than the 52 degree FOV. Alternatively, the imaging device 122 may be configured to have a narrow FOV in the range of 23-40 degrees, such as a 28 degree FOV or a 36 degree FOV. Additionally, the imaging device 122 may be configured to have a wide FOV in the range of 100-180 degrees. In some embodiments, the imaging device 122 may include a wide-angle bumper camera or a bumper camera with an FOV of up to 180 degrees. In some embodiments, the imaging device 122 may be a 7.2 Mpixel imaging device with an aspect ratio of about 2:1 (e.g., H×V=3800×1900 pixels) with a horizontal FOV of about 100 degrees. Such an imaging device may be used instead of a three-dimensional imaging device configuration. Due to large lens distortion, the vertical FOV of such an imaging device may be much lower than 50 degrees in implementations where the imaging device uses a radially symmetric lens. For example, such lenses are not radially symmetric, thereby allowing a vertical FOV greater than 50 degrees with a horizontal FOV of 100 degrees.

[0101] The first imaging device 122 may acquire a plurality of first images of a scene associated with the vehicle 200. The plurality of first images may each be acquired as a series of image scan lines, which may be captured using a rolling shutter. Each scan line may include a plurality of pixels.

[0102] The first imaging device 122 may have a scan rate associated with the acquisition of each of the first series of image scan lines. The scan rate may refer to the rate at which the image sensor can acquire image data associated with each pixel included in a particular scan line.

[0103] The imaging devices 122, 124, and 126 may include any suitable type and number of image sensors, including, for example, CCD sensors or CMOS sensors. In one embodiment, a CMOS image sensor may be utilized with a rolling shutter whereby each pixel in a row is read one at a time, and the scanning of the rows proceeds row by row until the entire image frame has been imaged. In some embodiments, the rows may be imaged sequentially from top to bottom for the frame.

[0104] In some embodiments, one or more of the imaging devices disclosed herein (e.g., imaging devices 122, 124, and 126) may constitute a high-resolution imager and may have a resolution of greater than 5M pixels, greater than 7M pixels, greater than 10M pixels, or more.

[0105] The use of a rolling shutter can result in pixels in different rows being exposed and imaged at different times, which can cause skew and other image artifacts in the captured image frame. On the other hand, if the imaging device 122 is configured to operate with a global or synchronous shutter, all pixels can be exposed during a common exposure period for the same amount of time. As a result, image data in a frame collected from a system utilizing a global shutter represents a snapshot of the entire FOV (such as FOV 202) at a particular time. Conversely, when applying a rolling shutter, each row in a frame is exposed and the data is imaged at a different time. Thus, a moving object may appear distorted in an imaging device with a rolling shutter. This phenomenon is described in more detail below.

[0106] The second imaging device 124 and the third imaging device 126 may be any type of imaging device. Like the first imaging device 122, each of the imaging devices 124 and 126 may include an optical axis. In one embodiment, each of the imaging devices 124 and 126 may include an Aptina M9V024 WVGA sensor with a global shutter. Alternatively, each of the imaging devices 124 and 126 may include a rolling shutter. Like the imaging device 122, the imaging devices 124 and 126 may be configured to include various lenses and optical elements. In some embodiments, the lenses associated with the imaging devices 124 and 126 may provide a FOV that is the same as the FOV associated with the imaging device 122 (such as FOV 202) or a narrower FOV (such as FOVs 204 and 206). For example, the imaging devices 124 and 126 may have an FOV of 40 degrees, 30 degrees, 26 degrees, 23 degrees, 20 degrees, or less than 20 degrees.

[0107] The imaging devices 124 and 126 may acquire multiple second and third images of a scene associated with the vehicle 200. Each of the multiple second and third images may be acquired as a second and third series of image scan lines, which may be imaged using a rolling shutter. Each scan line or row may have a number of pixels. The imaging devices 124 and 126 may have second and third scan rates associated with acquiring each image scan line included in the second and third series.

[0108] Each imaging device 122, 124, and 126 may be positioned in any suitable location and in any suitable orientation relative to vehicle 200. The relative positions of imaging devices 122, 124, and 126 may be selected to assist in fusing together information obtained from the imaging devices. For example, in some embodiments, the FOV associated with imaging device 124 (e.g., FOV 204) may partially or completely overlap with the FOV associated with imaging device 122 (e.g., FOV 202) and the FOV associated with imaging device 126 (e.g., FOV 206).

[0109] The imaging devices 122, 124, and 126 may be positioned on the vehicle 200 at any suitable relative height. In one example, there may be a height difference between the imaging devices 122, 124, and 126, which may provide sufficient parallax information to enable stereo analysis. For example, as shown in FIG. 2A, the two imaging devices 122 and 124 are at different heights. There may also be a lateral displacement difference between the imaging devices 122, 124, and 126, which may provide additional parallax information for stereo analysis by the processing unit 110, for example. The lateral displacement difference may be calculated as d x In some embodiments, a front displacement or a rear displacement (e.g., range displacement) may exist between imaging devices 122, 124, 126. For example, imaging device 122 may be positioned 0.5 to 2 meters or more behind imaging device 124 and / or imaging device 126. With this type of displacement, one of the imaging devices may be able to cover a potential blind spot of the other imaging device.

[0110] Imaging device 122 may have any suitable resolution capability (e.g., the number of pixels associated with the image sensor), and the resolution of the image sensor associated with imaging device 122 may be higher, lower, or the same as the resolution of the image sensors associated with imaging devices 124 and 126. In some embodiments, the image sensors associated with imaging device 122 and / or imaging devices 124 and 126 may have a resolution of 640×480, 1024×768, 1280×960, or any other suitable resolution.

[0111] The frame rate (e.g., the rate at which the imaging device acquires a set of pixel data for one image frame before moving on to capture pixel data associated with the next image frame) may be controllable. The frame rate associated with imaging device 122 may be higher, lower, or the same as the frame rate associated with imaging devices 124 and 126. The frame rates associated with imaging devices 122, 124, and 126 may depend on various factors that may affect the timing of the frame rate. For example, one or more of imaging devices 122, 124, and 126 may include a selectable pixel delay period imposed before or after acquisition of image data associated with a pixel of one or more of the image sensors in imaging devices 122, 124, and / or 126. In general, image data corresponding to each pixel may be acquired according to the device's clock rate (e.g., one pixel per clock cycle). Additionally, in embodiments including a rolling shutter, one or more of imaging devices 122, 124, and 126 may include a selectable horizontal blanking period that is imposed before or after acquisition of image data associated with a row of pixels of an image sensor in imaging devices 122, 124, and / or 126. Additionally, one or more of imaging devices 122, 124, and / or 126 may include a selectable vertical blanking period that is imposed before or after acquisition of image data associated with an image frame of imaging devices 122, 124, and 126.

[0112] These timing controls may enable the frame rates associated with imaging devices 122, 124, and 126 to be synchronized, even if the line scan rate of each imaging device is different. Additionally, as discussed in more detail below, among other factors (e.g., image sensor resolution, maximum line scan rate, etc.), these selectable timing controls may enable imaging from areas where the FOV of imaging device 122 overlaps with one or more of the FOVs of imaging devices 124 and 126 to be synchronized, even if the field of view of imaging device 122 differs from the FOV of imaging devices 124 and 126.

[0113] Frame rate timing for imaging devices 122, 124, and 126 may depend on the resolution of the associated image sensor. For example, if one device includes an image sensor with a resolution of 640×480 and the other device includes an image sensor with a resolution of 1280×960, assuming similar line scan rates for both devices, then capturing a frame of image data from the sensor with the higher resolution will require a longer period of time.

[0114] Another factor that may affect the timing of image data acquisition at imaging devices 122, 124, and 126 is the maximum line scan rate. For example, acquisition of a row of image data from an image sensor included in imaging devices 122, 124, and 126 requires some minimum amount of time. Assuming that no pixel delay period is added, this minimum amount of time to acquire a row of image data will be related to the maximum line scan rate of a particular device. A device that offers a higher maximum line scan rate has the potential to provide a higher frame rate than a device that has a lower maximum line scan rate. In some embodiments, one or both of imaging devices 124 and 126 may have a maximum line scan rate that is higher than the maximum line scan rate associated with imaging device 122. In some embodiments, the maximum line scan rate of imaging devices 124 and / or 126 may be 1.25 times, 1.5 times, 1.75 times, or 2 times or more than the maximum line scan rate of imaging device 122.

[0115] In another embodiment, imaging devices 122, 124, and 126 may have the same maximum line scan rate, but imaging device 122 may operate at a scan rate equal to or less than its maximum scan rate. The system may be configured such that one or both of imaging devices 124 and 126 operate at a line scan rate equal to the line scan rate of imaging device 122. In other examples, the system may be configured such that the line scan rate of imaging device 124 and / or imaging device 126 may be 1.25, 1.5, 1.75, or 2 times or more the line scan rate of imaging device 122.

[0116] In some embodiments, imaging devices 122, 124, and 126 may be asymmetric; that is, they may include cameras with different fields of view (FOV) and focal lengths. The fields of view of imaging devices 122, 124, and 126 may include any desired area of ​​the environment of vehicle 200, for example. In some embodiments, one or more of imaging devices 122, 124, and 126 may be configured to acquire image data from an environment in front of vehicle 200, an environment behind vehicle 200, an environment on either side of vehicle 200, or a combination thereof.

[0117] Additionally, the focal length associated with each imaging device 122, 124 and / or 126 may be selectable (e.g., by inclusion of an appropriate lens, etc.) such that each device captures images of objects at a desired distance range from vehicle 200. For example, in some embodiments, imaging devices 122, 124 and 126 may capture images of close-by objects within a few meters of the vehicle. Imaging devices 122, 124, 126 may also be configured to capture images of objects at greater distances from the vehicle (e.g., 25 m, 50 m, 100 m, 150 m or more). Further, the focal lengths of imaging devices 122, 124, and 126 may be selected such that one imaging device (e.g., imaging device 122) can capture images of objects relatively close to the vehicle (e.g., within 10 m or within 20 m), and the other imaging devices (e.g., imaging devices 124 and 126) can capture images of objects farther away from vehicle 200 (e.g., greater than 20 m, greater than 50 m, greater than 100 m, greater than 150 m, etc.).

[0118] According to some embodiments, the FOV of one or more of imaging devices 122, 124, and 126 may have a wide angle. For example, it may be advantageous to have an FOV of 140 degrees for imaging devices 122, 124, and 126, particularly those that may be used to image areas near vehicle 200. For example, imaging device 122 may be used to capture images of areas to the right or left of vehicle 200, and in such embodiments, it may be desirable for imaging device 122 to have a wide FOV (e.g., at least 140 degrees).

[0119] The field of view associated with each of the imaging devices 122, 124, and 126 may depend on the respective focal length. For example, as the focal length increases, the corresponding field of view decreases.

[0120] Imaging devices 122, 124, and 126 may be configured to have any suitable field of view. In one particular example, imaging device 122 may have a horizontal FOV of 46 degrees, imaging device 124 may have a horizontal FOV of 23 degrees, and imaging device 126 may have a horizontal FOV between 23 and 46 degrees. In another example, imaging device 122 may have a horizontal FOV of 52 degrees, imaging device 124 may have a horizontal FOV of 26 degrees, and imaging device 126 may have a horizontal FOV between 26 and 52 degrees. In some embodiments, the ratio between the FOV of imaging device 122 and the FOV of imaging device 124 and / or imaging device 126 may vary from 1.5 to 2.0. In other embodiments, the ratio may vary from 1.25 to 2.25.

[0121] System 100 may be configured such that the field of view of imaging device 122 at least partially or completely overlaps the field of view of imaging device 124 and / or imaging device 126. In some embodiments, system 100 may be configured such that the fields of view of imaging devices 124 and 126, for example, fall within the field of view of imaging device 122 (e.g., are smaller than the field of view of imaging device 122) and share a common center with the field of view of imaging device 122. In other embodiments, imaging devices 122, 124, and 126 may image adjacent FOVs or may have partially overlapping FOVs. In some embodiments, the fields of view of imaging devices 122, 124, and 126 may be aligned such that the center of imaging device 124 and / or 126, which has a narrower FOV, may be located in the bottom half of the field of view of device 122, which has a wider FOV.

[0122] 2F is a diagrammatic representation of an exemplary vehicle control system according to disclosed embodiments. As shown in FIG. 2F, vehicle 200 may include a throttle system 220, a brake system 230, and a steering system 240. System 100 may provide inputs (e.g., control signals) to one or more of throttle system 220, brake system 230, and steering system 240 via one or more data links (e.g., one or more of any wired and / or wireless links or links that transmit data). For example, based on analysis of images acquired by imaging devices 122, 124, and / or 126, system 100 may provide control signals to one or more of throttle system 220, brake system 230, and steering system 240 to navigate vehicle 200 (e.g., by accelerating, turning, lane shifting, etc.). Additionally, system 100 may receive inputs indicative of operating conditions of vehicle 200 (e.g., speed, whether vehicle 200 is braking and / or turning, etc.) from one or more of throttle system 220, braking system 230, and steering system 24. Further details are provided below in conjunction with Figures 4-7.

[0123] As shown in FIG. 3A , the vehicle 200 may also include a user interface 170 for interacting with a driver or passenger of the vehicle 200. For example, the user interface 170 in a vehicle application may include a touch screen 320, knobs 330, buttons 340, and a microphone 350. The driver or passenger of the vehicle 200 may also interact with the system 100 using a steering wheel (e.g., located on or near a steering column of the vehicle 200, including, for example, a turn signal handle), buttons (e.g., located on a steering wheel of the vehicle 200), and the like. In some embodiments, the microphone 350 may be positioned adjacent to the rearview mirror 310. Similarly, in some embodiments, the imaging device 122 may be 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., alerts) via the speaker 360.

[0124] 3B-3D are diagrams of an exemplary camera mount 370 configured to be positioned behind a rearview mirror (e.g., rearview mirror 310) and facing a vehicle windshield, according to disclosed embodiments. As shown in FIG. 3B, camera mount 370 may include imaging devices 122, 124, and 126. Imaging devices 124 and 126 may be positioned behind glare shield 380, which may be in direct contact with the vehicle windshield and may include a film and / or composition of anti-reflective material. For example, glare shield 380 may be positioned such that the shield is aligned facing the vehicle windshield with a matching slope. In some embodiments, each of imaging devices 122, 124, and 126 may be positioned behind glare shield 380, for example, as shown in FIG. 3D. The disclosed embodiments are not limited to any particular configuration of imaging devices 122, 124, and 126, camera mount 370, and glare shield 380. FIG. 3C is a view of the camera mount 370 shown in FIG. 3B from the front.

[0125] As will be appreciated by those skilled in the art having the benefit of this disclosure, many variations and / or modifications may be made to the above disclosed embodiments. For example, not all components are essential to the operation of system 100. Furthermore, any component may be located in any suitable portion of system 100, and the components may be rearranged in various configurations while still providing the functionality of the disclosed embodiments. Thus, the configurations discussed above are examples, and regardless of the configuration described above, system 100 may provide a wide range of functionality for analyzing the surroundings of vehicle 200 and navigating vehicle 200 in response to the analysis.

[0126] As discussed in more detail below, in accordance with various disclosed embodiments, system 100 may provide various features related to autonomous driving and / or driver assistance technology. For example, system 100 may analyze image data, location data (e.g., GPS location information), map data, speed data, and / or data from sensors included in vehicle 200. System 100 may collect data for analysis, for example, from image capture unit 120, location sensor 130, and other sensors. Additionally, system 100 may analyze the collected data to identify whether vehicle 200 should take a particular action and then automatically take the determined action without human intervention. For example, when vehicle 200 navigates without human intervention, system 100 may automatically control braking, acceleration, and / or steering of vehicle 200 (e.g., by sending control signals to one or more of throttle system 220, braking system 230, and steering system 240). Additionally, system 100 may analyze the collected data and issue warnings and / or alerts to vehicle occupants based on the analysis of the collected data. Further details regarding various embodiments provided by system 100 are provided below.

[0127] Forward-looking multi-imaging system

[0128] As discussed above, the system 100 may provide a driving assistance function using a multi-camera system. The multi-camera system may use one or more cameras facing the forward direction of the vehicle. In other embodiments, the multi-camera system may include one or more cameras facing the side of the vehicle or the rear of the vehicle. In one embodiment, for example, the system 100 may use a two-camera imaging system, in which a first camera and a second camera (e.g., imaging devices 122 and 124) may be positioned at the front and / or side of the vehicle (e.g., vehicle 200). The first camera may have a field of view that is larger, smaller, or partially overlapping that of the second camera. Furthermore, the first camera may be connected to a first image processor to perform monocular image analysis of the image provided by the first camera, and the second camera may be connected to a second image processor to perform monocular image analysis of the image provided by the second camera. The outputs (e.g., processed information) of the first and second image processors may be combined. In some embodiments, the second image processor may receive images from both the first and second cameras to perform stereo analysis. In another embodiment, the system 100 may use a three-camera imaging system, where each camera has a different field of view. Thus, such a system may make decisions based on information derived from objects located at various distances both in front of and to the sides of the vehicle. References to monocular image analysis may refer to cases where image analysis is performed based on images captured from a single viewpoint (e.g., a single camera). Stereo image analysis may refer to cases where image analysis is performed based on two or more images captured with one or more of the imaging parameters changed. For example, captured images suitable for performing stereo analysis may include images captured from two or more different positions, images captured from different fields of view, images captured using different focal lengths, images captured with parallax information, etc.

[0129] For example, in one embodiment, system 100 may implement a three camera configuration using imaging devices 122, 124, and 126. In such a configuration, imaging device 122 may provide a narrow field of view (e.g., 34 degrees or other value selected from the range of about 20 to 45 degrees, etc.), imaging device 124 may provide a wide field of view (e.g., 150 degrees or other value selected from the range of about 100 to about 180 degrees), and imaging device 126 may provide a medium field of view (e.g., 46 degrees or other value selected from the range of about 35 to about 60 degrees). In some embodiments, imaging device 126 may operate as a main or primary camera. Imaging devices 122, 124, and 126 may be positioned substantially side-by-side (e.g., 6 cm apart) behind rearview mirror 310. Additionally, in some embodiments, as discussed above, one or more of the imaging devices 122, 124, and 126 may be mounted behind a glare shield 380 that is flush with the windshield of the vehicle 200. Such a shield may operate to minimize the impact of any reflections from the interior of the vehicle on the imaging devices 122, 124, and 126.

[0130] In another embodiment, as discussed above in connection with Figures 3B and 3C, the wide field of view camera (e.g., imaging device 124 in the example above) may be mounted lower than the narrow main field of view camera (e.g., devices 122 and 126 in the example above). This configuration may provide a free line of sight from the wide field of view camera. To reduce reflections, the camera may be mounted near the windshield of vehicle 200 and may include a polarizer to attenuate reflected light.

[0131] A three-camera system may provide certain performance characteristics. For example, some embodiments may include the ability to verify the detection of an object by one camera based on the detection results from another camera. In the three-camera configuration discussed above, the processing unit 110 may include, for example, three processing devices (e.g., three EyeQ series processor chips as discussed above), each processing device dedicated to processing images captured by one or more of the imaging devices 122, 124, and 126.

[0132] In a three-camera system, the first processing device may receive images from both the main camera and the narrow FOV camera and perform vision processing of the narrow FOV camera to detect, for example, other vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. Additionally, the first processing device may calculate pixel discrepancies between the images from the main camera and the narrow camera and create a 3D reconstruction of the vehicle 200's environment. The first processing device may then combine the 3D reconstruction with 3D map data or 3D information calculated based on information from another camera.

[0133] The second processing device may receive images from the main camera and perform vision processing to detect other vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. Furthermore, the second processing device may calculate the camera displacement, calculate pixel discrepancies between successive images based on the displacement, and create a 3D reconstruction (e.g., structure-from-motion) of the scene. The second processing device may send the structure-from-motion based on the 3D reconstruction to the first processing device and combine the structure-from-motion with the stereoscopic 3D image.

[0134] The third processing device may receive images from the wide FOV camera and process the images to detect vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. The third processing device may further execute additional processing instructions to analyze the images and identify moving objects in the images, such as vehicles changing lanes, pedestrians, etc.

[0135] In some embodiments, having image-based information streams captured and processed independently may provide an opportunity to provide redundancy in the system. Such redundancy may include, for example, using a first imaging device and images processed from that device to verify and / or capture information obtained by capturing and processing image information from at least a second imaging device.

[0136] In some embodiments, system 100 may use two imaging devices (e.g., imaging devices 122 and 124) in providing navigation assistance to vehicle 200, and a third imaging device (e.g., imaging device 126) may be used to provide redundancy and verify analysis of data received from the other two imaging devices. For example, in such a configuration, imaging devices 122 and 124 may provide images for stereo analysis by system 100 for navigating vehicle 200, and imaging device 126 may provide images for monocular analysis by system 100 to provide redundancy and verification of information derived based on images captured from imaging devices 122 and / or imaging device 124. That is, imaging device 126 (and corresponding processing device) may be viewed as providing a redundant subsystem that provides a check on the analysis derived from imaging devices 122 and 124 (e.g., to provide an automatic emergency braking (AEB) system). Additionally, in some embodiments, redundancy and validation of received data can be supplemented based on information received from one or more sensors (e.g., radar, lidar, acoustic sensors, information received from one or more transceivers outside the vehicle, etc.).

[0137] Those skilled in the art will recognize that the above camera configurations, camera placements, camera numbers, camera locations, etc. are merely exemplary. These components, etc. described for an overall system, may be assembled and used in a variety of different configurations without departing from the scope of the disclosed embodiments. Further details regarding the use of multi-camera systems to provide driver assistance and / or autonomous vehicle functions follow below.

[0138] 4 is an example functional block diagram of memory 140 and / or 150 that may be stored / programmed with instructions to perform one or more operations in accordance with the disclosed embodiments. Although reference is made below to memory 140, those skilled in the art will recognize that instructions may be stored in memory 140 and / or 150.

[0139] 4, memory 140 may store monocular image analysis module 402, stereo image analysis module 404, velocity and acceleration module 406, and navigation response module 408. The disclosed embodiments are not limited to any particular configuration of memory 140. Furthermore, application processor 180 and / or image processor 190 may execute instructions stored in any of modules 402, 404, 406, and 408 included in memory 140. Those skilled in the art will appreciate that references to processing unit 110 in the following discussion may refer to application processor 180 and image processor 190 individually or collectively. Thus, any steps of the following processes may be performed by one or more processing devices.

[0140] In one embodiment, the monocular image analysis module 402 may store instructions (e.g., computer vision software) that, when executed by the processing unit 110, perform monocular image analysis of a set of images acquired by one of the imaging devices 122, 124, and 126. In some embodiments, the processing unit 110 may combine information from the set of images with additional sensory information (e.g., information from radar, lidar, etc.) to perform the monocular image analysis. As described below in connection with FIGS. 5A-5D, the monocular image analysis module 402 may include instructions to detect a set of features in the set of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazards, and any other features associated with the vehicle's environment. Based on the analysis, the system 100 may cause one or more navigational responses in the vehicle 200 (e.g., via the processing unit 110), such as turns, lane shifts, and acceleration changes, as discussed below in connection with the navigation response module 408.

[0141] In one embodiment, stereo image analysis module 404 may store instructions (such as computer vision software) that, when executed by processing unit 110, perform stereo image analysis of a first and second set of images acquired by a combination of imaging devices selected from imaging devices 122, 124, and 126. In some embodiments, processing unit 110 may combine information from the first and second sets of images with additional sensory information (e.g., information from radar) to perform stereo image analysis. For example, stereo image analysis module 404 may include instructions to perform stereo image analysis based on a first set of images acquired by imaging device 124 and a second set of images acquired by imaging device 126. As described below in connection with FIG. 6, stereo image analysis module 404 may include instructions to detect a set of features in the first and second sets of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, and hazards. Based on the analysis, processing unit 110 may cause one or more navigational responses in vehicle 200, such as turns, lane shifts, and acceleration changes, as described below in connection with navigation response module 408. Additionally, in some embodiments, stereo image analysis module 404 may implement techniques related to trained systems (such as neural networks or deep neural networks) or untrained systems, such as systems that may be configured to use computer vision algorithms to detect and / or label objects in an environment where sensory information has been imaged and processed. In one embodiment, stereo image analysis module 404 and / or other image processing modules may be configured to use a combination of trained and untrained systems.

