Augmented Reality with Multi-View

Abstract

Systems and methods for generating augmented image data for vehicle navigation are disclosed. In one implementation, a system includes a processor programmed to receive an image captured by a camera of a host vehicle; segment the image by identifying a first portion of the at least one image and a second portion of the at least one image; receive a point cloud generated based on an output of a LIDAR; determine a location for an augmented reality object relative to the image based on the first portion of the segmented image and at least a portion of the point cloud; select or generate an augmented reality object; and augment the image to include a representation of the augmented reality object.

Description

Cross-reference to related patent applications

[0001] This application claims priority to U.S. Provisional Application No. 63 / 618,629, filed January 8, 2024, and U.S. Provisional Application No. 63 / 679,291, filed August 5, 2024. The aforementioned applications are incorporated herein by reference in their entirety. BACKGROUND Field of technology

[0002] The present disclosure relates generally to vehicle navigation and, more particularly, to systems and methods for enhancing images captured from the surroundings of a vehicle. Background information

[0003] As technology advances, the goal of a fully autonomous vehicle capable of navigating roadways is on the horizon. Autonomous vehicles may need to consider a variety of factors and make appropriate decisions based on those factors to reach an intended destination safely and accurately. For example, an autonomous vehicle may need to process and interpret visual information (e.g., information captured by a camera) and may also use information from other sources (e.g., a GPS device, a speed sensor, an accelerometer, a suspension sensor, etc.). At the same time, to navigate to a destination, an autonomous vehicle may also need to know its location within a specific roadway (e.g.,a specific lane within a multi-lane road), navigate alongside other vehicles, avoid obstacles and pedestrians, observe traffic signals and signs, and travel from one road to another at appropriate intersections or junctions. Harnessing and interpreting large amounts of information collected by an autonomous vehicle as it travels toward its destination presents a variety of design challenges. The sheer volume of data (e.g., captured imagery, map data, GPS data, sensor data, etc.) that an autonomous vehicle may need to analyze, retrieve, and / or store presents challenges that can actually limit or even negatively impact autonomous navigation.Furthermore, when an autonomous vehicle relies on traditional mapping technology for navigation, the sheer volume of data required to store and update the map presents formidable challenges. SUMMARY

[0004] Embodiments consistent with the present disclosure provide systems and methods for enhancing images captured from the surroundings of a vehicle.

[0005] In one embodiment, a system for generating augmented image data for vehicle navigation may include at least one processor. The at least one processor may be programmed to receive at least one image captured by a camera of the host vehicle from an environment of the host vehicle; segment the at least one image, wherein segmenting the image includes identifying a first portion of the at least one image and a second portion of the at least one image, wherein the first portion is different from the second portion; receive a point cloud generated based on an output of a LIDAR; determine a location for an augmented reality object relative to the at least one image based on the first portion of the segmented image and at least a portion of the point cloud; select or generate an augmented reality object;and augment the at least one image to include a representation of the augmented reality object, wherein the at least one augmented image includes a representation of the environment of the host vehicle and the representation of the augmented reality object;

[0006] In one embodiment, a method for generating augmented image data for vehicle navigation may include receiving at least one image captured by a camera of the host vehicle from an environment of the host vehicle; segmenting the at least one image, wherein segmenting the image includes identifying a first portion of the at least one image and a second portion of the at least one image, the first portion being different from the second portion; receiving a point cloud generated based on an output of a LIDAR; determining a location for an augmented reality object relative to the at least one image based on the first portion of the segmented image and at least a portion of the point cloud; selecting or generating an augmented reality object;and augmenting the at least one image to include a representation of the augmented reality object, wherein the at least one augmented image includes a representation of the surroundings of the host vehicle and the representation of the augmented reality object;

[0007] In accordance with other disclosed embodiments, non-transitory computer-readable storage media may store program instructions executed by at least one processor and performing any of the methods described herein.

[0008] The foregoing general description and the following detailed description are exemplary and explanatory only and do not limit the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various disclosed embodiments. In the drawings: Fig. 1 is a schematic diagram of an exemplary system in accordance with the disclosed embodiments. Fig. 2A is a schematic side view illustration of an exemplary vehicle including a system, in accordance with the disclosed embodiments. Fig. 2B is a schematic plan view of the Fig. 2A, in accordance with the disclosed embodiments. Fig. 2C is a schematic top view illustration of another embodiment of a vehicle including a system, in accordance with the disclosed embodiments. Fig. 2D is a schematic top view illustration of yet another embodiment of a vehicle including a system in accordance with the disclosed embodiments. Fig. 2E is a schematic top view illustration of yet another embodiment of a vehicle including a system in accordance with the disclosed embodiments. Fig. 2F is a schematic diagram of exemplary vehicle control systems, in accordance with the disclosed embodiments. Fig. 3A is a schematic representation of an interior of a vehicle including a rearview mirror and a user interface for a vehicle imaging system, in accordance with the disclosed embodiments. Fig. 3B is an illustration of an example of a camera mount configured to be positioned behind a rearview mirror and against a vehicle windshield, in accordance with the disclosed embodiments. Fig. 3C is an illustration of the Fig. 3B from a different perspective, in accordance with the disclosed embodiments. Fig. 3D is an illustration of an example of a camera mount configured to be positioned behind a rearview mirror and against a vehicle windshield, in accordance with the disclosed embodiments. Fig. 4 is an exemplary block diagram of a memory configured to store instructions for performing one or more operations, in accordance with the disclosed embodiments. Fig. 5A is a flowchart illustrating an exemplary process for initiating one or more navigation responses based on monocular image analysis, in accordance with the disclosed embodiments. Fig. 5B is a flowchart illustrating an exemplary process for detecting one or more vehicles and / or pedestrians in a set of images, in accordance with the disclosed embodiments. Fig. 5C is a flowchart illustrating an exemplary process for detecting road markings and / or lane geometry information in a set of images, in accordance with the disclosed embodiments. Fig. 5D is a flowchart illustrating an exemplary process for detecting traffic lights in a set of images, in accordance with the disclosed embodiments. Fig. 5E is a flowchart illustrating an example process for initiating one or more navigation responses based on a vehicle path, in accordance with the disclosed embodiments. Fig. 5F is a flowchart illustrating an exemplary process for determining whether a leading vehicle is changing lanes, in accordance with the disclosed embodiments. Fig. 6 is a flowchart illustrating an exemplary process for initiating one or more navigation responses based on stereo image analysis, in accordance with the disclosed embodiments. Fig. 7 is a flowchart illustrating an exemplary process for initiating one or more navigation responses based on an analysis of three sets of images, in accordance with the disclosed embodiments. Fig. Figure 8 shows a sparse map for providing autonomous vehicle navigation, in accordance with the disclosed embodiments. Fig. 9A illustrates a polynomial representation of portions of a road segment, in accordance with the disclosed embodiments. Fig. 9B illustrates a curve in three-dimensional space representing a target trajectory of a vehicle for a particular road segment contained in a sparse map, in accordance with the disclosed embodiments. Fig. 10 illustrates exemplary landmarks that may be included in a sparse map in accordance with the disclosed embodiments. Fig. Figure 11A shows polynomial representations of trajectories, in accordance with the disclosed embodiments. The Fig. 11B and Fig. 11C show target trajectories along a multi-lane road, in accordance with the disclosed embodiments. Fig. 11D shows an exemplary road signature profile, in accordance with the disclosed embodiments. Fig. 12 is a schematic diagram of a system that utilizes crowdsourced data received from a plurality of vehicles for autonomous vehicle navigation, in accordance with the disclosed embodiments. Fig. 13 illustrates an exemplary road navigation model for autonomous vehicles represented by a plurality of three-dimensional splines, in accordance with the disclosed embodiments. Fig. 14 shows a map skeleton generated by combining location information from multiple trips, in accordance with the disclosed embodiments. Fig. 15 shows an example of a longitudinal alignment of two trips with example signs as landmarks, in accordance with the disclosed embodiments. Fig. 16 shows an example of a longitudinal alignment of many trips with an example sign as a landmark, in accordance with the disclosed embodiments. Fig. 17 is a schematic illustration of a system for generating trip data using a camera, a vehicle, and a server, in accordance with the disclosed embodiments. Fig. 18 is a schematic illustration of a system for crowdsourcing a sparse map, in accordance with the disclosed embodiments. Fig. 19 is a flowchart illustrating an exemplary process for generating a sparse map for autonomous vehicle navigation along a road segment, in accordance with the disclosed embodiments. Fig. 20 illustrates a block diagram of a server, in accordance with the disclosed embodiments. Fig. 21 illustrates a block diagram of a memory in accordance with the disclosed embodiments. Fig. 22 illustrates a process for clustering vehicle trajectories associated with vehicles in accordance with the disclosed embodiments. Fig. 23 illustrates a navigation system for a vehicle that may be used for autonomous navigation in accordance with the disclosed embodiments. The Fig. 24A, Fig. 24B, Fig. 24C and Fig. 24D illustrate exemplary lane markings that may be detected in accordance with the disclosed embodiments. Fig. 24E shows exemplary mapped lane markings, in accordance with the disclosed embodiments. Fig. 24F shows an exemplary anomaly associated with detecting a lane marking in accordance with the disclosed embodiments. Fig. 25A shows an exemplary image of a vehicle's surroundings for navigation based on the depicted lane markings, in accordance with the disclosed embodiments. Fig. 25B illustrates a lateral localization correction of a vehicle based on mapped lane markings in a road navigation model, in accordance with the disclosed embodiments. The Fig. 25C and Fig. 25D provide conceptual illustrations of a localization technique for locating a host vehicle along a target trajectory using mapped features contained in a sparse map. Fig. 26A is a flowchart illustrating an exemplary process for mapping a lane marking for use in autonomous vehicle navigation, in accordance with the disclosed embodiments. Fig. 26B is a flowchart illustrating an exemplary process for autonomously navigating a host vehicle along a road segment using mapped lane markings, in accordance with the disclosed embodiments. Fig. 27A illustrates an example image depicting an environment of a host vehicle in accordance with the disclosed embodiments. Fig. 27B illustrates an example segmented image that may be generated based on a captured image, in accordance with the disclosed embodiments. Fig. 28 illustrates example techniques for positioning an augmented reality object within an environment, in accordance with the disclosed embodiments. Fig. 29A illustrates an example of an augmented image depicting an environment of a host vehicle in accordance with the disclosed embodiments. Fig. Figure 29B illustrates an example of a transparent display for displaying an enhanced image, in accordance with the disclosed embodiments. The Fig. 30A and Fig. 30B illustrate further examples of augmented images depicting an environment of a host vehicle in accordance with the disclosed embodiments. Fig. 31 is a block diagram illustrating an example process for training a target system using augmented images, in accordance with the disclosed embodiments. Fig. 32 is a flowchart illustrating an example process for generating enhanced image data, in accordance with the disclosed embodiments. DETAILED DESCRIPTION

[0010] The following detailed description refers to the accompanying drawings. Where possible, the same reference numerals are used in the drawings and the following description to refer to the same or similar parts. While several illustrative embodiments are described herein, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the components illustrated in the drawings, and the illustrative methods described herein may be modified by replacing, rearranging, removing, or adding steps to the disclosed methods. Accordingly, the following detailed description is not limited to the disclosed embodiments and examples. Instead, the proper scope is defined by the appended claims. Overview of autonomous vehicles

[0011] As used throughout this disclosure, the term "autonomous vehicle" refers to a vehicle capable of implementing at least one navigation change without driver input. A "navigation change" refers to a change in one or more of the vehicle's steering, braking, or acceleration. To be autonomous, a vehicle does not have to be fully automatic (e.g., operating entirely without a driver or without driver input). Rather, an autonomous vehicle includes those capable of operating under driver control during certain periods and without driver control during other periods. Autonomous vehicles may also include vehicles that control only some aspects of vehicle navigation, such as steering (e.g., to maintain a vehicle course between vehicle lane restrictions), but may defer other aspects to the driver (e.g., braking).In some cases, autonomous vehicles can handle some or all aspects of braking, speed control, and / or steering the vehicle.

[0012] Because human drivers typically rely on visual cues and observations to steer a vehicle, transportation infrastructures are built accordingly, with lane markings, traffic signs, and traffic lights all designed to provide visual information to drivers. Given these design characteristics of transportation infrastructures, an autonomous vehicle may include a camera and a processing unit that analyzes visual information captured from the vehicle's surroundings. The visual information may include, for example, transportation infrastructure components (e.g., lane markings, traffic signs, traffic lights, etc.) observable by drivers and other obstacles (e.g., other vehicles, pedestrians, debris, etc.).Additionally, an autonomous vehicle may also use stored information, such as information that provides a model of the vehicle's surroundings when navigating. For example, the vehicle may use GPS data, sensor data (e.g., from an accelerometer, a speed sensor, a suspension sensor, etc.), and / or other mapping data to provide information regarding its surroundings while the vehicle is traveling, and the vehicle (as well as other vehicles) may use the information to locate itself on the model.

[0013] In some embodiments in this disclosure, an autonomous vehicle may use information obtained while navigating (e.g., from a camera, a GPS device, an accelerometer, a speed sensor, a suspension sensor, etc.). In other embodiments, an autonomous vehicle may use information obtained from previous navigations by the vehicle (or by other vehicles) while navigating. In still other embodiments, an autonomous vehicle may use a combination of information obtained while navigating and information obtained from previous navigations. The following sections provide an overview of a system consistent with the disclosed embodiments, followed by an overview of a forward-facing imaging system and methods consistent with the system.The following sections disclose systems and methods for constructing, using, and updating a sparse map for autonomous vehicle navigation. System overview

[0014] Fig. 1 is a block diagram representation of a system 100, consistent with the exemplary disclosed embodiments. The system 100 may include various components depending on the requirements of a particular implementation. In some embodiments, the system 100 may include a processing unit 110, an image capture unit 120, a position sensor 130, one or more storage 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 other suitable processing device.Similarly, the image capture unit 120 may include any number of image capture devices and components depending on the requirements of a particular application. In some embodiments, the image capture unit 120 may include one or more image capture devices (e.g., cameras), such as the image capture device 122, the image capture device 124, and the image capture device 126. The system 100 may also include a data interface 128 communicatively connecting the processing device 110 to the image capture device 120. For example, the data interface 128 may include one or more wired and / or wireless connections for transmitting the image data captured by the image capture device 120 to the processing unit 110.

[0015] The wireless transceiver 172 may include one or more devices configured to exchange transmissions over an air interface to one or more networks (e.g., cellular, Internet, etc.) using a radio frequency, an infrared frequency, a magnetic field, or an electric field. The wireless transceiver 172 may use any known standard for transmitting and / or receiving data (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 also include communications (one-way or two-way) between the host vehicle and one or more target vehicles in a vicinity of the host vehicle (e.g.,to facilitate the coordination of the navigation of the host vehicle with respect to or together with target vehicles in the vicinity of the host vehicle) or even involve a broadcast transmission to unspecified receivers in an vicinity of the transmitting vehicle.

[0016] Both application processor 180 and image processor 190 may include various types of processing devices. For example, one or both of application processor 180 and image processor 190 may include a microprocessor, pre-processors (such as an image pre-processor), a graphics processing unit (GPU), a central processing unit (CPU), support circuitry, digital signal processors, integrated circuits, memory, or any other type of device suitable for executing applications and for image processing and analysis. In some embodiments, application processor 180 and / or image processor 190 may include any type of single- or multi-core processor, mobile device microcontroller, central processing unit, etc.Various processing devices may be used, including, for example, processors available from manufacturers such as Intel®, AMD®, etc., or GPUs available from manufacturers such as NVIDIA®, ATI®, etc., and may include various architectures (e.g., x86 processor, ARM®, etc.).

[0017] In some embodiments, the application processor 180 and / or the image processor 190 may include any of the EyeQ series of processor chips available from Mobileye®. These processor configurations each include multiple processing units with local memory and instruction sets. Such processors may include video inputs for receiving image data from multiple image sensors and may also include video output capabilities. In one example, the EyeQ2® uses 90 nm micrometer technology operating at 332 MHz. The EyeQ2® architecture consists of two floating-point hyper-threaded 32-bit RISC CPUs (MIPS32® 34K® cores), five Vision Computing Engines (VCE), three Vector Microcode Processors (VMP®), Denali 64-bit mobile DDR controller, 128-bit internal Sonics interconnect, dual 16-bit video input and 18-bit video output controllers, 16-channel DMA, and multiple peripherals.The MIPS34K CPU manages the five VCEs, three VMP™, and the DMA, the second MIPS34K CPU and the multi-channel DMA, as well as the other peripherals. The five VCEs, three VMP®, and the MIPS34K CPU can perform intensive vision computations required by multifunction bundle applications. In another example, the EyeQ3®, which is a third-generation processor and six times more powerful than the EyeQ2®, may be used in the disclosed embodiments. In other examples, the EyeQ4® and / or the EyeQ5® may be used in the disclosed embodiments. Of course, any newer or future EyeQ processing devices may also be used with the disclosed embodiments.

[0018] Any of the processing devices disclosed herein may be configured to perform particular functions. Configuring a processing device, such as any of the described EyeQ processors or other controller or microprocessor, to perform particular functions may involve programming computer-executable instructions and providing those instructions to the processing device for execution during operation of the processing device. In some embodiments, configuring a processing device may involve programming the processing device directly with architectural instructions.For example, processing devices such as field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and the like may be configured using, for example, one or more hardware description languages (HDLs).

[0019] In other embodiments, configuring a processing device may include storing executable instructions in a memory accessible to the processing device during operation. For example, the processing device may access the memory to obtain and execute the stored instructions during operation. In any event, the processing device configured to perform the sensing, image analysis, and / or navigation functions disclosed herein represents a specialized hardware-based system that controls multiple hardware-based components of a host vehicle.

[0020] Although Fig. 1 depicts two separate processing devices included within 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 more than two processing devices. Further, in some embodiments, system 100 may include one or more processing units 110 without including other components, such as image acquisition unit 120.

[0021] 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, digital signal processors, integrated circuits, memory, or any other type of device for image processing and analysis. The image preprocessor may include a video processor for acquiring, digitizing, and processing the image material from the image sensors. The CPU may include any number of microcontrollers or microprocessors. The GPU may also include any number of microcontrollers or microprocessors.The support circuitry may be any number of circuits generally well known in the art, including cache, power supply, 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, tape storage, removable storage, and other types of storage. In one case, the memory may be separate from the processing unit 110. In another case, the memory may be integrated into the processing unit 110.

[0022] Each memory 140, 150 may include software instructions that, when executed by a processor (e.g., application processor 180 and / or image processor 190), may control the operation of various aspects of system 100. These storage units may include various databases and image processing software, as well as a trained system, such as a neural network or a deep neural network. The storage units may include random access memory (RAM), read-only memory (ROM), flash memory, disk drives, optical storage, tape storage, removable storage, and / or any other types of storage. In some embodiments, the storage units 140, 150 may be separate from the application processor 180 and / or image processor 190. In other embodiments, these storage units may be integrated into the application processor 180 and / or image processor 190.

[0023] 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 receivers may determine user position and velocity 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.

[0024] In some embodiments, the system 100 may include components such as a speed sensor (e.g., a speedometer, a tachometer) for measuring a speed of the vehicle 200 and / or an accelerometer (either single-axis or multi-axis) for measuring the acceleration of the vehicle 200.

[0025] The user interface 170 may include any device suitable for providing information to or receiving input from one or more users of the system 100. In some embodiments, the user interface 170 may include user input devices, including, for example, a touchscreen, a microphone, a keyboard, pointing devices, scroll wheels, cameras, knobs, buttons, etc. With such input devices, a user may be able to provide information input or commands to the system 100 by typing instructions or information, providing voice commands, selecting menu options on a screen using buttons, pointers, or eye-tracking capabilities, or through any other suitable techniques for communicating information to the system 100.

[0026] The user interface 170 may be equipped with one or more processing devices configured to provide and receive information to or from a user and to process that information for use by, for example, the application processor 180. In some embodiments, such processing devices may execute instructions to detect and track eye movements, receive and interpret voice commands, detect and interpret touches and / or gestures on a touchscreen, respond to keyboard inputs or menu selections, etc. In some embodiments, the user interface 170 may include a display, a speaker, a tactile device, and / or any other device for providing output information to a user.

[0027] The map database 160 may include any type of database for storing map data useful to the system 100. In some embodiments, the map database 160 may include data relating to the position in a reference coordinate system of various elements, including roads, water features, geographic features, businesses, points of interest, restaurants, gas stations, etc. The map database 160 may store not only the locations of such objects, but also descriptors relating to those elements, including, for example, names associated with any of the stored features. In some embodiments, the map database 160 may be physically located with other components of the system 100. Alternatively or additionally, the map database 160, or a portion thereof, may be remotely located with respect to other components of the system 100 (e.g., processing unit 110).In such embodiments, information may be downloaded from the map database 160 via a wired or wireless data connection to a network (e.g., via a cellular network and / or the Internet, etc.). In some cases, the map database 160 may store a sparse data model that includes polynomial representations of certain road features (e.g., lane markings) or target trajectories for the host vehicle. Systems and methods for generating such a map are described below with reference to FIGS. Fig. 8-19 discussed.