[0142] In one embodiment, the speed and acceleration module 406 may store software configured to analyze data received from one or more computational and electromechanical devices in the vehicle 200 configured to alter 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 of the vehicle 200 based on data derived from the execution of the monocular image analysis module 402 and / or the stereo image analysis module 404. Such data may include, for example, a target position, speed and / or acceleration, the position and / or speed of the vehicle 200 relative to nearby vehicles, pedestrians or road objects, and the position information of the vehicle 200 relative to lane markings of the road. In addition, the processing unit 110 may calculate a target speed of the vehicle 200 based on sensory input (e.g., information from a radar) and input from other systems of the vehicle 200, such as the throttle system 220, the braking system 230 and / or the steering system 240 of the vehicle 200. Based on the calculated target speed, the processing unit 110 may send electronic signals to the throttle system 220, the braking system 230 and / or the steering system 240 of the vehicle 200 to trigger a change in speed and / or acceleration, for example by physically reducing the brakes or accelerator of the vehicle 200.

[0143] In one embodiment, the navigation response module 408 may store executable software by the processing unit 110 to determine a desired navigation response based on data derived from execution of the monocular image analysis module 402 and / or the stereo image analysis module 404. Such data may include position and speed information associated with nearby vehicles, pedestrians, and road objects, target position information for the vehicle 200, and the like. Additionally, in some embodiments, the navigation response may be based (partially or fully) on map data, a predetermined position of the vehicle 200, and / or a relative speed or relative acceleration between the vehicle 200 and one or more objects detected from execution of the monocular image analysis module 402 and / or the stereo image analysis module 404. The navigation response module 408 may also determine a desired navigation response based on sensory inputs (e.g., information from a radar) and inputs from other systems of the vehicle 200, such as the throttle system 220, the braking system 230, and the steering system 240 of the vehicle 200. Based on the desired navigational response, processing unit 110 may trigger the desired navigational response by sending electronic signals to throttle system 220, braking system 230, and steering system 240 of vehicle 200 to, for example, turn the steering wheel of vehicle 200 to achieve a predetermined angle of rotation. In some embodiments, processing unit 110 may use the output of navigational response module 408 (e.g., the desired navigational response) as an input to the execution of velocity and acceleration module 406 to calculate a speed change for vehicle 200.

[0144] Additionally, any of the modules disclosed herein (e.g., modules 402, 404, and 406) may implement techniques related to trained systems (such as neural networks or deep neural networks) or untrained systems.

[0145] FIG. 5A is a flow chart illustrating an example process 500A for generating one or more navigational responses based on monocular image analysis, according to disclosed embodiments. In step 510, processing unit 110 may receive a plurality of images via data interface 128 between processing unit 110 and image acquisition unit 120. For example, a camera (such as imaging device 122 having field of view 202) included in image acquisition unit 120 may capture a plurality of images of an area in front of vehicle 200 (or, for example, to the side or rear of the vehicle) and transmit them to processing unit 110 via a data connection (e.g., digital, wired, USB, wireless, Bluetooth, etc.). Processing unit 110 may execute monocular image analysis module 402 to analyze the plurality of images in step 520, as described in further detail below in connection with FIGS. 5B-5D. By performing the analysis, processing unit 110 may detect a set of features in the set of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, and traffic lights.

[0146] Processing unit 110 may also execute monocular image analysis module 402 in step 520 to detect various road hazards, such as truck tire parts, fallen road signs, loose cargo, and small animals. Road hazards may vary in structure, shape, size, and color, which may make such hazards more difficult to detect. In some embodiments, processing unit 110 may execute monocular image analysis module 402 to perform multi-frame analysis on multiple images to detect road hazards. For example, processing unit 110 may estimate camera motion between consecutive image frames and calculate pixel discrepancies between frames to build a 3D map of the road. Processing unit 110 may then use the 3D map to detect the road surface and hazards present on the road surface.

[0147] In step 530, processing unit 110 may execute navigation response module 408 to cause one or more navigation responses in vehicle 200 based on the analysis performed in step 520 and the techniques described above in connection with FIG. 4. The navigation responses may include, for example, a turn, a lane shift, an acceleration change, and the like. In some embodiments, processing unit 110 may use data derived from execution of speed and acceleration module 406 to cause one or more navigation responses. Furthermore, multiple navigation responses may be performed simultaneously, sequentially, or any combination thereof. For example, processing unit 110 may cause vehicle 200 to cross a lane and then, for example, accelerate, for example, by sequentially sending control signals to steering system 240 and throttle system 220 of vehicle 200. Alternatively, processing unit 110 may cause vehicle 200 to brake and simultaneously shift lanes, for example, by simultaneously sending control signals to braking system 230 and steering system 240 of vehicle 200.

[0148] FIG. 5B is a flow chart illustrating an example process 500B for detecting one or more vehicles and / or pedestrians in a set of images according to the disclosed embodiments. Processing unit 110 may execute monocular image analysis module 402 to perform process 500B. In step 540, processing unit 110 may identify a set of candidate objects representing vehicles and / or pedestrians that may be present. For example, processing unit 110 may scan one or more images, compare the images to one or more predefined patterns, and identify locations within each image that may contain a target object (e.g., a vehicle, a pedestrian, or parts thereof). The predefined patterns may be specified to achieve a low rate of "false hits" and a low rate of "misses." For example, processing unit 110 may use a low similarity threshold to the predefined patterns to identify candidate objects as possible vehicles or pedestrians. By doing so, processing unit 110 may be able to reduce the probability of missing (e.g., not identifying) a candidate object representing a vehicle or pedestrian.

[0149] At step 542, processing unit 110 may filter the set of candidate objects to eliminate certain candidates (e.g., irrelevant or less relevant objects) based on classification criteria. Such criteria may be derived from various characteristics associated with object types stored in a database (e.g., a database stored in memory 140). The characteristics may include the object's shape, size, texture, and position (e.g., relative to vehicle 200), etc. Thus, processing unit 110 may use one or more sets of criteria to reject false candidates from the set of candidate objects.

[0150] At step 544, processing unit 110 may analyze multiple image frames to identify whether objects in the set of candidate objects represent vehicles and / or pedestrians. For example, processing unit 110 may track detected candidate objects across successive frames and accumulate per-frame data associated with the detected objects (e.g., size, position relative to vehicle 200, etc.). Additionally, processing unit 110 may estimate parameters of the detected objects and compare the per-frame position data of the objects to predicted positions.

[0151] In step 546, processing unit 110 may construct a set of measurements of the detected objects. Such measurements may include, for example, position, velocity, and acceleration values ​​(relative to vehicle 200) associated with the detected objects. In some embodiments, processing unit 110 may construct the measurements based on estimation techniques using a series of time-based observations, such as a Kalman filter or linear quadratic estimation (LQE), and / or based on modeling data available for different object types (e.g., cars, trucks, pedestrians, bicycles, road signs, etc.). A Kalman filter may be based on a measurement of the scale of the object, where the scale measurement is proportional to the time to impact (e.g., the amount of time it takes vehicle 200 to reach the object). Thus, by performing steps 540-546, processing unit 110 may identify vehicles and pedestrians appearing in the set of captured images and derive information (e.g., position, velocity, size) associated with the vehicles and pedestrians. Based on the identification and derived information, processing unit 110 may cause one or more navigation responses at vehicle 200, as described above in connection with FIG. 5A.

[0152] In step 548, processing unit 110 may perform optical flow analysis of one or more images to reduce the probability of detecting "false hits" and the probability of missing candidate objects representing vehicles or pedestrians. Optical flow analysis may refer to, for example, analyzing movement patterns for vehicle 200 in one or more images associated with other vehicles and pedestrians that are different from the movement of the road surface. Processing unit 110 may calculate the movement of candidate objects by observing different positions of the object across multiple image frames captured at different times. Processing unit 110 may use the position and time values ​​as inputs to a mathematical model to calculate the movement of the candidate objects. Thus, optical flow analysis may provide another way to detect vehicles and pedestrians in the vicinity of vehicle 200. Processing unit 110 may perform optical flow analysis in combination with steps 540-546 to provide redundancy for detecting vehicles and pedestrians and increase the reliability of system 100.

[0153] FIG. 5C is a flow chart illustrating an example process 500C for detecting road marks and / or lane geometry information in a set of images according to the disclosed embodiments. Processing unit 110 may execute monocular image analysis module 402 to perform process 500C. In step 550, processing unit 110 may detect a set of objects by scanning one or more images. To detect segments of lane marks, lane geometry information, and other related road marks, processing unit 110 may filter the set of objects to exclude those determined to be irrelevant (e.g., small holes, small rocks, etc.). In step 552, processing unit 110 may group together segments detected in step 550 that belong to the same road mark or lane mark. Based on the grouping, processing unit 110 may develop a model, such as a mathematical model, to represent the detected segments.

[0154] In step 554, processing unit 110 may construct a set of measurements associated with the detected segment. In some embodiments, processing unit 110 may create a projection of the detected segment from the image plane to the real world plane. The projection may be characterized using a third order polynomial with coefficients corresponding to physical properties such as the position, slope, curvature, and curvature derivative of the detected road. In generating the projection, processing unit 110 may take into account road surface changes and pitch and roll rates associated with vehicle 200. In addition, processing unit 110 may model road elevation by analyzing the position and motion cues present on the road surface. Furthermore, processing unit 110 may estimate pitch and roll rates associated with vehicle 200 by tracking a set of feature points in one or more images.

[0155] In step 556, processing unit 110 may perform a multi-frame analysis, for example, by tracking the detection segment across successive image frames and accumulating frame-by-frame data associated with the detection segment. When processing unit 110 performs a multi-frame analysis, the set of measurements constructed in step 554 may become more reliable and may be associated with an increasingly higher degree of confidence. Thus, by performing steps 550, 552, 554, and 556, processing unit 110 may identify road marks appearing in the set of captured images and derive lane geometry information. Based on the identification and derived information, processing unit 110 may cause one or more navigation responses in vehicle 200, as described above in connection with FIG. 5A.

[0156] In step 558, processing unit 110 may further develop a safety model of vehicle 200 in the vehicle's surroundings, taking into account additional information sources. Processing unit 110 may use the safety model to define situations in which system 100 may safely execute autonomous control of vehicle 200. To develop the safety model, in some embodiments, processing unit 110 may consider the positions and movements of other vehicles, detected road edges and barriers, and / or general road shape descriptions extracted from map data (such as data from map database 160). By taking into account additional information sources, processing unit 110 may provide redundancy in detecting road marks and lane geometry, increasing the reliability of system 100.

[0157] FIG. 5D is a flow chart illustrating an example process 500D for detecting traffic lights in a set of images according to disclosed embodiments. Processing unit 110 may execute monocular image analysis module 402 to perform process 500D. In step 560, processing unit 110 may scan the set of images and identify objects that appear at locations in the images that are likely to contain traffic lights. For example, processing unit 110 may filter the identified objects to build a set of candidate objects that excludes objects that are unlikely to correspond to traffic lights. Filtering may be based on various characteristics associated with traffic lights, such as shape, size, texture, and location (e.g., relative to vehicle 200). Such characteristics may be based on multiple examples of traffic lights and traffic control signals and stored in a database. In some embodiments, processing unit 110 may perform a multi-frame analysis on the set of candidate objects reflecting possible traffic lights. For example, processing unit 110 may track the candidate objects across consecutive image frames, estimate the real-world positions of the candidate objects, and filter out objects that are moving (and therefore unlikely to be traffic lights). In some embodiments, processing unit 110 may perform color analysis on the candidate objects to identify the relative location of the detected colors represented within the potential traffic lights.

[0158] In step 562, processing unit 110 may analyze the geometry of the intersection. The analysis may be based on any combination of (i) the number of lanes detected on either side of vehicle 200, (ii) marks (such as arrow marks) detected on the road, and (iii) a description of the intersection extracted from map data (such as data from map database 160). Processing unit 110 may perform the analysis using information derived from execution of monocular analysis module 402. In addition, processing unit 110 may identify a correspondence between traffic lights detected in step 560 and lanes appearing in the vicinity of vehicle 200.

[0159] As vehicle 200 approaches the intersection, in step 564, processing unit 110 may update a confidence associated with the analyzed intersection geometry and the detected traffic lights. For example, the number of traffic lights estimated to appear at the intersection compared to the number that actually appear at the intersection may affect the confidence. Thus, based on the confidence, processing unit 110 may delegate control to the driver of vehicle 200 to improve safety conditions. By performing steps 560, 562, and 564, processing unit 110 may identify traffic lights appearing in the set of captured images and analyze the intersection geometry information. Based on the identification and analysis, processing unit 110 may cause one or more navigation responses in vehicle 200, as described above in connection with FIG. 5A.

[0160] 5E is a flowchart illustrating an example process 500E for generating one or more navigational responses in vehicle 200 based on a vehicle path, according to disclosed embodiments. In step 570, processing unit 110 may construct an initial vehicle path associated with vehicle 200. The vehicle path may be represented using a set of points represented by coordinates (x,y), with distances d between two points in the set of points being expressed as i may be in the range of 1 to 5 meters. In one embodiment, processing unit 110 may construct an initial vehicle path using two polynomials, such as left and right road polynomials. Processing unit 110 may calculate a geometry midpoint between the two polynomials and, if there is a predefined offset (offset 0 may correspond to driving in the center of the lane), offset each point in the resulting vehicle path by the predefined offset (e.g., smart lane offset). The offset may be in a direction perpendicular to the segment between any two points in the vehicle path. In another embodiment, processing unit 110 may use one polynomial and an estimated lane width to offset each point in the vehicle path by half the estimated lane width plus a predefined offset (e.g., smart lane offset).

[0161] In step 572, processing unit 110 may update the vehicle path constructed in step 570. Processing unit 110 may update the vehicle path by calculating the distance d k is the distance d i A higher resolution may be used to reconstruct the vehicle path constructed in step 570 such that the distance d k may be in the range of 0.1 to 0.3 meters. Processing unit 110 may reconstruct the vehicle path using a parabolic spline algorithm, which may result in a cumulative distance vector S corresponding to the total length of the vehicle path (i.e., based on the set of points representing the vehicle path).

[0162] In step 574, the processing unit 110 calculates the look-ahead point ((x l ,z l ) in coordinates. The processing unit 110 may extract look-ahead points from the cumulative distance vector S, and the look-ahead points may be associated with a look-ahead distance and a look-ahead time. The look-ahead distance may have a lower bound range of 10-20 meters and may be calculated as the product of the speed of the vehicle 200 and the look-ahead time. For example, as the speed of the vehicle 200 decreases, the look-ahead distance may also decrease (e.g., until a lower bound is reached). The look-ahead time, which may range from 0.5 to 1.5 seconds, may be inversely proportional to the gain of one or more control loops associated with producing a navigation response in the vehicle 200, such as a heading error tracking control loop. For example, the gain of the heading error tracking control loop may depend on the bandwidth of the yaw rate loop, the steering actuator loop, and the vehicle lateral dynamics, etc. Thus, the higher the gain of the heading error tracking control loop, the shorter the look-ahead time.

[0163] In step 576, processing unit 110 may determine a heading error and yaw rate command based on the look ahead point identified in step 574. Processing unit 110 may calculate the arctangent of the look ahead point, e.g., arctan(x l / z l) to determine the heading error. Processing unit 110 may determine the yaw rate command as the product of the heading error and a high-level control gain. The high-level control gain may be equal to (2 / look ahead time) if the look ahead distance is not at a lower limit. If the look ahead distance is at a lower limit, the high-level control gain may be equal to (2×velocity of vehicle 200 / look ahead distance).

[0164] 5F is a flow chart illustrating an example process 500F for determining whether a leading vehicle is changing lanes, according to the disclosed embodiments. In step 580, processing unit 110 may determine navigation information associated with a leading vehicle (e.g., a vehicle traveling in front of vehicle 200). For example, processing unit 110 may determine a position, a velocity (e.g., direction and speed), and / or an acceleration of the leading vehicle using the techniques described above in connection with FIGs. 5A and 5B. Processing unit 110 may also determine one or more road polynomials, lookahead points (associated with vehicle 200), and / or a snail trail (e.g., a set of points describing a path taken by a leading vehicle) using the techniques described above in connection with FIGs. 5E.

[0165] In step 582, processing unit 110 may analyze the navigation information identified in step 580. In one embodiment, processing unit 110 may calculate a distance (e.g., along the trail) between the snail trail and the road polynomial. If the difference in this distance along the trail exceeds a predetermined threshold (e.g., 0.1-0.2 meters for straight roads, 0.3-0.4 meters for gently curving roads, and 0.5-0.6 meters for sharply curving roads), processing unit 110 may determine that the leading vehicle is likely changing lanes. If multiple vehicles are detected to be traveling in front of vehicle 200, processing unit 110 may compare the snail trails associated with each vehicle. Based on the comparison, processing unit 110 may determine that a vehicle whose snail trail does not match the snail trails of the other vehicles is likely changing lanes. Processing unit 110 may further compare the curvature of the snail trail (associated with the leading vehicle) to the expected curvature of the road segment along which the leading vehicle is traveling. The expected curvature may be extracted from map data (e.g., data from map database 160), from road polynomials, from snail trails of other vehicles, from prior knowledge about the road, etc. If the difference between the snail trail curvature and the expected curvature of the road segment exceeds a predetermined threshold, processing unit 110 may determine that the leading vehicle is likely changing lanes.

[0166] In another embodiment, processing unit 110 may compare the leading vehicle's instantaneous position to a look-ahead point (associated with vehicle 200) over a particular time period (e.g., 0.5-1.5 seconds). If the cumulative sum of the difference in distance and divergence between the leading vehicle's instantaneous position and the look-ahead point during the particular time period exceeds a predetermined threshold (e.g., 0.3-0.4 meters for straight roads, 0.7-0.8 meters for gently curving roads, and 1.3-1.7 meters for sharply curving roads), processing unit 110 may determine that the leading vehicle is likely changing lanes. In another embodiment, processing unit 110 may analyze the geometry of the snail trail by comparing the lateral distance traveled along the trail to the expected curvature of the snail trail. The expected radius of curvature is calculated as: (δ z 2 +δ x 2 ) / 2 / (δ x ) in which σ x represents the lateral movement distance, and σ z represents the longitudinal travel distance. If the difference between the lateral travel distance and the expected curvature exceeds a predetermined threshold (e.g., 500-700 meters), processing unit 110 may determine that the leading vehicle is likely changing lanes. In another embodiment, processing unit 110 may analyze the position of the leading vehicle. If the position of the leading vehicle obscures the road polynomial (e.g., the leading vehicle overlaps the road polynomial), processing unit 110 may determine that the leading vehicle is likely changing lanes. If the position of the leading vehicle is such that another vehicle is detected ahead of the leading vehicle and the snail trails of the two vehicles are not parallel, processing unit 110 may determine that the (closer) leading vehicle is likely changing lanes.

[0167] In step 584, processing unit 110 may determine whether leading vehicle 200 is changing lanes based on the analyses performed in step 582. For example, processing unit 110 may make that determination based on a weighted average of the individual analyses performed in step 582. Under such a scheme, for example, a determination by processing unit 110 that it is likely that the leading vehicle is changing lanes based on a particular type of analysis may be assigned a value of "1" (with a "0" representing a determination that it is unlikely that the leading vehicle is changing lanes). Different weights may be assigned to different analyses performed in step 582, and the disclosed embodiments are not limited to any particular combination of analyses and weights.

[0168] 6 is a flow chart illustrating an example process 600 for generating one or more navigation responses based on stereo image analysis, according to disclosed embodiments. In step 610, processing unit 110 may receive a first and second plurality of images via data interface 128. For example, a camera included in image acquisition unit 120 (such as imaging devices 122 and 124 having fields of view 202 and 204) may capture a first and second plurality of images of an area in front of vehicle 200 and transmit them to processing unit 110 via a digital connection (e.g., USB, wireless, Bluetooth, etc.). In some embodiments, processing unit 110 may receive the first and second plurality of images via two or more data interfaces. The disclosed embodiments are not limited to any particular data interface configuration or protocol.

[0169] In step 620, processing unit 110 may execute stereo image analysis module 404 to perform stereo image analysis of the first and second plurality of images to create a 3D map of the road ahead of the vehicle and detect features in the images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, and road hazards. The stereo image analysis may be performed similar to the steps described above in connection with FIGS. 5A-5D. For example, processing unit 110 may execute stereo image analysis module 404 to detect candidate objects (e.g., vehicles, pedestrians, road marks, traffic lights, road hazards, etc.) in the first and second plurality of images, filter out a subset of the candidate objects based on various criteria, perform a multi-frame analysis, construct measurements, and identify confidence levels for the remaining candidate objects. In performing the above steps, processing unit 110 may consider information from both the first and second plurality of images, rather than information from only one set of images. For example, processing unit 110 may analyze differences in pixel-level data (or other subsets of data from the two streams of captured images) of a candidate object that appears in both the first and second multiple images. As another example, processing unit 110 may estimate the position and / or velocity (e.g., relative to vehicle 200) of a candidate object by observing that an object appears in one of the multiple images but not in the other, or other differences that may exist for an object appearing in the two image streams. For example, the position, velocity, and / or acceleration relative to vehicle 200 may be determined based on the trajectory, location, movement characteristics, etc. of features associated with the object that appears in one or both of the image streams.

[0170] In step 630, processing unit 110 may execute navigation response module 408 to generate one or more navigational responses in vehicle 200 based on the analysis performed in step 620 and the techniques described above in connection with FIG. 4. The navigational responses may include, for example, turns, lane shifts, acceleration changes, speed changes, braking, etc. In some embodiments, processing unit 110 may generate one or more navigational responses using data derived from execution of speed and acceleration module 406. Furthermore, multiple navigational responses may be performed simultaneously, sequentially, or any combination thereof.

[0171] FIG. 7 is a flow chart illustrating an example process 700 for generating one or more navigation responses based on an analysis of three sets of images, according to disclosed embodiments. In step 710, processing unit 110 may receive a first, second, and third plurality of images via data interface 128. For example, cameras included in image acquisition unit 120 (such as imaging devices 122, 124, and 126 having fields of view 202, 204, and 206) may capture a first, second, and third plurality of images of an area in front of and / or to the side of vehicle 200 and transmit them to processing unit 110 via a digital connection (e.g., USB, wireless, Bluetooth, etc.). In some embodiments, processing unit 110 may receive the first, second, and third plurality of images via three or more data interfaces. For example, imaging devices 122, 124, and 126 may each have an associated data interface that communicates data to processing unit 110. The disclosed embodiments are not limited to any particular data interface configuration or protocol.

[0172] In step 720, processing unit 110 may analyze the first, second, and third pluralities of images to detect features in the images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, and road hazards. The analysis may be performed similar to the steps described above in connection with FIGS. 5A-5D and 6. For example, processing unit 110 may perform monocular image analysis on each of the first, second, and third pluralities of images (e.g., via execution of monocular image analysis module 402 and based on the steps described above in connection with FIGS. 5A-5D). Alternatively, processing unit 110 may perform stereo image analysis on the first and second pluralities of images, the second and third pluralities of images, and / or the first and third pluralities of images (e.g., via execution of stereo image analysis module 404 and based on the steps described above in connection with FIG. 6). Processed information corresponding to the analysis of the first, second, and / or third pluralities of images may be combined. In some embodiments, processing unit 110 may perform a combination of monocular image analysis and stereo image analysis. For example, processing unit 110 may perform monocular image analysis on the first plurality of images (e.g., via execution of monocular image analysis module 402) and stereo image analysis on the second and third plurality of images (e.g., via execution of stereo image analysis module 404). The configuration of imaging devices 122, 124, and 126 - including their respective positions and fields of view 202, 204, and 206 - may affect the type of analysis performed on the first, second, and third plurality of images. The disclosed embodiments are not limited to a particular configuration of imaging devices 122, 124, and 126 or the type of analysis performed on the first, second, and third plurality of images.

[0173] In some embodiments, processing unit 110 may perform tests on system 100 based on the images acquired and analyzed in steps 710 and 720. Such tests may provide an indicator of the overall performance of system 100 with a particular configuration of imaging devices 122, 124, and 126. For example, processing unit 110 may identify the rate of "false hits" (e.g., when system 100 incorrectly determines the presence of a vehicle or pedestrian) and "misses."

[0174] At step 730, processing unit 110 may cause one or more navigation responses in vehicle 200 based on information derived from two of the first, second, and third pluralities of images. The selection of two of the first, second, and third pluralities of images may depend on various factors, such as, for example, the number, type, and size of objects detected in each of the plural images. Processing unit 110 may make the selection based on the quality and resolution of the images, the effective field of view reflected in the images, the number of frames captured, and the extent to which one or more objects of interest actually appear in the frames (e.g., the percentage of frames in which the object appears, the proportion in which the object appears in each such frame, etc.), etc.

[0175] In some embodiments, processing unit 110 may select information derived from two of the first, second, and third pluralities of images by identifying the extent to which information derived from one image source is consistent with information derived from the other image source. For example, processing unit 110 may combine processed information derived from each of imaging devices 122, 124, and 126 (whether monocular, stereo, or any combination of the two) to identify visual indicators (e.g., lane marks, detected vehicles and / or their positions and / or paths, detected traffic lights, etc.) that are consistent across the captured images from each of imaging devices 122, 124, and 126. Processing unit 110 may also filter out information that is inconsistent across the captured images (e.g., vehicles changing lanes, lane models showing vehicles 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 identifying consistent and inconsistent information.

[0176] The navigational responses may include, for example, turns, lane shifts, acceleration changes, etc. Processing unit 110 may generate one or more navigational responses based on the analysis performed in step 720 and the techniques described above in connection with FIG. 4. Processing unit 110 may also generate one or more navigational responses using data derived from execution of velocity and acceleration module 406. In some embodiments, processing unit 110 may generate one or more navigational responses based on a relative position, relative velocity, and / or relative acceleration between vehicle 200 and an object detected in any of the first, second, and third multiple images. The multiple navigational responses may be performed simultaneously, sequentially, or any combination thereof.

[0177] Sparse Road Models for Autonomous Vehicle Navigation

[0178] In some embodiments, the disclosed systems and methods may use a sparse map for autonomous vehicle navigation. In particular, the sparse map may be for autonomous vehicle navigation along road segments. For example, the sparse map may provide sufficient information for navigating an autonomous vehicle without storing and / or updating large amounts of data. As discussed in more detail below, an autonomous vehicle may use the sparse map to navigate one or more roads based on one or more stored trajectories.

[0179] Sparse Maps for Autonomous Vehicle Navigation

[0180] In some embodiments, the disclosed systems and methods may generate sparse maps for autonomous vehicle navigation. For example, the sparse map may provide sufficient information for navigation without requiring excessive data storage or data transfer rates. As discussed in more detail below, a vehicle (which may be an autonomous vehicle) may navigate one or more roads using the sparse map. For example, in some embodiments, the sparse map may include data related to the road and potential landmarks along the road that may be sufficient for vehicle navigation, but also present a small data footprint. For example, the sparse data map, described in more detail below, may require significantly less storage space and data transfer bandwidth compared to a digital map that includes detailed map information, such as image data collected along the road.

[0181] For example, rather than storing detailed representations of road segments, a sparse data map may store three-dimensional polynomial representations of preferred vehicle paths along roads. These paths may require little data storage space. Additionally, in the described sparse data maps, landmarks may be identified and included in the sparse map road model to aid in navigation. These landmarks may be located at any interval suitable to enable navigation of a vehicle, although in some cases, it is not necessary to identify and include such landmarks in the model at high density and short intervals. Rather, in some cases, navigation may be possible based on landmarks at least 50 meters, at least 100 meters, at least 500 meters, at least 1 kilometer, or at least 2 kilometers apart. As discussed in more detail in other sections, sparse maps may be generated based on data collected or measured by vehicles equipped with various sensors and devices, such as imaging devices, global positioning system sensors, motion sensors, etc., as the vehicle travels along a road. In some cases, sparse maps may be generated based on data collected during multiple trips of one or more vehicles along a particular road. Generating a sparse map using multiple trips of one or more vehicles can be referred to as "crowdsourcing" the sparse map.

[0182] According to the disclosed embodiments, an autonomous vehicle system may use a sparse map for navigation. For example, the disclosed systems and methods may deliver a sparse map for generating a road navigation model for an autonomous vehicle and navigate the autonomous vehicle along a road segment using the sparse map and / or the generated road navigation model. A sparse map according to the present disclosure may include one or more three-dimensional contours that may represent predetermined trajectories that an autonomous vehicle may traverse as it travels along an associated road segment.

[0183] A sparse map according to the present disclosure may also include data representing one or more road features. Such road features may include recognized landmarks, road signature profiles, and any other road-related features useful for navigating a vehicle. A sparse map according to the present disclosure may enable autonomous navigation of a vehicle based on a relatively small amount of data included in the sparse map. Even without including detailed representations of roads, such as data detailing road edges, road curvatures, images associated with road segments, or other physical features associated with road segments, the disclosed embodiments of the sparse map may require relatively little storage space (and relatively little bandwidth when portions of the sparse map are transferred to the vehicle) but may still adequately provide autonomous vehicle navigation. The small data footprint of the disclosed sparse maps, discussed in more detail below, may be achieved in some embodiments by storing representations of road-related elements that require a small amount of data but still enable autonomous navigation.

[0184] For example, rather than storing detailed representations of various aspects of a road, the disclosed sparse map may store a polynomial representation of one or more trajectories that a vehicle may follow along the road. Thus, using the disclosed sparse map, rather than storing (or having to transfer) details about the physical properties of the road to enable navigation along the road, a vehicle may be navigated along a particular road segment, in some cases, without having to interpret the physical aspects of the road, but rather by aligning its travel path to a trajectory (e.g., a polynomial spline) along the particular road segment. In this manner, a vehicle may be navigated primarily based on a stored trajectory (e.g., a polynomial spline), which may require much less storage space than approaches that include storing road images, road parameters, road layouts, etc.

[0185] In addition to the stored polynomial representation of the trajectory along the road segment, the disclosed sparse map may also include small data objects that may represent road features. In some embodiments, the small data objects may include digital signatures derived from digital images (or digital signals) acquired by sensors (e.g., cameras or other sensors such as suspension sensors) mounted on a vehicle traveling along the road segment. The digital signatures may be of reduced size compared to the signals acquired by the sensors. In some embodiments, the digital signatures may be created to be compatible with a classifier function configured to detect and identify road features from signals acquired by the sensors during the travel, for example. In some embodiments, the digital signatures may be created such that they have as small a footprint as possible while retaining the ability to correlate or match road features with the stored signatures based on images of the road features captured by a camera mounted on a vehicle subsequently traveling along the same road segment (or digital signals generated by a sensor, if the stored signature is not based on images and / or includes other data).

[0186] In some embodiments, the size of the data object may be further associated with the uniqueness of the road feature. For example, for a road feature detectable by a vehicle-mounted camera, if the vehicle-mounted camera system is coupled to a classifier that can distinguish image data corresponding to the road feature as being associated with a particular type of road feature, e.g., a road sign, and if such road sign is locally unique in the region (e.g., there are no identical or same type of road signs nearby), it may be sufficient to store data indicative of the type of road feature and its location.

[0187] As discussed in more detail below, road features (e.g., landmarks along a road segment) may be stored as small data objects that may represent the road features in a relatively few bytes while providing sufficient information to recognize and use such features for navigation. In one example, road signs may be identified as recognized landmarks upon which navigation of a vehicle may be based. Representations of road signs may be stored in a sparse map, for example, to include a few bytes of data indicating the type of landmark (e.g., a stop sign) and a few bytes of data indicating the location of the landmark (e.g., coordinates). Navigating based on such data perspective representations of landmarks (e.g., using representations sufficient to locate, recognize, and navigate based on the landmarks) may provide a desired level of navigation functionality associated with sparse maps without significantly increasing the data overhead associated with sparse maps. Such lean representations of landmarks (and other road features) may take advantage of sensors and processors on board such vehicles that are configured to detect, identify, and / or classify specific road features.

[0188] For example, if a sign or a particular type of sign is locally unique in a particular area (e.g., if there are no other signs or there are no other signs of the same type), the sparse map may use data indicative of the type of landmark (sign or particular type of sign), and during navigation (e.g., autonomous navigation) when a camera mounted on an autonomous vehicle captures an image of an area containing a sign (or particular type of sign), a processor may process the image, detect the sign (if in fact present in the image), classify the image as a sign (or as a particular type of sign), and correlate the location of the image with the location of the sign stored in the sparse map.

[0189] Generating a sparse map

[0190] In some embodiments, the sparse map may include at least one line representation of a road surface feature extending along the road segment and a number of landmarks associated with the road segment. In certain aspects, the sparse map may be generated via "crowdsourcing," e.g., via image analysis of a number of images acquired as one or more vehicles traverse the road segment.

[0191] 8 illustrates a sparse map 800 that one or more vehicles, e.g., vehicle 200 (which may be an autonomous vehicle), may access to provide autonomous vehicle navigation. Sparse map 800 may be stored in a memory, such as memory 140 or 150. Such a memory device may include any type of non-transitory storage device or computer-readable medium. For example, in some embodiments, memory 140 or 150 may include a hard drive, a compact disk, a flash memory, a magnetic-based memory device, an optical-based memory device, etc. In some embodiments, sparse map 800 may be stored in a database (e.g., map database 160), which may be stored in memory 140 or 150, or other types of storage devices.

[0192] In some embodiments, sparse map 800 may be stored on a storage device or non-transitory computer-readable medium (e.g., a storage device included in a navigation system onboard vehicle 200) onboard vehicle 200. A processor (e.g., processing unit 110) onboard vehicle 200 may access sparse map 800 stored on a storage device or computer-readable medium onboard vehicle 200 to generate navigation instructions for guiding autonomous vehicle 200 as the vehicle traverses road segments.

[0193] However, the sparse map 800 need not be stored locally with respect to the vehicle. In some embodiments, the sparse map 800 may be stored on a storage device or computer-readable medium provided on a remote server in communication with the vehicle 200 or a device associated with the vehicle 200. A processor (e.g., processing unit 110) on board the vehicle 200 may receive the data included in the sparse map 800 from the remote server and execute the data to guide the autonomous navigation of the vehicle 200. In such an embodiment, the remote server may store all or only a portion of the sparse map 800. Thus, a storage device or computer-readable medium on board the vehicle 200 and / or one or more additional vehicles may store the remaining portions of the sparse map 800.

[0194] Further, in such embodiments, the sparse map 800 may be accessible to multiple vehicles (e.g., tens, hundreds, thousands, or millions of vehicles, etc.) traversing various road segments. It should also be noted that the sparse map 800 may include multiple sub-maps. For example, in some embodiments, the sparse map 800 may include hundreds, thousands, millions, or more sub-maps that may be used in navigating a vehicle. Such sub-maps may be referred to as local maps, and a vehicle traveling along a road may access any number of local maps that are relevant to the location in which the vehicle is traveling. The local map areas of the sparse map 800 may be stored with a Global Navigation Satellite System (GNSS) key as an index into a database of the sparse map 800. Thus, steering angle calculations for navigating a host vehicle in the system may be performed without reliance on the GNSS position of the host vehicle, road features, or landmarks, although such GNSS information may be used to look up the relevant local map.

[0195] In general, the sparse map 800 may be generated based on data collected from one or more vehicles as they travel along a road. For example, sensors (e.g., cameras, speedometers, GPS, accelerometers, etc.) mounted on one or more vehicles may be used to record the trajectory of one or more vehicles traveling along a road, and a polynomial representation of a preferred trajectory of the vehicle for subsequent travel along the road may be determined based on the collected trajectories traveled by the one or more vehicles. Similarly, data collected by one or more vehicles may assist in identifying potential landmarks along a particular road. Data collected from traversing vehicles may also be used to identify road profile information such as road width profile, road roughness profile, traffic line spacing profile, road conditions, etc. Using the collected information, the sparse map 800 may be generated and distributed (e.g., for local storage or via on-the-fly data transmission) for use in navigating one or more autonomous vehicles. However, in some embodiments, map generation may not end with the initial birth of the map. As discussed in more detail below, the sparse map 800 may be continuously or periodically updated based on data collected from vehicles as they continue to traverse roads included in the sparse map 800.

[0196] 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 of various map elements, including, for example, landmark locations, road profile locations, etc. The locations of the map elements included in the sparse map 800 may be obtained using GPS data collected from vehicles traversing roads. For example, a vehicle passing an identified landmark may determine the location of the identified landmark using GPS location information associated with the vehicle and determine the location of the identified landmark relative to the vehicle (e.g., based on image analysis of data collected from one or more cameras mounted on the vehicle). Such location determination of the identified landmark (or any other feature included in the sparse map 800) may be repeated as additional vehicles pass the location of the identified landmark. Some or all of the additional location determinations may be used to refine the location information stored in the sparse map 800 in association with the identified landmark. For example, in some embodiments, multiple location measurements associated with a particular feature stored in the sparse map 800 may be averaged together. However, any other mathematical operation may be used to refine the stored location of the map element based on multiple determined locations of the map element.

[0197] The sparse maps of the disclosed embodiments may enable autonomous navigation of a vehicle using a relatively small amount of stored data. In some embodiments, the sparse map 800 may have a data density (e.g., including data representing trajectories, landmarks, and any other stored road features) of less than 2MB per kilometer of road, less than 1MB per kilometer of road, less than 500KB per kilometer of road, or less than 100KB per kilometer of road. In some embodiments, the data density of the sparse map 800 may be less than 10KB per kilometer of road, or less than 2KB per kilometer of road (e.g., 1.6KB per kilometer), or less than or equal to 10KB per kilometer of road, or less than or equal to 20KB per kilometer of road. In some embodiments, most, if not all, of the roads in the United States may be navigated autonomously using a sparse map having a total of 4GB or less of data. These data density values ​​may represent averages across the entire sparse map 800, across local maps within the sparse map 800, and / or across specific road segments within the sparse map 800.

[0198] As noted above, the sparse map 800 may include a representation of multiple target trajectories 810 for guiding autonomous driving or navigation along a road segment. Such target trajectories may be stored as cubic splines. The target trajectories stored in the sparse map 800 may be determined, for example, based on two or more reconstructed trajectories of a previous traversal of a vehicle along a particular road segment. A road segment may be associated with a single target trajectory or multiple target trajectories. For example, on a two-lane road, a first target trajectory may be stored to represent an intended driving path along the road in a first direction, and a second target trajectory may be stored to represent an intended driving path along the road in another direction (e.g., opposite the first direction). Additional target trajectories may be stored for a particular road segment. For example, on a multi-lane road, one or more target trajectories may be stored that represent the intended driving path of the vehicle in one or more lanes associated with the multi-lane road. In some embodiments, each lane of the multi-lane road may be associated with its own target trajectory. In other embodiments, there may be fewer stored target trajectories than there are lanes on a multi-lane road. In such cases, a vehicle navigating a multi-lane road may use any of the stored target trajectories to guide navigation, taking into account the amount of lane offset from the lane in which the target trajectory is stored (e.g., if a vehicle is traveling in the leftmost lane of a three-lane highway and a target trajectory is stored only for the center lane of the highway, the vehicle may navigate using the target trajectory of the center lane, taking into account the amount of lane offset between the center lane and the leftmost lane, when generating navigation instructions).

[0199] In some embodiments, the target trajectory may represent an ideal path that the vehicle should take as it travels. The target trajectory may be located, for example, approximately in the center of the travel lane. In other cases, the target trajectory may be located elsewhere relative to the road segment. For example, the target trajectory may approximately coincide with the center of the road, the edge of the road, or the edge of a lane, etc. In such cases, navigation based on the target trajectory may include a determined amount of offset to maintain relative to the position of the target trajectory. Additionally, in some embodiments, the determined amount of offset to maintain relative to the position of the target trajectory may differ based on the type of vehicle (e.g., a passenger car including two axles may have a different offset along at least a portion of the target trajectory than a truck including three or more axles).

[0200] The sparse map 800 may also include data related to a number of predefined landmarks 820 associated with particular road segments, local maps, etc. As discussed in more detail below, these landmarks may be used to navigate the autonomous vehicle. For example, in some embodiments, the landmarks may be used to determine the vehicle's current position relative to a stored target trajectory. Using this position information, the autonomous vehicle may be able to adjust its heading to match the direction of the target trajectory at the determined location.

[0201] The multiple landmarks 820 may be identified and stored in the sparse map 800 at any suitable interval. In some embodiments, the landmarks may be stored at a relatively high density (e.g., every few meters or more). However, in some embodiments, significantly larger landmark spacing values ​​may be used. For example, in the sparse map 800, the identified (or recognized) landmarks may be spaced 10 meters, 20 meters, 50 meters, 100 meters, 1 kilometer, or 2 kilometers apart. In some cases, the identified landmarks may be located more than 2 kilometers apart.

[0202] Between landmarks, and thus determining the vehicle's position relative to the target trajectory, the vehicle may navigate based on dead reckoning, in which the vehicle uses sensors to determine egomotion and estimates its position relative to the target trajectory. Errors may accumulate during dead reckoning navigation, which may cause the position determination relative to the target trajectory to become less accurate over time. The vehicle may use landmarks present in the sparse map 800 (and their known positions) to remove dead reckoning-induced errors in the position determination. In this manner, identified landmarks included in the sparse map 800 may act as navigation anchors from which the vehicle's precise position relative to the target trajectory may be determined. Since some error may be tolerated in the position determination, the identified landmarks do not need to be available to the autonomous vehicle at all times. Rather, as noted above, adequate navigation may be possible based on landmark spacing of 10 meters, 20 meters, 50 meters, 100 meters, 500 meters, 1 kilometer, 2 kilometers, or even more. In some embodiments, a density of one identified landmark per kilometer of road may be sufficient to maintain longitudinal positioning accuracy within 1 meter, and therefore it is not necessary to store in sparse map 800 all potential landmarks that appear along a road segment.

[0203] Additionally, in some embodiments, lane marks may be used to locate the vehicle between landmark intervals. By using lane marks between landmark intervals, buildup during dead reckoning navigation may be minimized.

[0204] In addition to the target trajectory and identified landmarks, the sparse map 800 may include information related to various other road features. For example, FIG. 9A shows a representation of the curves along a particular road segment that may be stored in the sparse map 800. In some embodiments, a single lane of a road may be modeled by a three-dimensional polynomial description of the left and right sides of the road. Such polynomials representing the left and right sides of a single lane are shown in FIG. 9A. Regardless of the number of lanes a road may have, polynomials may be used to represent the road in a manner similar to that shown in FIG. 9A. For example, the left and right sides of a multi-lane road may be represented by polynomials similar to those shown in FIG. 9A, and intermediate lane markings included in a multi-lane road (e.g., dashed markings representing lane boundaries, solid yellow lines representing boundaries between lanes traveling in different directions, etc.) may also be represented using polynomials as shown in FIG. 9A.

[0205] As shown in FIG. 9A, lane 900 may be represented using a polynomial (e.g., a first order, second order, third order, or any suitable order polynomial). For purposes of illustration, lane 900 is shown as a two-dimensional lane and the polynomials are shown as two-dimensional polynomials. As shown in FIG. 9A, lane 900 includes a left side 910 and a right side 920. In some embodiments, multiple polynomials may be used to represent positions on each side of a road or lane boundary. For example, each of left side 910 and right side 920 may be represented by multiple polynomials of any suitable length. In some cases, the polynomials may be approximately 100 m long, although other lengths longer or shorter than 100 m may also be used. Additionally, polynomials may overlap one another to facilitate seamless transitions in navigating based on subsequently encountered polynomials as the host vehicle travels along the road. For example, each of the left side 910 and right side 920 may be represented by multiple third order polynomials separated into segments of approximately 100 meters in length (an example of a first predetermined range) and overlapping each other by approximately 50 meters. The polynomials representing the left side 910 and right side 920 may or may not be in the same order. For example, in some embodiments, some polynomials may be second order polynomials, some may be third order polynomials, and some may be fourth order polynomials.

[0206] In the example shown in FIG. 9A, the left side 910 of the lane 900 is represented by two groups of third order polynomials. The first group includes polynomial sections 911, 912, and 913. The second group includes polynomial sections 914, 915, and 916. The two groups are substantially parallel to each other but follow the location on each side of the road. Polynomial sections 911, 912, 913, 914, 915, and 916 are approximately 100 meters long and overlap adjacent sections in the series by approximately 50 meters. However, as previously mentioned, polynomials of different lengths and different amounts of overlap may also be used. For example, the polynomials may be 500 m, 1 km, or more long and the amount of overlap may vary from 0 to 50 m, 50 to 100 m, or more than 100 m. 9A is shown as representing polynomials extending in 2D space (e.g., on the surface of a piece of paper), it should be understood that these polynomials may represent curves extending in three dimensions (e.g., including a height component) to represent changes in elevation of the road segment in addition to XY curvature. In the example shown in FIG. 9A, the right side 920 of lane 900 is further represented by a first group having polynomial sections 921, 922, and 923, and a second group having polynomial sections 924, 925, and 926.

[0207] Returning to the target trajectories of the sparse map 800, FIG. 9B illustrates a cubic polynomial that represents a target trajectory of a vehicle traveling along a particular road segment. The target trajectory represents not only the XY path that the host vehicle should travel along a particular road segment, but also the elevation changes that the host vehicle experiences as it travels along the road segment. Thus, each target trajectory in the sparse map 800 may be represented by one or more cubic polynomials, such as cubic polynomial 950 illustrated in FIG. 9B. The sparse map 800 may include multiple 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 cubic polynomial segments.

[0208] With regard to the data footprint of the polynomial curves stored in the sparse map 800, in some embodiments, each third order polynomial is represented by four parameters, each of which may require 4 bytes of data. A suitable representation may be obtained with a third order polynomial requiring approximately 192 bytes of data for every 100 meters. This may translate to approximately 200KB per hour of data usage / transfer requirements for a host vehicle traveling approximately 100km / hr.

[0209] The sparse map 800 may describe the lane network using a combination of geometry descriptors and metadata. The geometry may be described with polynomials or splines as described above. The metadata may describe the number of lanes, special characteristics (such as carpool lanes), and possibly other sparse labels. The total footprint of such metrics may be negligible.

[0210] Thus, a sparse map according to embodiments of the present disclosure may include at least one line representation of a road surface feature extending along a road segment, with each line representation representing a path along the road segment that substantially corresponds to the road surface feature. In some embodiments, as discussed above, the at least one line representation of the road surface feature may include a spline, a polynomial representation, or a curve. Further, in some embodiments, the road surface feature may include at least one of a road edge or a lane marking. Furthermore, as discussed below with respect to "crowdsourcing," the road surface feature may be identified by image analysis of multiple images acquired as one or more vehicles traverse the road segment.

[0211] As previously discussed, the sparse map 800 may include a number of predefined landmarks associated with a road segment. Rather than storing actual images of the landmarks and relying, for example, on image recognition analysis based on captured and stored images, each landmark in the sparse map 800 may be represented and recognized using less data than a stored actual image would require. The data representing the landmarks may include sufficient information to describe or identify the landmarks along the road. Storing data describing characteristics of the landmarks rather than actual images of the landmarks may reduce the size of the sparse map 800.

[0212] FIG. 10 illustrates examples of types of landmarks that may be represented in the sparse map 800. Landmarks may include any visible and identifiable object along a road segment. Landmarks may be selected to be fixed and not change frequently with respect to location and / or content. Landmarks included in the sparse map 800 may be useful in determining the position of the vehicle 200 relative to a target trajectory as the vehicle traverses a particular road segment. Examples of landmarks may include traffic signs, directional signs, general signs (e.g., rectangular signs), roadside furniture (e.g., lamp posts, reflectors, etc.), and any other suitable categories. In some embodiments, lane markings on roads may also be included as landmarks in the sparse map 800.

[0213] Examples of landmarks shown in FIG. 10 include traffic signs, directional signs, roadside furniture, and general signs. Traffic signs may include, for example, speed limit signs (e.g., speed limit sign 1000), yield signs (e.g., yield sign 1005), route number signs (e.g., route number sign 1010), traffic light signs (e.g., traffic light sign 1015), and stop signs (e.g., stop sign 1020). Directional signs may include signs including one or more arrows indicating one or more directions to different locations. For example, directional signs may include freeway signs 1025 with arrows to direct vehicles to different roads or locations, exit signs 1030 with arrows to direct vehicles to exit a road, and the like. Thus, at least one of the plurality of landmarks may include a road sign.

[0214] A general sign may be non-traffic related. For example, a general sign may include a billboard used for advertising or a welcome board adjacent to a border between two countries, states, counties, cities, or towns. Figure 10 shows a general sign 1040 ("Joe's Restaurant"). As shown in Figure 10, the general sign 1040 may have a rectangular shape, however, the general sign 1040 may have other shapes such as a square, a circle, a triangle, etc.

[0215] Landmarks may also include roadside furniture. Roadside furniture may be objects that are not signs and may not be traffic or directional related. For example, roadside furniture may include lamp posts (e.g., lamp posts 1035), power poles, traffic light poles, etc.

[0216] Landmarks may also include beacons that may be specially designed for use in autonomous vehicle navigation systems. For example, such beacons may include freestanding structures placed at predetermined intervals to aid in navigating a host vehicle. Such beacons may also include visual / graphical information added to existing road signs (e.g., icons, emblems, bar codes, etc.) that may be identified or recognized by vehicles traveling along a road segment. Such beacons may also include electronic components. In such embodiments, electronic beacons (e.g., RFID tags, etc.) may be used to transmit non-visual information to the host vehicle. Such information may include, for example, landmark-specific and / or landmark location information that the host vehicle may use in determining its position along a target trajectory.