[0028] The image capture devices 122, 124, and 126 may each include any type of device suitable for capturing at least one image from an environment. Furthermore, any number of image capture devices may be used to capture images for input to the image processor. Some embodiments may include only a single image capture device, while other embodiments may include two, three, or even four or more image capture devices. The image capture devices 122, 124, and 126 are described below with reference to the Fig. 2B-2E are described in more detail.

[0029] The system 100 or various components thereof may be integrated into various different platforms. In some embodiments, the system 100 may be included in a vehicle 200, as shown in Fig. 2A. For example, the vehicle 200 may be equipped with a processing unit 110 and any of the other components of the system 100, as described above with respect to Fig. 1. While in some embodiments, the vehicle 200 may be equipped with only a single image capture device (e.g., a camera), in other embodiments, such as those described in connection with the Fig. 2B-2E, multiple image capture devices may be used. For example, each of the image capture devices 122 and 124 of the vehicle 200, as shown in Fig. 2A, be part of an ADAS (Advanced Driver Assistance Systems) imaging set.

[0030] The image capture devices included in the vehicle 200 as part of the image capture unit 120 may be positioned at any suitable location. In some embodiments, as shown in the Fig. 2A-2E and 3A-3C, the image capture device 122 may be located near the rearview mirror. This position may provide a line of sight similar to that of the driver of the vehicle 200, which may assist in determining what is and is not visible to the driver. The image capture device 122 may be positioned anywhere near the rearview mirror, but placing the image capture device 122 on the driver's side of the mirror may further assist in obtaining images representative of the driver's field of view and / or line of sight.

[0031] Other locations for the image capture devices of image capture unit 120 may also be used. For example, image capture device 124 may be located on or in a bumper of vehicle 200. Such a location may be particularly suitable for image capture devices with a wide field of view. The line of sight of the bumper-located image capture devices may differ from that of the driver, and therefore, the bumper image capture device and the driver may not always see the same objects. The image capture devices (e.g., image capture devices 122, 124, and 126) may also be located in other locations.For example, the image capture devices may be located on or in one or both of the side mirrors of the vehicle 200, on the roof of the vehicle 200, on the hood of the vehicle 200, on the trunk of the vehicle 200, and on the sides of the vehicle 200, mounted on, positioned behind, or positioned in front of one of the windows of the vehicle 200, and mounted in or near light figures on the front and / or rear of the vehicle 200, etc.

[0032] In addition to the image capture devices, the vehicle 200 may include various other components of the system 100. For example, the processing unit 110 may be included in the vehicle 200, either integrated with or separate from an engine control unit (ECU) of the vehicle. The vehicle 200 may also be equipped with a position sensor 130, such as a GPS receiver, and may also include a map database 160 and storage units 140 and 150.

[0033] As previously discussed, the wireless transceiver 172 may transmit and / or receive data over one or more networks (e.g., cellular networks, the Internet, etc.). For example, the wireless transceiver 172 may upload data collected by the system 100 to one or more servers and download data from the one or more servers. For example, the wireless transceiver 172 may receive periodic or on-demand updated data stored in the map database 160, the memory 140, and / or the storage 150 via the wireless transceiver 172. Similarly, the wireless transceiver 172 may upload any data (e.g., images captured by the image capture unit 120, data received from the position sensor 130 or other sensors, vehicle control systems, etc.) from the system 100 and / or any data processed by the processing unit 110 to the one or more servers.

[0034] System 100 may upload data to a server (e.g., to the cloud) based on a privacy level setting. For example, system 100 may implement privacy level settings to regulate or limit the types of data (including metadata) sent to the server that can uniquely identify a vehicle and / or a driver / owner of a vehicle. Such settings may be set by the user, for example, via wireless transceiver 172, initialized by factory default settings, or by data received by wireless transceiver 172.

[0035] In some embodiments, the system 100 may upload data according to a "high" privacy level, and when configured, the system 100 may transmit data (e.g., location information related to a route, captured images, etc.) without any details about the specific vehicle and / or driver / owner. For example, when uploading data according to a "high" privacy setting, the system 100 may not include a vehicle identification number (VIN) or a name of a driver or owner of the vehicle and may instead transmit data such as captured images and / or limited location information related to a route.

[0036] Other privacy levels are contemplated. For example, system 100 may transmit data to a server according to a "medium" privacy level and include additional information not included under a "high" privacy level, such as a make and / or model of a vehicle and / or a vehicle type (e.g., a passenger car, an SUV, a 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 data and include information sufficient to uniquely identify a specific vehicle, a specific owner / driver, and / or a portion or the entirety of a route traveled by the vehicle.Such “low” privacy level data may include one or more of, for example, a VIN, a driver / owner name, a vehicle’s point of origin before departure, an intended destination of the vehicle, a make and / or model of the vehicle, a type of vehicle, etc.

[0037] Fig. 2A is a schematic side view illustration of an exemplary vehicle imaging system, in accordance with the disclosed embodiments. Fig. 2B is a schematic top view illustration of the Fig. 2A. As shown in Fig. 2B, the disclosed embodiments may include a vehicle 200 including in its body a system 100 having a first image capture device 122 positioned near the rearview mirror and / or near the driver of the vehicle 200, a second image capture device 124 positioned on or in a bumper area (e.g., one of the bumper areas 210) of the vehicle 200, and a processing unit 110.

[0038] As in Fig. 2C, the image capture devices 122 and 124 may both be positioned near the rearview mirror and / or near the driver of the vehicle 200. In addition, although two image capture devices 122 and 124 may be Fig. 2B and Fig. 2C, it should be understood that other embodiments may include more than two image capture devices. For example, in the Fig. 2D and Fig. 2E, the first, second, and third image capture devices 122, 124, and 126 are included in the system 100 of the vehicle 200.

[0039] As in Fig. 2D, the image capture device 122 may be positioned near the rearview mirror and / or near the driver of the vehicle 200, and the image capture devices 124 and 126 may be positioned on or in a bumper area (e.g., one of the bumper areas 210) of the vehicle 200. And as in Fig. 2E, the image capture devices 122, 124, and 126 may be positioned near the rearview mirror and / or near the driver's seat of the vehicle 200. The disclosed embodiments are not limited to any particular number and configuration of the image capture devices, and the image capture devices may be positioned at any suitable location within and / or on the vehicle 200.

[0040] It is understood that the disclosed embodiments are not limited to vehicles and may be applied in other contexts. It is also understood that the disclosed embodiments are not limited to any particular vehicle type 200 and may be applicable to all vehicle types, including automobiles, trucks, trailers, and other vehicle types.

[0041] The first image capture device 122 may include any suitable type of image capture device. The image capture device 122 may include an optical axis. In one case, the image capture device 122 may include an Aptina M9V024 WVGA sensor with a global shutter. In other embodiments, the image capture device 122 may provide a resolution of 1280x960 pixels and include a rolling shutter. The image capture 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 for the image capture device. In some embodiments, the image capture device 122 may be associated with a 6 mm lens or a 12 mm lens.In some embodiments, the image capture device 122 may be configured to capture images with a desired field of view (FOV) 202, as shown in FIG. Fig. 2D. For example, the image capture device 122 may be configured to have a regular FOV, such as within a range of 40 degrees to 56 degrees, including a 46-degree FOV, 50-degree FOV, 52-degree FOV, or greater. Alternatively, the image capture device 122 may be configured to have a narrow FOV in the range of 23 to 40 degrees, such as a 28-degree FOV or 36-degree FOV. Additionally, the image capture device 122 may be configured to have a wide FOV in the range of 100 to 180 degrees. In some embodiments, the image capture device 122 may include a wide-angle bumper camera or one with up to a 180-degree FOV. In some embodiments, the image capture device 122 may be a 7.2M pixel image capture device with an aspect ratio of about 2:1 (e.g., HxV = 3800x1900 pixels) with about 100 degrees horizontal FOV.Such an image capture device can be used instead of a three-image capture device configuration. Due to significant lens distortion, the vertical FOV of such an image capture device can be significantly less than 50 degrees in implementations where the image capture device uses a radially symmetric lens. For example, such a lens may not be radially symmetric, which would allow a vertical FOV of greater than 50 degrees with a horizontal FOV of 100 degrees.

[0042] The first image capture device 122 may capture a plurality of first images relative to a scene associated with the vehicle 200. Each of the plurality of first images may be 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.

[0043] The first image capture device 122 may have a sampling rate associated with the acquisition of each of the first series of image scan lines. The sampling rate may refer to a rate at which an image sensor can acquire image data associated with each pixel included in a particular scan line.

[0044] The image capture 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 employed in conjunction with a rolling shutter, such that each pixel in a row is read sequentially, and the rows are scanned on a row-by-row basis until an entire image frame has been captured. In some embodiments, the rows may be captured sequentially from top to bottom with respect to the frame.

[0045] In some embodiments, one or more of the image capture devices disclosed herein (e.g., image capture devices 122, 124, and 126) may form a high-resolution imager and have a resolution of greater than 5M pixels, 7M pixels, 10M pixels, or more.

[0046] The use of a rolling shutter can result in pixels in different rows being exposed and captured at different times, which can introduce distortion and other image artifacts into the captured frame. On the other hand, if the image capture device 122 is configured to operate with a global or synchronous shutter, all pixels may be exposed for the same time and during a common exposure period. As a result, the image data in a frame collected by a system using a global shutter represents a snapshot of the entire FOV (such as FOV 202) at a particular time. In contrast, in a rolling shutter application, each row in a frame is exposed, and data is captured at different times. Therefore, moving objects may appear distorted in an image capture device with a rolling shutter.This phenomenon is described in more detail below.

[0047] The second image capture device 124 and the third image capture device 126 may be any type of image capture device. Like the first image capture device 122, each of the image capture devices 124 and 126 may include an optical axis. In one embodiment, each of the image capture devices 124 and 126 may include an Aptina M9V024 WVGA sensor with a global shutter. Alternatively, each of the image capture devices 124 and 126 may include a rolling shutter. Like the image capture device 122, the image capture devices 124 and 126 may be configured to include various lenses and optical elements. In some embodiments, the lenses associated with image capture devices 124 and 126 may provide FOVs (such as FOVs 204 and 206) that are equal to or narrower than an FOV (such as FOV 202) associated with image capture device 122.For example, image capture devices 124 and 126 may have FOVs of 40 degrees, 30 degrees, 26 degrees, 23 degrees, 20 degrees, or less.

[0048] Image capture devices 124 and 126 may capture a plurality of second and third images relative to a scene associated with vehicle 200. Each of the plurality of second and third images may be captured as a second and third series of image scan lines, which may be captured using a rolling shutter. Each scan line or row may include a plurality of pixels. Image capture devices 124 and 126 may have second and third sampling rates associated with the capture of each of the image scan lines included in the second and third series.

[0049] Each image capture device 122, 124, and 126 may be positioned at any suitable position and orientation relative to the vehicle 200. The relative positioning of the image capture devices 122, 124, and 126 may be selected to assist in merging the information captured by the image capture devices. For example, in some embodiments, an FOV (such as FOV 204) associated with the image capture device 124 may partially or completely overlap with an FOV (such as FOV 202) associated with the image capture device 122 and an FOV (such as FOV 206) associated with the image capture device 126.

[0050] The image capture devices 122, 124, and 126 may be located at any suitable relative heights on the vehicle 200. In one case, there may be a height difference between the image capture devices 122, 124, and 126 that may provide sufficient parallax information to enable stereo analysis. For example, as shown in Fig. As shown in Figure 2A, the two image capture devices 122 and 124 are located at different heights. There may also be a lateral displacement difference between the image capture devices 122, 124, and 126, which, for example, provides additional parallax information for stereo analysis by the processing unit 110. The difference in lateral displacement can be determined by the x be referred to as in the Fig. 2C and Fig. 2D. In some embodiments, a forward or backward offset (e.g., distance offset) may exist between the image capture devices 122, 124, and 126. For example, the image capture device 122 may be located 0.5 to 2 meters or more behind the image capture device 124 and / or the image capture device 126. This type of offset may allow one of the image capture devices to cover potential blind spots of the other image capture device(s).

[0051] The image capture devices 122 may have any suitable resolution capability (e.g., number of pixels associated with the image sensor), and the resolution of the image sensor(s) associated with the image capture device 122 may be higher, lower, or equal to the resolution of the image sensor(s) associated with the image capture devices 124 and 126. In some embodiments, the image sensor(s) associated with the image capture device 122 and / or the image capture devices 124 and 126 may have a resolution of 640 x 480, 1024 x 768, 1280 x 960, or any other suitable resolution.

[0052] The frame rate (e.g., the rate at which an image capture device captures a set of pixel data of one image frame before moving to capture pixel data associated with the next image frame) may be controllable. The frame rate associated with image capture device 122 may be higher, lower, or equal to the frame rate associated with image capture devices 124 and 126. The frame rate associated with image capture devices 122, 124, and 126 may depend on a variety of factors that may affect the timing of the frame rate. For example, one or more of image capture devices 122, 124, and 126 may include a selectable pixel delay period imposed before or after the capture of image data associated with one or more pixels of an image sensor in image capture device 122, 124, and / or 126.In general, image data corresponding to each pixel may be captured according to a clock rate for the device (e.g., one pixel per clock cycle). Additionally, in embodiments including a rolling shutter, one or more of the image capture devices 122, 124, and 126 may include a selectable horizontal blanking period imposed before or after capturing image data associated with a row of pixels of an image sensor in the image capture device 122, 124, and / or 126. Further, one or more of the image capture devices 122, 124, and / or 126 may include a selectable vertical blanking period imposed before or after capturing image data associated with a frame of the image capture device 122, 124, and / or 126.

[0053] These timing controls may enable synchronization of frame rates associated with image capture devices 122, 124, and 126, even if the line scan rates are different. Additionally, as discussed in more detail below, these selectable timing controls, among other factors (e.g., image sensor resolution, maximum line scan rates, etc.), may enable synchronization of image capture from an area where the FOV of image capture device 122 overlaps with one or more FOVs of image capture devices 124 and 126, even if the field of view of image capture device 122 differs from the FOVs of image capture devices 124 and 126.

[0054] The frame rate timing in image capture devices 122, 124, and 126 may depend on the resolution of the associated image sensors. For example, if similar line scan rates are assumed for both devices, if one device includes an image sensor with a resolution of 640 x 480 and another device includes an image sensor with a resolution of 1280 x 960, then more time will be required to capture a frame of image data from the higher resolution sensor.

[0055] Another factor that can affect the timing of image data acquisition in image capture devices 122, 124, and 126 is the maximum line scan rate. For example, acquiring a burst of image data from an image sensor included in image capture devices 122, 124, and 126 requires a minimum amount of time. Assuming no pixel delay periods are added, this minimum amount of time for acquiring a burst of image data is related to the maximum line scan rate for a particular device. Devices that offer higher maximum line scan rates have the potential to provide higher frame rates than devices with lower maximum line scan rates. In some embodiments, one or more of image capture devices 124 and 126 may have a maximum line scan rate that is higher than a maximum line scan rate associated with image capture device 122.In some embodiments, the maximum line scan rate of image capture device 124 and / or 126 may be 1.25, 1.5, 1.75, or 2 times or more a maximum line scan rate of image capture device 122.

[0056] In another embodiment, image capture devices 122, 124, and 126 may have the same maximum line scan rate, but image capture device 122 may operate at a scan rate less than or equal to its maximum scan rate. The system may be configured such that one or more of image capture devices 124 and 126 operate at a line scan rate equal to the line scan rate of image capture device 122. In other cases, the system may be configured such that the line scan rate of image capture device 124 and / or image capture device 126 may be 1.25, 1.5, 1.75, or 2 times or more the line scan rate of image capture device 122.

[0057] In some embodiments, the image capture devices 122, 124, and 126 may be asymmetric. That is, they may include cameras with different fields of view (FOV) and focal lengths. The fields of view of the image capture devices 122, 124, and 126 may, for example, include any desired area relative to an environment of the vehicle 200. In some embodiments, one or more of the image capture devices 122, 124, and 126 may be configured to capture image data from an environment in front of the vehicle 200, behind the vehicle 200, on the sides of the vehicle 200, or combinations thereof.

[0058] Furthermore, the focal length associated with each image capture device 122, 124, and / or 126 may be selectable (e.g., by incorporating appropriate lenses, etc.) such that each device captures images of objects within a desired range of distances relative to the vehicle 200. For example, in some embodiments, the image capture devices 122, 124, and 126 may capture images of close-up objects within a few meters of the vehicle. The image capture devices 122, 124, and 126 may also be configured to capture images of objects at distances farther from the vehicle (e.g., 25 m, 50 m, 100 m, 150 m, or more). Furthermore, the focal lengths of the image capture devices 122, 124 and 126 may be selected such that an image capture device (e.g., image capture device 122) can acquire images of objects relatively close to the vehicle (e.g.,within 10 m or within 20 m), while the other image capture devices (e.g., image capture devices 124 and 126) can capture images of objects further away (e.g., greater than 20 m, 50 m, 100 m, 150 m, etc.) from the vehicle 200.

[0059] According to some embodiments, the FOV of one or more image capture devices 122, 124, and 126 may have a wide angle. For example, it may be advantageous to have an FOV of 140 degrees, particularly for image capture devices 122, 124, and 126 that may be used to capture images of the area proximate to vehicle 200. For example, image capture device 122 may be used to capture images of the area to the right or left of vehicle 200, and in such embodiments, it may be desirable for image capture device 122 to have a wide FOV (e.g., at least 140 degrees).

[0060] The field of view associated with each of the image capture devices 122, 124, and 126 may depend on the respective focal lengths. For example, as the focal length increases, the corresponding field of view decreases.

[0061] The image capture devices 122, 124, and 126 may be configured to have any suitable fields of view. In one particular example, the image capture device 122 may have a horizontal FOV of 46 degrees, the image capture device 124 may have a horizontal FOV of 23 degrees, and the image capture device 126 may have a horizontal FOV between 23 and 46 degrees. In another case, the image capture device 122 may have a horizontal FOV of 52 degrees, the image capture device 124 may have a horizontal FOV of 26 degrees, and the image capture device 126 may have a horizontal FOV between 26 and 52 degrees. In some embodiments, a ratio of the FOV of the image capture device 122 to the FOVs of the image capture device 124 and / or the image capture device 126 may vary from 1.5 to 2.0. In other embodiments, this ratio may vary between 1.25 and 2.25.

[0062] The system 100 may be configured such that a field of view of the image capture device 122 at least partially or completely overlaps with a field of view of the image capture device 124 and / or the image capture device 126. In some embodiments, the system 100 may be configured such that the fields of view of the image capture devices 124 and 126, for example, fall within (e.g., are narrower than) and share a common center with the field of view of the image capture device 122. In other embodiments, the image capture devices 122, 124, and 126 may capture adjacent FOVs or have a partial overlap in their FOVs. In some embodiments, the fields of view of the image capture devices 122, 124, and 126 may be aligned such that a center of the narrower FOV image capture devices 124 and / or 126 may be located in a lower half of the field of view of the wider FOV device 122.

[0063] Fig. 2F is a schematic representation of exemplary vehicle control systems, in accordance with the disclosed embodiments. As in Fig. 2F, the vehicle 200 may include a throttle system 220, a braking system 230, and a steering system 240. The system 100 may provide inputs (e.g., control signals) to one or more of a throttle system 220, a braking system 230, and a steering system 240 via one or more data connections (e.g., any wired and / or wireless connection or connections for transmitting data). For example, based on an analysis of images captured by the image capture devices 122, 124, and / or 126, the system 100 may provide control signals to one or more of a throttle system 220, a braking system 230, and a steering system 240 to navigate the vehicle 200 (e.g., by initiating acceleration, a turn, a lane shift, etc.).Further, the system 100 may receive inputs from one or more of a throttle system 220, a braking system 230, and a steering system 24 indicative of operating conditions of the vehicle 200 (e.g., speed, whether the vehicle 200 is braking and / or turning, etc.). Further details are provided in connection with the following. Fig. 4-7 provided.

[0064] As in Fig. 3A, the vehicle 200 may also include a user interface 170 for interacting with a driver or a passenger of the vehicle 200. For example, in a vehicle application, the user interface 170 may include a touchscreen 320, knobs 330, buttons 340, and a microphone 350. A driver or passenger of the vehicle 200 may also use handles (e.g., located on or near the steering column of the vehicle 200, including, for example, turn signal handles), buttons (e.g., located on the steering wheel of the vehicle 200), and the like to interact with the system 100. In some embodiments, the microphone 350 may be positioned adjacent to a rearview mirror 310. Similarly, in some embodiments, the image capture device 122 may be located near the rearview mirror 310. In some embodiments, the user interface 170 may also include one or more speakers 360 (e.g.,speakers of a vehicle audio system). For example, the system 100 may provide various notifications (e.g., alerts) through the speakers 360.