[0217] In some embodiments, landmarks included in the sparse map 800 may be represented by data objects of a predefined size. The data representing the landmarks may include any suitable parameters for identifying a particular landmark. For example, in some embodiments, landmarks stored in the sparse map 800 may include parameters such as the physical size of the landmark (e.g., to support estimation of distance to the landmark based on a known size / scale), distance to the previous landmark, lateral offset, height, type code (e.g., landmark type - type of directional sign, traffic sign, etc.), GPS coordinates (e.g., to support global localization), and any other suitable parameters. Each parameter may be associated with a data size. For example, the landmark size may be stored using 8 bytes of data. The distance to the previous landmark, lateral offset, and height may be specified using 12 bytes of data. A type code associated with a landmark such as a directional sign or traffic sign may require approximately 2 bytes of data. For a general sign, an image signature that allows identification of the general sign may be stored using 50 bytes of data storage. The GPS location of a landmark may be associated with 16 bytes of data storage. These data sizes for each parameter are merely examples and other data sizes may be used.

[0218] Representing landmarks in the sparse map 800 in this manner may provide a lean solution for efficiently representing landmarks in a database. In some embodiments, signs may be referred to as semantic and non-semantic signs. Semantic signs may include any class of signs with a standardized meaning (e.g., speed limit signs, warning signs, directional signs, etc.). Non-semantic signs may include any signs that are not associated with a standardized meaning (e.g., general advertising signs, signs identifying business establishments, etc.). For example, each semantic sign may be represented by 38 bytes of data (e.g., 8 bytes of size, distance to previous landmark, lateral offset, 12 bytes of height, 2 bytes of type code, 16 bytes of GPS coordinates). The sparse map 800 may use a tag system to represent landmark types. In some cases, each traffic sign or directional sign may be associated with a unique tag and stored in the database as part of the landmark ID. For example, the database may include as many as 1000 different tags to represent various traffic signs and as many as 10,000 different tags to represent directional signs. Of course, any suitable number of tags may be used, and additional tags may be created as needed. A generic sign may, in some embodiments, be represented using less than about 100 bytes (e.g., about 86 bytes, including 8 bytes for size, 12 bytes for distance to previous landmark, lateral offset, and height, 50 bytes for image signature, and 16 bytes for GPS coordinates).

[0219] Thus, for semantic road signs that do not require image signatures, the data density impact on the sparse map 800 can be as much as about 760 bytes per kilometer, even with a relatively high landmark density of about 1 per 50 meters (e.g., 20 landmarks per kilometer x 38 bytes per landmark = 760 bytes). For generic signs that include an image signature component, the data density impact is still about 1.72 KB per kilometer (e.g., 20 landmarks per kilometer x 86 bytes per landmark = 1,720 bytes). For semantic road signs, this corresponds to a data usage of about 76 KB per hour for a vehicle traveling at 100 km / hr. For generic signs, this corresponds to about 170 KB per hour for a vehicle traveling at 100 km / hr.

[0220] In some embodiments, a generally rectangular object, such as a rectangular sign, may be represented in the sparse map 800 with 100 bytes of data or less. A representation of a generally rectangular object (e.g., generic sign 1040) in the sparse map 800 may include a condensed image signature (e.g., condensed image signature 1045) associated with the generally rectangular object. This condensed image signature may be used, for example, to aid in the identification of the generic sign, for example, as a recognized landmark. Such a condensed image signature (e.g., image information derived from actual image data representing the object) may avoid the need to store an actual image of the object or the need for comparative image analysis to be performed on the actual image in order to recognize the landmark.

[0221] 10 , the sparse map 800 may include or store a condensed image signature 1045 associated with the generic sign 1040, rather than an actual image of the generic sign 1040. For example, after an imaging device (e.g., imaging device 122, 124, or 126) captures an image of the generic sign 1040, a processor (e.g., image processor 190 or any other processor capable of processing images, either on-board or remotely located relative to the host vehicle) may perform image analysis to extract / create a condensed image signature 1045 that includes a unique signature or pattern associated with the generic sign 1040. In one embodiment, the condensed image signature 1045 may include a shape, color pattern, brightness pattern, or any other feature that may be extracted from an image of the generic sign 1040 to describe the generic sign 1040.

[0222] For example, in FIG. 10, the circles, triangles, and stars shown in the condensed image signature 1045 may represent regions of different colors. The patterns represented by the circles, triangles, and stars may be stored in the sparse map 800, for example, within the 50 bytes designated to include the image signature. In particular, the circles, triangles, and stars are not meant to necessarily indicate that such shapes are stored as part of the image signature. Rather, these shapes are intended to conceptually represent recognizable regions having distinguishable color differences, text areas, graphic shapes, or other variants of characteristics that may be associated with generic signs. Such condensed image signatures may be used to identify landmarks in the form of generic signs. For example, the condensed image signatures may be used to perform a sameness analysis based on a comparison of image data captured using, for example, a camera mounted on an autonomous vehicle to the stored condensed image signature.

[0223] Thus, the plurality of landmarks may be identified by image analysis of a plurality of images acquired as one or more vehicles traverse the road segment. As described below with respect to "crowdsourcing," in some embodiments, the image analysis to identify the plurality of landmarks may include accepting a potential landmark if a ratio of images in which the landmark appears to images in which the landmark does not appear exceeds a threshold. Further, in some embodiments, the image analysis to identify the plurality of landmarks may include rejecting a potential landmark if a ratio of images in which the landmark does not appear to images in which the landmark appears exceeds a threshold.

[0224] Returning to the target trajectory that the host vehicle may use to navigate a particular road segment, FIG. 11A illustrates a polynomial representation trajectory that is imaged during the process of building or maintaining a sparse map 800. The polynomial representation of the target trajectory included in the sparse map 800 may be determined based on two or more reconstructed trajectories of the vehicle's previous trajectories along the same road segment. In some embodiments, the polynomial representation of the target trajectory included in the sparse map 800 may be an aggregate of two or more reconstructed trajectories of the vehicle's previous trajectories along the same road segment. In some embodiments, the polynomial representation of the target trajectory included in the sparse map 800 may be an average of two or more reconstructed trajectories of the vehicle's previous trajectories along the same road segment. Other mathematical operations may also be used to construct a target trajectory along a road path based on reconstructed trajectories collected from vehicles traversing along the road segment.

[0225] As shown in FIG. 11A, a road segment 1100 may be traveled by multiple vehicles 200 at different times. Each vehicle 200 may collect data related to the path the vehicle took along the road segment. The path traveled by a particular vehicle may be determined based on camera data, accelerometer information, speed sensor information, and / or GPS information, among other potential sources of information. Such data may be used to reconstruct trajectories of the vehicles traveling along the road segment, and based on these reconstructed trajectories, a target trajectory (or target trajectories) for the particular road segment may be determined. Such target trajectories may represent a preferred path for a host vehicle (e.g., guided by an autonomous navigation system) as the vehicle travels along the road segment.

[0226] 11A , a first reconstructed trajectory 1101 may be determined based on data received from a first vehicle traversing the road segment 1100 during a first time period (e.g., day 1), a second reconstructed trajectory 1102 may be obtained from a second vehicle traversing the road segment 1100 during a second time period (e.g., day 2), and a third reconstructed trajectory 1103 may be obtained from a third vehicle traversing the road segment 1100 during a third time period (e.g., day 3). Each of the trajectories 1101, 1102, and 1103 may be represented by a polynomial, such as a three-dimensional polynomial. Note that in some embodiments, any of the reconstructed trajectories may be provided to and assembled by a vehicle traversing the road segment 1100.

[0227] Additionally or alternatively, such a reconstructed trajectory may be determined at 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, position, speed, detected road geometry, and / or detected landmarks, among others) to one or more servers. The servers may reconstruct the trajectory of the vehicles 200 based on the received data. The servers may also generate a target trajectory based on the first, second, and third trajectories 1101, 1102, and 1103 to guide the navigation of an autonomous vehicle that subsequently travels along the same road segment 1100. Although the target trajectory may be associated with a single previous traversal of the road segment, in some embodiments, each target trajectory included in the sparse map 800 may be determined based on two or more reconstructed trajectories of vehicles traversing the same road segment. In FIG. 11A, the target trajectory is represented by 1110. In some embodiments, the target trajectory 1110 may be generated based on an average of the first, second, and third trajectories 1101, 1102, and 1103. In some embodiments, the target trajectory 1110 included in the sparse map 800 may be an aggregation (e.g., a weighted combination) of two or more reconstructed trajectories.

[0228] 11B and 11C further illustrate the concept of a target trajectory associated with road segments present within the geographic region 1111. As shown in FIG. 11B, a first road segment 1120 within the geographic region 1111 may include a multi-lane road including two lanes 1122 designated for vehicle travel in a first direction and two additional lanes 1124 designated for vehicle travel in a second direction opposite the first direction. The lanes 1122 and 1124 may be separated by a double yellow line 1123. The geographic region 1111 may also include a branch road segment 1130 that intersects with the road segment 1120. The road segment 1130 may include a two-lane road, with each lane designated for a different direction of travel. The geographic region 1111 may also include other road features, such as a stop line 1132, a stop sign 1134, a speed limit sign 1136, and a hazard sign 1138.

[0229] 11C , the sparse map 800 may include a local map 1140 including a road model to assist in autonomous navigation of a vehicle within the geographic region 1111. For example, the local map 1140 may include target trajectories for one or more lanes associated with road segments 1120 and / or 1130 within the geographic region 1111. For example, the local map 1140 may include target trajectories 1141 and / or 1142 that the autonomous vehicle may access or rely on when crossing lane 1122. Similarly, the local map 1140 may include target trajectories 1143 and / or 1144 that the autonomous vehicle may access or rely on when crossing lane 1124. Additionally, the local map 1140 may include target trajectories 1145 and / or 1146 that the autonomous vehicle may access or rely on when crossing road segment 1130. Target trajectory 1147 represents a preferred path for the autonomous vehicle to follow when transitioning from lane 1120 (specifically, corresponding to target trajectory 1141 associated with the rightmost lane of lane 1120) to road segment 1130 (specifically, corresponding to target trajectory 1145 associated with a first side of road segment 1130). Similarly, target trajectory 1148 represents a preferred path for the autonomous vehicle to follow when transitioning from road segment 1130 (specifically, corresponding to target trajectory 1146) to a portion of road segment 1124 (specifically, corresponding to target trajectory 1143 associated with the left lane of lane 1124, as shown).

[0230] The sparse map 800 may also include representations of other road-related features associated with the geographic region 1111. For example, the sparse map 800 may also include a representation of one or more landmarks identified in the geographic region 1111. Such landmarks may include a first landmark 1150 associated with a stop line 1132, a second landmark 1152 associated with a stop sign 1134, a third landmark 1154 associated with a speed limit sign 1154, and a fourth landmark 1156 associated with a hazard sign 1138. Such landmarks may be used, for example, to assist an autonomous vehicle in determining its current position relative to any of the indicated target trajectories, so that the vehicle may adjust its heading to match the direction of the target trajectory at the determined location.

[0231] In some embodiments, the sparse map 800 may also include a road signature profile. Such a road signature profile may be associated with any identifiable / measurable variation in at least one parameter associated with a road. For example, in some cases, such a profile may be associated with changes in road surface information, such as changes in surface roughness of a particular road segment, changes in road width across a particular road segment, changes in distance between dashed lines drawn along a particular road segment, changes in curvature of the road along a particular road segment, etc. FIG. 11D illustrates an example of a road signature profile 1160. While the profile 1160 may represent any of the parameters listed above or other parameters, in one example, the profile 1160 may represent a measure of road surface roughness, e.g., obtained by monitoring one or more sensors that provide an output indicative of an amount of suspension displacement as the vehicle travels along a particular road segment.

[0232] Alternatively, or simultaneously, profile 1160 may represent changes in road width determined based on image data acquired via a camera mounted on a vehicle traveling a particular road segment. Such a profile may be useful, for example, to determine a particular position of an autonomous vehicle relative to a particular target trajectory. That is, as the autonomous vehicle traverses a road segment, the autonomous vehicle may measure a profile associated with one or more parameters associated with the road segment. If the measured profile can be correlated / matched with a predefined profile that plots the change in the parameter with respect to position along the road segment, the measured predefined profile may be used (e.g., by overlapping corresponding sections of the measured predefined profile) to determine the current position along the road segment, and thus the current position relative to the target trajectory of the road segment.

[0233] In some embodiments, the sparse map 800 may include different trajectories based on different characteristics associated with users of the autonomous vehicle, environmental conditions, and / or other parameters related to the trip. For example, in some embodiments, different trajectories may be generated based on preferences and / or profiles of different users. The sparse map 800 including such different trajectories may be provided to different autonomous vehicles of different users. For example, some users may prefer to avoid toll roads, while other users may prefer to take the shortest or fastest route, regardless of whether the route has toll roads or not. The disclosed system may generate different sparse maps with different trajectories based on the preferences or profiles of such different users. As another example, some users may prefer to travel in fast moving lanes, while other users may prefer to always maintain a center lane position.

[0234] Different trajectories may be generated and included in the sparse map 800 based on different environmental conditions, such as day and night, snow, rain, fog, etc. An autonomous vehicle traveling in different environmental conditions may provide a sparse map 800 generated based on such different environmental conditions. In some embodiments, a camera provided on the autonomous vehicle may detect the environmental conditions and provide such information to a server that generates and provides the sparse map. For example, the server may generate or update an already generated sparse map 800 to include trajectories that may be more suitable or safer for autonomous traveling under the detected environmental conditions. The update of the sparse map 800 based on the environmental conditions may be performed dynamically as the autonomous vehicle travels along the road.

[0235] Other different parameters related to travel may also be used as the basis for generating and providing different sparse maps for different autonomous vehicles. For example, when an autonomous vehicle is traveling at high speed, it may have difficulty turning. A trajectory associated with a particular lane, rather than a road, may be included in the sparse map 800 so that the autonomous vehicle may stay within a particular lane as it follows a particular trajectory. If an image captured by a camera mounted on the autonomous vehicle indicates that the vehicle has drifted outside the lane (e.g., crossed a lane marking), an action may be triggered within the vehicle to return the vehicle to the designated lane according to the particular trajectory.

[0236] Crowdsourcing sparse maps

[0237] In some embodiments, the disclosed systems and methods may generate sparse maps for autonomous vehicle navigation. For example, the disclosed systems and methods may use crowdsourced data to generate sparse maps that one or more autonomous vehicles may use to navigate along a system of roads. As used herein, "crowdsourcing" refers to receiving data from various vehicles (e.g., autonomous vehicles) traveling a road segment at different times and using such data to generate and / or update a road model. The model may then be transmitted to vehicles traveling later along the road segment or to other vehicles to aid in the navigation of the autonomous vehicle. The road model may include multiple target trajectories that represent preferred trajectories for the autonomous vehicle to follow when traversing the road segment. The target trajectories may be the same as reconstructed actual trajectories collected from vehicles traversing the road segment and transmitted from the vehicles to a server. In some embodiments, the target trajectories may differ from actual trajectories previously taken by one or more vehicles when traversing the road segment. The target trajectories may be generated (e.g., by averaging or any other suitable operation) based on the actual trajectories.

[0238] The vehicle trajectory data that the vehicle may upload to the server may correspond to the vehicle's actual reconstructed trajectory, or may correspond to a recommended trajectory that may be based on or related to the vehicle's actual reconstructed trajectory, but may differ from the actual reconstructed trajectory. For example, the vehicle may modify the actual reconstructed trajectory and transmit (e.g., recommend) the modified actual trajectory to the server. The road model may use the recommended modified trajectory as a target trajectory for autonomous navigation of other vehicles.

[0239] In addition to trajectory information, other information for potential use in constructing the sparse data map 800 may include information related to potential landmark candidates. For example, by crowdsourcing information, the disclosed systems and methods may identify potential landmarks within an environment and refine the locations of the landmarks. The landmarks may be used by the autonomous vehicle's navigation system to determine and / or adjust the vehicle's position along a target trajectory.

[0240] The reconstructed trajectory that the vehicle may generate as it travels along a road may be obtained by any suitable method. In some embodiments, the reconstructed trajectory may be developed by piecing together segments of the vehicle's motion using, for example, egomotion estimation (e.g., 3D translation and 3D rotation of the camera and thus the body of the vehicle). Estimates of rotation and translation may be determined based on analysis of images captured by one or more imaging devices along with information from other sensors or devices, such as inertial sensors and speed sensors. For example, the inertial sensors may include accelerometers or other suitable sensors configured to measure changes in translation and / or rotation of the vehicle body. The vehicle may include a speed sensor to measure the speed of the vehicle.

[0241] In some embodiments, the egomotion of the camera (and therefore the vehicle body) may be estimated based on optical flow analysis of captured images. Optical flow analysis of a series of images identifies pixel movements from the series of images and determines the vehicle's motion based on the identified movements. The egomotion may be integrated over time along a road segment to reconstruct a trajectory associated with the road segment traversed by the vehicle.

[0242] Data collected by multiple vehicles on multiple runs along a road segment at different times (e.g., reconstructed trajectories) may be used to build a road model (e.g., including a target trajectory, etc.) included in sparse data map 800. Data collected by multiple vehicles on multiple runs along a road segment at different times may also be averaged to increase the accuracy of the model. In some embodiments, data regarding road geometry and / or landmarks may be received from multiple vehicles traveling a common road segment at different times. Such data received from different vehicles may be combined to generate and / or update a road model.

[0243] The geometry of the reconstructed trajectory (and the target trajectory) along the road segment may be represented by a curve in three-dimensional space, which may be a spline connecting three-dimensional polynomials. The reconstructed trajectory curve may be determined from an analysis of a video stream or multiple images captured by a camera mounted on the vehicle. In some embodiments, a position is identified in each frame or image that is a few meters ahead of the vehicle's current position. This position is where the vehicle is expected to travel within a given time period. This operation may be repeated for each frame, and at the same time, the vehicle may calculate the egomotion (rotation and translation) of the camera. At each frame or image, a short-range model of the desired path is generated by the vehicle in the reference frame mounted on the camera. The short-range models may be stitched together to obtain a three-dimensional model of the road in a coordinate frame, which may be any coordinate frame or a predetermined coordinate frame. The three-dimensional model of the road may then be fitted by a spline that may include or connect one or more polynomials of appropriate order.

[0244] One or more detection modules may be used to conclude a short-distance road model in each frame. For example, a bottom-up lane detection module may be used. The bottom-up lane detection module may be useful when lane markings are painted on the road. This module may look for edges in the image and assemble them together to form lane markings. A second module may be used together with the bottom-up lane detection module. The second module is an end-to-end deep neural network that may be trained to predict the correct short-distance path from the input image. In either module, the road model is detected in the image coordinate frame and may be transformed into a three-dimensional space that may be virtually connected to the camera.

[0245] Although the reconstructed trajectory modeling approach may result in error accumulation due to integration of egomotion over long periods of time, which may include noise components, such errors may not be significant because the generated model may provide sufficient accuracy for navigation at local scales. In addition, external sources of information, such as satellite images or geodetic measurements, may be used to undo the integrated errors. For example, the disclosed system and method may use a GNSS receiver to undo the accumulated errors. However, GNSS positioning signals are not always available and accurate. The disclosed system and method may enable steering applications that are weakly dependent on the availability and accuracy of GNSS positioning. In such systems, the use of GNSS signals may be limited. For example, in some embodiments, the disclosed system may use GNSS signals only for database indexing purposes.

[0246] In some embodiments, range scales (e.g., local scales) that may be relevant for autonomous vehicle navigation and steering applications may be as much as 50 meters, as much as 100 meters, as much as 200 meters, as much as 300 meters, etc. Such distances may be used because the geometric road model is primarily used for two purposes: to plan the trajectory ahead and to localize the vehicle on the road model. In some embodiments, when the control algorithm steers the vehicle according to a target point located 1.3 seconds ahead (or any other time, such as 1.5 seconds, 1.7 seconds, 2 seconds, etc.), the planning task may use the model over a typical range of 40 meters ahead (or any other suitable distance ahead, such as 20 meters, 30 meters, 50 meters, etc.). The localization task uses the road model over a typical range of 60 meters behind the vehicle (or any other suitable distance, such as 50 meters, 100 meters, 150 meters, etc.), following a method called "tail alignment" that is described in more detail in another section. The disclosed systems and methods can generate a model of the geometry with sufficient accuracy over a particular range, such as 100 meters, so that the planned trajectory does not deviate from the lane center by more than 30 cm.

[0247] As mentioned above, a 3D road model can be built from detecting short distance sections and stitching them together. Stitching can be made possible by calculating a 6 degree ego-motion model using video and / or images captured by cameras, data from inertial sensors reflecting the vehicle motion, and the host vehicle's speed signal. The accumulated error can be small enough at some local range scales, such as 100 meters or so. At all of these range scales, a particular road segment can be completed in a single run.

[0248] In some embodiments, multiple runs may be used to average the resulting model to further improve its accuracy. The same vehicle may run the same route multiple times, or multiple vehicles may send collected model data to a central server. In either case, a matching procedure may be performed to identify overlapping models and enable averaging to generate the target trajectory. The constructed model (e.g., including the target trajectory) may be used for steering once convergence criteria are met. Subsequent runs may be used to further improve the model and to accommodate infrastructure changes.

[0249] When multiple vehicles are connected to a central server, sharing of driving experience (such as sensor data) between multiple vehicles becomes possible. Each vehicle client may store a partial copy of a universal road model that may be relevant to its current location. A bidirectional update procedure between the vehicle and the server may be performed by the vehicle and the server. The small footprint concept discussed above allows the disclosed system and method to perform bidirectional updates using a very small bandwidth.

[0250] Information related to the potential landmark may also be determined and forwarded to the central server. For example, the disclosed systems and methods may determine one or more physical characteristics of a potential landmark based on one or more images that include the landmark. The physical characteristics may include the physical size of the landmark (e.g., height, width), the distance of the landmark from the vehicle, the distance of the landmark to the previous landmark, the lateral position of the landmark (e.g., the position of the landmark relative to the driving lane), the GPS coordinates of the landmark, the type of landmark, an identification of text on the landmark, etc. For example, the vehicle may analyze one or more images captured by a camera to detect potential landmarks, such as speed limit signs.

[0251] The vehicle may determine the distance from the vehicle to the landmark based on an analysis of one or more images. In some embodiments, the distance may be determined based on an analysis of the image of the landmark using suitable image analysis methods such as scaling and / or optical flow methods. In some embodiments, the disclosed systems and methods may be configured to determine a type or classification of the potential landmark. If the vehicle determines that a particular potential landmark corresponds to a predefined type or classification stored in the sparse map, it may be sufficient for the vehicle to communicate an indication of the landmark type or classification along with its location to the server. The server may store such an indication. Later, another vehicle may take an image of the landmark, process the image (e.g., using a classifier), and compare the results of processing the image with the indication of the landmark type stored in the server. There may be various types of landmarks, different types of landmarks may be associated with different types of data that are uploaded and stored in the server, different processes on board the vehicle may detect the landmarks and communicate information about the landmarks to the server, and the system on board the vehicle may receive the landmark data from the server and use the landmark data to identify the landmark in autonomous navigation.

[0252] In some embodiments, multiple autonomous vehicles traveling on a road segment may communicate with a server. The vehicles (or clients) may generate curves that describe their travel in any coordinate frame (e.g., by egomotion integration). The vehicles may detect landmarks and place them in the same frame. The vehicles may upload the curves and landmarks to the server. The server may collect data from the vehicles over multiple travels and generate a unified road model. For example, as discussed below with respect to FIG. 19, the server may generate a sparse map with a unified road model using the uploaded curves and landmarks.

[0253] The server may also distribute the model to clients (e.g., vehicles). For example, the server may distribute sparse maps to one or more vehicles. The server may continuously or periodically update the model as it receives new data from the vehicles. For example, the server may process the new data to evaluate whether the data contains information that should trigger an update or the creation of new data on the server. The server may distribute updated models or updates to the vehicles to provide navigation for the autonomous vehicles.

[0254] The server may use one or more criteria to determine whether new data received from a vehicle should trigger a model update or the creation of new data. For example, if the new data indicates that a previously recognized landmark at a particular location is no longer present or has been replaced with another landmark, the server may determine that the new data should trigger a model update. As another example, if the new data indicates that a road segment is closed, and this is corroborated by data received from other vehicles, the server may determine that the new data should trigger a model update.

[0255] The server may distribute the updated model (or the updated portion of the model) to one or more vehicles traveling a road segment with which the update to the model is associated. The server may also distribute the updated model to vehicles about to travel a road segment with which the update to the model is associated, or to vehicles on a planned trip that includes the road segment. For example, while the autonomous vehicle is traveling along another road segment before reaching the road segment with which the update is associated, the server may distribute the update or the updated model to the autonomous vehicle before the vehicle reaches the road segment.

[0256] In some embodiments, a remote server may collect trajectories and landmarks from multiple clients (e.g., vehicles traveling along a common road segment). The server may use the landmarks to match curves and create an average road model based on the trajectories collected from multiple vehicles. The server may also calculate the most likely path at each node or junction of the road graph and road segment. For example, the remote server may align the trajectories to generate a crowdsourced sparse map from the collected trajectories.

[0257] The server may average landmark properties received from multiple vehicles traveling along a common road segment, such as the distance from one landmark to another (e.g., the previous landmark along the road segment) measured by the multiple vehicles, to determine arc-length parameters and support position location and speed calibration along the path of each client vehicle. The server may average physical dimensions of landmarks measured by multiple vehicles traveling along a common road segment and recognizing the same landmark. The averaged physical dimensions may be used to support distance estimation, such as the distance from the vehicle to the landmark. The server may average lateral positions of landmarks (e.g., the position of the landmark from the lane in which the vehicle is traveling) measured by multiple vehicles traveling along a common road segment and recognizing the same landmark. The averaged lateral positions may be used to support lane assignment. The server may average GPS coordinates of landmarks measured by multiple vehicles traveling along the same road segment and recognizing the same landmark. The averaged GPS coordinates of landmarks may be used to support global localization or positioning of landmarks within the road model.

[0258] In some embodiments, the server may identify model changes, such as construction, detours, new signs, sign removal, etc., based on data received from the vehicle. The server may update the model continuously, periodically, or instantly as it receives new data from the vehicle. The server may distribute model updates or updated models to the vehicle to provide autonomous navigation. For example, as discussed further below, the server may use crowdsourced data to filter out "ghost" landmarks detected by the vehicle.

[0259] In some embodiments, the server may analyze the driver's intervention during the autonomous driving. The server may analyze data received from the vehicle at the time and location where the intervention occurs and / or data received prior to the time where the intervention occurs. The server may identify a particular portion of data that caused or is closely related to the intervention, e.g., data indicating a temporary lane closure configuration, data indicating a pedestrian on the road. The server may update the model based on the identified data. For example, the server may modify one or more trajectories stored in the model.

[0260] FIG. 12 is a schematic diagram of a system for generating sparse maps using crowdsourcing (and distributing and navigating using crowdsourced sparse maps). FIG. 12 shows a road segment 1200 including one or more lanes. Multiple vehicles 1205, 1210, 1215, 1220, and 1225 may travel on the road segment 1200 at the same time or at different times (although they are shown in FIG. 12 as appearing on the road segment 1200 at the same time). At least one of the vehicles 1205, 1210, 1215, 1220, and 1225 may be an autonomous vehicle. To simplify this example, it is assumed that all of the vehicles 1205, 1210, 1215, 1220, and 1225 are autonomous vehicles.

[0261] Each vehicle may be similar to vehicles disclosed in other embodiments (e.g., vehicle 200) and may include components or devices included or associated with vehicles disclosed in other embodiments. Each vehicle may be equipped with an imaging device or camera (e.g., imaging device 122 or camera 122). Each vehicle may communicate with a remote server 1230 through one or more networks (e.g., via a cellular network and / or the Internet, etc.) over wireless communication path 1235, as shown by the dotted line. Each vehicle may transmit data to and receive data from server 1230. For example, server 1230 may collect data from multiple vehicles traveling road segment 1200 at different times and process the collected data to generate an autonomous vehicle road navigation model or model update. Server 1230 may transmit the autonomous vehicle road navigation model or model update to the vehicles that sent the data to server 1230. Server 1230 may transmit the autonomous vehicle road navigation model or model update to other vehicles that subsequently travel road segment 1200.

[0262] Navigation information collected (e.g., detected, sensed, or measured) by the vehicles 1205, 1210, 1215, 1220, and 1225 as they travel along the road segment 1200 may be transmitted to the server 1230. In some embodiments, the navigation information may be associated with a common road segment 1200. The navigation information may include trajectories associated with each of the vehicles 1205, 1210, 1215, 1220, and 1225 as they travel along the road segment 1200. In some embodiments, the trajectories may be reconstructed based on data sensed by various sensors and devices provided to the vehicles 1205. For example, the trajectories may be reconstructed based on at least one of accelerometer data, speed data, landmark data, road geometry or profile data, vehicle position data, and ego-motion data. In some embodiments, the trajectory may be reconstructed based on data from inertial sensors, such as accelerometers, and the speed of the vehicle 1205 as sensed by a speed sensor. Additionally, in some embodiments, the trajectory may be determined (e.g., by a processor on board each of the vehicles 1205, 1210, 1215, 1220, and 1225) based on sensed ego-motion of the cameras, which may indicate three-dimensional translation and / or three-dimensional rotation (or rotational motion). The ego-motion of the cameras (and thus the vehicle body) may be determined from an analysis of one or more images captured by the cameras.

[0263] In some embodiments, the trajectory of the vehicle 1205 may be determined by a processor on board the vehicle 1205 and transmitted to the server 1230. In other embodiments, the server 1230 may receive data sensed by various sensors and devices on board the vehicle 1205 and determine the trajectory based on the data received from the vehicle 1205.

[0264] In some embodiments, navigation information transmitted from vehicles 1205, 1210, 1215, 1220, and 1225 to server 1230 may include data regarding the road surface, the geometry of the road, or the road profile. The geometry of road segment 1200 may include lane structure and / or landmarks. The lane structure may include the total number of lanes of road segment 1200, lane types (e.g., unidirectional lanes, bidirectional lanes, travel lanes, overtaking lanes, etc.), markings on the lanes, lane widths, etc. In some embodiments, navigation information may include lane assignments, such as which lane of multiple lanes the vehicle is traveling in. For example, a numeric value "3" may be associated with a lane assignment to indicate that the vehicle is traveling in the third lane from the left or right. As another example, a lane assignment may be associated with a text value "center lane" to indicate that the vehicle is traveling in the center lane.

[0265] The server 1230 may store the navigation information on a non-transitory computer-readable medium, such as a hard drive, compact disk, tape, memory, etc. The server 1230 may generate (e.g., via a processor included in the server 1230) at least a portion of an autonomous vehicle road navigation model of the common road segment 1200 based on navigation information received from multiple vehicles 1205, 1210, 1215, 1220, and 1225, and may store the model as part of a sparse map. The server 1230 may determine a trajectory associated with each lane based on crowd-sourced data (e.g., navigation information) received from multiple vehicles (e.g., 1205, 1210, 1215, 1220, and 1225) traveling the lanes of the road segment at different times. The server 1230 may generate the autonomous vehicle road navigation model or a portion of the model (e.g., an updated portion) based on the multiple trajectories determined based on the crowd-sourced navigation data. Server 1230 may transmit the model or updated portions of the model to one or more of autonomous vehicles 1205, 1210, 1215, 1220, and 1225 traveling road segment 1200 or any other autonomous vehicles that later travel the road segment to update an existing autonomous vehicle road navigation model provided to the vehicle's navigation system. The autonomous vehicle road navigation model may be used by the autonomous vehicles as they navigate autonomously along the common road segment 1200.

[0266] As described above, the autonomous vehicle road navigation model may be included in a sparse map (e.g., sparse map 800 shown in FIG. 8 ). Sparse map 800 may include a sparse record of data related to road geometry and / or landmarks along the roads, providing sufficient information to guide the autonomous navigation of an autonomous vehicle, but without requiring excessive data storage. In some embodiments, the autonomous vehicle road navigation model may be stored separately from sparse map 800, and may use map data from sparse map 800 when the model is executed for navigation. In some embodiments, the autonomous vehicle road navigation model may use the map data included in sparse map 800 to determine a target trajectory along road segment 1200 to guide the autonomous navigation of autonomous vehicles 1205, 1210, 1215, 1220, and 1225, or other vehicles that subsequently travel along road segment 1200. For example, when the autonomous vehicle road navigation model is executed by a processor included in a navigation system of the vehicle 1205, the model may cause the processor to compare a trajectory determined based on navigation information received from the vehicle 1205 with a predetermined trajectory included in the sparse map 800 to verify and / or correct the current course of the vehicle 1205.

[0267] In the autonomous vehicle road navigation model, the geometry of the road features or the target trajectory may be encoded by a curve in three-dimensional space. In one embodiment, the curve may be a three-dimensional spline including one or more connected three-dimensional polynomials. As one skilled in the art will appreciate, a spline may be a numerical function that is piecewise defined by a series of polynomials for fitting the data. The spline for fitting the three-dimensional geometry data of the road may include a linear spline (first order), a quadratic spline (second order), a cubic spline (third order), or any other spline (other order), or a combination thereof. The spline may include one or more three-dimensional polynomials of different orders that connect (e.g., fit) the data points of the three-dimensional geometry data of the road. In some embodiments, the autonomous vehicle road navigation model may include a three-dimensional spline that corresponds to a common road segment (e.g., road segment 1200) or a target trajectory along a lane of the road segment 1200.

[0268] As described above, the autonomous vehicle road navigation model included in the sparse map may include other information, such as an 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) mounted on the vehicle 1205 may process the image of the landmark to extract the identification of the landmark. Landmark-specific information may be stored in the sparse map 800, rather than the actual image of the landmark. The landmark-specific information may require much less storage space than the actual image. Other sensors or systems (e.g., a GPS system) may also provide specific identification of the landmark (e.g., the location of the landmark). The landmarks may include at least one of a traffic sign, an arrow mark, a lane mark, a dashed lane mark, a traffic light, a stop line, a directional sign (e.g., a highway exit sign with an arrow indicating a direction, a highway sign with an arrow pointing in a different direction or location), a landmark beacon, or a light pole. A landmark beacon refers to a device (e.g., an RFID device) installed along a road segment that transmits or reflects a signal to a receiver installed in a vehicle, such that when the vehicle passes by the device, the beacon received by the vehicle and the location of the device (e.g., determined from the GPS location of the device) can be used as a landmark included in the autonomous vehicle road navigation model and / or sparse map 800.

[0269] The identification of the at least one landmark may include a location of the at least one landmark. The location of the landmark may be determined based on position measurements performed using sensor systems (e.g., global positioning systems, inertial-based positioning systems, landmark beacons, etc.) associated with the multiple vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, the location of the landmark may be determined by averaging position measurements detected, collected, or received by sensor systems on the different vehicles 1205, 1210, 1215, 1220, and 1225 over multiple runs. For example, the vehicles 1205, 1210, 1215, 1220, and 1225 may transmit position measurement data to the server 1230, which may average the position measurements and use the average position measurement as the location of the landmark. The location of the landmark may be continually refined by measurements received from the vehicles on subsequent runs.

[0270] The identification of the landmark may include the size of the landmark. A processor provided in the vehicle (e.g., 1205) may estimate the physical size of the landmark based on an analysis of the image. The server 1230 may receive multiple estimates of the physical size of the same landmark from different vehicles over different trips. The server 1230 may average the different estimates to arrive at the physical size of the landmark and store the size of the landmark in the road model. The estimate of the physical size may be used to further determine or estimate the distance of the landmark from the vehicle. The distance to the landmark may be estimated based on the current speed of the vehicle and a scale of magnification based on the position of the landmark as it appears in the image relative to the magnification focus of the camera. For example, the distance to the landmark may be estimated as Z=V×dt×R / D, where V is the speed of the vehicle, R is the distance in the image from the landmark to the magnification focus at time t1, D is the change in the distance of the landmark in the image from t1 to t2, and dt represents (t2-t1). For example, the distance to a landmark may be estimated by Z=V×dt×R / D, where V is the speed of the vehicle, R is the distance in the image between the landmark and the magnification focus, dt is the time interval, and D is the image displacement of the landmark along the epipolar line. The above equation and other equivalent equations may be used to estimate the distance to a landmark, such as Z=V×ω / Δω, where V is the speed of the vehicle, ω is the image length (e.g., object width), and Δω is the change in that image length per unit time.

[0271] If the physical size of the landmark is known, the distance to the landmark may also be determined based on the following formula: Z=f×W / ω, where f is the focal length, W is the size of the landmark (e.g., height or width), and ω is the number of pixels the landmark passes through in the image. From the above formula, the change in distance Z is ΔZ=f×W×Δω / ω 2 +f×ΔW / ω, where ΔW decays to zero through averaging and Δω is the number of pixels that represent the accuracy of the bounding box in the image. An estimate of the physical size of the landmark can be calculated on the server side by averaging multiple observations. The resulting error in the distance estimation can be very small. There are two sources of error that can occur when using the above formula, namely ΔW and Δω. The contribution to the distance error is ΔZ=f×W×Δω / ω 2is given by f × ΔW / ω, where ΔW decays to zero through averaging, and therefore ΔZ is determined by Δω (e.g., the inaccuracy of the image bounding box).

[0272] For a landmark of unknown dimensions, the distance to the landmark may be estimated by tracking feature points on the landmark between successive frames. For example, a particular feature displayed on a speed limit sign may be tracked between two or more image frames. Based on these tracked features, a distance distribution for each feature point may be generated. A distance estimate may be extracted from the distance distribution. For example, the most frequent distance appearing in the distance distribution may be used as the distance estimate. As another example, the mean of the distance distribution may be used as the distance estimate.

[0273] FIG. 13 illustrates an example autonomous vehicle road navigation model represented by a number of three-dimensional splines 1301, 1302, and 1303. The curves 1301, 1302, and 1303 illustrated in FIG. 13 are for illustrative purposes only. Each spline may include one or more three-dimensional polynomials connecting a number of data points 1310. Each polynomial may be a first order polynomial, a second order polynomial, a third order polynomial, or any suitable combination of polynomials having different orders. Each data point 1310 may be associated with navigation information received from the vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, each data point 1310 may be associated with data related to landmarks (e.g., size, location, and landmark identification information) and / or road signature profiles (e.g., road geometry, road roughness profile, road curvature profile, road width profile). In some embodiments, some data points 1310 may be associated with data related to landmarks and other data points may be associated with data related to road signature profiles.

[0274] FIG. 14 shows raw position data 1410 (e.g., GPS data) received from five separate runs. One run may be separate from another run if separate vehicles crossed at the same time, if the same vehicle crossed at separate times, or if separate vehicles crossed at separate times. To account for errors in the position data 1410 and different positions of vehicles in the same lane (e.g., one vehicle may be closer to the left side of the lane than another vehicle), the remote server 1230 may generate a map skeleton 1420 using one or more statistical techniques to determine whether changes in the raw position data 1410 represent actual deviations or statistical errors. Each route in the skeleton 1420 may be linked back to the raw data 1410 that formed that route. For example, the route between A and B in the skeleton 1420 is linked to raw data 1410 from runs 2, 3, 4, and 5, but not from run 1. While the skeleton 1420 may not be detailed enough to be used for navigating a vehicle (e.g., because it combines travel from multiple lanes on the same road, unlike the splines described above), it may provide useful topological information and may be used to define intersections.

[0275] FIG. 15 illustrates an example in which additional detail may be generated for a sparse map in a section of a map skeleton (e.g., section A to B in skeleton 1420). As shown in FIG. 15, data (e.g., egomotion data, road mark data, etc.) may be shown as a function of position S (or S1 or S2) along the run. Server 1230 may identify landmarks in the sparse map by identifying unique matches between landmarks 1501, 1503, and 1505 of run 1510 and landmarks 1507 and 1509 of run 1520. Such a matching algorithm may lead to the identification of landmarks 1511, 1513, and 1515. However, one skilled in the art will recognize that other matching algorithms may be used. For example, probability optimization may be used in place of or in combination with a unique match. Server 1230 may align runs longitudinally to align matching landmarks. For example, the server 1230 may select one run (e.g., run 1520) as a reference run and then shift and / or elastically stretch the other run (e.g., run 1510) for alignment.

[0276] FIG. 16 shows an example of registered landmark data for use with a sparse map. In the example of FIG. 16, landmark 1610 includes a road sign. The example of FIG. 16 further shows data from multiple runs 1601, 1603, 1605, 1607, 1609, 1611, and 1613. In the example of FIG. 16, the data from run 1613 consists of "ghost" landmarks, and server 1230 may identify the landmark as a "ghost" because none of runs 1601, 1603, 1605, 1607, 1609, and 1611 include identification of a landmark in the vicinity of an identified landmark in run 1613. Thus, server 1230 may accept a potential landmark if the ratio of images in which the landmark appears to images in which the landmark does not appear exceeds a threshold, and / or may reject a potential landmark if the ratio of images in which the landmark does not appear to images in which the landmark appears exceeds a threshold.

[0277] FIG. 17 illustrates a system 1700 for generating trip data that may be used to crowdsource a sparse map. As illustrated in FIG. 17, the system 1700 may include a camera 1701 and a location device 1703 (e.g., a GPS locator). The camera 1701 and the location device 1703 may be mounted on a vehicle (e.g., one of the vehicles 1205, 1210, 1215, 1220, and 1225). The camera 1701 may generate multiple types of data, such as ego-motion data, traffic sign data, road data, etc. The camera data and location data may be segmented into trip segments 1705. For example, each trip segment 1705 may have camera data and location data for trips of less than 1 km.

[0278] In some embodiments, system 1700 may remove redundancy in drive segment 1705. For example, if a landmark appears in multiple images from camera 1701, system 1700 may remove the redundant data such that drive segment 1705 includes only the location of the landmark and one copy of any metadata associated with the landmark. As a further example, if a lane marking appears in multiple images from camera 1701, system 1700 may remove the redundant data such that drive segment 1705 includes only the location of the lane marking and one copy of any metadata associated with the lane marking.

[0279] System 1700 also includes a server (e.g., server 1230), which may receive trip segments 1705 from the vehicles and recombine the trip segments 1705 into a single trip 1707. Such an arrangement may reduce bandwidth requirements when transferring data between the vehicles and the server, and the server may also store data related to an entire trip.

[0280] FIG. 18 illustrates the system 1700 of FIG. 17 further configured for crowdsourcing a sparse map. As in FIG. 17, the system 1700 includes a vehicle 1810 that captures trip data using, for example, a camera (which generates, for example, ego-motion data, traffic sign data, road data, etc.) and a location device (e.g., a GPS locator). As in FIG. 17, the vehicle 1810 segments the collected data into trip segments (shown in FIG. 18 as "DS1 1", "DS2 1", "DSN 1"). The server 1230 then receives the trip segments and reconstructs the trip (shown in FIG. 18 as "trip 1") from the received segments.

[0281] As further shown in FIG. 18, system 1700 also receives data from additional vehicles. For example, vehicle 1820 also captures trip data using, for example, a camera (which generates ego-motion data, traffic sign data, road data, etc.) and a location device (e.g., a GPS locator). Similar to vehicle 1810, vehicle 1820 segments the collected data into trip segments (shown in FIG. 18 as “DS1 2”, “DS2 2”, “DSN 2”). Server 1230 then receives the trip segments and reconstructs the trip (shown in FIG. 18 as “Journey 2”) from the received segments. Any number of additional vehicles may be used. For example, FIG. 18 also includes “Car N” which captures trip data, segments it into trip segments (shown in FIG. 18 as “DS1 N”, “DS2 N”, “DSN N”), and transmits it to server 1230 for reconstruction into a trip (shown in FIG. 18 as “Journey N”).

[0282] As shown in FIG. 18, the server 1230 may build a sparse map (shown as “Map”) using reconstructed trips (e.g., “Run 1”, “Run 2”, and “Run N”) collected from multiple vehicles (e.g., “Car 1” (also referred to as vehicle 1810), “Car 2” (also referred to as vehicle 1820), and “Car N”).

[0283] 19 is a flow chart illustrating an example process 1900 for generating a sparse map for autonomous vehicle navigation along a road segment. The process 1900 may be performed by one or more processing devices included in the server 1230.

[0284] Process 1900 may include receiving a number of images captured as one or more vehicles traverse the road segment (step 1905). Server 1230 may receive the images from a camera included in one or more of vehicles 1205, 1210, 1215, 1220, and 1225. For example, camera 122 may capture one or more images of an environment surrounding vehicle 1205 as vehicle 1205 travels along road segment 1200. In some embodiments, server 1230 may receive pruned image data, in which redundancy is removed by a processor on vehicle 1205, as discussed above with respect to FIG.

[0285] The process 1900 may further include identifying at least one line representation of a road surface feature extending along the road segment based on the multiple images (step 1910). Each line representation may represent a path along the road segment that substantially corresponds to a road surface feature. For example, the server 1230 may analyze the environmental images received from the camera 122 to identify road edges or lane markings and determine a trajectory of travel along the road segment 1200 associated with the road edges or lane markings. In some embodiments, the trajectory (or line representation) may include a spline, a polynomial representation, or a curve. The server 1230 may determine a trajectory of travel of the vehicle 1205 based on the egomotion (e.g., three-dimensional translational and / or three-dimensional rotational motion) of the camera received in step 1905.

[0286] The process 1900 may also include identifying a number of landmarks associated with the road segment based on the multiple images (step 1910). For example, the server 1230 may analyze the environmental images received from the camera 122 to identify one or more landmarks, such as road signs, along the road segment 1200. The server 1230 may identify the landmarks using an analysis of the multiple images acquired as one or more vehicles traverse the road segment. To enable crowdsourcing, the analysis may include rules for accepting and rejecting possible landmarks associated with the road segment. For example, the analysis may include accepting a potential landmark if a ratio of images in which the landmark appears to images in which the landmark does not appear exceeds a threshold, and / or rejecting a potential landmark if a ratio of images in which the landmark does not appear to images in which the landmark appears exceeds a threshold.

[0287] Process 1900 may include other operations or steps performed by server 1230. For example, the navigation information may include a target trajectory for a vehicle to travel along the road segment, and process 1900 may include clustering, by server 1230, vehicle trajectories associated with a plurality of vehicles traveling on the road segment, and determining a target trajectory based on the clustered vehicle trajectories, as discussed in more detail below. Clustering the vehicle trajectories may include clustering, by server 1230, a plurality of trajectories associated with the vehicles traveling on the road segment into a plurality of clusters based on at least one of an absolute heading of the vehicles or a lane assignment of the vehicles. Generating the target trajectory may include averaging, by server 1230, the clustered trajectories. As a further example, process 1900 may include aligning the data received in step 1905. As discussed above, other processes or steps performed by server 1230 may also be included in process 1900.

[0288] The disclosed system and method may include other features. For example, the disclosed system may use local coordinates rather than global coordinates. For autonomous driving, some systems may display data in global coordinates. For example, they may use longitude-latitude coordinates on the Earth's surface. To use a map for steering, the host vehicle may determine its position and orientation relative to the map. It seems natural to use an on-board GPS device to locate the vehicle on the map and find the rotational transformation between the body's reference frame and the world's reference frame (e.g., north, east, and down). Once the body's reference frame is aligned with the map's reference frame, the desired route may be represented in the body's reference frame and steering commands may be calculated or generated.

[0289] The disclosed system and method may enable autonomous vehicle navigation (e.g., steering control) with a low-footprint model that may be collected by the autonomous vehicle itself without the aid of expensive surveying equipment. To support autonomous navigation (e.g., steering applications), the road model may include a sparse map with the geometry of the road, its lane structure, and landmarks that may be used to determine the location or position of the vehicle along the trajectory included in the model. As discussed above, the generation of the sparse map may be performed by a remote server that communicates with and receives data from the vehicles traveling on the road. The data may include sensed data, a reconstructed trajectory based on the sensed data, and / or a recommended trajectory that may represent a corrected reconstructed trajectory. As discussed below, the server may transmit the model to the vehicle or other vehicles that subsequently travel on the road to assist autonomous navigation.

[0290] FIG. 20 shows a block diagram of the server 1230. The server 1230 may include a communication unit 2005, which may include both hardware components (e.g., communication control circuits, switches, and antennas) and software components (e.g., communication protocols, computer code). For example, the communication unit 2005 may include at least one network interface. The server 1230 may communicate with the vehicles 1205, 1210, 1215, 1220, and 1225 through the communication unit 2005. For example, the server 1230 may receive navigation information transmitted from the vehicles 1205, 1210, 1215, 1220, and 1225 through the communication unit 2005. The server 1230 may distribute an autonomous vehicle road navigation model to one or more autonomous vehicles through the communication unit 2005.

[0291] The server 1230 may include at least one non-transitory storage medium 2010, such as a hard drive, compact disc, tape, etc. The storage device 1410 may be configured to store data such as navigation information received from the vehicles 1205, 1210, 1215, 1220, and 1225 and / or an autonomous vehicle road navigation model that the server 1230 generates based on the navigation information. The storage device 2010 may be configured to store any other information, such as a sparse map (e.g., sparse map 800 discussed above with respect to FIG. 8).

[0292] In addition to, or instead of, the storage device 2010, the server 1230 may include a memory 2015. The memory 2015 may be similar to or different from the memory 140 or 150. The memory 2015 may be a non-transitory memory, such as a flash memory, a random access memory, etc. The memory 2015 may be configured to store data, such as computer code or instructions executable by a processor (e.g., the processor 2020), map data (e.g., data for the sparse map 800), an autonomous vehicle road navigation model, and / or navigation information received from the vehicles 1205, 1210, 1215, 1220, and 1225.