[0065] The Fig. 3B-3D are illustrations of an exemplary camera mount 370 configured to be positioned behind a rearview mirror (e.g., rearview mirror 310) and against a vehicle windshield, in accordance with the disclosed embodiments. As shown in Fig. 3B, the camera mount 370 may include image capture devices 122, 124, and 126. The image capture devices 124 and 126 may be positioned behind an anti-glare shield 380, which may be flush with the vehicle windshield and may include a composition of film and / or anti-reflective materials. For example, the anti-glare shield 380 may be positioned so that the shield aligns with a vehicle windshield having a matching slope. In some embodiments, each of the image capture devices 122, 124, and 126 may be positioned behind the anti-glare shield 380, such as in Fig. 3D. The disclosed embodiments are not limited to any particular configuration of the image capture devices 122, 124, and 126, the camera mount 370, and the glare shield 380. Fig. 3C is an illustration of the camera mount 370 shown in Fig. 3B is shown from a front perspective.

[0066] As will be apparent to those skilled in the art having the benefit of this disclosure, numerous variations and / or modifications may be made to the above-disclosed embodiments. For example, not all components are essential to the operation of the system 100. Further, any component may be located in any suitable part of the system 100, and the components may be rearranged into a variety of configurations while providing the functionality of the disclosed embodiments. Therefore, the above configurations are examples, and regardless of the configurations discussed above, the system 100 may provide a wide range of functionality to analyze the environment of the vehicle 200 and navigate the vehicle 200 in response to the analysis.

[0067] As discussed in more detail below, and in accordance with various disclosed embodiments, system 100 may provide a variety of features related to autonomous driving and / or driver assistance technology. For example, system 100 may analyze image data, position data (e.g., GPS location information), map data, speed data, and / or data from sensors included in vehicle 200. System 100 may collect the data for analysis, for example, from image capture unit 120, position sensor 130, and other sensors. Further, system 100 may analyze the collected data to determine whether or not vehicle 200 should perform a particular action and then automatically perform the particular action without human intervention.For example, when the vehicle 200 is navigating without human intervention, the system 100 may automatically control the braking, acceleration, and / or steering of the vehicle 200 (e.g., by sending control signals to one or more of a throttle system 220, a braking system 230, and a steering system 240). Further, the system 100 may analyze the collected data and issue warnings and / or alarms to vehicle occupants based on the analysis of the collected data. Additional details regarding the various embodiments provided by the system 100 are provided below. Forward-facing multi-imaging system

[0068] As discussed above, system 100 may provide driver assistance functionality utilizing a multi-camera system. The multi-camera system may utilize one or more cameras facing in the forward direction of a vehicle. In other embodiments, the multi-camera system may include one or more cameras facing the side of a vehicle or the rear of the vehicle. For example, in one embodiment, system 100 may utilize a two-camera imaging system, where a first camera and a second camera (e.g., image capture devices 122 and 124) may be positioned on the front and / or sides of a vehicle (e.g., vehicle 200). The first camera may have a field of view that is larger than, smaller than, or partially overlapping the field of view of the second camera.Additionally, the first camera may be connected to a first image processor to perform monocular image analysis of images provided by the first camera, and the second camera may be connected to a second image processor to perform monocular image analysis of images 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 camera and the second camera to perform stereo analysis. In another embodiment, system 100 may use a three-camera imaging system, with each of the cameras having a different field of view.Such a system can therefore make decisions based on information derived from objects located at varying distances both to the front and to the sides of the vehicle. References to monocular image analysis may refer to cases where image analysis is performed based on images acquired from a single viewpoint (e.g., from a single camera). Stereo image analysis may refer to cases where image analysis is performed based on two or more images acquired with one or more variations of an image acquisition parameter. For example, acquired images suitable for performing stereo image analysis may include images acquired from two or more different positions, from different fields of view, using different focal lengths, along with parallax information, etc.

[0069] For example, in one embodiment, system 100 may implement a three-camera configuration using image capture devices 122, 124, and 126. In such a configuration, image capture device 122 may provide a narrow field of view (e.g., 34 degrees or other values selected from a range of about 20 to 45 degrees, etc.), image capture device 124 may provide a wide field of view (e.g., 150 degrees or other values selected from a range of about 100 to about 180 degrees), and image capture device 126 may provide an intermediate field of view (e.g., 46 degrees or other values selected from a range of about 35 to about 60 degrees). In some embodiments, image capture device 126 may function as the main or primary camera.The image capture devices 122, 124, and 126 may be positioned behind the rearview mirror 310 and positioned substantially side by side (e.g., 6 cm apart). Further, in some embodiments, as discussed above, one or more of the image capture devices 122, 124, and 126 may be mounted behind the glare shield 380, which is flush with the windshield of the vehicle 200. Such shielding may serve to minimize the impact of reflections from inside the vehicle on the image capture devices 122, 124, and 126.

[0070] In another embodiment, as described above in connection with the Fig. 3B and Fig. 3C, the wide field of view camera (e.g., image capture device 124 in the above example) may be mounted lower than the narrow field of view camera and main field of view camera (e.g., image capture devices 122 and 126 in the above example). This configuration may provide a clear line of sight from the wide field of view camera. To reduce reflections, the cameras may be mounted near the windshield of the vehicle 200 and include polarizers on the cameras to attenuate reflected light.

[0071] A three-camera system may provide certain performance characteristics. For example, some embodiments may include the ability to validate the detection of objects by one camera based on detection results from another camera. In the three-camera configuration discussed above, for example, processing unit 110 may include three processing devices (e.g., three EyeQ series of processor chips, as discussed above), with each processing device dedicated to processing images captured by one or more of image capture devices 122, 124, and 126.

[0072] In a three-camera system, a first processing device may receive images from both the main camera and the narrow FOV camera and perform image processing of the narrow FOV camera to detect, for example, other vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. Furthermore, the first processing device may calculate a pixel disparity between the images from the main camera and the narrow FOV camera and create a 3D reconstruction of the surroundings of the vehicle 200. The first processing device may then combine the 3D reconstruction with 3D map data or with 3D information calculated based on information from another camera.

[0073] The second processing device can receive images from the main camera and perform image processing to detect other vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. Additionally, the second processing device can calculate a camera displacement and, based on the displacement, calculate a disparity of pixels between consecutive images and create a 3D reconstruction of the scene (e.g., a structure from motion). The second processing device can send the structure from a motion-based 3D reconstruction to the first processing device to be combined with the stereo 3D images.

[0074] 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 images to identify moving objects in the image, such as vehicles changing lanes, pedestrians, etc.

[0075] In some embodiments, streams of image-based information that are independently captured and processed may provide a way to provide redundancy in the system. Such redundancy may include, for example, using a first image capture device and the images processed by that device to validate and / or supplement information obtained by capturing and processing image information from at least one second image capture device.

[0076] In some embodiments, system 100 may use two image capture devices (e.g., image capture devices 122 and 124) to provide navigation assistance for vehicle 200 and a third image capture device (e.g., image capture device 126) to provide redundancy and validate the analysis of data received from the other two image capture devices. For example, in such a configuration, image capture devices 122 and 124 may provide images for stereo analysis by system 100 for navigating vehicle 200, while image capture device 126 may provide images for monocular analysis by system 100 to provide redundancy and validation of information obtained based on images acquired by image capture device 122 and / or image capture device 124.That is, the image capture device 126 (and a corresponding processing device) may be considered a redundant subsystem for providing verification of the analysis derived from the image capture devices 122 and 124 (e.g., to provide an automatic emergency braking (AEB) system). Furthermore, in some embodiments, the redundancy and validation of the received data may be supplemented based on information received from one or more sensors (e.g., radar, lidar, acoustic sensors, information received from one or more transceivers external to a vehicle, etc.).

[0077] One skilled in the art will recognize that the above camera configurations, camera placements, number of cameras, camera locations, etc., are merely examples. These components and others described with respect to the overall system may be assembled and used in a variety of different configurations without departing from the scope of the disclosed embodiments. Further details regarding the use of a multi-camera system to provide driver assistance and / or autonomous vehicle functionality follow below.

[0078] Fig. Figure 4 is an exemplary functional block diagram of memory 140 and / or 150, which may be stored / programmed with instructions for performing one or more operations, in accordance with the disclosed embodiments. Although the following refers to memory 140, one of ordinary skill in the art will recognize that instructions may be stored in memory 140 and / or 150.

[0079] As in Fig. 4, memory 140 may store a monocular image analysis module 402, a stereo image analysis module 404, a velocity and acceleration module 406, and a navigation response module 408. The disclosed embodiments are not limited to any particular configuration of memory 140. Further, application processor 180 and / or image processor 190 may execute the instructions stored in any of modules 402, 404, 406, and 408 included in memory 140. One of ordinary skill in the art will understand that references in the following discussions to processing unit 110 may refer individually or collectively to application processor 180 and image processor 190. Accordingly, steps of any of the following processes may be performed by one or more processing devices.

[0080] In one embodiment, the monocular image analysis module 402 may store instructions (such as computer vision software) that, when executed by the processing unit 110, perform a monocular image analysis of a set of images acquired by one of the image capture devices 122, 124, and 126. In some embodiments, the processing unit 110 may combine information from a set of images with additional sensory information (e.g., information from radar, lidar, etc.) to perform the monocular image analysis. As described in connection with the following Fig. 5A-5D, the monocular image analysis module 402 may include instructions for detecting a set of features within the set of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazardous objects, and any other feature associated with an environment of a vehicle. Based on the analysis, the system 100 (e.g., via the processing unit 110) may initiate one or more navigational responses within the vehicle 200, such as a turn, a lane shift, a change in acceleration, and the like, as discussed below in connection with the navigational response module 408.

[0081] 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 image capture devices selected from any of image capture devices 122, 124, and 126. In some embodiments, processing unit 110 may combine information from the first and second sets of images with additional sensory information (e.g., information from radar) to perform the stereo image analysis.For example, the stereo image analysis module 404 may include instructions for performing a stereo image analysis based on a first set of images acquired by the image capture device 124 and a second set of images acquired by the image capture device 126. As described in connection with the following. Fig. 6, the stereo image analysis module 404 may include instructions for detecting a set of features within the first and second sets of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazardous objects, and the like. Based on the analysis, the processing unit 110 may initiate one or more navigational responses within the vehicle 200, such as a turn, a lane shift, a change in acceleration, and the like, as discussed below in connection with the navigational response module 408.Furthermore, in some embodiments, the stereo image analysis module 404 may implement techniques associated with a trained system (such as a neural network or a deep neural network) or an untrained system, such as a system that may be configured to use computer vision algorithms to detect and / or label objects in an environment from which sensory information has been acquired and processed. In one embodiment, the stereo image analysis module 404 and / or other image processing modules may be configured to use a combination of a trained and untrained system.

[0082] In one embodiment, the speed and acceleration module 406 may store software configured to analyze data received from one or more computing devices and electromechanical devices within the vehicle 200 that are configured to cause a change in the speed and / or acceleration of the vehicle 200. For example, the processing unit 110 may execute instructions associated with the speed and acceleration module 406 to calculate a target speed for the vehicle 200 based on data derived from the execution of the monocular image analysis module 402 and / or the stereo image analysis module 404.Such data may include, for example, a target position, speed, and / or acceleration, the position and / or speed of the vehicle 200 relative to a nearby vehicle, pedestrian, or road object, position information for the vehicle 200 relative to lane markings of the road, and the like. Additionally, the processing unit 110 may calculate a target speed for the vehicle 200 based on sensory inputs (e.g., information from radar) and inputs from other systems of the vehicle 200, such as the throttle system 220, the braking system 230, and / or the steering system 240 of the vehicle 200.Based on the calculated target speed, the processing unit 110 may transmit electronic signals to the throttle system 220, the braking system 230, and / or the steering system 240 of the vehicle 200 to trigger a change in speed and / or acceleration, for example, by physically depressing the brake or releasing the accelerator of the vehicle 200.

[0083] In one embodiment, the navigation response module 408 may store software executable by the processing unit 110 to determine a desired navigation response based on data derived from the execution of the monocular image analysis module 402 and / or the stereo image analysis module 404. Such data may include position and velocity information associated with nearby vehicles, pedestrians, and road objects, target position information for the vehicle 200, and the like.Additionally, in some embodiments, the navigation response may be based (partially or entirely) on map data, a predetermined position of the vehicle 200, and / or a relative velocity or acceleration between the vehicle 200 and one or more objects detected by the execution of the monocular image analysis module 402 and / or the stereo image analysis module 404. The navigation response module 408 may also determine a desired navigation response based on sensory inputs (e.g., information from 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 navigation response, processing unit 110 may transmit electronic signals to the throttle system 220, the braking system 230, and the steering system 240 of the vehicle 200 to trigger a desired navigation response, for example, by turning the steering wheel of the vehicle 200 to achieve a rotation of a predetermined angle. In some embodiments, processing unit 110 may use the output of the navigation response module 408 (e.g., the desired navigation response) as an input to execute the speed and acceleration module 406 to calculate a change in speed of the vehicle 200.

[0084] Furthermore, each of the modules disclosed herein (e.g., modules 402, 404, and 406) may implement techniques associated with a trained system (such as a neural network or a deep neural network) or an untrained system.

[0085] Fig. 5A is a flowchart illustrating an exemplary process 500A for initiating one or more navigation responses based on monocular image analysis, in accordance with the disclosed embodiments. At step 510, the processing unit 110 may receive a plurality of images via the data interface 128 between the processing unit 110 and the image capture unit 120. For example, a camera included in the image capture unit 120 (such as the image capture device 122 with the field of view 202) may capture a plurality of images of an area in front of the vehicle 200 (or, for example, to the sides or rear of a vehicle) and transmit them to the processing unit 110 via a data connection (e.g., digital, wired, USB, wireless, Bluetooth, etc.).The processing unit 110 may execute the monocular image analysis module 402 to analyze the plurality of images at step 520, as described below in connection with FIGS. Fig. 5B-5D. By performing the analysis, the processing unit 110 may recognize a set of features within the set of images, such as lane markings, vehicles, pedestrians, street signs, highway exit ramps, traffic lights, and the like.

[0086] The processing unit 110 may also execute the monocular image analysis module 402 to detect various road hazards at step 520, such as, for example, pieces of a truck tire, fallen road signs, loose cargo, small animals, and the like. Road hazards can vary in texture, shape, size, and color, which may make detection of such hazards more difficult. In some embodiments, the processing unit 110 may execute the monocular image analysis module 402 to perform multi-frame analysis on the plurality of images to detect road hazards. For example, the processing unit 110 may estimate camera motion between consecutive frames and calculate the disparities in pixels between the frames to create a 3D map of the road.The processing unit 110 can then use the 3D map to detect the road surface as well as the hazards existing above the road surface.

[0087] At step 530, the processing unit 110 may execute the navigation response module 408 to determine one or more navigation responses in the vehicle 200 based on the analysis performed at step 520 and the techniques described above in connection with Fig. 4. Navigational responses may include, for example, a turn, a lane shift, a change in acceleration, and the like. In some embodiments, the processing unit 110 may use data derived from the execution of the speed and acceleration module 406 to prompt the one or more navigational responses. Additionally, multiple navigational responses may occur simultaneously, sequentially, or in any combination thereof. For example, the processing unit 110 may cause the vehicle 200 to change lanes and then accelerate, for example, by sequentially transmitting control signals to the steering system 240 and the throttle system 220 of the vehicle 200.Alternatively, the processing unit 110 may cause the vehicle 200 to brake while simultaneously switching lanes, for example, by simultaneously transmitting control signals to the braking system 230 and the steering system 240 of the vehicle 200.

[0088] Fig. 5B is a flowchart illustrating an exemplary process 500B for detecting one or more vehicles and / or pedestrians in a set of images, in accordance with the disclosed embodiments. The processing unit 110 may execute the monocular image analysis module 402 to implement the process 500B. At step 540, the processing unit 110 may determine a set of candidate objects representing possible vehicles and / or pedestrians. For example, the processing unit 110 may scan one or more images, compare the images to one or more predetermined patterns, and identify possible locations within each image that may contain objects of interest (e.g., vehicles, pedestrians, or portions thereof). The predetermined patterns may be configured to achieve a high false hit rate and a low false miss rate.For example, processing unit 110 may use a low threshold of similarity to predetermined patterns to identify candidate objects as possible vehicles or pedestrians. This may allow processing unit 110 to reduce the likelihood that a candidate object representing a vehicle or pedestrian is missing (e.g., not identified).

[0089] At step 542, the processing unit 110 may filter the set of candidate objects to exclude certain candidates (e.g., irrelevant or less relevant objects) based on classification criteria. Such criteria may be derived from various properties associated with object types stored in a database (e.g., a database stored in memory 140). Properties may include object shape, dimensions, texture, position (e.g., relative to the vehicle 200), and the like. Thus, the processing unit 110 may use one or more sets of criteria to reject false candidates from the set of candidate objects.

[0090] At step 544, processing unit 110 may analyze multiple frames of images to determine whether objects in the set of candidate objects represent vehicles and / or pedestrians. For example, processing unit 110 may track a detected candidate object across consecutive frames and accumulate frame data associated with the detected object (e.g., size, position relative to vehicle 200, etc.). Additionally, processing unit 110 may estimate parameters for the detected object and compare the frame position data of the object to a predicted position.

[0091] At step 546, processing unit 110 may create a set of measurements for the detected objects. Such measurements may include, for example, position, velocity, and acceleration values (relative to vehicle 200) associated with the detected objects. In some embodiments, processing unit 110 may create the measurements based on estimation techniques using a set of time-based observations, such as Kalman filters or linear quadratic estimation (LQE), and / or based on available modeling data for different object types (e.g., cars, trucks, pedestrians, bicycles, road signs, etc.). The Kalman filters may be based on a measurement of the scale of an object, where the scale measurement is proportional to a time to collision (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 within the set of captured images and derive information (e.g., position, speed, size) associated with the vehicles and pedestrians. Based on the identification and the derived information, processing unit 110 may initiate one or more navigational responses in vehicle 200, as described above in connection with [Figure 54]. Fig. 5A.

[0092] At step 548, processing unit 110 may perform optical flow analysis of one or more images to reduce the likelihood of detecting a "false hit" and missing a candidate object representing a vehicle or pedestrian. Optical flow analysis may refer, for example, to analyzing motion patterns relative to vehicle 200 in one or more images that are associated with other vehicles and pedestrians and that are distinct from road surface motion. Processing unit 110 may calculate the motion of candidate objects by observing the various positions of the objects across multiple frames captured at different times. Processing unit 110 may use the position and time values as inputs to mathematical models for calculating the motion of the candidate objects.Thus, optical flow analysis may provide another method for detecting vehicles and pedestrians that are near 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.

[0093] Fig. 5C is a flowchart illustrating an exemplary process 500C for detecting road markings and / or lane geometry information in a set of images, in accordance with the disclosed embodiments. Processing unit 110 may execute monocular image analysis module 402 to implement process 500C. At step 550, processing unit 110 may detect a set of objects by scanning one or more images. To detect segments of lane markings, lane geometry information, and other relevant road markings, processing unit 110 may filter the set of objects to exclude those determined to be irrelevant (e.g., small potholes, small rocks, etc.). At step 552, processing unit 110 may group together the segments detected in step 550 that belong to the same road marking or lane marking.Based on the grouping, the processing unit 110 may develop a model to represent the detected segments, such as a mathematical model.

[0094] At step 554, processing unit 110 may construct a set of measurements associated with the detected segments. In some embodiments, processing unit 110 may create a projection of the detected segments from the image plane to the real plane. The projection may be characterized using a third-degree polynomial having 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 consider changes in the road surface, as well as pitch and roll rates associated with vehicle 200. Additionally, processing unit 110 may model the road elevation by analyzing position and motion cues present on the road surface.Further, the processing unit 110 may estimate the pitch and roll rates associated with the vehicle 200 by tracking a set of feature points in the one or more images.

[0095] At step 556, the processing unit 110 may perform a multi-frame analysis, for example, by tracking the detected segments across successive image frames and accumulating frame data associated with detected segments. As the processing unit 110 performs a multi-frame analysis, the set of measurements created at step 554 may become more reliable and associated with an increasingly higher level of confidence. Thus, by performing steps 550, 552, 554, and 556, the processing unit 110 may identify road markings appearing within the set of captured images and derive lane geometry information. Based on the identification and the derived information, the processing unit 110 may initiate one or more navigation responses in the vehicle 200, as described above in connection with Fig. 5A.

[0096] At step 558, processing unit 110 may consider additional information sources to further develop a safety model for vehicle 200 in the context of its environment. Processing unit 110 may use the safety model to define a context in which system 100 can safely perform autonomous control of vehicle 200. To develop the safety model, in some embodiments, processing unit 110 may consider the position and movement of other vehicles, detected road edges and obstacles, and / or general road shape descriptions extracted from map data (such as data from map database 160). By considering additional information sources, processing unit 110 may provide redundancy for detecting road markings and lane geometry and increase the reliability of system 100.

[0097] Fig. 5D is a flowchart illustrating an exemplary process 500D for detecting traffic lights in a set of images, in accordance with the disclosed embodiments. Processing unit 110 may execute monocular image analysis module 402 to implement process 500D. At step 560, processing unit 110 may scan the set of images and identify objects that appear at locations in the images that are likely to contain traffic lights. For example, processing unit 110 may filter the identified objects to create a set of candidate objects, excluding those objects that are unlikely to correspond to traffic lights. The filtering may be based on various characteristics associated with traffic lights, such as shape, dimensions, texture, position (e.g., relative to vehicle 200), and the like.Such properties may be based on multiple examples of traffic lights and traffic control signals and stored in a database. In some embodiments, processing unit 110 may perform a multi-frame analysis on the set of candidate objects that reflect possible traffic lights. For example, processing unit 110 may track the candidate objects over consecutive frames, estimate the real-world position of the candidate objects, and filter out those objects that are moving (which are unlikely to be traffic lights). In some embodiments, processing unit 110 may perform a color analysis on the candidate objects and identify the relative position of the detected colors that appear within possible traffic lights.