[0293] The server 1230 may include at least one processing device 2020 configured to execute computer codes or instructions stored in the memory 2015 to perform various functions. For example, the processing device 2020 may analyze navigation information received from the vehicles 1205, 1210, 1215, 1220, and 1225 and generate an autonomous vehicle road navigation model based on the analysis. The processing device 2020 may control the communication unit 1405 to distribute the autonomous vehicle road navigation model to one or more autonomous vehicles (e.g., one or more of the vehicles 1205, 1210, 1215, 1220, and 1225, or any vehicle that subsequently travels the road segment 1200). The processing device 2020 may be similar to or different from the processors 180, 190, or the processing unit 110.

[0294] 21 shows a block diagram of a memory 2015 that may store computer code or instructions for performing one or more operations for generating a road navigation model for use in autonomous vehicle navigation. As shown in FIG. 21, the memory 2015 may store one or more modules for performing operations for processing vehicle navigation information. For example, the memory 2015 may include a model generation module 2105 and a model distribution module 2110. The processor 2020 may execute instructions stored in any of the modules 2105 and 2110 included in the memory 2015.

[0295] The model generation module 2105 may store instructions that, when executed by the processor 2020, may generate at least a portion of an autonomous vehicle road navigation model of a common road segment (e.g., road segment 1200) based on navigation information received from the vehicles 1205, 1210, 1215, 1220, and 1225. For example, in generating the autonomous vehicle road navigation model, the processor 2020 may cluster vehicle trajectories along the common road segment 1200 into different clusters. The processor 2020 may determine a target trajectory along the common road segment 1200 based on the clustered vehicle trajectories for each of the different clusters. Such an operation may include finding an average or mean trajectory of the clustered vehicle trajectories in each cluster (e.g., by averaging data representing the clustered vehicle trajectories). In some embodiments, the target trajectory may be associated with a single lane of the common road segment 1200.

[0296] The road model and / or sparse map may store trajectories associated with road segments. These trajectories may be referred to as target trajectories and are provided to the autonomous vehicle for autonomous navigation. The target trajectories may be received from multiple vehicles or may be generated based on actual trajectories or recommended trajectories (actual trajectories with some modifications) received from multiple vehicles. The target trajectories included in the road model or sparse map may be continuously updated (e.g., averaged) with new trajectories received from other vehicles.

[0297] A vehicle traveling along a road segment may collect data by various sensors. The data may include landmarks, road signature profiles, vehicle movements (e.g., accelerometer data, speed data), vehicle position (e.g., GPS data) and may reconstruct the actual trajectory itself or may transmit the data to a server, which may reconstruct the actual trajectory of the vehicle. In some embodiments, the vehicle may transmit data related to the trajectory (e.g., curves in any reference frame), landmark data, and lane assignments along the traveled path to the server 1230. Different vehicles traveling along the same road segment over multiple trips may have different trajectories. The server 1230 may identify the path or trajectory associated with each lane from the trajectories received from the vehicles through a clustering process.

[0298] 22 illustrates a process of clustering vehicle trajectories associated with vehicles 1205, 1210, 1215, 1220, and 1225 to determine a target trajectory for a common road segment (e.g., road segment 1200). The target trajectory or multiple target trajectories determined from the clustering process may be included in the autonomous vehicle road navigation model or sparse map 800. In some embodiments, the vehicles 1205, 1210, 1215, 1220, and 1225 traveling along road segment 1200 may transmit multiple trajectories 2200 to server 1230. In some embodiments, server 1230 may generate trajectories based on landmark, road geometry, and vehicle motion information received from vehicles 1205, 1210, 1215, 1220, and 1225. To generate the autonomous vehicle road navigation model, the server 1230 may cluster the vehicle trajectory 1600 into multiple clusters 2205, 2210, 2215, 2220, 2225, and 2230, as shown in FIG. 22.

[0299] Clustering may be performed using various criteria. In some embodiments, all trips in a cluster may be similar in terms of absolute heading along the road segment 1200. The absolute heading may be obtained from GPS signals received by the vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, dead reckoning may be used to obtain the absolute heading. As one skilled in the art will appreciate, dead reckoning may be used to determine the current position and therefore heading of the vehicles 1205, 1210, 1215, 1220, and 1225 using previously determined positions, estimated speeds, etc. Trajectories clustered by absolute heading may be useful for identifying routes along a road.

[0300] In some embodiments, all trips in a cluster may be similar with respect to lane assignment along a trip of road segment 1200 (e.g., same lane before and after an intersection). Trajectories clustered by lane assignment may be useful for identifying lanes along a road. In some embodiments, both criteria (e.g., absolute heading and lane assignment) may be used for clustering.

[0301] In each cluster 2205, 2210, 2215, 2220, 2225, and 2230, the trajectories may be averaged to obtain a target trajectory associated with the particular cluster. For example, trajectories from multiple runs associated with the same lane cluster may be averaged. The average trajectory may be a target trajectory associated with a particular lane. To average a cluster of trajectories, the server 1230 may select a reference frame for an arbitrary trajectory C0. For all other trajectories (C1, ..., Cn), the server 1230 may find a rigid transformation that maps C to C0, where i = 1, 2, ..., n, where n is a positive integer and corresponds to the total number of trajectories included in the cluster. The server 1230 may calculate the average curve or trajectory in the C0 reference frame.

[0302] In some embodiments, the landmarks may define arc lengths that are consistent between different runs, and the arc lengths may be used to align the track with the lanes. In some embodiments, lane marks before and after an intersection may be used to align the track with the lanes.

[0303] To assemble lanes from the trajectory, server 1230 may select a reference frame for any lane. Server 1230 may map overlapping lanes to the selected reference frame. Server 1230 may continue mapping until all lanes are in the same reference frame. Lanes that are next to each other may be aligned as if they were the same lane and may later be shifted laterally.

[0304] Landmarks recognized along a road segment may be mapped to a common reference frame, first at the lane level and then at the intersection level. For example, the same landmark may be recognized multiple times by multiple vehicles on multiple runs. Data for the same landmark received on different runs may be slightly different. Such data may be averaged and mapped to the same reference frame, such as the C0 reference frame. Additionally or alternatively, the variance of data for the same landmark received on multiple runs may be calculated.

[0305] In some embodiments, each lane of road segment 120 may be associated with a target trajectory and specific landmarks. The target trajectory or multiple such target trajectories may be included in an autonomous vehicle road navigation model that may be used by other autonomous vehicles that subsequently travel along the same road segment 1200. Landmarks identified by vehicles 1205, 1210, 1215, 1220, and 1225 while they travel along road segment 1200 may be recorded in association with the target trajectory. The target trajectory and landmark data may be continuously or periodically updated with new data received from other vehicles on subsequent trips.

[0306] For localization of an autonomous vehicle, the disclosed system and method may use an extended Kalman filter. The vehicle's position may be determined based on 3D position data and / or 3D orientation data, prediction of future positions beyond the vehicle's current position by integration of egomotion. The vehicle's localization may be corrected or adjusted by observing images of landmarks. For example, if the vehicle detects a landmark in an image captured by a camera, the landmark may be compared to known landmarks stored in the road model or sparse map 800. The known landmarks may have known positions (e.g., GPS data) along a target trajectory stored in the road model and / or sparse map 800. Based on the current speed and images of the landmarks, the distance from the vehicle to the landmarks may be estimated. The vehicle's position along the target trajectory may be adjusted based on the distance to the landmarks and the known positions of the landmarks (stored in the road model or sparse map 800). The landmark position / location data (e.g., average values ​​from multiple runs) stored in the road model and / or sparse map 800 may be presumed to be accurate.

[0307] In some embodiments, the disclosed system may form a closed-loop subsystem in which an estimate of the position of the vehicle's six degrees of freedom (e.g., three-dimensional position data and three-dimensional orientation data) may be used to navigate (e.g., steer the wheels) the autonomous vehicle to reach a desired point (e.g., 1.3 seconds ahead of a stored point). The measured data from the steering and actual navigation may then be used to estimate the six degrees of freedom position.

[0308] In some embodiments, poles along roads, such as lampposts and power or cable poles, may be used as landmarks for vehicle location. Other landmarks, such as traffic signs, traffic lights, arrows on the road, stop lines, and static features or signatures of objects along road segments, may also be used as landmarks for vehicle location. When poles are used for location, the x observation of the pole (i.e., the viewing angle from the vehicle) may be used rather than the y observation (i.e., the distance to the pole), since the bottom of the pole may be occluded and not on the road plane.

[0309] FIG. 23 illustrates a navigation system for a vehicle that may be used for autonomous navigation using crowdsourced sparse maps. For purposes of illustration, the vehicle is referred to as vehicle 1205. The vehicle illustrated in FIG. 23 may be any other vehicle disclosed herein, including, for example, vehicles 1210, 1215, 1220, and 1225, as well as vehicle 200 illustrated in other embodiments. As illustrated in FIG. 12, the vehicle 1205 may communicate with a server 1230. The vehicle 1205 may include an imaging device 122 (e.g., camera 122). The vehicle 1205 may include a navigation system 2300 configured to provide navigation guidance for the vehicle 1205 to travel along a road (e.g., road segment 1200). The vehicle 1205 may also include other sensors, such as a speed sensor 2320 and an accelerometer 2325. The speed sensor 2320 may be configured to detect the speed of the vehicle 1205. The accelerometer 2325 may be configured to detect acceleration or deceleration of the vehicle 1205. 23 may be an autonomous vehicle and the navigation system 2300 may be used to provide navigation guidance for the autonomous drive. Alternatively, the vehicle 1205 may be a non-autonomous human-controlled vehicle and the navigation system 2300 may still be used to provide navigation guidance.

[0310] The navigation system 2300 may include a communication unit 2305 configured to communicate with the server 1230 through the communication path 1235. The navigation system 2300 may also include a GPS unit 2310 configured to receive and process GPS signals. The navigation system 2300 may further include at least one processor 2315 configured to process data such as GPS signals, map data from the sparse map 800 (which may be stored in a storage device onboard the vehicle 1205 and / or received from the server 1230), road geometry sensed by the road profile sensor 2330, images captured by the camera 122, and / or an autonomous vehicle road navigation model received from the server 1230. The road profile sensor 2330 may include different types of devices for measuring different types of road profiles such as road surface roughness, road width, road height, road curvature, etc. For example, the road profile sensor 2330 may include a device that measures the suspension movement of the vehicle 2305 to derive a road roughness profile. In some embodiments, the road profile sensor 2330 may include a radar sensor to measure the distance from the vehicle 1205 to the side of the road (e.g., a barrier on the side of the road), thereby measuring the width of the road. In some embodiments, the road profile sensor 2330 may include a device configured to measure the elevation above and below the road. In some embodiments, the road profile sensor 2330 may include a device configured to measure the road curvature. For example, a camera (e.g., camera 122 or another camera) may be used to capture an image of the road indicative of the road curvature. The vehicle 1205 may use such an image to detect the road curvature.

[0311] At least one processor 2315 may be programmed to receive at least one environmental image associated with the vehicle 1205 from the camera 122. The at least one processor 2315 may analyze the at least one environmental image to determine navigation information associated with the vehicle 1205. The navigation information may include a trajectory associated with the travel of the vehicle 1205 along the road segment 1200. The at least one processor 2315 may determine the trajectory based on the motion of the camera 122 (and thus the vehicle), such as three-dimensional translational motion and three-dimensional rotational motion. In some embodiments, the at least one processor 2315 may determine the translational and rotational motion of the camera 122 based on an analysis of multiple images acquired by the camera 122. In some embodiments, the navigation information may include lane assignment information (e.g., whether the vehicle 1205 is traveling in that lane along the road segment 1200). Navigation information sent 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 sent from server 1230 to vehicle 1205 to provide autonomous navigation guidance to vehicle 1205.

[0312] The at least one processor 2315 may also be programmed to transmit navigation information from the vehicle 1205 to the server 1230. In some embodiments, the navigation information may be transmitted to the server 1230 along with road information. The road position information may include at least one of a GPS signal received by the GPS unit 2310, landmark information, road geometry, lane information, and the like. The at least one processor 2315 may receive an autonomous vehicle road navigation model or a portion of the model from the server 1230. The autonomous vehicle road navigation model received from the server 1230 may include at least one update based on the navigation information transmitted from the vehicle 1205 to the server 1230. The portion of the model transmitted from the server 1230 to the vehicle 1205 may include an updated portion of the model. The at least one processor 2315 may cause at least one navigation action by the vehicle 1205 (e.g., steering, such as making a turn, braking, accelerating, passing another vehicle, etc.) based on the received autonomous vehicle road navigation model or the updated portion of the model.

[0313] The at least one processor 2315 may be configured to communicate with various sensors and components included in the vehicle 1205, including the communication unit 1705, the GPS unit 2315, the camera 122, the speed sensor 2320, the accelerometer 2325, and the road profile sensor 2330. The at least one processor 2315 may collect information or data from the various sensors and components and transmit the information or data to the server 1230 through the communication unit 2305. Alternatively or additionally, the various sensors or components of the vehicle 1205 may also communicate with the server 1230 and transmit data or information collected by the sensors or components to the server 1230.

[0314] In some embodiments, the vehicles 1205, 1210, 1215, 1220, and 1225 may communicate with each other and share navigation information with each other, such that at least one of the vehicles 1205, 1210, 1215, 1220, and 1225 may generate an autonomous vehicle road navigation model using crowdsourcing, for example, based on information shared by other vehicles. In some embodiments, the vehicles 1205, 1210, 1215, 1220, and 1225 may share navigation information with each other, and each vehicle may update its own autonomous vehicle road navigation model, which is provided to the vehicle. In some embodiments, at least one of the vehicles 1205, 1210, 1215, 1220, and 1225 (e.g., vehicle 1205) may function as a hub vehicle. At least one processor 2315 of the hub vehicle (e.g., vehicle 1205) may perform some or all of the functions performed by the server 1230. For example, the at least one processor 2315 of the hub vehicle may communicate with and receive navigation information from the other vehicles. The at least one processor 2315 of the hub vehicle may generate an autonomous vehicle road navigation model or updates to the model based on the shared information received from the other vehicles. The at least one processor 2315 of the hub vehicle may transmit the autonomous vehicle road navigation model or updates to the model to the other vehicles to provide autonomous navigation guidance.

[0315] Mapping of lane marks and navigation based on the mapped lane marks

[0316] As mentioned above, the road navigation model and / or sparse map 800 of the autonomous vehicle may include a number of mapped lane marks associated with road segments. As discussed in more detail below, these mapped lane marks may be used when the autonomous vehicle navigates. For example, in some embodiments, the mapped lane marks may be used to determine a lateral position and / or orientation relative to a planned trajectory. Using this position information, the autonomous vehicle may be able to adjust its heading to match the direction of the target trajectory at the determined location.

[0317] Vehicle 200 may be configured to detect lane markings on a given road segment. A road segment may include any markings on a road to guide vehicular traffic on the road. For example, lane markings may be solid or dashed lines indicating the ends of travel lanes. Lane markings may also include double lines, such as double solid lines, double dashed lines, or a combination of solid and dashed lines, indicating whether passing is permitted in the adjacent lane. Lane markings may also include freeway on- and off-ramp markings, such as deceleration lanes on exit ramps, or dotted lines indicating that a lane is turn-only or is ending. Markings may further indicate work zones, temporary lane shifts, travel paths through intersections, medians, reserved lanes (e.g., bike lanes, HOV lanes, etc.), or other miscellaneous markings (e.g., crosswalks, speed humps, railroad crossings, stop lines, etc.).

[0318] Vehicle 200 may capture images of surrounding lane markings using cameras such as imaging devices 122 and 124 included in image acquisition unit 120. Vehicle 200 may analyze the images to detect point locations associated with the lane marks based on features identified in one or more of the captured images. These point locations may be uploaded to a server to represent the lane marks in sparse map 800. Depending on the camera's location and field of view, lane marks on both sides of the vehicle may be detected simultaneously from a single image. In other embodiments, different cameras may be used to capture images on multiple sides of the vehicle. Rather than uploading actual images of the lane marks, the marks may be stored in sparse map 800 as a spline or a series of points, thus reducing the size of sparse map 800 and / or the data that must be uploaded remotely by the vehicle.

[0319] 24A-24D show exemplary point locations that may be detected by vehicle 200 to represent a particular lane mark. 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 recognize a series of end points, corner points, or various other point locations associated with a particular lane mark. FIG. 24A shows a continuous lane mark 2410 that may be detected by vehicle 200. Lane mark 2410 may represent an outer edge of a roadway represented by a continuous white line. As shown in FIG. 24A, vehicle 200 may be configured to detect multiple end location points 2411 along the lane mark. Location points 2411 may be collected to represent the lane mark at any interval sufficient to create a lane mark mapped in a sparse map. For example, lane markings may be represented by one point for every meter of a detected end, one point for every five meters of a detected end, or other suitable intervals. In some embodiments, the interval may be determined by other factors rather than a set interval, such as, for example, based on the point where the vehicle 200 has the highest confidence ranking of the detected point locations. Although FIG. 24A shows end location points on the inside end of the lane marking 2410, points may be collected on the outside end of the line or along both ends. Additionally, although a single line is shown in FIG. 24A, similar end points may be detected for double solid lines. For example, point 2411 may be detected along one or both ends of the solid line.

[0320] Vehicle 200 may also image different lane marks depending on the type or shape of the lane mark. FIG. 24B illustrates an exemplary dashed lane mark 2420 that may be detected by vehicle 200. Rather than identifying end points as in FIG. 24A, the vehicle may detect a series of corner points 2421 representing the corners of the lane dashed line to define the complete boundary of the dashed line. Although FIG. 24B illustrates that each corner of a given dashed line mark is located, vehicle 200 may detect or upload a subset of the points shown in the figure. For example, vehicle 200 may detect the front end or front corner of a given dashed line mark, or may detect the two corner points closest to the interior of the lane. Furthermore, not all dashed line marks may be imaged, for example, vehicle 200 may image and / or record points representing a sample of dashed line marks (e.g., every other, every third, every fifth, etc.) or points representing dashed line marks at predefined intervals (e.g., every meter, every five meters, every ten meters, etc.). Corner points may also be detected for similar lane markings, such as markings indicating that a lane is for an exit ramp, markings indicating that a particular lane is about to end, or various other lane markings that may have detectable corner points. Corner points may also be detected for lane markings that are comprised of double dashed lines or a combination of solid and dashed lines.

[0321] In some embodiments, the points uploaded to the server to generate the mapped lane markings may represent other points than the detected end or corner points. FIG. 24C shows a series of points that may represent the centerline of a particular lane marking. For example, a continuous lane 2410 may be represented by a centerline point 2441 along the centerline 2440 of the lane marking. In some embodiments, the vehicle 200 may be configured to detect these center points using various image recognition techniques, such as convolutional neural networks (CNNs), scale invariant feature transforms (SIFTs), histogram of oriented gradients (HOG) features, or other techniques. Alternatively, the vehicle 200 may detect other points, such as the end points 2411 shown in FIG. 24A, and may calculate the centerline point 2441, for example, by detecting points along each end and determining the midpoint between the end points. Similarly, a dashed lane marking 2420 may be represented by a centerline point 2451 along the centerline 2450 of the lane marking. The centerline points may be located at the ends of the dashed lines, as shown in FIG. 24C, or at various other locations along the centerline. For example, each dashed line may be represented by a single point at the geometric center of the dashed line. The points may also be spaced at predetermined intervals along the centerline (e.g., every 1 meter, every 5 meters, every 10 meters, etc.). The centerline points 2451 may be detected directly by the vehicle 200, or may be calculated based on other detected reference points, such as corner points 2421, as shown in FIG. 24B. Centerlines may also be used to represent other lane marking types, such as double lines, using techniques similar to those described above.

[0322] In some embodiments, the vehicle 200 may identify points that represent other features, such as vertices between two intersecting lane marks. FIG. 24D illustrates an example point that represents an intersection between two lane marks 2460 and 2465. The vehicle 200 may calculate a vertex 2466 that represents the intersection between the two lane marks. For example, one of the lane marks 2460 or 2465 may represent a train intersection area or other intersection area within a road segment. Although the lane marks 2460 and 2465 are shown intersecting perpendicularly to one another, various other configurations may be detected. For example, the lane marks 2460 and 2465 may intersect at other angles, or one or both of the lane marks may terminate at the vertex 2466. Similar techniques may also be applied to intersections between dashed lines or other lane mark types. In addition to the vertex 2466, various other points 2467 may also be detected to provide further information about the orientation of the lane marks 2460 and 2465.

[0323] Vehicle 200 may associate real-world coordinates with each detected point of the lane marking. For example, a location identifier including the coordinates of each point may be generated and uploaded to a server for mapping the lane marking. The location identifier may further include other identifying information about the point, including whether the point represents a corner point, an end point, a center point, etc. Accordingly, vehicle 200 may be configured to determine the real-world location of each point based on an analysis of the image. For example, vehicle 200 may detect other features in the image, such as the various landmarks described above, to identify the real-world location of the lane marking. This may include determining the location of the lane marking in the image relative to the detected landmarks, or determining the location of the vehicle based on the detected landmarks and then determining the distance from the vehicle (or the target trajectory of the vehicle) to the lane marking. If landmarks are not available, the location of the lane marking point may be determined relative to the vehicle's position determined based on dead reckoning. The real-world coordinates included in the location identifier may be expressed as absolute coordinates (e.g., latitude / longitude coordinates) or may be related to other features, such as based on a longitudinal position along the target trajectory and a lateral distance from the target trajectory. The location identifiers may then be uploaded to a server to generate lane markings that are mapped in a navigation model (such as sparse map 800). In some embodiments, the server may construct splines that represent the lane markings of the road segment. Alternatively, vehicle 200 may generate the splines and upload them to the server for recording in the navigation model.

[0324] 24E shows an example of an exemplary navigation model or sparse map of the corresponding road segment including mapped lane markings. The sparse map may include a target trajectory 2475 for the vehicle to follow along the road segment. As described above, the target trajectory 2475 may represent an ideal path that the vehicle would take when traveling the corresponding road segment, or may be located elsewhere on the road (e.g., on the centerline of the road, etc.). The target trajectory 2475 may be calculated in various ways as described above, for example, based on an aggregation (e.g., a weighted combination) of two or more reconstructed trajectories of vehicles traversing the same road segment.

[0325] In some embodiments, the target trajectory may be generated equally for all vehicle types and all road, vehicle, and / or environmental conditions. However, in other embodiments, various other factors or variables may also be considered in generating the target trajectory. Different target trajectories may be generated for different types of vehicles (e.g., private cars, light trucks, and full trailers). For example, a target trajectory with a relatively tighter turning radius may be generated for a small private car than for a large semi-trailer truck. In some embodiments, road, vehicle, and environmental conditions may also be considered. For example, different target trajectories may be generated for different road conditions (e.g., wet, snowy, icy, dry, etc.), vehicle conditions (e.g., tire conditions or estimated tire conditions, braking conditions or estimated braking conditions, fuel level, etc.), or environmental factors (e.g., time of day, visibility, weather, etc.). The target trajectory may also depend on one or more aspects or characteristics of a particular road segment (e.g., speed limit, frequency and size of turns, gradient, etc.). In some embodiments, various user settings may also be used to determine target trajectories, such as a set driving mode (eg, desired aggressive driving, economy mode, etc.).

[0326] The sparse map may also include mapped lane marks 2470 and 2480 representing lane marks along the road segment. The mapped lane marks may be represented by a number of location identifiers 2471 and 2481. As explained above, the location identifiers may include locations in real-world coordinates of points associated with the detected lane marks. Similar to the target trajectory of the model, the lane marks may also include elevation data and may be represented as curves in three-dimensional space. For example, the curves may be splines connecting three-dimensional polynomials of appropriate degree, where the curves may be calculated based on the location identifiers. The mapped lane marks may also include other information or metadata about the lane marks, such as an identifier for the type of lane mark (e.g., between two lanes having the same direction of travel, between two lanes having opposite directions of travel, edge of the road, etc.) and / or other characteristics of the lane mark (e.g., solid line, dashed line, single line, double line, yellow line, white line, etc.). In some embodiments, the mapped lane marks may be continuously updated within the model, for example, using crowdsourcing techniques. The same vehicle may upload location identifiers during multiple occasions traveling the same road segment, or data may be selected from multiple vehicles (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 vehicles and stored in the system. Once the mapped lane marks have been updated and refined, the updated road navigation model and / or sparse map may be distributed to multiple autonomous vehicles.

[0327] Generating the lane marks mapped in the sparse map may also include detecting and / or mitigating errors based on anomalies in the image or the actual lane marks themselves. FIG. 24F illustrates an example anomaly 2495 associated with the detection of a lane mark 2490. The anomaly 2495 may appear in an image captured by the vehicle 200, for example, from an object blocking the camera's view of the lane mark, dirt on the lens, etc. In some cases, the anomaly may be due to the lane mark itself, which may be damaged, worn, or partially covered, for example, by dirt, debris, water, snow, or other substances on the road. The anomaly 2495 may cause an erroneous point 2491 to be detected by the vehicle 200. The sparse map 800 may provide a correctly mapped lane mark and filter out the error. In some embodiments, the vehicle 200 may detect the error point 2491, for example, by detecting the anomaly 2495 in the image or by identifying the error based on lane mark points detected before and after the anomaly. Based on the detection of the anomaly, the vehicle may exclude point 2491 or may adjust it to match other detected points. In other embodiments, the error may be corrected after the point is uploaded by determining that the point is outside of an expected threshold, for example, based on other points uploaded during the same trip, or based on an aggregation of data from previous trips along the same road segment.

[0328] The lane marks mapped in the navigation model and / or the sparse map may also be used for navigation by an autonomous vehicle traversing the corresponding road. For example, a vehicle navigating along a target trajectory may periodically use the mapped lane marks in the sparse map to align itself to the target trajectory. As described above, between landmarks, the vehicle may navigate based on dead reckoning, in which the vehicle uses sensors to determine egomotion and estimate a position relative to the target trajectory. Errors may accumulate over time, causing the vehicle's position determination relative to the target trajectory to become less and less accurate. Thus, the vehicle may use lane marks occurring in the sparse map 800 (and their known positions) to reduce dead reckoning-induced errors in position determination. In this manner, the identified lane marks included in the sparse map 800 may serve as navigation anchors from which the vehicle's precise position relative to the target trajectory may be determined.

[0329] FIG. 25A illustrates an exemplary image 2500 of the vehicle's surroundings that may be used for navigation based on mapped lane marks. Image 2500 may be captured by vehicle 200, for example, via imaging devices 122 and 124 included in image acquisition unit 120. Image 2500 may include an image of at least one lane mark 2510, as shown in FIG. 25A. Image 2500 may also include one or more landmarks 2521, such as road signs, used for navigation as described above. Some elements shown in FIG. 25A, such as elements 2511, 2530, and 2520, that are not visible in captured image 2500 but are detected and / or determined by vehicle 200, are also shown for reference.

[0330] Using the various techniques described above with respect to Figures 24A-24D and 24F, the vehicle may analyze the image 2500 to identify the lane markings 2510. Various points 2511 may be detected that correspond to features of the lane markings in the image. For example, the points 2511 may correspond to an end of a lane mark, a corner of a lane mark, a midpoint of a lane mark, an apex between two intersecting lane marks, or various other features or locations. The points 2511 may be detected to correspond to locations of points stored in a navigation model received from a server. For example, if a sparse map is received that includes points representing centerlines of mapped lane marks, the points 2511 may also be detected based on the centerlines of the lane markings 2510.

[0331] The vehicle may also determine a longitudinal position, represented by element 2520, located along the target trajectory. The longitudinal position 2520 may be determined from the image 2500, for example, by detecting landmarks 2521 in the image 2500 and comparing the measured position to known landmark positions stored in the road model or sparse map 800. The position of the vehicle along the target trajectory may then be determined based on the distance to the landmarks and the known positions of the landmarks. The longitudinal position 2520 may also be determined from images other than those used to determine the positions of the lane marks. For example, the longitudinal position 2520 may be determined by detecting landmarks in images from other cameras in the image acquisition unit 120 taken at or near the same time as the image 2500. In some cases, the vehicle may not be near any landmarks or other reference points to determine the longitudinal position 2520. In such cases, the vehicle may navigate based on dead reckoning and thus use sensors to determine egomotion and estimate the longitudinal position 2520 relative to the target trajectory. The vehicle may also determine a distance 2530 that represents the actual distance between the vehicle observed in the captured image and the lane markings 2510. The angle of the camera, the speed of the vehicle, the width of the vehicle, or various other factors may be considered in determining the distance 2530.

[0332] FIG. 25B illustrates a lateral localization correction of a vehicle based on a mapped lane mark in a road navigation model. As described above, the vehicle 200 may determine a distance 2530 between the vehicle 200 and a lane mark 2510 using one or more images captured by the vehicle 200. The vehicle 200 may also access a road navigation model, such as a sparse map 800, which may include a mapped lane mark 2550 and a target trajectory 2555. The mapped lane mark 2550 may be modeled using the techniques described above, for example, using crowdsourced location identifiers captured by multiple vehicles. The target trajectory 2555 may also be generated using various techniques described above. The vehicle 200 may also determine or estimate a longitudinal position 2520 along the target trajectory 2555, as described above with respect to FIG. 25A. The vehicle 200 may then determine an expected distance 2540 based on the lateral distance between the target trajectory 2555 and the mapped lane mark 2550 corresponding to the longitudinal position 2520. The lateral localization of the vehicle 200 may be corrected or adjusted by comparing the actual distance 2530 measured using the captured images with the expected distance 2540 from the model.

[0333] FIG. 26A is a flow chart illustrating an example process 2600A for mapping lane marks for use in autonomous vehicle navigation, according to disclosed embodiments. In step 2610, the process 2600A may include receiving two or more location identifiers associated with the detected lane marks. For example, step 2610 may be performed by the server 1230 or one or more processors associated with the server. The location identifiers may include a location in real-world coordinates of a point associated with the detected lane mark, as described above with respect to FIG. 24E. In some embodiments, the location identifiers may also include other data, such as additional information about the road segment or lane mark. Additional data, such as accelerometer data, speed data, landmark data, road geometry or profile data, vehicle position data, ego-motion data, or various other forms of data described above, may also be received during step 2610. The location identifiers may be generated by vehicles, such as vehicles 1205, 1210, 1215, 1220, and 1225, based on images captured by the vehicles. For example, the identifier may be determined based on capturing at least one image representative of the host vehicle's environment from a camera associated with the host vehicle, analyzing the at least one image to detect lane marks in the host vehicle's environment, and analyzing the at least one image to determine a location of the detected lane mark relative to a location associated with the host vehicle. As discussed above, the lane marks may include a variety of different mark types, and the location identifier may correspond to various points associated with the lane mark. For example, if the detected lane mark is part of a dashed line marking a lane boundary, the point may correspond to a detected corner of the lane mark. If the detected lane mark is part of a solid line marking a lane boundary, the point may correspond to a detected end of the lane mark at various intervals, as discussed above. In some embodiments, the point may correspond to a centerline of the detected lane mark, as shown in FIG. 24C, or may correspond to at least one of an apex between two intersecting lane marks and two other points associated with the intersecting lane marks, as shown in FIG. 24D.

[0334] At step 2612, process 2600A may include associating the detected lane markings with corresponding road segments. For example, server 1230 may analyze the real-world coordinates or other information received during step 2610 and compare the coordinates or other information to position information stored in the autonomous vehicle road navigation model. Server 1230 may determine the road segment in the model that corresponds to the real-world road segment on which the lane marking was detected.

[0335] In step 2614, process 2600A may include updating an autonomous vehicle road navigation model associated with the corresponding road segment based on the two or more position identifiers associated with the detected lane marks. For example, the autonomous vehicle road navigation model may be a sparse map 800, and server 1230 may update the sparse map to include or adjust the lane marks mapped to the model. Server 1230 may update the model based on various methods or processes described above with respect to FIG. 24E. In some embodiments, updating the autonomous vehicle road navigation model may include storing one or more indicators of the positions in real-world coordinates of the detected lane marks. The autonomous vehicle road navigation model may include at least one target trajectory for the vehicle to follow along the corresponding road segment, as shown in FIG. 24E.

[0336] At step 2616, process 2600A may include distributing the updated autonomous vehicle road navigation model to multiple autonomous vehicles. For example, server 1230 may distribute the updated autonomous vehicle road navigation model to vehicles 1205, 1210, 1215, 1220, and 1225 that may use the model for navigation. The autonomous vehicle road navigation model may be distributed over wireless communication path 1235, via one or more networks (e.g., via a cellular network and / or the Internet, etc.), as shown in FIG.

[0337] In some embodiments, the lane mark may be mapped using data received from multiple vehicles, such as via crowdsourcing techniques, as described above with respect to FIG. 24E. For example, process 2600A may include receiving a first communication from a first host vehicle including a location identifier associated with the detected lane mark, and receiving a second communication from a second host vehicle including an additional location identifier associated with the detected lane mark. For example, the second communication may be received from a subsequent vehicle traveling the same road segment, i.e., from the same vehicle traveling subsequently along the same road segment. Process 2600A may further include refining a determination of at least one location associated with the detected lane mark based on the location identifier received in the first communication and based on the additional location identifier received in the second communication. This may include using an average of the multiple location identifiers and / or filtering out "ghost" identifiers that may not reflect a real-world location of the lane mark.

[0338] FIG. 26B is a flow chart illustrating an example process 2600B for autonomously navigating a host vehicle along a road segment using the mapped lane marks. Process 2600B may be performed, for example, by processing unit 110 of autonomous vehicle 200. In step 2620, process 2600B may include receiving an autonomous vehicle road navigation model from a server-based system. In some embodiments, the autonomous vehicle road navigation model may include a target trajectory of the host vehicle along the road segment and a position identifier associated with one or more lane marks associated with the road segment. For example, vehicle 200 may receive sparse map 800 or another road navigation model developed using process 2600A. In some embodiments, the target trajectory may be represented as a cubic spline, for example, as shown in FIG. 9B. As described above with respect to Figures 24A-24F, the location identifier may include the location in real-world coordinates of a point associated with the lane mark (e.g., a corner point of a dashed lane mark, an end point of a solid lane mark, a vertex between two intersecting lane marks and other points associated with the intersecting lane marks, a centerline associated with a lane mark, etc.).

[0339] In step 2621, process 2600B may include receiving at least one image representative of the vehicle's environment. The image may be received from an imaging device of the vehicle, such as via imaging 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.

[0340] In step 2622, process 2600B may include determining a longitudinal position of the host vehicle along the target trajectory. As discussed above with respect to FIG. 25A, this may be based on other information in the captured images (e.g., landmarks, etc.) or by dead-reckoning of the vehicle between detected landmarks.

[0341] In step 2623, process 2600B may include determining an expected lateral distance to the lane mark based on the determined longitudinal position of the host vehicle along the target trajectory and based on two or more position identifiers associated with the at least one lane mark. For example, vehicle 200 may use sparse map 800 to determine the expected lateral distance to the lane mark. As shown in FIG. 25B, a longitudinal position 2520 along the target trajectory 2555 may be determined in step 2622. Using sparse map 800, vehicle 200 may determine an expected distance 2540 to a mapped lane mark 2550 that corresponds to longitudinal position 2520.

[0342] In step 2624, process 2600B may include analyzing at least one image to identify at least one lane marking. Vehicle 200 may identify lane markings in the image using various image recognition techniques or algorithms, for example, as described above. For example, lane markings 2510 may be detected via image analysis of image 2500, as shown in FIG.

[0343] In step 2625, process 2600B may include determining an actual lateral distance to the at least one lane marking based on an analysis of the at least one image. For example, the vehicle may determine a distance 2530 representing the actual distance between the vehicle and lane marking 2510, as shown in FIG. 25A. The angle of the camera, the speed of the vehicle, the width of the vehicle, the position of the camera relative to the vehicle, or various other factors may be considered in determining the distance 2530.

[0344] In step 2626, process 2600B may include determining an autonomous steering action of the host vehicle based on a difference between the predicted lateral distance to the at least one lane mark and the determined actual lateral distance to the at least one lane mark. For example, as described above with respect to FIG. 25B, vehicle 200 may compare actual distance 2530 to predicted distance 2540. The difference between the actual distance and the predicted distance may indicate an error (and its magnitude) between the actual position of the vehicle and a target trajectory that the vehicle will follow. Thus, the vehicle may determine an autonomous steering action or other autonomous action based on the difference. For example, as shown in FIG. 25B, if actual distance 2530 is less than predicted distance 2540, the vehicle may determine an autonomous steering action to steer the vehicle left, away from lane mark 2510. Thus, the position of the vehicle relative to the target trajectory may be modified. Process 2600B may be used, for example, to improve navigation of the vehicle between landmarks.

[0345] Navigation based on road vector fields

[0346] As described above, vehicles, such as autonomous or semi-autonomous vehicles, may navigate based on road navigation maps. Such maps may provide information about drivable routes, lane meanings, intersections, etc., so that a host vehicle may navigate road segments and build accurate predictions for the future. In some embodiments, a vehicle may navigate based on a sparse map, as described above, that may provide sufficient information for navigation without requiring excessive data storage or data transfer rates. In some cases, localization issues may cause the sparse map or other navigation data to be inaccurate or unavailable. Other issues with maps may occur due to changes in road or lane markings, changes in traffic patterns, the addition of more traffic signs, or other real-world changes that may affect navigation. Additionally, there may be areas where map data is unavailable. Based on these and other potential issues with road navigation maps, it may be desirable to have alternative and / or additional methods for navigation based on sensor data.

[0347] Thus, the present disclosure may include a vehicle navigation system that analyzes images captured by an imaging device of a host vehicle. The system may analyze one or more pixels in the captured image to generate a predicted or estimated path at various points in the image. The estimated path may include any representation of movement that a vehicle or other agent (e.g., pedestrian, animal, etc.) associated with that point may have. In some embodiments, the estimated path may be an estimated path of the host vehicle. Additionally or alternatively, the estimated path may be determined relative to other vehicles, such as target vehicles imaged in the image. In some embodiments, the estimated path may represent the path of the virtual vehicle at various points where the virtual vehicle is not included in the image.

[0348] The estimated path may be represented in various forms. For example, the estimated path may include a set of points indicating the expected positions of the vehicle or other agent at various times in the future. The estimated path may be represented as a spline indicating the path the vehicle is expected to follow over a certain period of time. In some embodiments, the estimated path may be represented as a vector at a given point indicating the expected movement that a vehicle placed at that point should have. The vector prediction may be based on features detected in the image, such as road directional arrows, road edges, road curvature, or other road topology features detected in the image. Motion vector predictions at several points throughout the image may be aggregated to form an array or vector field of vectors indicating the expected movement at each point. The resulting vector field may be used, for example, to predict future paths of the host vehicle and / or other target vehicles in the host vehicle's environment. The resulting paths may be used in addition to or instead of sparse maps or other road navigation map data.

[0349] FIG. 27 is a diagram of an example image 2700 that may be captured by a host vehicle, according to disclosed embodiments. For example, image 2700 may be captured from the environment of host vehicle 200 using image capture unit 120, as described in detail above. Image 2700 may include road surface 2730 traveled by host vehicle 200. The road may include multiple travel lanes, as indicated by lane markings 2734 and 2736. In the example shown in FIG. 27, image 2700 may include an intersection or road entry point, as indicated by driveway 2732. The image may also include other vehicles, such as target vehicle 2720, which may enter the roadway from driveway 2732 in this example. Image 2700 may also include directional arrow 2738, indicating that the lane associated with directional arrow 2738 is a turn-only lane. Image 2700 may include other features, such as road edges 2740 and 2742, guard rails 2744, median strip 2746, etc.

[0350] The vehicle navigation system may be configured to detect road topology features from within the image 2700. Road topology features, as used herein, may include any natural or man-made features contained within the environment of the host vehicle. Such road topology features may indicate the configuration, layout, and traffic patterns of sections of roads. Road topology features may include any of the features contained within the image 2700, as described above. FIG. 28 is a diagram 2800 of another exemplary image with additional exemplary road topology features that may be detected by a host vehicle, according to disclosed embodiments. In some embodiments, the road topology features may include features on the surface of the road. For example, the road topology features may include directional arrows, such as turn arrows (e.g., directional arrow 2738 shown in FIG. 27), merge arrows, U-turn arrows, etc. The road topology features may include other road markings, such as lane markings, HOV lane markings, bike lane markings, stop lines, crosswalks (e.g., crosswalk 2840 shown in FIG. 28), or other markings on the surface of the road.

[0351] In some embodiments, the road topology features may include road edges. For example, image 2800 may include road edges 2810 and 2820 shown in FIG. 28 (and / or road edges 2740 and 2742 shown in FIG. 27). A road edge may be any transition from a drivable surface to a non-drivable surface or object. For example, road edge 2810 may be the end of a dirt, gravel, or grass surface area that indicates the edge of a road. Road edge 2820 may include an elevated structure such as a concrete curb or barrier that may indicate the edge of a road. Other barriers or obstacles such as guard rails 2744 may be detected as road topology features. In some embodiments, the road topology features may include the shape or curvature of the road. For example, image 2800 may include a curve 2850 that may be detected by a vehicle navigation system. Other features associated with the shape of the road may include merging lanes, exit ramps, intersections, lane divisions, etc.

[0352] Such road topology features may be detected using any suitable means. For example, a vehicle navigation system may use computer vision algorithms to detect and / or label objects in an environment in which an image is captured and processed, as described in various embodiments above. In some embodiments, the road topology features may be detected at least in part based on height. For example, multiple images captured at different times may indicate the height of objects in the image. In the image 2800, the road surface 2830 may be determined to have a different height than the road edge 2820, which may be used to detect the road topology features. In some embodiments, one or more of the road topology features may be identified based on a trained machine learning process. For example, a set of images containing the road topology features, along with labels or other identifying information for the features, may be input into a machine learning model as training data. The resulting trained model may be used to analyze the images and identify road topology features in the images. Various other machine learning algorithms may be used, including logistic regression, linear regression, regression, random forests, K-nearest neighbor (KNN) models, K-Means models, decision trees, cox proportional hazards regression models, naive Bayes models, support vector machine (SVM) models, gradient boosting algorithms, deep learning models, or any other type of machine learning model or algorithm.

[0353] Based on these detected features in the image, the vehicle navigation system may be configured to determine an estimated path through the environment for the vehicle associated with one or more points. In some embodiments, the estimated path may be represented as a prediction of the movement the vehicle would make at that point. FIG. 29 is a diagram of an example vector field overlaid on an image 2700, according to disclosed embodiments. As shown in FIG. 29, the vehicle navigation system may determine a predicted motion vector 2920 at point 2910. As explained above, the predicted motion vector 2920 may represent the predicted movement a vehicle located at point 2910 would make. In some embodiments, the predicted motion vector 2920 may represent the movement a vehicle traveling along a path that includes point 2910 would take. Because a vehicle is larger than a single point in an image, representative anchor points for the vehicle (e.g., an edge of the vehicle, a center point of the vehicle, etc.) may be assumed, and the motion vector may represent the movement a vehicle having an anchor point at that location would make. For example, predicted motion vector 2920 may indicate the direction the vehicle should travel based on the center point of the vehicle (or any other point on the vehicle) passing through point 2910. In some embodiments, the generated vector may have a predetermined length. For example, vector 2920 may represent a path connecting point 2910 to a second point a predetermined distance away from point 2910 (e.g., 0.5 meters, 1 meter, 2 meters, 5 meters, etc.) along the expected path of the vehicle at point 2910.

[0354] This process may be performed for several points in the image 2700, as shown in FIG. 29, to generate an array of vectors (i.e., vector field) 2930. Each of the vectors in the vector field 2930 may represent a direction in which a vehicle located at that point (e.g., having a center point or other point at that location) is expected or predicted to travel. The resulting array of vectors may map the expected travel path of each point in the image. For example, as shown in FIG. 29, a vehicle near a driveway 2732 may be expected to travel on the driveway near an entrance, and a vehicle on another side of the driveway may be expected to travel on the roadway. Similarly, the array of vectors may indicate that a vehicle in a turn lane (i.e., the lane associated with the turn arrow 2738 described above) may be expected to turn left, as indicated by a vector in that lane. Notably, the vector field may be determined without reliance on lane markings, such as lane marking 2734. This may be advantageous because lane markings may not always be present in all vehicle environments, such as urban environments. Additionally, lane markings may be difficult to detect due to, for example, limitations in image detection technology, wear or damage to the lane markings, debris or other material (e.g., dirt, snow, water, etc.) covering the lane markings, or objects obscuring the lane markings from view.

[0355] In some embodiments, vectors in the vector field 2930 may be determined for every pixel included in the captured image. Thus, the resulting vector field may have a resolution comparable to that of the captured image. In other embodiments, each point (e.g., point 2910) may be associated with a group of pixels. For example, point 2910 may represent a group of contiguous pixels (e.g., 2 pixels, 5 pixels, 10 pixels, 20 pixels, 50 pixels, etc.), and a predicted motion vector may be determined for each group of pixels in the image. The system may be configured to determine predicted motion vectors only for pixels associated with the road surface. For example, a trained neural network may be configured to detect the road surface 2730 and may generate vectors only for pixels (or groups of pixels) associated with the road surface 2730. In some embodiments, this may be inherent to the process of generating the vector field. For example, a vehicle navigation system may not generate vectors (or may generate vectors of zero magnitude) for pixels that are not associated with the road surface.

[0356] The vector field 2930 may be generated according to any suitable technique. In some embodiments, a trained neural network (or another form of machine learning model) may be deployed to generate the vector field for the input image. The model may be trained using a set of training data including a set of training images that have been examined and characterized such that each pixel (or group of pixels) is associated with a predicted motion vector. Such characterization may be performed, for example, by one or more human reviewers who analyze the captured images in the training data set and specify predicted motion vectors for the pixels of the captured images. As a result, the trained neural network model may be configured to determine a motion vector for each pixel in the input image.

[0357] The effectiveness and accuracy of a neural network may depend on the quality and quantity of available training data in a training dataset. Thus, there may be advantages provided by using a large training dataset (e.g., including hundreds, thousands, or millions of specified captured images). However, it may be practical or impossible to generate such a large dataset using a manual inspection process. To address this challenge, the system may use a set of image analysis algorithms designed to specifically identify the predicted motion vector for each pixel in the image. In some embodiments, this may be based on information contained in the sparse map. For example, an algorithm may be implemented to interpolate between multiple drivable paths contained in the sparse map to generate a corresponding vector field in the road segment. This vector field may be correlated to multiple images captured along the road segment to generate training data. Based on the resulting training dataset, a neural network model may be deployed to determine the vector fields of other input images.

[0358] The resulting vector field 2930 may be used to determine additional information about the host vehicle's environment. In some embodiments, a predicted path of the host vehicle or target vehicle, or a predicted path not associated with the vehicle, may be determined. FIG. 30 illustrates an example predicted path that may be determined according to disclosed embodiments. The vehicle navigation system may determine predicted paths 3010, 3012, 3014, and 3020 as shown in FIG. 30. The predicted paths 3010, 3012, 3014, and 3020 may be determined, at least in part, based on the vector field 2930. For example, a starting point for the predicted path may be established, and the system may track a series of predicted motion vectors through the vector field 2930 to generate the predicted path.

[0359] A predicted path may be generated at various locations within the image 2700. The predicted path 3010 may represent a predicted path of a host vehicle (e.g., the vehicle that captured the image). Thus, in embodiments where a sparse map or other navigation map is not available, the predicted path 3010 may be used to navigate the vehicle 200. In other embodiments, the predicted path 3010 may be used as a redundant path. For example, the predicted path 3010 may be used as a check to ensure that the information in the sparse map is accurate and up-to-date. Any discrepancies may be tracked and, in some embodiments, used to update the sparse map.

[0360] In some embodiments, the predicted path 3010 (or predetermined path 3012, 3014, and / or 3020) may be associated with a predetermined distance (D), as discussed above. In embodiments where each vector is associated with a predefined distance, the predicted path may be determined based on the number of vectors used to generate the path. For example, if each vector is 1 meter in length, then a predicted path generated by tracing 100 vectors will be 100 meters in length. The predicted path 3010 may originate from a point ahead of the host vehicle. In some embodiments, this may be a predetermined distance (e.g., 2 meters, 5 meters, 6 meters, 10 meters, etc.) ahead of the host vehicle. The predetermined path 3010 may follow a vector in the vector field 2930 for a predetermined distance (D) (e.g., 50 meters, 100 meters, 200 meters, etc.). In other embodiments, the predetermined path 3010 may extend as far as the path is visible in the image 2700.

[0361] A similar process may be used to generate predicted paths for other agents in the image 2700, such as the target vehicle 2720. For example, the vehicle navigation system may generate a predicted path 3020 that represents a predicted path of the target vehicle 2720. The predicted path 3020 may be determined in a similar manner to the predicted path 3010, for example, by tracing a vector ahead of the target vehicle 2720. In some embodiments, the vector may be traced for a predetermined distance, which may be the same for a predetermined distance D or may be different. The predicted path 3020 may originate from a point associated with the target vehicle 2720 detected in the image. For example, the path may include a center point of the target vehicle 2720. In some embodiments, a predicted path may be generated for multiple points associated with the target vehicle 2720. For example, a predicted path may be generated for points on either side of the target vehicle 2720 to generate a predicted corridor for the target vehicle 2720. This corridor may be used to predict an interaction between the host vehicle 200 and the target vehicle 2720. For example, the lateral separation between the predicted path 3010 and the predicted path 3020 (i.e., a corridor) may be analyzed to predict the future separation between the vehicles, which may be used to make navigation decisions.