[0098] At step 562, processing unit 110 may analyze the geometry of an intersection. The analysis may be based on any combination of: (i) the number of lanes detected on either side of vehicle 200, (ii) markers (such as arrow markers) detected on the road, and (iii) descriptions 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 the execution of monocular analysis module 402. Additionally, processing unit 110 may determine a correspondence between the traffic lights detected at step 560 and the lanes appearing near vehicle 200.

[0099] As the vehicle 200 approaches the intersection, the processing unit 110 may update the confidence level associated with the analyzed intersection geometry and the detected traffic lights at step 564. For example, the number of traffic lights estimated to appear at the intersection compared to the number actually appearing at the intersection may impact the confidence level. Thus, based on the confidence level, the processing unit 110 may delegate control to the driver of the vehicle 200 to improve safety conditions. By performing steps 560, 562, and 564, the processing unit 110 may identify traffic lights appearing within the set of captured images and analyze intersection geometry information.Based on the identification and analysis, the processing unit 110 may initiate one or more navigation responses in the vehicle 200, as described above in connection with . Fig. 5A.

[0100] Fig. 5E is a flowchart illustrating an exemplary process 500E for initiating one or more navigational responses in vehicle 200 based on a vehicle path, in accordance with the disclosed embodiments. At step 570, processing unit 110 may construct an initial vehicle path associated with vehicle 200. The vehicle path may be represented using a set of points expressed in coordinates (x, z) and the distance d ibetween two points in the set of points may range from 1 to 5 meters. In one embodiment, the processing unit 110 may construct the initial vehicle path using two polynomials, such as left and right road polynomials. The processing unit 110 may calculate the geometric midpoint between the two polynomials and offset each point included in the resulting vehicle path by a predetermined offset (e.g., a smart lane offset), if present (a zero offset may correspond to traveling in the center of a lane). The offset may be in a direction perpendicular to a segment between any two points in the vehicle path. In another embodiment, the processing unit 110 may use a polynomial and an estimated lane width to offset each point of the vehicle path by half the estimated lane width plus a predetermined offset (e.g.,an offset of an intelligent lane).

[0101] At step 572, the processing unit 110 may update the vehicle path constructed at step 570. The processing unit 110 may reconstruct the vehicle path constructed at step 570 using a higher resolution such that the distance d k between two points in the set of points representing the vehicle path is less than the distance d described above i For example, the distance d k fall within the range of 0.1 to 0.3 meters. The processing unit 110 may reconstruct the vehicle path using a parabolic spline algorithm, which may provide 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).

[0102] At step 574, the processing unit 110 may determine a lookahead point (expressed in coordinates as (x l , e.g. l)) based on the updated vehicle path constructed at step 572. The processing unit 110 may extract the look-ahead point from the cumulative range vector S, and the look-ahead point may be associated with a look-ahead distance and look-ahead time. The look-ahead distance, which may have a lower bound in the range of 10 to 20 meters, may be calculated as the product of the speed of the vehicle 200 and the look-ahead time. For example, as the speed of the vehicle 200 decreases, the look-ahead distance may also decrease (e.g., until it reaches the lower bound). 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 initiating a navigation response in the vehicle 200, such as the heading error tracking control loop.For example, the gain of the directional error tracking control loop may depend on the bandwidth of a yaw rate loop, a steering actuator loop, vehicle lateral dynamics, etc. Thus, the higher the gain of the directional error tracking control loop, the shorter the look-ahead time.

[0103] At step 576, the processing unit 110 may determine a heading error and a yaw rate command based on the look-ahead point determined at step 574. The processing unit 110 may determine the heading error by calculating the arctangent of the look-ahead point, e.g., arctan (x l / z l). Processing unit 110 may determine the yaw rate command as the product of the heading error and a high control gain. The high control gain may be equal to: (2 / look-ahead time) if the look-ahead distance is not at the lower limit. Otherwise, the high control gain may be equal to: (2 * vehicle speed 200 / look-ahead distance).

[0104] Fig. 5F is a flowchart illustrating an exemplary process 500F for determining whether a leading vehicle is changing lanes, in accordance with the disclosed embodiments. At step 580, the processing unit 110 may determine navigation information associated with a leading vehicle (e.g., a vehicle traveling in front of the vehicle 200). For example, the processing unit 110 may determine the position, speed (e.g., direction and speed), and / or acceleration of the leading vehicle using the methods described above in connection with the Fig. 5A and Fig. 5B. The processing unit 110 may also determine one or more road polynomials, a look-ahead point (associated with the vehicle 200), and / or a snail trail (e.g., a set of points describing a path taken by the preceding vehicle) using the techniques described above in connection with Fig. 5E described techniques.

[0105] At step 582, processing unit 110 may analyze the navigation information determined at step 580. In one embodiment, processing unit 110 may calculate the distance between a snail trail and a road polynomial (e.g., along the trail). If the variance of this distance along the trail exceeds a predetermined threshold (e.g., 0.1 to 0.2 meters on a straight road, 0.3 to 0.4 meters on a moderately curved road, and 0.5 to 0.6 meters on a road with sharp curves), processing unit 110 may determine that the leading vehicle is likely changing lanes. In the event that multiple vehicles are detected 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 other vehicles is likely to change lanes. Processing unit 110 may additionally compare the curvature of the snail trail (associated with the leading vehicle) with the expected curvature of the road segment in which the leading vehicle is traveling. The expected curvature may be extracted from map data (e.g., data from map database 160), road polynomials, snail trails of other vehicles, prior knowledge of the road, and the like. If the difference in the curvature of the snail trail and the expected curvature of the road segment exceeds a predetermined threshold, processing unit 110 may determine that the leading vehicle is likely to change lanes.

[0106] In another embodiment, processing unit 110 may compare the current position of the leading vehicle with the look-ahead point (associated with vehicle 200) over a specific period of time (e.g., 0.5 to 1.5 seconds). If the distance between the current position of the leading vehicle and the look-ahead point varies during the specific period of time and the cumulative sum of the variation exceeds a predetermined threshold (e.g., 0.3 to 0.4 meters on a straight road, 0.7 to 0.8 meters on a moderately curved road, and 1.3 to 1.7 meters on a road with sharp curves), processing unit 110 may determine that the leading vehicle is likely changing lanes.In another embodiment, the processing unit 110 may analyze the geometry of the snail trail by comparing the lateral distance traveled along the trail with the expected curvature of the snail trail. The expected radius of curvature may be determined according to the calculation: (δ z 2 + δ x 2 ) / 2 / (δ x ), where δ x represents the lateral distance traveled and δ zrepresents the traveled longitudinal distance. If the difference between the traveled lateral distance and the expected curvature exceeds a predetermined threshold (e.g., 500 to 700 meters), the processing unit 110 may determine that the leading vehicle is likely to change lanes. In another embodiment, the processing unit 110 may analyze the position of the leading vehicle. If the position of the leading vehicle obscures a road polynomial (e.g., the leading vehicle is overlaid on the road polynomial), then the processing unit 110 may determine that the leading vehicle is likely to change lanes.In case the position of the preceding vehicle is such that another vehicle is detected in front of the preceding vehicle and the snail trails of the two vehicles are not parallel, the processing unit 110 may determine that the (nearer) preceding vehicle is likely to change lanes.

[0107] At step 584, processing unit 110 may determine whether or not the leading vehicle 200 is changing lanes based on the analysis performed at step 582. For example, processing unit 110 may make the determination based on a weighted average of the individual analyses performed at step 582. Under such a scheme, for example, a decision by processing unit 110 that the leading vehicle is likely to change lanes may be assigned a value of "1" (and "0" to represent a determination that the leading vehicle is not likely to change lanes) based on a particular type of analysis. Different analyses performed at step 582 may be assigned different weights, and the disclosed embodiments are not limited to any particular combination of analyses and weights.

[0108] Fig. 6 is a flowchart illustrating an exemplary process 600 for initiating one or more navigation responses based on stereo image analysis, in accordance with the disclosed embodiments. At step 610, the processing unit 110 may receive a first and second plurality of images via the data interface 128. For example, cameras included in the image capture unit 120 (such as image capture devices 122 and 124 with fields of view 202 and 204) may capture a first and second plurality of images of an area in front of the vehicle 200 and transmit them to the processing unit 110 via a digital connection (e.g., USB, wireless, Bluetooth, etc.). In some embodiments, the processing unit 110 may receive the first and second plurality of images via two or more data interfaces.The disclosed embodiments are not limited to any particular data interface configurations or protocols.

[0109] At step 620, the processing unit 110 may execute the stereo image analysis module 404 to perform a stereo image analysis of the first and second plurality of images to create a 3D map of the road ahead of the vehicle and to detect features within the images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, road hazards, and the like. The stereo image analysis may be performed in a manner consistent with the methods described above in connection with the Fig. 5A-5D. For example, the processing unit 110 may execute the stereo image analysis module 404 to detect candidate objects (e.g., vehicles, pedestrians, road markings, traffic lights, road hazards, etc.) within the first and second pluralities of images, filter out a subset of the candidate objects based on various criteria, and perform a multi-frame analysis, take measurements, and determine a confidence level for the remaining candidate objects. In performing the above steps, the processing unit 110 may consider information from both the first and second pluralities of images, as well as information from one set of images alone.For example, processing unit 110 may analyze the differences in pixel-level data (or other data subsets from the two streams of acquired images) for a candidate object that appears in both the first and second plurality of images. As another example, processing unit 110 may estimate a position and / or velocity of a candidate object (e.g., relative to vehicle 200) by observing that the object appears in one of the plurality of images but not the other, or relative to other differences that may exist relative to objects that appear when the two image streams are compared. For example, position, velocity, and / or acceleration relative to vehicle 200 may be determined based on trajectories, positions, motion characteristics, etc. of features associated with an object that appears in one or both of the image streams.

[0110] At step 630, the processing unit 110 may execute the navigation response module 408 to determine one or more navigation responses in the vehicle 200 based on the analysis performed at step 620 and the techniques described above in connection with Fig. 4. Navigational responses may include, for example, a turn, a lane shift, a change in acceleration, a change in speed, braking, and the like. In some embodiments, processing unit 110 may use data derived from the execution of speed and acceleration module 406 to prompt the one or more navigational responses. Additionally, multiple navigational responses may occur simultaneously, sequentially, or in any combination thereof.

[0111] Fig. 7 is a flowchart illustrating an example process 700 for initiating one or more navigation responses based on an analysis of three sets of images, in accordance with disclosed embodiments. At step 710, the processing unit 110 may receive a first, second, and third plurality of images via the data interface 128. For example, cameras included in the image capture unit 120 (such as image capture devices 122, 124, and 126 with 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 the vehicle 200 and transmit them to the processing unit 110 via a digital connection (e.g., USB, wireless, Bluetooth, etc.). In some embodiments, the processing unit 110 may receive the first, second, and third plurality of images via three or more data interfaces.For example, each of the image capture devices 122, 124, 126 may have an associated data interface for communicating data to the processing unit 110. The disclosed embodiments are not limited to any particular data interface configurations or protocols.

[0112] At step 720, the processing unit 110 may analyze the first, second, and third plurality of images to identify features within the images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, road hazards, and the like. The analysis may be performed in a manner consistent with the methods described above in connection with the Fig. 5A-5D and 6. For example, the processing unit 110 may perform monocular image analysis (e.g., via execution of the monocular image analysis module 402 and based on the steps described above in connection with the Fig. 5A-5D) on each of the first, second, and third plurality of images. Alternatively, the processing unit 110 may perform a stereo image analysis (e.g., via execution of the stereo image analysis module 404 and based on the steps described above in connection with Fig. 6) on the first and second plurality of images, the second and third plurality of images, and / or the first and third plurality of images. The processed information corresponding to the analysis of the first, second, and / or third plurality of images may be combined. In some embodiments, the processing unit 110 may perform a combination of monocular and stereo image analyses. For example, the processing unit 110 may perform monocular image analysis (e.g., via execution of the monocular image analysis module 402) on the first plurality of images and stereo image analysis (e.g., via execution of the stereo image analysis module 404) on the second and third plurality of images.The configuration of the image capture devices 122, 124, and 126—including their respective locations and fields of view 202, 204, and 206—may influence the types of analyses performed on the first, second, and third pluralities of images. The disclosed embodiments are not limited to any particular configuration of the image capture devices 122, 124, and 126 or the types of analyses performed on the first, second, and third pluralities of images.

[0113] In some embodiments, processing unit 110 may perform testing on system 100 based on the images captured and analyzed at steps 710 and 720. Such testing may provide an indicator of the overall performance of system 100 for particular configurations of image capture devices 122, 124, and 126. For example, processing unit 110 may determine the proportion of "false hits" (e.g., cases where system 100 incorrectly determined the presence of a vehicle or pedestrian) and "misses."

[0114] At step 730, the processing unit 110 may initiate one or more navigation responses in the vehicle 200 based on information derived from two of the first, second, and third plurality of images. The selection of two of the first, second, and third plurality of images may depend on various factors, such as the number, types, and sizes of objects detected in each of the plurality of images. The processing unit 110 may also make the selection based on image quality and resolution, the effective field of view reflected in the images, the number of captured frames, the extent to which one or more objects of interest actually appear in the frames (e.g., the percentage of frames in which an object appears, the proportion of the object that appears in each of those frames, etc.), and the like.

[0115] In some embodiments, processing unit 110 may select information derived from two of the first, second, and third pluralities of images by determining the extent to which information derived from one image source is consistent with information derived from other image sources. For example, processing unit 110 may combine the processed information derived from each of image capture devices 122, 124, and 126 (whether through monocular analysis, stereo analysis, or any combination of the two) and determine visual indicators (e.g., lane markings, a detected vehicle and its location and / or path, a detected traffic light, etc.) that are consistent across the images captured by each of image capture devices 122, 124, and 126. Processing unit 110 may also exclude information that is inconsistent across the captured images (e.g.,a vehicle changing lanes, a lane model indicating a vehicle that is too close to the vehicle 200, etc.). Thus, the processing unit 110 may select information derived from two of the first, second, and third pluralities of images based on the determinations of consistent and inconsistent information.

[0116] Navigational responses may include, for example, a turn, a lane shift, a change in acceleration, and the like. The processing unit 110 may determine the one or more navigational responses based on the analysis performed at step 720 and the techniques described above in connection with Fig. 4. The processing unit 110 may also use data derived from the execution of the speed and acceleration module 406 to initiate the one or more navigation responses. In some embodiments, the processing unit 110 may initiate the one or more navigation responses based on a relative position, relative speed, and / or relative acceleration between the vehicle 200 and an object detected within any of the first, second, and third pluralities of images. Multiple navigation responses may occur simultaneously, sequentially, or in any combination thereof. Sparse road model for autonomous vehicle navigation

[0117] 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 a road segment. For example, the sparse map may provide sufficient information for an autonomous vehicle to navigate without storing and / or updating a large amount 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. Sparse map for autonomous vehicle navigation

[0118] In some embodiments, the disclosed systems and methods may generate a sparse map for autonomous vehicle navigation. For example, the sparse map may provide sufficient information for navigation without requiring excessive data storage or excessive data transfer rates. As discussed in more detail below, a vehicle (which may be an autonomous vehicle) may use the sparse map to navigate one or more roads. For example, in some embodiments, the sparse map may include data related to a road and potentially landmarks along the road that may be sufficient for vehicle navigation, but which also has small data footprints.For example, the sparse data maps described in more detail below can require significantly less storage space and data transmission bandwidth compared to digital maps that include detailed map information, such as imagery collected along a road.

[0119] For example, instead of storing detailed representations of a road segment, the sparse data map may store three-dimensional polynomial representations of preferred vehicle paths along a road. These paths may require very little data storage space. Further, landmarks identified in the described sparse data maps may be included in the sparse map road model to aid in navigation. These landmarks may be located at any spacing suitable to enable vehicle navigation, but in some cases, such landmarks need not be identified and included in the model with high densities and short spacings.Rather, in some cases, navigation may be possible based on landmarks that are 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, the sparse map may be generated based on data collected or measured by vehicles equipped with various sensors and devices, such as image capture devices, global positioning system sensors, motion sensors, etc., as the vehicles travel along roadways. In some cases, the sparse map may be generated based on data collected during multiple trips by one or more vehicles along a particular roadway.Creating a sparse map using multiple trips of one or more vehicles can be referred to as crowdsourcing a sparse map.

[0120] In accordance with the disclosed embodiments, an autonomous vehicle system may use a sparse map for navigation. For example, the disclosed systems and methods may distribute a sparse map to generate a road navigation model for an autonomous vehicle and may navigate an autonomous vehicle along a road segment using a sparse map and / or a generated road navigation model. Sparse maps in accordance with the present disclosure may include one or more three-dimensional contours that may represent predetermined trajectories that autonomous vehicles may traverse when traveling along associated road segments.

[0121] Sparse maps in accordance with the present disclosure may also include data representing one or more road features. Such road features may include detected landmarks, road signature profiles, and any other road-related features useful in navigating a vehicle. Sparse maps in accordance with the present disclosure may enable autonomous navigation of a vehicle based on relatively small amounts of data contained in the sparse map.For example, rather than including detailed representations of a road, such as road edges, road curvature, images associated with road segments, or data detailing other physical features associated with a road segment, the disclosed sparse map embodiments may require relatively little storage space (and relatively little bandwidth when transmitting portions of the sparse map to a vehicle) yet 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 features that require small amounts of data yet still enable autonomous navigation.

[0122] For example, instead of storing detailed representations of various aspects of a road, the disclosed sparse maps may store polynomial representations of one or more trajectories that a vehicle may follow along the road. Thus, instead of having to store (or transmit) details regarding the physical nature of the road to enable navigation along the road, a vehicle may be navigated along a particular road segment using the disclosed sparse maps without, in some cases, having to interpret physical aspects of the road, but rather by aligning its path of travel with a trajectory (e.g., a polynomial spline) along the particular road segment. In this way, the vehicle may navigate primarily based on the stored trajectory (e.g.,a polynomial spline), which can require much less storage space than an approach that involves storing roadway images, road parameters, road layout, etc.

[0123] In addition to the stored polynomial representations of trajectories along a road segment, the disclosed sparse maps may also include small data objects that may represent a road feature. In some embodiments, the small data objects may include digital signatures derived from a digital image (or a digital signal) obtained from a sensor (e.g., a camera or other sensor, such as a suspension sensor) onboard a vehicle traveling along the road segment. The digital signature may have a reduced size relative to the signal detected by the sensor. In some embodiments, the digital signature may be generated to be compatible with a classifier function configured to detect and identify the road feature from the signal detected by the sensor, for example, during a subsequent trip.In some embodiments, a digital signature may be generated such that the digital signature has a footprint that is as small as possible while maintaining the ability to correlate or match the road feature with the stored signature based on an image (or a digital signal generated by a sensor if the stored signature is not based on an image and / or includes other data) of the road feature captured by a camera onboard a vehicle traveling along the same road segment at a subsequent time.

[0124] In some embodiments, a size of the data objects may further be associated with a uniqueness of the road feature. For example, for a road feature detectable by a camera onboard a vehicle, and if the camera system onboard the vehicle is coupled to a classifier capable of distinguishing the image data corresponding to that road feature as associated with a particular type of road feature, for example, a road sign, and if such a road sign is locally unique in that area (e.g., there is no identical road sign or road sign of the same type nearby), it may be sufficient to store data indicating the type of road feature and its location.

[0125] As discussed in more detail below, road features (e.g., landmarks along a road segment) can be stored as small data objects that can represent a road feature in relatively few bytes, while providing sufficient information to recognize and use such a feature for navigation. In one example, a road sign can be identified as a recognized landmark upon which a vehicle's navigation can be based. A representation of the road sign can be stored in the sparse map to include, for example, a few bytes of data indicating a type of landmark (e.g., a stop sign) and a few bytes of data indicating a location of the landmark (e.g., coordinates). Navigating based on such data light representations of the landmarks (e.g.,A sparse representation of landmarks (i.e., using representations sufficient to locate, recognize, and navigate based on the landmarks) can provide a desired level of navigation functionality associated with sparse maps without significantly increasing the data overhead associated with the sparse maps. This sparse representation of landmarks (and other road features) can utilize the sensors and processors included onboard such vehicles that are configured to detect, identify, and / or classify specific road features.

[0126] For example, if a sign or even a particular type of sign is locally unique in a given area (e.g., if there is no other sign or no other sign of the same type), the sparse map may use data indicating a type of landmark (a sign or a particular type of sign), and during navigation (e.g., autonomous navigation), when a camera onboard an autonomous vehicle captures an image of the area that includes a sign (or a particular type of sign), the processor may process the image, detect the sign (if it is actually 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 as stored in the sparse map.