[0362] In some embodiments, predicted paths may be generated for other locations not associated with the vehicle. For example, predicted paths 3012 and 3014 may be generated for lanes adjacent to the lane in which the host vehicle 200 is traveling. As shown in FIG. 30, predicted path 3014 may indicate that a vehicle traveling in a lane to the left of the host vehicle 200 is expected to turn. In some embodiments, the predicted path may branch into multiple possible paths. For example, a vehicle traveling along predicted path 3012 may turn into a private road 2732 or continue straight. Thus, predicted path 3012 may be split to indicate multiple paths based on an analysis of vector field 2930, as shown in FIG.

[0363] As discussed above, the vector field 2930 may be determined without consideration of lane marks, such as lane marking 2734. Thus, in areas where lane marks are detected, they may be used to adjust or calibrate the predicted path. For example, lane marks 2734 and 2736 may be detected by a vehicle navigation system but not used to generate the vector field 2930. The locations of lane marks 2734 and 2736 may then be used to adjust the vector field 2930 and / or the predicted path 3010. For example, various points in the vector field 2930 may be adjusted or fixed to align with the centerlines of lane marks 2734 and 2736. Additional centerlines may be interpolated throughout the image to align the vectors of the vector field 2930. As another example, the predicted path 3010 may be shifted to be centered within the lane based on the detected lane marks 2734 and 2736. In such embodiments where lane marks are not detected in the image or lane marks are unreliable, the vector field 2930 and the predicted path may not be adjusted based on the lane marks.

[0364] While images 2700 and 2800 represent images taken from a front camera of the host vehicle 200, it should be understood that various other camera angles may be used to generate the vector field and associated predicted path. For example, images taken from the side, corner, or rear of the vehicle may be used to generate the vector field using the process described above. Thus, the predicted path of the lanes adjacent to the host vehicle may be determined along with the predicted path of the vehicle traveling next to the host vehicle 200. In some embodiments, multiple cameras may be used to generate the vector field. For example, a side camera or corner camera may be used to extend the range of the main front camera. The resulting vector files may be combined to generate a vector field for a larger area around the host vehicle 200.

[0365] In some embodiments, in addition to following the trajectory along the vector field 2930, additional predictions of the target vehicle may be determined. For example, the speed of the target vehicle may be used to adjust the predicted path of the vehicle. Multiple images taken at different times (e.g., consecutive images, etc.) may be used to determine the speed of the target vehicle. To accomplish this, the change in the position of the vehicle may be tracked over two or more image frames, and the speed may be determined based on the time elapsed between the images. In some embodiments, this may include a lateral velocity, which may be a component of the vehicle's velocity that is perpendicular to the lane of travel and thus perpendicular to the vector field 2930. This lateral velocity may be used in determining the predicted path of the vehicle. For example, rather than following the trajectory of the vector field 2930, a predicted path of the vehicle may be generated that also moves laterally through the vector field 2930 according to the measured lateral velocity. This revised predicted path may be useful in determining the navigation operation of the host vehicle. For example, the navigation system may determine that the predicted path of the host vehicle and the predicted path of the target vehicle intersect. Based on the vector field, the navigation system can determine the distance ahead of vehicles whose paths intersect, and thus the host vehicle can decide whether to perform braking actions (e.g., to maintain a safe following distance), evasive actions, acceleration actions, etc.

[0366] FIG. 31 is a flow chart illustrating an exemplary process 3100 for navigating a host vehicle based on a vector field, according to the disclosed embodiments. The process 3100 may be performed by at least one processing device, such as the processing unit 110, as described above. It should be understood that throughout this disclosure, the term "processor" is used as an abbreviation for "at least one processor." In other words, a processor may include one or more structures that perform logical operations, regardless of whether such structures are collocated, connected, or distributed. In some embodiments, a non-transitory computer-readable medium may include instructions that, when executed by a processor, cause the processor to perform the process 3100. Furthermore, the process 3100 is not necessarily limited to the steps illustrated in FIG. 31, and any steps or processes of various embodiments described throughout this disclosure may also be included in the process 3100, including those described above with respect to FIGS. 27-30.

[0367] In step 3110, process 3100 may include receiving at least one image captured from a vehicle camera of the vehicle's environment. For example, image acquisition unit 120 may capture one or more images representative of the environment of host vehicle 200. The captured images may correspond to images such as those described above with respect to FIGS. 27-28.

[0368] In step 3120, the process 3100 may include analyzing the at least one image to identify road topology features within the vehicle's environment represented by the at least one image. As described above, the road topology features may be any natural or man-made object or other feature contained within the host vehicle's environment. In some embodiments, the road topology features may include a road surface, such as road surface 2730. The road topology may include other features, such as bends or curves in a road segment (e.g., curve 2850), edges of the road (e.g., road edges 2740, 2742, 2810, and / or 2820), barriers, curbs, walls, guard rails, etc. associated with road boundaries (e.g., road edges 2820). The road topology may include features, such as merge lanes, exit ramps, crosswalks (e.g., crosswalks 2840), intersections, lane divisions, driveways, or various other features of the road. In some embodiments, the height of one or more of the features described above, such as the height of the road surface or barriers, may be detected as a road topology feature. In some embodiments, analyzing the at least one image to identify road topology features may include analyzing the at least one image using a trained system, for example, the trained system may include a neural network, as described above.

[0369] Step 3120 may further include analyzing the at least one image to identify at least one point associated with the at least one image. For example, the at least one point may correspond to point 2910, as described above. The at least one point may be associated with at least one pixel of the at least one image, and in some embodiments, may be associated with multiple pixels of the at least one image. The at least one point may correspond to various locations within the at least one image. In some embodiments, the at least one point may be included along at least a portion of a predicted travel path of the vehicle that includes the at least one point. For example, the at least one point may be along a path of the host vehicle 200 or the target vehicle 2720. In some embodiments, the at least one point may not correspond to a location of the vehicle. For example, the at least one point may be selected as any point associated with the at least one image. In other embodiments, the at least one point may be determined based on a grid or other predefined interval.

[0370] In step 3130, the process 3100 may include determining an estimated path in the environment for the vehicle associated with the at least one point based on the identified road topology features. The estimated path may correspond to a predicted driving path from a location corresponding to the at least one point in the image, such as predicted paths 3010, 3012, 3014, and 3020. In some embodiments, the estimated path may be a predicted path of the host vehicle. Thus, the location may be ahead of the vehicle. In some embodiments, based on the current or planned speed of the vehicle, the vehicle may be located at a location within a predetermined time (e.g., 1 second, 2 seconds, 3 seconds, etc.). In some embodiments, the estimated path may correspond to a vector prediction of the vehicle's movement along a driving path ahead of the vehicle. The vector prediction of the vehicle's movement along a driving path ahead of the vehicle may be traversable by the vehicle within a predetermined time (e.g., 1 second, 2 seconds, 3 seconds, etc.). The process 3100 may further include predicting a heading direction of the vehicle based on the estimated path. In some embodiments, the predicted path may be associated with a target vehicle, such as target vehicle 2720. Thus, the process 3100 may include predicting the heading of the detected target vehicle based on the estimated path.

[0371] In step 3140, process 3100 may include having the vehicle perform a navigation action based on the estimated route. For example, the navigation action may be maintaining a current speed, maintaining a current direction, performing a braking action, performing a lateral movement (e.g., lane change, evasive action, etc.), accelerating, or a similar navigation action. In some embodiments, process 3100 may further include transmitting the estimated route to a location external to the vehicle. For example, the location may include another vehicle, such as the target vehicle 2720, another host vehicle (e.g., vehicles 1205-1225 described above), or another vehicle. In some embodiments, the location may be a server, such as server 1230 described above.

[0372] In some embodiments, the process 3100 may further include analyzing the at least one image to identify a second point associated with the at least one image. The processing unit 110 may be configured to determine an estimated second path within the environment of the vehicle associated with the second point based on the identified road topology features. The process 3100 may include causing the vehicle to perform a second navigation operation based on the estimated second path.

[0373] 32 is a flow chart illustrating an example process 3200 for navigating a host vehicle based on a vector field, according to disclosed embodiments. Process 3200 may be performed by at least one processing device, such as processing unit 110, as described above. In some embodiments, a non-transitory computer-readable medium may include instructions that, when executed by a processor, cause the processor to perform process 3200. Additionally, process 3100 is not necessarily limited to the steps illustrated in FIG. 32, and any steps or processes of the various embodiments described throughout this disclosure may also be included in process 3200, including those described above with respect to process 3100.

[0374] At step 3210, process 3200 may include receiving, from a vehicle camera, at least one image captured of the vehicle's environment. For example, image acquisition unit 120 may capture one or more images representative of the environment of host vehicle 200. The captured images may correspond to images such as those described above with respect to FIGS. 27-28.

[0375] In step 3220, process 3200 may include analyzing the at least one image to identify one or more road topology features within the vehicle's environment represented in the at least one image. For example, as described above, road topology features may include a road surface, a road surface height, a bend or curve in a road segment, an edge of a road, a barrier associated with a road boundary, a turn lane, a merge lane, an exit ramp, a crosswalk, an intersection, a lane division, a directional arrow, or any similar feature that may indicate a direction of travel for a vehicle.

[0376] In step 3230, the processor 3200 may include determining, for each of a plurality of segments of the at least one image, an estimated path at a location in the environment of the vehicle corresponding to one of the plurality of segments based on the identified one or more road topology features. In some embodiments, each of the plurality of segments may correspond to a single pixel of the at least one image. In other embodiments, each of the plurality of segments may correspond to multiple pixels of the at least one image. For example, a segment may correspond to multiple adjacent pixels. In some embodiments, the estimated path may correspond to a predicted path of the vehicle. Thus, one or more of the estimated paths correspond to a predicted driving path ahead of the detected target vehicle, and the one or more estimated paths correspond to a predicted driving path ahead of the vehicle.

[0377] In step 3240, process 3200 may include causing the vehicle to perform a navigation action based on at least one of the predicted paths associated with the segments of the at least one image. For example, the navigation action may include maintaining a current speed, maintaining a current heading, performing a braking action, performing a lateral movement, accelerating, or a similar navigation action.

[0378] Semantic Crane Explanation

[0379] As described above, a vehicle, such as an autonomous or semi-autonomous vehicle, may use imagery to assist in navigation in the vehicle's environment. As described above, a vehicle navigation system may analyze imagery to determine contextual information that may assist in determining a navigation operation. In some embodiments, the contextual information may include information about lanes included in the vehicle's environment. The vehicle navigation system may be configured to characterize the current lane in which the vehicle is traveling, as well as other surrounding lanes. For example, the system may determine a direction of travel associated with the lane, whether the lane is associated with an oncoming lane, use restrictions associated with the lane, or other information that may be relevant to determining a navigation operation. Such information may also be provided to a central server to generate and / or update a sparse map or other road map, as described in detail above.

[0380] FIG. 33 is a diagram of an example image 3300 that may be captured by a host vehicle for lane analysis, according to the disclosed embodiments. For example, the image 3300 may be captured from the environment of the host vehicle 200 using the image capture unit 120, as described in detail above. The image 3300 may include a representation of one or more lanes, such as lanes 3310, 3312, and 3314. A lane, as used herein, may refer to a portion of a road designated for use by a line of vehicles. In some cases, the lanes may be separated by lane marks, such as lane marks 3326 and 3322, as shown in FIG. 33. In the example image 3300, the lane 3310 may represent the current driving lane of the host vehicle. In other words, the host vehicle 200 may capture the image 3300 while traveling along the lane 3310. The lanes 3312 and 3314 may represent additional lanes in the environment of the vehicle 200, which may include driving lanes adjacent to the host vehicle's driving lane. Image 3300 may include other features or attributes that may provide context for characterizing lanes 3310, 3312, and / or 3314. For example, image 3300 may include a directional arrow 3320 associated with lane 3312, which may indicate that lane 3312 is a left turn lane, as shown.

[0381] The vehicle navigation system may analyze the image 3300 to characterize one or more lanes in the image. In some embodiments, the vehicle navigation system may analyze the image 3300 to identify the host vehicle's travel lane. In the example shown in FIG. 33, this may correspond to lane 3310. The lane 3310 may be identified using a variety of techniques. In some embodiments, the lane 3310 may be identified based on lane marks, such as lane marks 3324 and 3322. These lane marks may be identified based on various image analysis algorithms, such as object detection, edge detection, or other feature detection algorithms. Additional details regarding the detection of lanes and lane marks are described in detail above with respect to FIGS. 24A-D. In some embodiments, the lanes may be detected based on other features, such as road edges 3332 and 3334. The vehicle navigation system may be further configured to detect one or more additional lanes, such as lanes 3312 and 3314, as described above. The additional lanes may be identified using similar techniques as the lanes of the host vehicle.

[0382] The vehicle navigation system may further analyze the image 3330 to identify attributes associated with additional travel lanes. Attributes may include any quality or characteristic that may be detected from an image associated with a travel lane. For example, the system may identify a directional arrow 3320 associated with lane 3312. The directional arrow 3320 (and / or other attributes of the image 3300) may be detected using a variety of techniques. In some embodiments, the attributes may be detected using object recognition algorithms similar to the lane markings 3322 and 3324, as described above. Additional examples of attributes that may be identified by a vehicle navigation system are provided below with reference to FIG. 34.

[0383] The vehicle navigation system may be further configured to determine information indicative of additional travel lane characterization based on the attributes. Lane characterization may include any description or classification of the lane. In some embodiments, the lane may be characterized according to a set of predefined lane characteristic categories. Example characteristics may include lane presence, lane type, lane orientation, and / or lane direction. Lane presence may be a characteristic or value indicative of whether a particular lane is present. Lane presence may be represented in various forms, including a numeric value (e.g., "1" indicating lane is present, "0" indicating lane is not present), a text value (e.g., "present" / "not present", "yes" / "no", etc.), a numeric value, alphanumeric value, symbol or icon, or other various forms. The presence of a particular lane, which may be identified based on its spatial relationship to the host vehicle lane (e.g., 2 lanes to the left, 1 lane to the left, 1 lane to the right, 2 lanes to the right, etc.), may be determined based on image analysis, as described above.

[0384] The lane orientation characteristic may indicate whether the traffic direction associated with the lane is leading (i.e., in the same direction as the host vehicle) or oncoming (i.e., in the opposite direction from the host vehicle). Similar to lane occupancy values, lane orientation may be represented as a numeric value (e.g., 0=leading, 1=oncoming, etc.), a text value (e.g., "leading" / "oncoming", etc.), a numeric, alphanumeric, symbolic or iconic (e.g., arrows, etc.), or a variety of other formats. In some embodiments, a lane may be bidirectional, such as a center turn lane, a turn-use lane, a merge lane, etc.

[0385] The lane direction characteristic may indicate a target direction associated with the lane. For example, a lane that continues straight ahead may be designated as a "straight" lane, and a turning lane may be designated as a "left turn" or "right turn" lane. Various other lane direction classifications may be used, for example, indicating that the lane is a merge lane (e.g., "merge left" or "merge right"), an exit lane, etc. In some embodiments, the lane direction may be represented by a numeric value or code associated with various lane directions (e.g., 1=right, 2=left, 3=right, etc.). In some embodiments, a lane may be associated with multiple directions. For example, a lane may allow a vehicle to continue straight or turn right (e.g., as designated by a straight / right arrow, etc.). Thus, a lane may have a separate "straight / right" designation, or may have multiple designated lane directions (e.g., "straight" and "right turn").

[0386] Lane type characteristics may include restrictions associated with a lane that may specify the type or class of vehicle permitted to travel in the lane. For example, lane type may indicate whether a lane is a ride-sharing vehicle (HOV) lane or carpool lane, a truck-only lane, a no-trucks lane, a bus-only lane, a lane restricted by vehicle weight, a bicycle lane, an emergency vehicle-only lane, etc. Lane type may include additional classifications such as whether a lane is associated with a shoulder (e.g., a hard shoulder), whether a lane is open or closed for travel, whether a lane is a parking lane (e.g., along a row of parking meters, etc.). In some embodiments, lane type may be represented by a text value or string (e.g., "HOV", "bus only", etc.). In other embodiments, lane type may be represented by a numeric value or code associated with various lane types (e.g., 0=unknown, 1=shoulder, 3=bicycle lane, 4=carpool, etc.).

[0387] In some embodiments, the characterization of a lane may include a spatial relationship to a host vehicle lane. For example, a lane may be characterized as a particular number of lanes (e.g., 1, 2, 3, etc.) from a host vehicle lane in a particular direction (e.g., left turn or right turn). Various other characterizations may also be identified, including lane surface type (e.g., dirt, gravel, asphalt, concrete, etc.), lane condition (e.g., wet, dry, damaged, etc.), lane color, lane height, or various other characteristics. In some embodiments, one or more of these examples may be combined or included with another classification attribute, such as lane type, or may be used to identify other lane characterizations described above.

[0388] In some embodiments, determining information indicative of lane characterization may be based, at least in part, on historical information. Historical information may include any information associated with the lane or road segment recorded before the image was captured. In some embodiments, the historical information may be captured by the host vehicle (e.g., at a previous point along the same traveling road segment, during a previous trip along the same road segment, etc.). The historical information may thus be stored in a memory of the host vehicle, such as memory 140. In other embodiments, the historical information may be captured by another vehicle and thus stored in a central location (e.g., server 1230) or as part of a set of navigation information (e.g., map database 160). In some embodiments, the historical information may be included in or associated with a sparse map. The historical information may be captured any period of time before the current image was captured. For example, the historical information may be years, months, weeks, or days prior to the current analysis, or may be hours, minutes, or seconds prior to the analysis. In some embodiments, the historical information may be indices of lane characteristics captured by the host vehicle at a previous position along the road segment. For example, the host vehicle may detect a traffic sign indicating an upcoming lane split and may analyze the image to determine the lane characterization and confirm a previously identified split. For example, directional arrow 3320 may indicate that lane 3312 is a turn lane and therefore split from lanes 3310 and 3314.

[0389] The various image analysis and characterization steps described above may be performed using a variety of methods. In some embodiments, one or more of these steps may be performed using a trained machine learning model. For example, a neural network (e.g., a convolutional neural network, a recurrent neural network, etc.) may be trained to determine information indicative of lane characterizations based on a set of training data. The training data may include a set of images that have been surveyed and characterized such that each lane in the images is tagged with a particular lane characterization. Thus, the trained neural network model may be configured to output lane characterizations based on input images. In some embodiments, the neural network may be trained to use a series of two or more images to determine lane characterizations in accordance with disclosed embodiments.

[0390] In some embodiments, the neural network model (or a separate neural network model) may also be trained to identify lanes and / or attributes in an image. For example, a training dataset may include a set of training images with identified lanes (e.g., lanes 3310, 3312, and 3314) and / or attributes (e.g., directional arrow 3320) such that the trained neural network model is configured to detect and identify the lanes and attributes in input images. The resulting trained neural network may be...

Claims

1. 1. A navigation system for a vehicle, comprising: receiving at least one image captured from a camera of the vehicle of an environment of the vehicle; analyzing the at least one image to identify road topology features within the environment of the vehicle represented in the at least one image and a plurality of spaced apart points associated with the at least one image; determining a plurality of motion vectors associated with the plurality of points based on the road topology features, each of the plurality of motion vectors indicating a direction of travel associated with a corresponding point; determining an estimated path for the vehicle through the environment based on the plurality of motion vectors; causing the vehicle to perform a navigation operation based on the estimated route; and at least one processor programmed to: The estimated route corresponds to a route that the vehicle is predicted to travel from at least one of the plurality of points.

2. The navigation system of claim 1 , wherein the road topology features include a road surface.

3. The navigation system according to claim 1 or 2, wherein the road topology features include a road surface height.

4. A navigation system according to claim 1 , wherein the road topology features include at least one of a bend or curve in a road segment, an edge of a road, or a barrier associated with a road boundary.

5. The navigation system of claim 1 , wherein the road topology features include at least one of a turn lane, a merge lane, an exit ramp, a crosswalk, an intersection, or a lane division.

6. 6. The navigation system of claim 1, wherein analyzing the at least one image to identify the road topology features comprises analyzing the at least one image using a trained system.

7. The navigation system of claim 6 , wherein the trained system comprises a neural network.

8. A navigation system according to claim 1 , wherein said at least one point is associated with at least one pixel of said at least one image.

9. A navigation system according to claim 1 , wherein the at least one point is associated with a number of pixels of the at least one image.

10. A navigation system according to claim 1 , wherein the estimated path corresponds to a predicted driving path from a location corresponding to the at least one point in the image.

11. The navigation system of claim 10 , wherein the location is in front of the vehicle.

12. 12. A navigation system according to claim 10 or 11, wherein the vehicle can be positioned at the location within a predetermined time based on a current or planned speed of the vehicle.

13. The navigation system of claim 12, wherein the predetermined time is at least one second.

14. A navigation system according to any one of claims 1 to 13, wherein the estimated path corresponds to a vectorial prediction of the movement of the vehicle along a driving path ahead of the vehicle.

15. 15. The navigation system of claim 14, wherein the vector prediction of the movement of the vehicle along the driving path ahead of the vehicle is traversable by the vehicle within a predetermined time.

16. 16. The navigation system of claim 15, wherein the predetermined time is at least one second.

17. 17. The navigation system of claim 1, wherein the at least one processor is further programmed to transmit the estimated route to a location external to the vehicle.

18. The navigation system of claim 17 , wherein the location includes another vehicle.

19. 19. A navigation system according to claim 17 or 18, wherein the location includes a server.

20. 20. The navigation system of claim 1, wherein the at least one processor is further programmed to predict a heading of the vehicle based on the estimated route.

21. 21. The navigation system of claim 1, wherein the at least one processor is further programmed to predict a heading of a detected target vehicle based on the estimated path.

22. 22. The navigation system of claim 1, wherein the at least one point is selected as any point associated with the at least one image.

23. 22. A navigation system according to any one of claims 1 to 21, wherein said at least one point does not correspond to a vehicle position.

24. 22. A navigation system according to any one of claims 1 to 21, wherein the at least one point is included along at least a portion of a predicted travel route of the vehicle that includes the at least one point.

25. 25. The navigation system of claim 1, wherein the at least one processor is further programmed to analyze the at least one image to identify a second point associated with the at least one image.

26. 26. The navigation system of claim 25, wherein the at least one processor is further programmed to determine an estimated second path through the environment for the vehicle associated with the second point based on the identified road topology features.

27. 27. The navigation system of claim 26, wherein the at least one processor is further programmed to cause the vehicle to perform a second navigation operation based on the estimated second route.

28. 1. A navigation system for a vehicle, comprising: receiving at least one image captured from a camera of the vehicle of an environment of the vehicle; analyzing the at least one image to identify one or more road topology features in the environment of the vehicle represented in the at least one image and a plurality of points associated with the at least one image at predetermined intervals to detect a plurality of segments of the at least one image; determining a plurality of motion vectors associated with the plurality of points based on the road topology features, each of the plurality of motion vectors indicating a direction of travel associated with a corresponding point; determining a plurality of estimated paths based on the plurality of motion vectors; causing the vehicle to perform a navigation operation based on at least one of the estimated paths associated with the plurality of segments of the at least one image; and at least one processor programmed to: A navigation system, wherein each of the plurality of estimated routes corresponds to a route that the vehicle is predicted to travel from a corresponding one of the plurality of segments.

29. 30. The navigation system of claim 28, wherein each of the plurality of segments corresponds to a single pixel of the at least one image.

30. 29. The navigation system of claim 28, wherein each of the plurality of segments corresponds to a plurality of pixels of the at least one image.

31. The navigation system of claim 30, wherein the pixels are adjacent.

32. 32. A navigation system according to any one of claims 28 to 31, wherein one or more of the estimated routes correspond to a predicted driving path ahead of the vehicle.

33. 33. A navigation system according to any one of claims 28 to 32, wherein one or more of the estimated paths correspond to a predicted driving path ahead of a detected target vehicle.

34. 34. A navigation system according to any one of claims 28 to 33, wherein the one or more road topology features include a road surface.

35. 35. A navigation system according to any one of claims 28 to 34, wherein the one or more road topology features include a road surface height.

36. 36. A navigation system according to any one of claims 28 to 35, wherein the one or more road topology features include at least one of a bend or curve in a road segment, an edge of a road, or a barrier associated with a road boundary.

37. 37. The navigation system of any one of claims 28 to 36, wherein the one or more road topology features include at least one of a turn lane, a merging lane, an exit ramp, a crosswalk, an intersection, or a lane division.

Images