[0127] The sparse map may include any suitable representation of the objects identified along a road segment. In some cases, the objects may be referred to as semantic objects or non-semantic objects. Semantic objects may, for example, be objects associated with a predetermined type classification. This type classification may be useful to reduce the amount of data required to describe the semantic object detected in an environment, which may be useful both in the gathering phase (e.g., to reduce the costs associated with bandwidth usage for transmitting driving information from multiple gathering vehicles to a server) and during the navigation phase (e.g.,Map data reduction can be beneficial (for example, by speeding up the transmission of map tiles from a server to a navigating vehicle and also reducing the costs associated with bandwidth usage for such transmissions). Semantic object classification types can be assigned to any type of object or feature encountered along a roadway.

[0128] Semantic objects can be further divided into two or more logical groups. For example, in some cases, a group of semantic object types may be associated with predetermined dimensions. Such semantic objects can be specific speed limit signs, yield signs, merge signs, stop signs, traffic lights, directional arrows on the roadway, manhole covers, or any other type of object that can be associated with a standardized size. An advantage of such semantic objects is that very little data is required to represent / fully define the objects.For example, if the standardized size of a speed limit sign is known, a collection vehicle only needs to identify (by analyzing a captured image) the presence of a speed limit sign (of a detected type) along with an indication of a position of the detected speed limit sign (e.g., a 2D position in the captured image (or alternatively, a 3D position in real-world coordinates) of a center point of the sign or a specific corner of the sign) to provide sufficient information for map generation on the server side. When 2D image positions are transmitted to the server, a position associated with the captured image where the sign was detected may also be transmitted to allow the server to determine a real-world position of the sign (e.g., through structure-in-motion techniques using multiple captured images from one or more collection vehicles).Even with this limited information (requiring only a few bytes to define each detected object), the server can create the map including a fully represented speed limit sign based on the type classification (representative of a speed limit sign) received from one or more collection vehicles along with the position information for the detected sign.

[0129] Semantic objects may also include other detected object or feature types that are not associated with specific standardized features. Such objects or features may be potholes, tar seams, light poles, non-standardized signs, curbs, trees, branches, or other detected object types with one or more variable characteristics (e.g., variable dimensions). In such cases, in addition to transmitting an indication of the detected object or feature type (e.g., pothole, post, etc.) and the position information for the detected object or feature to a server, a collection vehicle may also transmit an indication of the size of the object or feature. The size may be expressed in 2D image dimensions (e.g.,with a bounding box or one or more dimension values) or expressed in real dimensions (determined by structure-in-motion calculations, based on the results of LIDAR or RADAR systems, based on the outputs of trained neural networks, etc.).

[0130] Non-semantic objects or features can include any detectable objects or features that do not fall into a recognized category or type, but can still provide valuable information for map generation. In some cases, such non-semantic features can be a detected corner of a building or a corner of a detected window of a building, a unique stone or object near a roadway, a piece of concrete on a roadside verge, or any other detectable object or feature. Upon detecting such an object or feature, one or more collection vehicles can transmit the position of one or more points (2D image points or 3D real-world points) associated with the detected object / feature to a map generation server. Additionally, a compressed or simplified image segment (e.g.,An image hash (or hash) can be generated for a region of the captured image containing the detected object or feature. This image hash can be calculated based on a predetermined image processing algorithm and can form an effective signature for the detected non-semantic object or feature. Such a signature can be useful for navigation relative to a sparse map containing the non-semantic feature or object, as a vehicle traversing the roadway can apply an algorithm similar to the algorithm used to generate the image hash to confirm / verify the presence of the mapped non-semantic feature or object in a captured image. Using this technique, non-semantic features can contribute to the richness of the sparse maps (e.g.,to improve their usefulness in navigation) without adding significant data overhead.

[0131] As noted, target trajectories can be stored in the sparse map. These target trajectories (e.g., 3D splines) can represent the preferred or recommended paths for each available lane of a roadway, each valid pedestrian path through an intersection, for merges and exits, etc. In addition to the target trajectories, other road features can also be detected, collected, and incorporated into the sparse maps as representative splines. Such features can include, for example, roadside verges, pavement markings, curbs, guardrails, or other objects or features extending along a roadway or road segment. Creating a sparse map

[0132] In some embodiments, a sparse map may include at least one line representation of a road surface feature extending along a road segment and a plurality of landmarks associated with the road segment. In certain aspects, the sparse map may be generated by crowdsourcing, for example, by image analysis of a plurality of images captured as one or more vehicles traverse the road segment.

[0133] Fig. 8 shows a sparse map 800 that can be accessed by one or more vehicles, e.g., vehicle 200 (which may be an autonomous vehicle), to provide autonomous vehicle navigation. Sparse map 800 may be stored in a memory, such as memory 140 or 150. Such storage devices may include any type of non-volatile storage device or computer-readable media. For example, in some embodiments, memory 140 or 150 may include hard drives, compact discs, flash memory, magnetic-based storage devices, optical-based storage devices, 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.

[0134] In some embodiments, the sparse map 800 may be stored on a storage device or non-transitory computer-readable medium provided onboard the vehicle 200 (e.g., a storage device included in a navigation system onboard the vehicle 200). A processor (e.g., processing unit 110) provided on the vehicle 200 may access the sparse map 800 stored in the storage device or computer-readable medium provided onboard the vehicle 200 to generate navigation instructions for guiding the autonomous vehicle 200 as the vehicle traverses a road segment.

[0135] However, the sparse map 800 does not need to be stored locally with respect to a vehicle. In some embodiments, the sparse map 800 may be stored on a storage device or computer-readable medium provided on a remote server that communicates with the vehicle 200 or a device associated with the vehicle 200. A processor (e.g., processing unit 110) provided on the vehicle 200 may receive data contained in the sparse map 800 from the remote server and may execute the data to guide the autonomous driving of the vehicle 200. In such embodiments, the remote server may store all or only a portion of the sparse map 800.Accordingly, the storage device or computer-readable medium provided on board the vehicle 200 and / or on board one or more additional vehicles may store the remaining portion(s) of the sparse map 800.

[0136] Furthermore, in such embodiments, the sparse map 800 may be made accessible to a plurality of vehicles traversing various road segments (e.g., dozens, hundreds, thousands, or millions of vehicles, etc.). It should also be noted that the sparse map 800 may include multiple submaps. For example, in some embodiments, the sparse map 800 may include hundreds, thousands, millions, or more submaps (e.g., map tiles) that may be used in navigating a vehicle. Such submaps may be referred to as local maps or map tiles, and a vehicle traveling along a roadway may access any number of local maps relevant to the location where the vehicle is traveling.The local map sections of the sparse map 800 may be stored with a Global Navigation Satellite System (GNSS) key as an index to the sparse map database 800. While the calculation of steering angles for navigating a host vehicle in the present system may be performed without dependence on a host vehicle's GNSS position, road features, or landmarks, such GNSS information may be used to retrieve relevant local maps.

[0137] In general, the sparse map 800 may be generated based on data collected from one or more vehicles as they travel along the roadways. For example, using sensors onboard the one or more vehicles (e.g., cameras, speedometers, GPS, accelerometers, etc.), the trajectories traveled by the one or more vehicles along a roadway may be recorded, and polynomial representations of a preferred trajectory for vehicles making subsequent trips along the roadway may be determined based on the collected trajectories traveled by the one or more vehicles. Similarly, data collected by the one or more vehicles may assist in identifying potential landmarks along a particular roadway.Data collected from crossing vehicles may also be used to identify road profile information, such as road width profiles, road roughness profiles, traffic line spacing profiles, road conditions, etc. Using the collected information, the sparse map 800 may be generated and distributed for use in navigating one or more autonomous vehicles (e.g., for local storage or via on-the-fly data transfer). However, in some embodiments, map generation may not end with the initial generation 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 those vehicles continue to traverse roadways included in the sparse map 800.

[0138] Data recorded in the sparse map 800 may include location information based on Global Positioning System (GPS) data. For example, location information may be included in the sparse map 800 for various map elements, including, for example, landmark locations, road profile locations, etc. Locations for map elements included in the sparse map 800 may be obtained using GPS data collected from vehicles traversing a roadway. For example, a vehicle passing an identified landmark may determine a location of the identified landmark using GPS position information associated with the vehicle and a determination of a location of the identified landmark relative to the vehicle (e.g.,based on image analysis of data collected by one or more cameras onboard the vehicle). Such location determinations of an 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 relative to the identified landmark. For example, in some embodiments, multiple position measurements relative to a particular feature stored in the sparse map 800 may be averaged together.However, any other mathematical operations can also be used to refine a stored location of a map element based on a plurality of specific locations for the map element.

[0139] In a specific example, the collection vehicles may traverse a specific road segment. Each collection vehicle captures images of its respective surroundings. The images may be collected at any suitable image acquisition rate (e.g., 9 Hz, etc.). Image analysis processor(s) onboard each collection vehicle analyze(s) the captured images to detect the presence of semantic and / or non-semantic features / objects. At a high level, the collection vehicles transmit information about the detections of the semantic and / or non-semantic objects / features, along with the positions associated with these objects / features, to a mapping server. In more detail, type indicators, dimension indicators, etc., may be transmitted along with the position information.The position information may include any suitable information that enables the mapping server to aggregate the detected objects / features into a sparse map useful for navigation. In some cases, the position information may include one or more 2D image locations (e.g., XY pixel locations) in a captured image in which the semantic or non-semantic features / objects were detected. Such image locations may correspond to a center of the feature / object, a corner, etc. In this scenario, to assist the mapping server in reconstructing the travel information and aligning the travel information from multiple collection vehicles, each collection vehicle may also provide the server with a location (e.g., a GPS location) where each image was captured.

[0140] In other cases, the collection vehicle may provide the server with one or more real-world 3D points associated with the detected objects / features. Such 3D points may be referenced to a predetermined origin (such as the origin of a driving segment) and may be determined using any suitable technique. In some cases, a structure-in-motion technique may be used to determine the real-world 3D position of a detected object / feature. For example, a particular object, such as a particular speed limit sign, may be detected in two or more captured images. Using information such as the known self-motion (speed, trajectory, GPS position, etc.) of the collection vehicle between the captured images, along with observed changes in the speed limit sign in the captured images (change in XY pixel position, change in size, etc.),), the real-world position of one or more points associated with the speed limit sign can be determined and forwarded to the mapping server. Such an approach is optional because it requires greater computational complexity for the collector vehicle's systems. The sparse map of the disclosed embodiments can enable autonomous navigation of a vehicle using relatively small amounts of stored data. In some embodiments, the sparse map 800 can have a data density (e.g., including data representing the target trajectories, landmarks, and any other stored road features) of less than 2 MB per kilometer of roads, less than 1 MB per kilometer of roads, less than 500 kB per kilometer of roads, or less than 100 kB per kilometer of roads.In some embodiments, the data density of the sparse map 800 may be less than 10 kB per kilometer of roads, or even less than 2 kB per kilometer of roads (e.g., 1.6 kB per kilometer), or no more than 10 kB per kilometer of roads, or no more than 20 kB per kilometer of roads. In some embodiments, most, if not all, of the roadways of the United States may be autonomously navigated using a sparse map that has a total of 4 GB or less of data. These data density values may represent an average over an entire sparse map 800, over a local map within the sparse map 800, and / or over a particular road segment within the sparse map 800.

[0141] As noted, the sparse map 800 may include representations of a plurality of target trajectories 810 for guiding autonomous driving or navigation along a road segment. Such target trajectories may be stored as three-dimensional splines. The target trajectories stored in the sparse map 800 may be determined, for example, based on two or more reconstructed trajectories of previous vehicle crossings along a particular road segment. A road segment may be associated with a single target trajectory or multiple target trajectories. For example, on a two-lane road, a first target trajectory may be stored to represent an intended path of travel along the road in a first direction, and a second target trajectory may be stored to represent an intended path of travel along the road in a different direction (e.g.,opposite to the first direction). Additional target trajectories may be stored with respect to a particular road segment. For example, on a multi-lane road, one or more target trajectories may be stored representing intended travel paths for vehicles in one or more lanes associated with the multi-lane road. In some embodiments, each lane of a multi-lane road may be associated with its own target trajectory. In other embodiments, fewer target trajectories may be stored than lanes present on a multi-lane road. In such cases, a vehicle navigating the multi-lane road may use any of the stored target trajectories to guide its navigation by taking into account an amount of lane offset from a lane for which a 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 center lane target trajectory by taking into account the amount of lane offset between the center lane and the leftmost lane when generating navigation instructions).

[0142] In some embodiments, the target trajectory may represent an ideal path that a vehicle should take when traveling. For example, the target trajectory may be located at an approximate center of a lane. In other cases, the target trajectory may be located elsewhere with respect to a road segment. For example, a target trajectory may approximately coincide with a center of a road, an edge of a road, or an edge of a lane, etc. In such cases, navigation based on the target trajectory may include a particular offset amount to be maintained with respect to the location of the target trajectory. Furthermore, in some embodiments, the particular offset amount to be maintained with respect to the location of the target trajectory may differ based on a vehicle type (e.g.,a passenger car with two axles may have a different offset than a truck with more than two axles along at least one section of the target trajectory).

[0143] Sparse map 800 may also include data related to a plurality of predetermined landmarks 820 associated with particular road segments, local maps, etc. As discussed in more detail below, these landmarks may be used in the navigation of the autonomous vehicle. For example, in some embodiments, the landmarks may be used to determine a current position of the vehicle relative to a stored target trajectory. With this position information, the autonomous vehicle may be able to adjust a heading to match a direction of the target trajectory at the particular location.

[0144] The plurality of landmarks 820 may be identified and stored in the sparse map 800 at any suitable spacing. In some embodiments, landmarks may be stored at relatively high densities (e.g., every few meters or more). However, in some embodiments, significantly larger landmark spacing values may be used. For example, landmarks identified (or recognized) in the sparse map 800 may be 10 meters, 20 meters, 50 meters, 100 meters, 1 kilometer, or 2 kilometers apart. In some cases, the identified landmarks may be at distances even greater than 2 kilometers apart.

[0145] The vehicle may navigate between landmarks, and therefore between determinations of the vehicle's position relative to a target trajectory, based on dead reckoning, in which the vehicle uses sensors to determine its own motion and estimate its position relative to the target trajectory. Because errors can accumulate during dead reckoning navigation, position determinations relative to the target trajectory may become increasingly inaccurate over time. The vehicle may use landmarks that appear in the sparse map 800 (and their known locations) to eliminate the dead reckoning-induced errors in the position determination. In this way, the identified landmarks contained in the sparse map 800 may serve as navigation anchors from which an accurate position of the vehicle relative to a target trajectory can be determined.Since some error in the position location may be acceptable, an identified landmark need not always be available to an autonomous vehicle. Rather, appropriate navigation may also be possible based on landmark spacing, as noted above, of 10 meters, 20 meters, 50 meters, 100 meters, 500 meters, 1 kilometer, 2 kilometers, or more. In some embodiments, a density of 1 identified landmark every 1 km of road may be sufficient to maintain longitudinal positioning accuracy within 1 m. Thus, not every potential landmark appearing along a road segment needs to be stored in the sparse map 800.

[0146] Furthermore, in some embodiments, lane markings may be used to locate the vehicle during landmark intervals. Using lane markings during landmark intervals may minimize the accumulation of dead reckoning errors during navigation.

[0147] In addition to target trajectories and identified landmarks, the sparse map 800 may include information related to various other road features. For example, Fig. 9A shows a representation of 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 how many lanes a road may have, the road can be represented using polynomials in a manner similar to that shown in Fig. 9A. For example, the left and right sides of a multi-lane road can be represented by polynomials similar to those shown in Fig. 9A, and inter-lane markings included on 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, such as those shown in Fig. 9A are shown.

[0148] As in Fig. 9A, a lane 900 may be represented using polynomials (e.g., first-order, second-order, third-order, or any suitable order polynomials). For illustration, the lane 900 is shown as a two-dimensional lane, and the polynomials are shown as two-dimensional polynomials. As shown in Fig. 9A, the lane 900 includes a left side 910 and a right side 920. In some embodiments, more than one polynomial may be used to represent a location on each side of the road or lane boundary. For example, each of the left side 910 and the right side 920 may be represented by a plurality of polynomials of any suitable length. In some cases, the polynomials may have a length of approximately 100 m, although other lengths greater or less than 100 m may also be used. Additionally, the polynomials may overlap with one another to facilitate seamless transitions when navigating based on subsequently encountered polynomials as a host vehicle travels along a roadway.For example, each of the left side 910 and the right side 920 may be represented by a plurality of third-order polynomials separated into segments of approximately 100 meters in length (an example of the first predetermined range) and overlapping each other by approximately 50 meters. The polynomials representing the left side 910 and the right side 920 may or may not be of the same order. For example, in some embodiments, some polynomials may be second-order polynomials, some third-order polynomials, and some fourth-order polynomials.

[0149] In the Fig. In the example shown in Figure 9A, the left side 910 of lane 900 is represented by two groups of third-order polynomials. The first group includes polynomial segments 911, 912, and 913. The second group includes polynomial segments 914, 915, and 916. The two groups, while substantially parallel to each other, track the locations of their respective sides of the road. Polynomial segments 911, 912, 913, 914, 915, and 916 have a length of approximately 100 meters and overlap adjacent segments in the series by approximately 50 meters. However, as previously noted, polynomials of different lengths and different amounts of overlap may also be used. For example, the polynomials can have lengths of 500 m, 1 km or more and the amount of overlap can vary from 0 to 50 m, 50 m to 100 m or more than 100 m. While Fig. 9A as representing polynomials extending in 2D space (e.g., on the surface of the paper), it is additionally understood that these polynomials may represent curves extending in three dimensions (e.g., including an elevation component) to represent elevation changes in a road segment in addition to the XY curvature. In the Fig. 9A, the right side 920 of the lane 900 is further represented by a first group of polynomial segments 921, 922 and 923 and a second group of polynomial segments 924, 925 and 926.

[0150] Referring again to the target trajectories of the sparse map 800, Fig. 9B shows a three-dimensional polynomial representing a target trajectory for a vehicle traveling along a particular road segment. The target trajectory represents not only the XY path a host vehicle should travel along a particular road segment, but also the elevation change the host vehicle will experience while traveling along the road segment. Thus, each target trajectory in the sparse map 800 can be represented by one or more three-dimensional polynomials, such as the one shown in Fig. 9B. Sparse map 800 may include a plurality of trajectories (e.g., millions or billions or more to represent trajectories of vehicles along various road segments along roadways throughout the world). In some embodiments, each target trajectory may correspond to a spline connecting three-dimensional polynomial segments.

[0151] Regarding the data footprint of polynomial curves stored in sparse map 800, in some embodiments, each third-degree polynomial can be represented by four parameters, each requiring four bytes of data. Suitable representations can be obtained with third-degree polynomials, which require approximately 192 bytes of data for every 100 m. This can translate to approximately 200 kB per hour for data usage / transmission requirements for a host vehicle traveling approximately 100 km / h.

[0152] Sparse map 800 may describe the lane network using a combination of geometry descriptors and metadata. The geometry may be described by polynomials or splines, as described above. The metadata may describe the number of lanes, special properties (such as a carpool lane), and possibly other sparse designations. The overall footprint of such indicators may be negligible.

[0153] Accordingly, a sparse map according to embodiments of the present disclosure may include at least one line representation of a road surface feature extending along the road segment, each line representation representing a path along the road segment that substantially corresponds to the road surface feature. In some embodiments, as discussed above, the at least one line representation of the road surface feature may include a spline, a polynomial representation, or a curve. Furthermore, in some embodiments, the road surface feature may include at least one of a road edge or a lane marker.Furthermore, as discussed below with respect to crowdsourcing, the road surface feature can be identified by image analysis of a plurality of images taken when one or more vehicles cross the road segment.

[0154] As previously noted, the sparse map 800 may include a plurality of predetermined landmarks associated with a road segment. Instead of storing actual images of the landmarks and relying, for example, on image recognition analysis based on captured images 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. Data representing landmarks may still include sufficient information to describe or identify the landmarks along a road. Storing data describing landmark characteristics rather than actual landmark images may reduce the size of the sparse map 800.

[0155] Fig. 10 illustrates examples of landmark types that may be represented in the sparse map 800. The landmarks may include any visible and identifiable objects along a road segment. The landmarks may be selected to be fixed and not change frequently in terms of their locations and / or content. The landmarks included in the sparse map 800 may be useful in determining a location 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 fixtures (e.g., lampposts, reflectors, etc.), and any other suitable category.In some embodiments, lane markings on the road may also be included as landmarks in the sparse map 800.

[0156] Examples of in Fig. The landmarks shown in Figure 10 include traffic signs, directional signs, roadside fixtures, and general signs. Traffic signs may include, for example, speed limit signs (e.g., speed limit sign 1000), yield signs (e.g., yield sign 1005), route number signs (e.g., route number sign 1010), traffic light signs (e.g., traffic light sign 1015), and stop signs (e.g., stop sign 1020). Directional signs may include a sign that includes one or more arrows indicating one or more directions to different locations. For example, directional signs may include a highway sign 1025 that includes arrows for directing vehicles to different roads or locations, an exit sign 1030 that includes an arrow for directing vehicles from a road, etc. Accordingly, at least one of the plurality of landmarks may include a road sign.

[0157] General signs don't have to be related to traffic. For example, general signs might include billboards used for advertising or a welcome sign adjacent to a border between two countries, states, counties, cities, or towns. Fig. 10 shows a generic sign 1040 ("Joe's Restaurant"). Although the generic sign 1040 may have a rectangular shape, as in Fig. 10, the general character 1040 may have other shapes, such as square, circle, triangle, etc.

[0158] Landmarks may also include roadside fixtures. Roadside fixtures may be objects other than signs and may not be related to traffic or directions. For example, roadside fixtures may include lampposts (e.g., lamppost 1035), power line posts, traffic light posts, etc.

[0159] Landmarks may also include beacons, which may be specifically designed for use in a navigation system of an autonomous vehicle. For example, such beacons may include stand-alone structures placed at predetermined intervals to assist in navigating a host vehicle. Such beacons may also include visual / graphical information added to existing road signs (e.g., icons, emblems, barcodes, etc.) that can be identified or recognized by a vehicle 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 a host vehicle.Such information may include, for example, landmark identification and / or landmark location information that a host vehicle may use in determining its position along a target trajectory.

[0160] In some embodiments, the landmarks included in sparse map 800 may be represented by a data object of a predetermined size. The data representing a landmark may include any suitable parameters for identifying a particular landmark. For example, in some embodiments, landmarks stored in sparse map 800 may include parameters such as a physical size of the landmark (e.g., to support estimating the distance to the landmark based on a known size / scale), a distance to a previous landmark, lateral offset, elevation, a type code (e.g., a landmark type—what type of directional sign, traffic sign, etc.), a GPS coordinate (e.g., to support global localization), and any other suitable parameters.Each parameter can be associated with a data size. For example, a landmark size can be stored using 8 bytes of data. A distance to a previous landmark, a lateral offset, and an elevation can be specified using 12 bytes of data. A type code associated with a landmark, such as a directional sign or a traffic sign, may require approximately 2 bytes of data. For general signs, an image signature that enables identification of the general sign can be stored using 50 bytes of data storage. The landmark GPS position can be associated with 16 bytes of data storage. These data sizes for each parameter are only examples, and other data sizes may also be used.Representing landmarks in the sparse map 800 in this manner may provide a lightweight solution for efficiently representing landmarks in the database. In some embodiments, objects may be referred to as standard semantic objects and non-standard semantic objects. A standard semantic object may include any class of objects for which there is a standardized set of properties (e.g., speed limit signs, warning signs, directional signs, traffic lights, etc., with known dimensions or other properties). A non-standard semantic object may include any object that is not associated with a standardized set of properties (e.g., general advertising signs, signs identifying business establishments, potholes, trees, etc., which may have variable dimensions).Each non-standard semantic object can be represented using 38 bytes of data (e.g., 8 bytes for size; 12 bytes for distance to a previous landmark, lateral offset, and elevation; 2 bytes for a type code; and 16 bytes for position coordinates). Standard semantic objects can be represented using even less data, since size information may not be required by the mapping server to fully represent the object in the sparse map.

[0161] The sparse map 800 may use a marker system to represent landmark types. In some cases, each traffic sign or directional sign may be associated with its own marker, which may be stored in the database as part of the landmark identification. For example, the database may contain on the order of 1,000 different markers to represent various traffic signs and on the order of about 10,000 different markers to represent directional signs. Of course, any suitable number of markers may be used, and additional markers may be generated as needed. Common signs may be represented using less than about 100 bytes in some embodiments (e.g.,approximately 86 bytes, including 8 bytes for size; 12 bytes for distance to a previous landmark, lateral offset, and elevation; 50 bytes for an image signature; and 16 bytes for GPS coordinates).

[0162] Thus, for semantic road signs that do not require an image signature, the data density impact on the sparse map 800, even at relatively high landmark densities of about 1 per 50 m, can be on the order of about 760 bytes per kilometer (e.g., 20 landmarks per km x 38 bytes per landmark = 760 bytes). Even for general signs that include an image signature component, the data density impact is about 1.72 kB per km (e.g., 20 landmarks per km x 86 bytes per landmark = 1,720 bytes). For semantic road signs, this corresponds to about 76 kB per hour of data usage for a vehicle traveling at 100 km / h. For general signs, this corresponds to about 170 kB per hour for a vehicle traveling at 100 km / h. It should be noted that in some environments (e.g.,in urban environments) there may be a much higher density of detected objects available for inclusion in the sparse map (perhaps more than one per meter). In some embodiments, a generally rectangular object, such as a rectangular sign, may be represented in the sparse map 800 by no more than 100 bytes of data. The representation of the generally rectangular object (e.g., the general sign 1040) in the sparse map 800 may include a condensed image signature (e.g., the condensed image signature 1045) associated with the generally rectangular object. This condensed image signature may be used, for example, to assist in the identification of a general sign, for example, as a recognized landmark. Such a condensed image signature (e.g.,Image information derived from actual image data representing an object) can avoid a need for storing an actual image of an object or a need for comparative image analysis performed on actual images to detect landmarks.

[0163] With reference to Fig. 10, the sparse card 800 may include or store a condensed image signature 1045 associated with a common indicia 1040, rather than an actual image of the common indicia 1040. For example, after an image capture device (e.g., image capture device 122, 124, or 126) captures an image of the common indicia 1040, a processor (e.g., image processor 190 or any other processor capable of processing images either onboard or remotely relative to a host vehicle) may perform image analysis to extract / generate the condensed image signature 1045 that includes a unique signature or pattern associated with the common indicia 1040.In one embodiment, the condensed image signature 1045 may include a shape, a color pattern, a brightness pattern, or any other feature that can be extracted from the image of the general character 1040 to describe the general character 1040.

[0164] For example, in Fig. 10, the circles, triangles, and stars shown in the condensed image signature 1045 represent areas of different colors. The pattern represented by the circles, triangles, and stars may be stored in the sparse map 800, e.g., within the 50 bytes designated as containing an image signature. In particular, the circles, triangles, and stars are not necessarily intended to indicate that such shapes are stored as part of the image signature. Rather, these shapes are intended to conceptually represent recognizable areas with recognizable color differences, text areas, graphic shapes, or other variations of properties that may be associated with a common character. Such condensed image signatures may be used to identify a landmark in the form of a common character.For example, the condensed image signature can be used to perform a like-not-like analysis based on a comparison of a stored condensed image signature with image data captured, for example, using a camera onboard an autonomous vehicle.

[0165] Accordingly, the plurality of landmarks may be identified by image analysis of the plurality of images captured as one or more vehicles traverse the road segment. As explained below with respect to "crowdsourcing," in some embodiments, the image analysis to identify the plurality of landmarks may include accepting potential landmarks if a ratio of images in which the landmark appears to images in which the landmark does not appear exceeds a threshold. Furthermore, in some embodiments, the image analysis to identify the plurality of landmarks may include rejecting potential landmarks if a ratio of images in which the landmark does not appear to images in which the landmark does appear exceeds a threshold.

[0166] Referring again to the target trajectories that a host vehicle can use to navigate a particular road segment, Fig. 11A shows polynomial representations of trajectories acquired during a process for creating or maintaining the sparse map 800. A polynomial representation of a target trajectory included in the sparse map 800 may be determined based on two or more reconstructed trajectories of previous vehicle crossings along the same road segment. In some embodiments, the polynomial representation of the target trajectory included in the sparse map 800 may be an aggregation of two or more reconstructed trajectories of previous vehicle crossings 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 the two or more reconstructed trajectories of previous vehicle crossings along the same road segment.Other mathematical operations can also be used to construct a target trajectory along a road path based on reconstructed trajectories collected from crossing vehicles along a road segment.

[0167] As in Fig. 11A, a road segment 1100 may be traversed at different times by a number of vehicles 200. Each vehicle 200 may collect data related to a path taken by the vehicle 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. Such data may be used to reconstruct trajectories of vehicles traveling along the road segment, and based on these reconstructed trajectories, a target trajectory (or multiple target trajectories) may be determined for the particular road segment. Such target trajectories may represent a preferred path of a host vehicle (e.g., guided by an autonomous navigation system) as the vehicle travels along the road segment.

[0168] In the Fig. 11A, a first reconstructed trajectory 1101 may be determined based on data received from a first vehicle traversing road segment 1100 in a first time period (e.g., day 1), a second reconstructed trajectory 1102 may be obtained from a second vehicle traversing road segment 1100 in a second time period (e.g., day 2), and a third reconstructed trajectory 1103 may be obtained from a third vehicle traversing road segment 1100 in a third time period (e.g., day 3). Each trajectory 1101, 1102, and 1103 may be represented by a polynomial, such as a three-dimensional polynomial. It should be noted that in some embodiments, each of the reconstructed trajectories may be assembled onboard the vehicles traversing road segment 1100.

[0169] Additionally or alternatively, such reconstructed trajectories may be determined on a server side based on information received from vehicles traversing road segment 1100. For example, in some embodiments, vehicles 200 may transmit data to one or more servers related to their movement along road segment 1100 (e.g., steering angle, heading, time, position, speed, detected road geometry, and / or detected landmarks, among others). The server may reconstruct trajectories for vehicles 200 based on the received data. The server may also generate a target trajectory for guiding the navigation of the autonomous vehicle traveling along the same road segment 1100 at a later time, based on the first, second, and third trajectories 1101, 1102, and 1103.While a target trajectory may be associated with a single previous crossing of a road segment, in some embodiments, each target trajectory included in the sparse map 800 may be determined based on two or more reconstructed trajectories of vehicles crossing the same road segment. In . Fig. In Figure 11A, the target trajectory is represented by 1110. In some embodiments, the target trajectory 1110 may be generated based on an average of the first, 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.

[0170] On the mapping server, the server may receive actual trajectories for a given road segment from multiple collector vehicles traversing the road segment. To generate a target trajectory for each valid path along the road segment (e.g., each lane, each direction of travel, each pedestrian path through an intersection, etc.), the received actual trajectories may be aligned. The alignment process may involve using detected objects / features identified along the road segment, along with aggregated positions of those detected objects / features, to correlate the actual, collected trajectories. Once aligned, an average or "best-fit" target trajectory for each available lane, etc., can be determined based on the aggregated, correlated / aligned actual trajectories.

[0171] The Fig. 11B and Fig. 11C further illustrate the concept of target trajectories associated with road segments present within a geographical area 1111. As in Fig. 11B, a first road segment 1120 within geographic area 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. Lanes 1122 and lanes 1124 may be separated by a double yellow line 1123. Geographic area 1111 may also include a branch road segment 1130 intersecting road segment 1120. Road segment 1130 may include a two-lane road, with each lane designated for a different direction of travel. The geographic area 1111 may also include other road features such as a stop line 1132, a stop sign 1134, a speed limit sign 1136, and a hazard sign 1138.

[0172] As in Fig. 11C, the sparse map 800 may include a local map 1140 that includes a road model for supporting autonomous navigation of vehicles within the geographic area 1111. For example, the local map 1140 may include target trajectories for one or more lanes associated with the road segments 1120 and / or 1130 within the geographic area 1111. For example, the local map 1140 may include target trajectories 1141 and / or 1142 that an autonomous vehicle may access or rely on when traversing the lanes 1122. Similarly, the local map 1140 may include target trajectories 1143 and / or 1144 that an autonomous vehicle may access or rely on when traversing the lanes 1124.Further, the local map 1140 may include target trajectories 1145 and / or 1146 that an autonomous vehicle may access or rely on when traversing the road segment 1130. The target trajectory 1147 represents a preferred path that an autonomous vehicle should follow when transitioning from the lanes 1120 (and specifically relative to the target trajectory 1141 associated with a rightmost lane of the lanes 1120) to the road segment 1130 (and specifically relative to a target trajectory 1145 associated with a first side of the road segment 1130).Similarly, target trajectory 1148 represents a preferred path that an autonomous vehicle should follow when transitioning from road segment 1130 (and specifically relative to target trajectory 1146) to a portion of road segment 1124 (and specifically, as shown, relative to a target trajectory 1143 associated with a left lane of lanes 1124).

[0173] Sparse map 800 may also include representations of other road-related features associated with geographic area 1111. For example, sparse map 800 may also include representations of one or more landmarks identified within geographic area 1111. Such landmarks may include a first landmark 1150 associated with stop line 1132, a second landmark 1152 associated with stop sign 1134, a third landmark 1156 associated with speed limit sign 1154, and a fourth landmark 1156 associated with hazard sign 1138.Such landmarks may, for example, be used to assist an autonomous vehicle in determining its current location relative to one of the displayed target trajectories, so that the vehicle can adjust its direction of travel to correspond to a direction of the target trajectory at the specific location.

[0174] In some embodiments, the sparse map 800 may also include road signature profiles. Such road signature profiles may be associated with any detectable / measurable variation in at least one parameter associated with a road. For example, in some cases, such profiles may be associated with variations in road surface information, such as variations in the surface roughness of a particular road segment, variations in road width across a particular road segment, variations in distances between dashed lines painted along a particular road segment, variations in road curvature along a particular road segment, etc. Fig. 11D shows an example of a road signature profile 1160. While the profile 1160 may represent any of the parameters mentioned above or others, in one example, the profile 1160 may represent a measure of road surface roughness, such as obtained by monitoring one or more sensors that provide outputs indicative of an amount of suspension displacement as a vehicle travels a particular road segment.

[0175] Alternatively or simultaneously, the profile 1160 may represent a variation in road width as determined based on image data obtained via a camera onboard a vehicle traveling a particular road segment. Such profiles may be useful, for example, in determining a particular location of an autonomous vehicle relative to a particular target trajectory. That is, when traversing a road segment, an 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 to a predetermined profile representing the parameter variation with respect to position along the road segment, the measured and predetermined profiles may be used (e.g.,by overlaying corresponding portions of the measured and predetermined profiles) to determine a current position along the road segment and therefore a current position relative to a target trajectory for the road segment.

[0176] In some embodiments, the sparse map 800 may include different trajectories based on various characteristics associated with an autonomous vehicle user, environmental conditions, and / or other parameters related to the trip. For example, in some embodiments, different trajectories may be generated based on different user preferences and / or profiles. 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 others may prefer to take the shortest or fastest routes, regardless of whether a toll road is present on the route.The disclosed systems can generate different sparse maps with different trajectories based on such different user preferences or profiles. As another example, some users may prefer to travel in a fast-moving lane, while others may prefer to maintain a position in the central lane at all times.

[0177] 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. Autonomous vehicles traveling under different environmental conditions may be provided with the sparse map 800 generated based on such different environmental conditions. In some embodiments, cameras deployed on autonomous vehicles may detect the environmental conditions and may provide such information back to a server that generates and provides sparse maps. For example, the server may generate or update an already generated sparse map 800 to include trajectories that may be more suitable or safer for autonomous driving under the detected environmental conditions.The updating of the sparse map 800 based on environmental conditions can be performed dynamically while the autonomous vehicles are driving on roads.

[0178] Other different parameters related to driving may also be used as a basis for generating and providing different sparse maps for different autonomous vehicles. For example, when an autonomous vehicle is traveling at a high speed, curves may be tighter. Trajectories associated with specific lanes, rather than roads, may be included in the sparse map 800 so that the autonomous vehicle can stay within a specific lane while following a specific trajectory. If an image captured by a camera onboard the autonomous vehicle indicates that the vehicle has drifted outside of the lane (e.g., crossed the lane marking), an action within the vehicle may be triggered to return the vehicle to the designated lane according to the specific trajectory. Crowdsourcing a sparse map

[0179] The disclosed sparse maps can be efficiently (and passively) generated through the power of crowdsourcing. For example, any private or commercial vehicle equipped with a camera (e.g., a simple, low-resolution camera often included as original equipment in today's vehicles) and a suitable image analysis processor can serve as a collection vehicle. No specialized equipment (e.g., high-resolution imaging and / or positioning systems) is required. As a result of the disclosed crowdsourcing technique, the generated sparse maps can be extremely accurate and include extremely refined positional information (enabling navigation error margins of 10 cm or less) without requiring specialized imaging or scanning equipment as input to the map generation process.Crowdsourcing also enables much faster (and more cost-effective) updates to the generated maps, as the mapping server system is constantly provided with new driving data from all roads traversed by private or commercial vehicles that are minimally equipped to also serve as collection vehicles. There is no need for dedicated vehicles equipped with high-resolution imaging and mapping sensors, thus avoiding the costs associated with building such specialized vehicles.Furthermore, updates to the sparse maps disclosed herein can be made much more quickly than systems that rely on dedicated, specialized mapping vehicles (which, due to their cost and specialized equipment, are typically limited to a fleet of specialized vehicles far below the number of private or commercial vehicles already available to perform the disclosed collection techniques).

[0180] The disclosed sparse maps generated through crowdsourcing can be extremely accurate because they can be generated based on many inputs from multiple (tens, hundreds, millions, etc.) collection vehicles that have collected travel data along a given road segment. For example, each collection vehicle traveling along a given road segment can record its actual trajectory and determine position information relative to detected objects / features along the road segment. This information is forwarded from multiple collection vehicles to a server. The actual trajectories are aggregated to generate a refined target trajectory for each valid travel path along the road segment.Additionally, the position information collected by the multiple collection vehicles for each of the detected objects / features along the road segment (semantic or non-semantic) can also be aggregated. As a result, the mapped position of each detected object / feature can be an average of hundreds, thousands, or millions of individually determined positions for each detected object / feature. Such a technique can provide extremely accurate mapped positions for the detected objects / features.

[0181] In some embodiments, the disclosed systems and methods may generate a sparse map for autonomous vehicle navigation. For example, disclosed systems and methods may use crowdsourcing data to generate a sparse map that one or more autonomous vehicles may use to navigate along a system of roads. As used herein, "crowdsourcing" means receiving data from different vehicles (e.g., autonomous vehicles) traveling on a road segment at different times, and using such data to generate and / or update the road model, including sparse map tiles. The model, or any of its sparse map tiles, may, in turn, be transmitted to the vehicles or other vehicles later traveling along the road segment to assist in autonomous vehicle navigation.The road model may include a plurality of target trajectories representing preferred trajectories that autonomous vehicles should follow when traversing a road segment. The target trajectories may be the same as a reconstructed actual trajectory collected from a vehicle traversing a road segment, which may be transmitted by the vehicle to a server. In some embodiments, the target trajectories may differ from actual trajectories previously taken by one or more vehicles when traversing a road segment. The target trajectories may be generated based on actual trajectories (e.g., by averaging or any other suitable operation).

[0182] The vehicle trajectory data that a vehicle can upload to a server may correspond to the actual reconstructed trajectory for the vehicle or may correspond to a recommended trajectory, which may be based on or referenced to the vehicle's actual reconstructed trajectory but may differ from the actual reconstructed trajectory. For example, vehicles can modify their actual reconstructed trajectories and submit (e.g., recommend) the modified actual trajectories to the server. The road model can use the recommended modified trajectories as target trajectories for autonomous navigation of other vehicles.

[0183] In addition to trajectory information, other information for potential use in creating a sparse data map 800 may include information related to potential landmark candidates. For example, the disclosed systems and methods may crowdsource information to identify potential landmarks in an environment and refine landmark positions. The landmarks may be used by a navigation system of autonomous vehicles to determine and / or adjust the vehicle's position along target trajectories.

[0184] The reconstructed trajectories that a vehicle may generate as the vehicle travels along a road may be obtained by any suitable method. In some embodiments, the reconstructed trajectories may be developed by stitching motion segments for the vehicle using, for example, ego-motion estimation (e.g., three-dimensional translation and three-dimensional rotation of the camera and thus the vehicle body). The rotation and translation estimation may be determined based on the analysis of images acquired by one or more image acquisition devices along with information from other sensors or devices, such as inertial sensors and velocity sensors.For example, the inertial sensors may include an accelerometer or other suitable sensors configured to measure changes in the translation and / or rotation of the vehicle body. The vehicle may include a speed sensor that measures a speed of the vehicle.

[0185] In some embodiments, the ego-motion of the camera (and thus the vehicle body) can be estimated based on optical flow analysis of the captured images. Optical flow analysis of an image sequence identifies the motion of pixels from the image sequence and determines the vehicle's movements based on the identified motion. The ego-motion can be integrated over time and along the road segment to reconstruct a trajectory associated with the road segment the vehicle followed.

[0186] Data (e.g., reconstructed trajectories) collected from multiple vehicles in multiple trips along a road segment at different times may be used to construct the road model (e.g., including target trajectories, etc.) included in the sparse data map 800. Data collected from multiple vehicles in multiple trips along a road segment at different times may also be averaged to increase model accuracy. In some embodiments, data regarding road geometry and / or landmarks may be received from multiple vehicles traveling through the shared road segment at different times. Such data received from different vehicles may be combined to generate the road model and / or update the road model.

[0187] The geometry of a reconstructed trajectory (and also a target trajectory) along a road segment can be represented by a curve in three-dimensional space, which can be a spline connecting three-dimensional polynomials. The reconstructed trajectory curve can be determined from the analysis of a video stream or a plurality of images captured by a camera mounted on the vehicle. In some embodiments, a location a few meters ahead of the vehicle's current position is identified in each frame or image. This location is the location the vehicle is expected to travel to in a predetermined period of time. This operation can be repeated frame by frame, and at the same time, the vehicle can calculate the camera's own motion (rotation and translation).For each frame or image, a near-field model of the desired path of the vehicle is generated in a reference frame attached to the camera. The near-field models can be combined to obtain a three-dimensional model of the road in a coordinate frame, which can be any or a predetermined coordinate frame. The three-dimensional model of the road can then be fitted by a spline, which can include or connect one or more polynomials of appropriate orders.

[0188] To complete the near-range road model at each frame, one or more detection modules can be used. For example, a ground-top lane detection module can be used. The ground-top lane detection module can be useful when lane markings are drawn on the road. This module can search for edges in the image and stitch them together to form the lane markings. A second module can be used together with the ground-top lane detection module. The second module is an end-to-end deep neural network that can be trained to predict the correct near-range path from an input image. In both modules, the road model can be detected in the image coordinate system and transformed into a three-dimensional space, which can be virtually attached to the camera.

[0189] Although the reconstructed trajectory modeling method may introduce an accumulation of errors due to the integration of ego-motion over a long period of time, which may include a noise component, such errors may be insignificant because the generated model can provide sufficient accuracy for navigation over a local scale. Additionally, it is possible to eliminate the integrated error by using external information sources, such as satellite imagery or geodetic measurements. For example, the disclosed systems and methods may use a GNSS receiver to eliminate accumulated errors. However, the GNSS positioning signals may not always be available and accurate. The disclosed systems and methods may enable a guidance application that is weakly dependent on the availability and accuracy of GNSS positioning. In such systems, the use of the GNSS signals may be limited.For example, in some embodiments, the disclosed systems may use the GNSS signals only for database indexing purposes.

[0190] In some embodiments, the range scale (e.g., local scale) that may be relevant for autonomous vehicle navigation may be on the order of 50 meters, 100 meters, 200 meters, 300 meters, etc. Such distances may be used because the geometric road model is used primarily for two purposes: planning the trajectory ahead and locating the vehicle on the road model. In some embodiments, the planning task may use the model over a typical range of 40 meters ahead (or another suitable distance ahead, such as 20 meters, 30 meters, 50 meters) when the control algorithm steers the vehicle according to a target point located 1.3 seconds ahead (or another time point, such as 1.5 seconds, 1.7 seconds, 2 seconds, etc.).The localization task uses the road model over a typical range of 60 meters behind the car (or any other suitable distances, such as 50 meters, 100 meters, 150 meters, etc.) according to a process called "tail alignment," which is described in more detail in another section. The disclosed systems and methods can generate a geometric model with sufficient accuracy over a given range, such as 100 meters, so that a planned trajectory does not deviate from the lane center by more than, for example, 30 cm.

[0191] As explained above, a three-dimensional road model can be constructed by detecting short-range segments and stitching them together. Stitching can be enabled by calculating a six-degree self-motion model using the video and / or images captured by the camera, data from the inertial sensors reflecting the vehicle's movements, and the host vehicle's speed signal. The accumulated error can be small enough over a local range scale, such as on the order of 100 meters. All of this can be completed in a single trip over a given road segment.

[0192] In some embodiments, multiple trips may be used to average the resulting model and further increase its accuracy. The same car may travel the same route multiple times, or multiple cars may send their collected model data to a central server. In either case, a reconciliation process may be performed to identify overlapping models and enable averaging to generate target trajectories. The constructed model (e.g., including the target trajectories) may be used for steering once a convergence criterion is met. Subsequent trips may be used for further model improvements and to adapt to infrastructure changes.

[0193] Sharing driving experience (such as captured data) between multiple cars becomes feasible when they are connected to a central server. Each vehicle client can store a partial copy of a universal road model that may be relevant to its current location. A bidirectional update process between the vehicles and the server can be performed by the vehicles and the server. The small footprint concept discussed above enables the disclosed systems and methods to perform the bidirectional updates using very small bandwidth.

[0194] Information related to potential landmarks may also be determined and forwarded to a central server. For example, the disclosed systems and methods may determine one or more physical characteristics of a potential landmark based on one or more images including the landmark. The physical characteristics may include a physical size (e.g., height, width) of the landmark, a distance from a vehicle to a landmark, a distance between the landmark and a previous landmark, the lateral position of the landmark (e.g., the position of the landmark relative to the lane), the GPS coordinates of the landmark, a type of landmark, identifying text on the landmark, etc.For example, a vehicle may analyze one or more images captured by a camera to detect a potential landmark, such as a speed limit sign.

[0195] The vehicle may determine a distance from the vehicle to the landmark or a position associated with the landmark (e.g., any semantic or non-semantic object or feature along a road segment) based on the analysis of the one or more images. In some embodiments, the distance may be determined based on the analysis of images of the landmark using a suitable image analysis method, such as a scaling method and / or an optical flow method. As previously noted, a position of the object / feature may include a 2D image position (e.g., an XY pixel position in one or more acquired images) of one or more points associated with the object / feature, or may include a real-world 3D position of one or more points (e.g., determined by structure-in-motion / optical flow techniques, LIDAR or RADAR information, etc.).In some embodiments, the disclosed systems and methods may be configured to determine a type or classification of a potential landmark. If the vehicle determines that a particular potential landmark corresponds to a predetermined type or classification stored in a sparse map, it may be sufficient for the vehicle to communicate to the server an indication of the type or classification of the landmark along with its location. The server may store such information. At a later time, during navigation, a navigating vehicle may acquire an image including a representation of the landmark, process the image (e.g.,using a classifier) and compare the resulting landmark to confirm the detection of the mapped landmark and use the mapped landmark in locating the navigating vehicle relative to the sparse map.

[0196] In some embodiments, multiple autonomous vehicles traveling on a road segment may communicate with a server. The vehicles (or clients) may generate a curve describing their travel (e.g., through ego-motion integration) in an arbitrary coordinate frame. The vehicles may detect landmarks and locate them within the same frame. The vehicles may upload the curve and landmarks to the server. The server may collect data from vehicles across multiple travels and generate a unified road model. For example, as described below with respect to Fig. 19, the server can generate a sparse map with the unified road model using the uploaded curves and landmarks.

[0197] The server can also distribute the model to clients (e.g., vehicles). For example, the server can distribute the sparse map to one or more vehicles. The server can update the model continuously or periodically when it receives new data from the vehicles. For example, the server can process the new data to evaluate whether the data contains information that should trigger an update or creation of new data on the server. The server can distribute the updated model or updates to the vehicles to provide autonomous vehicle navigation.

[0198] The server may use one or more criteria to determine whether new data received from the vehicles should trigger an update to the model or trigger the creation of new data. For example, if the new data indicates that a previously detected landmark at a particular location no longer exists or is replaced by another landmark, the server may determine that the new data should trigger an update to the model. As another example, if the new data indicates that a road segment has been closed, and this has been confirmed by data received from other vehicles, the server may determine that the new data should trigger an update to the model.

[0199] The server may distribute the updated model (or the updated portion of the model) to one or more vehicles traveling on the road segment with which the model updates are associated. The server may also distribute the updated model to vehicles about to travel on the road segment or vehicles whose planned trip includes the road segment with which the model updates are associated. For example, while an autonomous vehicle is traveling along another road segment before reaching the road segment with which an update is associated, the server may distribute the updates or the updated model to the autonomous vehicle before the vehicle reaches the road segment.

[0200] In some embodiments, the remote server may collect trajectories and landmarks from multiple clients (e.g., vehicles traveling along a common road segment). The server may match curves using landmarks and create an average road model based on the trajectories collected from the multiple vehicles. The server may also calculate a graph of roads and the most likely path at each node or associated with the road segment. For example, the remote server may align the trajectories to generate a sparse crowdsourced map from the collected trajectories.

[0201] The server may average landmark properties received from multiple vehicles that have traveled along the shared road segment, such as the distances between one landmark to another (e.g., a previous one along the road segment) as measured by multiple vehicles, to determine an arc length parameter and support along-path localization and speed calibration for each client vehicle. The server may average the physical dimensions of a landmark measured by multiple vehicles that have traveled along the shared road segment and recognize 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 determine lateral positions of a landmark (e.g.,The server averages the GPS coordinates of the landmark (position from the lane in which vehicles travel to the landmark) measured by multiple vehicles traveling along the common road segment and detecting the same landmark. The averaged lateral position can be used to support lane assignment. The server can average the GPS coordinates of the landmark measured by multiple vehicles traveling along the same road segment and detecting the same landmark. The averaged GPS coordinates of the landmark can be used to support global localization or positioning of the landmark in the road model.

[0202] In some embodiments, the server may identify model changes, such as construction, detours, new signs, sign removals, etc., based on data received from the vehicles. The server may update the model continuously, periodically, or immediately upon receiving new data from the vehicles. The server may distribute updates to the model or the updated model to vehicles to provide autonomous navigation. For example, as discussed further below, the server may use crowdsourced data to filter out "ghost" landmarks detected by vehicles.

[0203] In some embodiments, the server may analyze driver interventions during autonomous driving. The server may analyze data received from the vehicle at the time and location at which an intervention occurs and / or data received before the time the intervention occurred. The server may identify specific pieces of the data that caused or are closely related to the intervention, for example, data indicating a temporary lane closure device, data indicating a pedestrian in the road. The server may update the model based on the identified data. For example, the server may modify one or more trajectories stored in the model.

[0204] Fig. Figure 12 is a schematic illustration of a system that uses crowdsourcing to generate a sparse map (as well as distribute and navigate using a sparse crowdsourced map). Fig. 12 shows a road segment 1200 that includes one or more lanes. A plurality of vehicles 1205, 1210, 1215, 1220, and 1225 may travel on the road segment 1200 simultaneously or at different times (although in Fig. 12 as appearing simultaneously on road segment 1200). At least one of vehicles 1205, 1210, 1215, 1220, and 1225 may be an autonomous vehicle. To simplify the present example, it is assumed that all vehicles 1205, 1210, 1215, 1220, and 1225 are autonomous vehicles.

[0205] Each vehicle may be similar to vehicles disclosed in other embodiments (e.g., vehicle 200) and may include components or devices included in or associated with vehicles disclosed in other embodiments. Each vehicle may be equipped with an image capture device or camera (e.g., image capture device 122 or camera 122). Each vehicle may communicate with a remote server 1230 over one or more networks (e.g., over a cellular network and / or the Internet, etc.) via wireless communication paths 1235, as indicated by the dashed lines. Each vehicle may transmit data to and receive data from the server 1230.For example, server 1230 may collect data from multiple vehicles traveling on road segment 1200 at different times and process the collected data to generate an autonomous vehicle's road navigation model or an update to the model. Server 1230 may transmit the autonomous vehicle's road navigation model or the update to the model to the vehicles that transmitted data to server 1230. Server 1230 may transmit the autonomous vehicle's road navigation model or the update to the model to other vehicles traveling on road segment 1200 at later times.

[0206] As vehicles 1205, 1210, 1215, 1220, and 1225 travel on road segment 1200, navigation information collected (e.g., detected, sensed, or measured) by vehicles 1205, 1210, 1215, 1220, and 1225 may be transmitted to server 1230. In some embodiments, the navigation information may be associated with the shared road segment 1200. The navigation information may include a trajectory associated with each of vehicles 1205, 1210, 1215, 1220, and 1225 as each vehicle travels over road segment 1200. In some embodiments, the trajectory may be reconstructed based on data collected by various sensors and devices provided on vehicle 1205.For example, the trajectory may be reconstructed based on at least one of accelerometer data, velocity data, landmark data, road geometry or profile data, vehicle positioning data, and self-motion data. In some embodiments, the trajectory may be reconstructed based on data from inertial sensors, such as accelerometers, and the velocity of the vehicle 1205 detected by a speed sensor. Additionally, in some embodiments, the trajectory may be determined based on detected self-motion of the camera (e.g., by a processor onboard each of the vehicles 1205, 1210, 1215, 1220, and 1225), which may indicate three-dimensional translation and / or three-dimensional rotations (or rotational motions). The self-motion of the camera (and thus the vehicle body) may be determined from the analysis of one or more images captured by the camera.

[0207] In some embodiments, the trajectory of the vehicle 1205 may be determined by a processor provided onboard the vehicle 1205 and transmitted to the server 1230. In other embodiments, the server 1230 may receive data collected by the various sensors and devices provided in the vehicle 1205 and determine the trajectory based on the data received from the vehicle 1205.

[0208] In some embodiments, the navigation information transmitted from vehicles 1205, 1210, 1215, 1220, and 1225 to server 1230 may include data regarding the road surface, road geometry, or road profile. The geometry of road segment 1200 may include a lane structure and / or landmarks. The lane structure may include the total number of lanes of road segment 1200, the type of lanes (e.g., one-way, two-way, travel, passing, etc.), markings on the lanes, width of the lanes, etc. In some embodiments, the navigation information may include a lane assignment, e.g., which of a plurality of lanes a vehicle is traveling in. For example, the lane assignment may be associated with a numerical value of "3," indicating that the vehicle is traveling in the third lane from the left or right.As another example, the lane assignment may be associated with a text value “center lane” that indicates that the vehicle is traveling in the center lane.

[0209] The server 1230 may store the navigation information on a non-transitory computer-readable medium, such as a hard disk, a compact disc, a tape, a memory, etc. The server 1230 may generate (e.g., through a processor included in the server 1230) at least a portion of an autonomous vehicle road navigation model for the shared road segment 1200 based on the navigation information received from the plurality of vehicles 1205, 1210, 1215, 1220, and 1225, and may store the model as a portion of a sparse map. The server 1230 may determine a trajectory associated with each lane based on crowdsourced data (e.g., navigation information) received from multiple vehicles (e.g., 1205, 1210, 1215, 1220, and 1225) traveling in a lane of the road segment at different times.The server 1230 may generate the autonomous vehicle's road navigation model or a portion of the model (e.g., an updated portion) based on a plurality of trajectories determined based on the crowdsourced navigation data. The server 1230 may transmit the model or the updated portion of the model to one or more autonomous vehicles 1205, 1210, 1215, 1220, and 1225 traveling on the road segment 1200, or any other autonomous vehicles traveling on the road segment at a later time, to update an existing autonomous vehicle road navigation model provided in a navigation system of the vehicles. The autonomous vehicle road navigation model may be used by the autonomous vehicles in autonomously navigating along the shared road segment 1200.

[0210] As explained above, the road navigation model of the autonomous vehicle can be used in a sparse map (e.g., the one shown in Fig. 8). Sparse map 800 may include a sparse record of data related to road geometry and / or landmarks along a road that may provide sufficient information to guide autonomous navigation of an autonomous vehicle, yet may not require excessive data storage. In some embodiments, the autonomous vehicle's road navigation model may be stored separately from sparse map 800 and may use map data from sparse map 800 when executing the model for navigation.In some embodiments, the autonomous vehicle's road navigation model may use map data included in the sparse map 800 to determine target trajectories along the road segment 1200 to guide the autonomous navigation of the autonomous vehicles 1205, 1210, 1215, 1220, and 1225 or other vehicles later traveling along the road segment 1200. For example, if the autonomous vehicle's 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 the trajectories determined based on the navigation information received from the vehicle 1205 with predetermined trajectories included in the sparse map 800 to validate and / or correct the current travel path of the vehicle 1205.

[0211] In the autonomous vehicle's road navigation model, the geometry of a road feature or 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 connecting three-dimensional polynomials. As one of ordinary skill in the art would understand, a spline may be a numerical function defined piecewise by a series of polynomials for fitting data. A 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 splines (other orders), or a combination thereof. The spline may include one or more three-dimensional polynomials of different orders that connect (e.g., fit) data points of the three-dimensional geometry data of the road.In some embodiments, the road navigation model for autonomous vehicles may include a three-dimensional spline corresponding to a target trajectory along a common road segment (e.g., road segment 1200) or a lane of road segment 1200.

[0212] As explained above, the road navigation model for autonomous vehicles included in the sparse map may include other information, such as the identification of at least one landmark along the road segment 1200. The landmark may be visible within a field of view of a camera (e.g., camera 122) installed on each of the vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, the camera 122 may capture an image of a landmark. A processor (e.g., processor 180, 190, or processing unit 110) provided on the vehicle 1205 may process the image of the landmark to extract identification information for the landmark. The landmark identification information may be stored in the sparse map 800, rather than an actual image of the landmark.The landmark identification information may require much less storage space than an actual image. Other sensors or systems (e.g., GPS system) may also provide certain landmark identification information (e.g., location of the landmark). The landmark may include at least one of a traffic sign, an arrow marker, a lane marker, a dashed lane marker, a traffic light, a stop line, a directional sign (e.g., a highway exit sign with an arrow indicating one direction, a highway sign with arrows pointing in different directions or locations), a landmark beacon, or a lamppost. A landmark beacon refers to a device (e.g.,an RFID device) installed along a road segment that transmits or reflects a signal to a receiver installed on a vehicle such that, as the vehicle passes the device, the beacon received by the vehicle and the location of the device (e.g., determined from the GPS location of the device) can be used as a landmark to be incorporated into the autonomous vehicle road navigation model and / or the sparse map 800.

[0213] Identifying at least one landmark may include a position of the at least one landmark. The position of the landmark may be determined based on position measurements made using sensor systems (e.g., global positioning systems, inertial positioning systems, landmark beacons, etc.) associated with the plurality of vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, the position of the landmark may be determined by averaging the position measurements detected, collected, or received by sensor systems on various vehicles 1205, 1210, 1215, 1220, and 1225 over multiple trips.For example, vehicles 1205, 1210, 1215, 1220, and 1225 may transmit position measurement data to server 1230, which may average the position measurements and use the averaged position measurement as the landmark position. The landmark position may be continuously refined using measurements received from vehicles in subsequent trips.

[0214] The identification of the landmark may include a size of the landmark. The processor provided on a vehicle (e.g., 1205) may estimate the physical size of the landmark based on the analysis of the images. 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 a physical size for the landmark and store this landmark size in the road model. The physical size estimate may be used to further determine or estimate a distance from the vehicle to the landmark.The distance to the landmark can be estimated based on the current speed of the vehicle and an extent scale based on the position of the landmark appearing in the images with respect to the camera's extent focus. For example, the distance to the landmark can be estimated by Z = V*dt*R / D, where V is the speed of the vehicle, R is the distance in the image from the landmark at time t1 to the extent focus, and D is the change in distance for the landmark in the image from t1 to t2. dt represents (t2-t1). For example, the distance to the landmark can be estimated by Z = V*dt*R / D, where V is the speed of the vehicle, R is the distance in the image between the landmark and the extent focus, dt is a time interval, and D is the image displacement of the landmark along the epipolar line.Other equations equivalent to the above equation, such as Z = V * ω / Δω, can be used to estimate the distance to the landmark. Here, V is the vehicle speed, ω is an image length (such as the object width), and Δω is the change in this image length in unit time.

[0215] If the physical size of the landmark is known, the distance to the landmark can also be determined based on the following equation: Z = f*W / ω, where f is the focal length, W is the size of the landmark (e.g., height or width), ω is the number of pixels as the landmark leaves the image. From the above equation, a change in distance Z can be calculated using ΔZ = f * W * Δω / ω2 + f * ΔW / w, where ΔW decays to zero by averaging, and where Δω is the number of pixels representing a bounding box accuracy in the image. A value estimating the physical size of the landmark can be calculated by averaging multiple observations on the server side. The resulting error in the distance estimation can be very small. There are two sources of error that can arise when using the above formula, namely ΔW and Δω.Their contribution to the range error is given by ΔZ = f * W * Δω / ω2 + f * ΔW / ω. However, ΔW decays to zero upon averaging; therefore, ΔZ is determined by Δω (e.g., the inaccuracy of the bounding box in the image).

[0216] For landmarks of unknown dimensions, the distance to the landmark can be estimated by tracking feature points on the landmark between consecutive frames. For example, certain features appearing on a speed limit sign can be tracked between two or more frames. Based on these tracked features, a distance distribution can be generated per feature point. The distance estimate can be extracted from the distance distribution. For example, the most frequently appearing distance in the distance distribution can be used as the distance estimate. As another example, the average of the distance distribution can be used as the distance estimate.

[0217] Fig. 13 illustrates an exemplary road navigation model for autonomous vehicles, represented by a plurality of three-dimensional splines 1301, 1302, and 1303. The Fig. The curves 1301, 1302, and 1303 shown in Figure 13 are for illustrative purposes only. Each spline may include one or more three-dimensional polynomials connecting a plurality of data points 1310. Each polynomial may be a first-order polynomial, a second-order polynomial, a third-order polynomial, or a combination of any suitable polynomials having different orders. Each data point 1310 may be associated with the 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., landmark size, location, and 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 others may be associated with data related to road signature profiles.

[0218] Fig. 14 illustrates raw location data 1410 (e.g., GPS data) received from five separate trips. A trip may be separated from another trip if it was traversed simultaneously by separate vehicles, at separate times by the same vehicle, or at separate times by separate vehicles. To account for errors in the location data 1410 and for different locations of vehicles within the same lane (e.g., one vehicle may travel closer to the left side of a lane than another), the server 1230 may generate a map skeleton 1420 using one or more statistical techniques to determine whether variations in the raw location data 1410 represent actual deviations or statistical errors. Each path within the skeleton 1420 may be linked back to the raw data 1410 that formed the path.For example, the path between A and B within skeleton 1420 is linked to the raw data 1410 from trips 2, 3, 4, and 5, but not from trip 1. Skeleton 1420 may not be detailed enough to be used to navigate a vehicle (e.g., because it combines trips from multiple lanes on the same road, unlike the splines described above), but can provide useful topological information and can be used to define intersections.

[0219] Fig. 15 illustrates an example by which additional details for a sparse map can be generated within a segment of a map skeleton (e.g., segments A to B within skeleton 1420). As in Fig. 15, the data (e.g., self-motion data, road marking data, and the like) may be shown as a function of position S (or S1 or S2) along the trip. Server 1230 may identify landmarks for the sparse map by identifying unique matches between landmarks 1501, 1503, and 1505 of trip 1510 and landmarks 1507 and 1509 of trip 1520. Such a matching algorithm may result in the identification of landmarks 1511, 1513, and 1515. However, one of ordinary skill in the art would recognize that other matching algorithms may be used. For example, probabilistic optimization may be used instead of, or in combination with, a unique match. Server 1230 may longitudinally align the trips to align the matched landmarks. For example, the server 1230 may request a trip (e.g.Select one ride (e.g., ride 1520) as a reference ride and then move and / or elastically stretch the other ride(s) (e.g., ride 1510) for alignment.

[0220] Fig. Figure 16 shows an example of aligned landmark data for use in a sparse map. In the example from Fig. 16, the landmark 1610 includes a street sign. The example from Fig. 16 further shows data from a number of trips 1601, 1603, 1605, 1607, 1609, 1611 and 1613. In the example from Fig. 16, the data from trip 1613 consists of a "ghost" landmark, and server 1230 may identify it as such because none of trips 1601, 1603, 1605, 1607, 1609, and 1611 include an identification of a landmark near the identified landmark in trip 1613. Accordingly, server 1230 may accept potential landmarks if a ratio of images in which the landmark appears to images in which the landmark does not appear exceeds a threshold and / or reject potential landmarks if a ratio of images in which the landmark does not appear to images in which the landmark does appear exceeds a threshold.

[0221] Fig. 17 illustrates a system 1700 for generating trip data that can be used to crowdsource a sparse map. As in Fig. As shown in Figure 17, the system 1700 may include a camera 1701 and a localization device 1703 (e.g., a GPS locator). The camera 1701 and the localization 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 a variety of data of several types, e.g., self-motion data, traffic sign data, road data, or the like. The camera data and location data may be segmented into trip segments 1705. For example, the trip segments 1705 may each include camera data and location data from less than 1 km of trip.

[0222] In some embodiments, system 1700 may eliminate redundancies in trip segments 1705. For example, if a landmark appears in multiple images from camera 1701, system 1700 may remove the redundant data so that trip segments 1705 contain only a copy of the location and any metadata related to the landmark. As another example, if a lane marking appears in multiple images from camera 1701, system 1700 may remove the redundant data so that trip segments 1705 contain only a copy of the location and any metadata related to the lane marking.

[0223] System 1700 also includes a server (e.g., server 1230). Server 1230 may receive trip segments 1705 from the vehicle and recombine trip segments 1705 into a single trip 1707. Such an arrangement may enable reduced bandwidth requirements when transferring data between the vehicle and the server, while also allowing the server to store data related to an entire trip.

[0224] Fig. 18 represents the system 1700 of Fig. 17, which is further configured for crowdsourcing a sparse map. As in Fig. 17, the system 1700 includes the vehicle 1810, which collects travel data, for example, using a camera (e.g., producing self-motion data, traffic sign data, road data, or the like) and a localization device (e.g., a GPS locator). As in Fig. 17, the vehicle 1810 segments the collected data into trip segments (represented as “DS1 1”, “DS2 1”, “DSN 1” in Fig. 18). The server 1230 then receives the trip segments and reconstructs a trip (shown as “Trip 1” in Fig. 18) from the received segments.

[0225] As further stated in Fig. 18, the system 1700 also receives data from additional vehicles. For example, the vehicle 1820 also collects trip data, for example, using a camera (e.g., generating self-motion data, traffic sign data, road data, or the like) and a localization device (e.g., a GPS locator). Similar to the vehicle 1810, the vehicle 1820 segments the collected data into trip segments (represented as "DS1 2," "DS2 2," "DSN 2" in Fig. 18). The server 1230 then receives the trip segments and reconstructs a trip (shown as “Trip 2” in Fig. 18) from the received segments. Any number of additional vehicles can be used. For example, Fig. 18 also “AUTO N”, which records trip data, segments them into trip segments (represented as “DS1 N”, “DS2 N”, “DSN N” in Fig. 18) and sends it to the server 1230 for reconstruction into a trip (represented as “Trip N” in Fig. 18) sends.

[0226] As in Fig. 18, the server 1230 may construct a sparse map (represented as "MAP") using the reconstructed trips (e.g., "Trip 1," "Trip 2," and "Trip N") collected from a plurality of vehicles (e.g., "CAR 1" (also referred to as vehicle 1810), "CAR 2" (also referred to as vehicle 1820), and "CAR N").

[0227] Fig. 19 is a flowchart illustrating an exemplary process 1900 for generating a sparse map for autonomous vehicle navigation along a road segment. Process 1900 may be performed by one or more processing devices included in server 1230.

[0228] Process 1900 may include receiving a plurality of images captured as one or more vehicles traverse the road segment (step 1905). Server 1230 may receive images from cameras included in one or more of vehicles 1205, 1210, 1215, 1220, and 1225. For example, camera 122 may capture one or more images of the environment surrounding vehicle 1205 as vehicle 1205 travels along road segment 1200. In some embodiments, server 1230 may also receive remote image data that includes redundancies eliminated by a processor on vehicle 1205, as described above with respect to Fig. 17 discussed.

[0229] The process 1900 may further include identifying, based on the plurality of images, at least one line representation of a road surface feature extending along the road segment (step 1910). Each line representation may represent a path along the road segment that substantially corresponds to the road surface feature. For example, the server 1230 may analyze the environmental images received from the camera 122 to identify a road edge or a lane marking and determine a travel trajectory along the road segment 1200 associated with the road edge or lane marking. In some embodiments, the trajectory (or line representation) may include a spline, a polynomial representation, or a curve. The server 1230 may determine the travel trajectory of the vehicle 1205 based on camera ego-motions (e.g.,three-dimensional translation and / or three-dimensional rotational movements) received at step 1905.

[0230] Process 1900 may also include identifying, based on the plurality of images, a plurality of landmarks associated with the road segment (step 1910). For example, server 1230 may analyze the environmental images received from camera 122 to identify one or more landmarks, such as the street sign along road segment 1200. Server 1230 may identify the landmarks using analysis of the plurality of images captured as one or more vehicles traverse the road segment. To enable crowdsourcing, the analysis may include rules regarding accepting and rejecting possible landmarks associated with the road segment.For example, the analysis may include accepting potential landmarks 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 potential landmarks if a ratio of images in which the landmark does not appear to images in which the landmark does appear exceeds a threshold.

[0231] Process 1900 may include other operations or steps performed by server 1230. For example, the navigation information may include a target trajectory for vehicles to travel along a road segment, and process 1900 may include clustering, by server 1230, vehicle trajectories related to multiple vehicles traveling on the road segment and determining the target trajectory based on the clustered vehicle trajectories, as discussed in more detail below. Clustering vehicle trajectories may include clustering, by server 1230, the multiple trajectories related to the vehicles traveling on the road segment into a plurality of clusters based on at least one of the absolute direction of travel of the vehicles or the lane assignment of the vehicles.Generating the target trajectory may include averaging, by server 1230, the clustered trajectories. As another example, process 1900 may include aligning data received in step 1905. Other processes or steps performed by server 1230, as described above, may also be included in process 1900.

[0232] The disclosed systems and methods may include other features. For example, the disclosed systems may use local coordinates instead of global coordinates. For autonomous driving, some systems may present data in world coordinates. For example, longitude and latitude coordinates on the Earth's surface may be used. To use the map for steering, the host vehicle may determine its position and orientation relative to the map. It seems natural to use an onboard GPS device to position the vehicle on the map and to find the rotational transformation between the body reference frame and the world reference frame (e.g., north, east, and down). Once the body reference frame is aligned with the map reference frame, the desired route can be expressed in the body reference frame, and the steering commands can be calculated or generated.

[0233] The disclosed systems and methods can enable autonomous vehicle navigation (e.g., steering control) with small-footprint models that can be collected by the autonomous vehicles themselves without the aid of expensive surveying equipment. To support autonomous navigation (e.g., steering applications), the road model can include a sparse map containing the geometry of the road, its lane structure, and landmarks that can be used to determine the location or position of vehicles along a trajectory included in the model. As discussed above, the generation of the sparse map can be performed by a remote server that communicates with vehicles traveling on the road and receives data from the vehicles.The data may include acquired data, trajectories reconstructed based on the acquired data, and / or recommended trajectories, which may represent modified reconstructed trajectories. As discussed below, the server may transmit the model back to the vehicles or other vehicles later traveling on the road to support autonomous navigation.

[0234] Fig. 20 illustrates a block diagram of server 1230. Server 1230 may include a communications unit 2005, which may include both hardware components (e.g., communications control circuits, switches, and antenna) and software components (e.g., communications protocols, computer codes). For example, communications unit 2005 may include at least one network interface. Server 1230 may communicate with vehicles 1205, 1210, 1215, 1220, and 1225 via communications unit 2005. For example, server 1230 may receive navigation information transmitted from vehicles 1205, 1210, 1215, 1220, and 1225 via communications unit 2005. Server 1230 may distribute the autonomous vehicle's road navigation model to one or more autonomous vehicles via communications unit 2005.

[0235] The server 1230 may include at least one non-volatile storage medium 2010, such as a hard disk, a compact disc, a tape, etc. The storage device 1210 may be configured to store data, such as navigation information received from the vehicles 1205, 1210, 1215, 1220, and 1225 and / or the road navigation model of the autonomous vehicle 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., the sparse map 800 described above with respect to Fig. 8 is discussed).

[0236] In addition to or instead of storage device 2010, server 1230 may include memory 2015. Memory 2015 may be similar to or different from memory 140 or 150. Memory 2015 may be non-volatile memory, such as flash memory, random access memory, etc. Memory 2015 may be configured to store data, such as computer code or instructions executable by a processor (e.g., processor 2020), map data (e.g., sparse map 800 data), the autonomous vehicle's road navigation model, and / or navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225.

[0237] Server 1230 may include at least one processing device 2020 configured to execute computer code or instructions stored in memory 2015 to perform various functions. For example, processing device 2020 may analyze the navigation information received from vehicles 1205, 1210, 1215, 1220, and 1225 and generate the autonomous vehicle's road navigation model based on the analysis. Processing device 2020 may control communication unit 1405 to distribute the autonomous vehicle's road navigation model to one or more autonomous vehicles (e.g., one or more of vehicles 1205, 1210, 1215, 1220, and 1225, or any vehicle traveling on road segment 1200 at a later time). The processing device 2020 may be similar to or different from the processor 180, 190 or the processing unit 110.

[0238] Fig. 21 illustrates a block diagram of memory 2015, which may store computer code or instructions for performing one or more operations to generate a road navigation model for use in autonomous vehicle navigation. As shown in Fig. As shown in Figure 21, memory 2015 may store one or more modules for performing operations for processing vehicle navigation information. For example, memory 2015 may include a model generation module 2105 and a model distribution module 2110. Processor 2020 may execute the instructions stored in any of modules 2105 and 2110 included in memory 2015.

[0239] The model generation module 2105 may store instructions that, when executed by the processor 2020, may generate at least a portion of an autonomous vehicle road navigation model for a shared 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 shared road segment 1200 into different clusters. The processor 2020 may determine a target trajectory along the shared road segment 1200 based on the clustered vehicle trajectories for each of the different clusters. Such an operation may include finding a mean or average trajectory of the clustered vehicle trajectories (e.g.,by averaging data representing the clustered vehicle trajectories) in each cluster. In some embodiments, the target trajectory may be associated with a single lane of the common road segment 1200.

[0240] The road model and / or sparse map may store trajectories associated with a road segment. These trajectories may be referred to as target trajectories, which are provided to autonomous vehicles for autonomous navigation. The target trajectories may be received from multiple vehicles or may be generated based on actual trajectories or recommended trajectories (actual trajectories with some modifications) received from multiple vehicles. The target trajectories contained in the road model or sparse map may be continuously updated (e.g., averaged) with new trajectories received from other vehicles.

[0241] Vehicles traveling along a road segment may collect data through various sensors. The data may include landmarks, road signature profile, vehicle motion (e.g., accelerometer data, speed data), vehicle position (e.g., GPS data), and may either reconstruct the actual trajectories themselves or transmit the data to a server that reconstructs the actual trajectories for the vehicles. In some embodiments, the vehicles may transmit data related to a trajectory (e.g., a curve in any reference frame), landmark data, and lane assignment along the travel path to the server 1230. Different vehicles traveling along the same road segment on multiple trips may have different trajectories.The server 1230 may identify routes or trajectories associated with each lane from the trajectories received from vehicles through a clustering process.

[0242] Fig. Figure 22 illustrates a process for clustering vehicle trajectories associated with vehicles 1205, 1210, 1215, 1220, and 1225 to determine a target trajectory for the shared road segment (e.g., road segment 1200). The target trajectory or a plurality of target trajectories determined from the clustering process may be included in the road navigation model or sparse map 800 of the autonomous vehicle. In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 traveling along road segment 1200 may transmit a plurality of 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 road navigation model of the autonomous vehicle, the server 1230 may cluster vehicle trajectories 1600 into a plurality of clusters 2205, 2210, 2215, 2220, 2225, and 2230, as shown in FIG. Fig. 22 shown.

[0243] Clustering can be performed using various criteria. In some embodiments, all trips in a cluster can be similar in terms of absolute direction of travel along road segment 1200. The absolute direction of travel can be obtained from GPS signals received from vehicles 1205, 1210, 1215, 1220, and 1225. In some embodiments, the absolute direction of travel can be obtained using dead reckoning. Dead reckoning, as one of ordinary skill in the art would understand, can be used to determine the current position and thus the direction of travel of vehicles 1205, 1210, 1215, 1220, and 1225 using previously determined position, estimated speed, etc. Trajectories clustered by absolute direction of travel can be useful for identifying routes along roadways.

[0244] In some embodiments, all trips in a cluster may be similar in terms of lane assignment (e.g., in the same lane before and after an intersection) along the journey on road segment 1200. Trajectories clustered by lane assignment may be useful for identifying lanes along the roadways. In some embodiments, both criteria (e.g., absolute direction of travel and lane assignment) may be used for clustering.

[0245] In each cluster 2205, 2210, 2215, 2220, 2225, and 2230, trajectories may be averaged to obtain a target trajectory associated with the specific cluster. For example, the trajectories of multiple trips associated with the same lane cluster may be averaged. The averaged trajectory may be a target trajectory associated with a specific lane. To average a cluster of trajectories, the server 1230 may select a reference frame of an arbitrary trajectory C0. For all other trajectories (C1,..., Cn), the server 1230 may find a rigid transformation mapping Ci to C0, where i = 1, 2,..., n, where n is a positive integer equal to the total number of trajectories contained in the cluster. The server 1230 may calculate a mean curve or trajectory in the C0 reference frame.

[0246] In some embodiments, the landmarks may define an arc length alignment between different trips, which may be used to align trajectories with lanes. In some embodiments, lane markings before and after an intersection may be used to align trajectories with lanes.

[0247] To assemble lanes from the trajectories, server 1230 may select a reference frame of any lane. Server 1230 may map partially overlapping lanes to the selected reference frame. Server 1230 may continue the mapping until all lanes are in the same reference frame. Lanes that are adjacent to each other may be aligned as if they were the same lane and later shifted laterally.

[0248] Landmarks detected along the road segment can be mapped to the common reference frame, first at the lane level, then at the intersection level. For example, the same landmarks may be detected multiple times by multiple vehicles in multiple trips. The data regarding the same landmarks received in different trips may be slightly different. Such data can be averaged and mapped to the same reference frame, such as the C0 reference frame. Additionally or alternatively, the variance of the data for the same landmark received in multiple trips can be calculated.

[0249] In some embodiments, each lane of road segment 120 may be associated with a target trajectory and specific landmarks. The target trajectory, or a plurality of such target trajectories, may be included in the autonomous vehicle's road navigation model, which may later be used by other autonomous vehicles traveling along the same road segment 1200. Landmarks identified by vehicles 1205, 1210, 1215, 1220, and 1225 as the vehicles travel along road segment 1200 may be recorded in association with the target trajectory. The target trajectory and landmark data may be updated continuously or periodically with new data received from other vehicles in subsequent trips.

[0250] To locate an autonomous vehicle, the disclosed systems and methods may use an extended Kalman filter. The vehicle's location may be determined based on three-dimensional position data and / or three-dimensional orientation data, predicting the future location prior to the vehicle's current location by integrating self-motion. The vehicle's location may be corrected or adjusted through image observations of landmarks. For example, if the vehicle detects a landmark within an image captured by the camera, the landmark may be compared to a known landmark stored within the road model or sparse map 800. The known landmark may include a known location (e.g., GPS data) along a target trajectory stored in the road model and / or sparse map 800.Based on the current speed and landmark images, the distance from the vehicle to the landmark can be estimated. The vehicle's location along a target trajectory can be adjusted based on the distance to the landmark and the known location of the landmark (stored in the road model or sparse map 800). The landmark position / location data (e.g., averages of multiple trips) stored in the road model and / or sparse map 800 can be assumed to be accurate.

[0251] In some embodiments, the disclosed system may form a closed-loop subsystem in which the six-degree-of-freedom location estimate of the vehicle (e.g., three-dimensional position data plus three-dimensional orientation data) may be used to navigate the autonomous vehicle (e.g., steer its wheel) to reach a desired point (e.g., 1.3 seconds prior in the stored location). In turn, data measured from the steering and actual navigation may be used to estimate the six-degree-of-freedom location.

[0252] In some embodiments, poles along a road, such as lampposts and utility or cable line poles, can be used as landmarks to locate vehicles. Other landmarks, such as traffic signs, traffic lights, arrows in the road, stop lines, and static features or signatures of an object along the road segment can also be used as landmarks to locate the vehicle. When poles are used for localization, the x-observation of the poles (i.e., the viewpoint from the vehicle) can be used instead of the y-observation (i.e., the distance to the pole), because the bottoms of the poles can be obscured and they are sometimes not at the street level.

[0253] Fig. Figure 23 illustrates a navigation system for a vehicle that can be used for autonomous navigation using a sparse crowdsourced map. For illustration purposes, the vehicle is referenced as vehicle 1205. The Fig. 23 may be any other vehicle disclosed herein, including, for example, vehicles 1210, 1215, 1220, and 1225, as well as vehicle 200 shown in other embodiments. As shown in Fig. 12, the vehicle 1205 may communicate with the server 1230. The vehicle 1205 may include an image capture 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 on 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. The Fig. The vehicle 1205 shown in Figure 23 may be an autonomous vehicle, and the navigation system 2300 may be used to provide navigation guidance for autonomous driving. Alternatively, the vehicle 1205 may be a non-autonomous, human-driven vehicle, and the navigation system 2300 may still be used to provide navigation guidance.

[0254] The navigation system 2300 may include a communication unit 2305 configured to communicate with the server 1230 via 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 on a storage device provided onboard the vehicle 1205 and / or received from the server 1230), road geometry captured by a 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 elevation, road curvature, etc. For example, the road profile sensor 2330 may include a device that measures the movement of a suspension of the vehicle 2305 to derive the road roughness profile. In some embodiments, the road profile sensor 2330 may include radar sensors to measure the distance from the vehicle 1205 to the sides of the road (e.g., barriers on the sides 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 of the road up and down. In some embodiments, the road profile sensor 2330 may include a device configured to measure the curvature of the road. For example, a camera (e.g.,Camera 122 or another camera) may be used to capture images of the road showing road curvatures. Vehicle 1205 may use such images to detect road curvatures.

[0255] The 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 related to the vehicle 1205. The navigation information may include a trajectory related to the travel of the vehicle 1205 along the road segment 1200. The at least one processor 2315 may determine the trajectory based on movements of the camera 122 (and thus the vehicle), such as three-dimensional translation and three-dimensional rotational movements. In some embodiments, the at least one processor 2315 may determine the translational and rotational movements of the camera 122 based on the analysis of a plurality of images captured by the camera 122.In some embodiments, the navigation information may include lane assignment information (e.g., which lane the vehicle 1205 is traveling in along the road segment 1200). The navigation information transmitted from the vehicle 1205 to the server 1230 may be used by the server 1230 to generate and / or update a road navigation model of the autonomous vehicle, which may be transmitted back from the server 1230 to the vehicle 1205 to provide autonomous navigation guidance for the vehicle 1205.

[0256] The at least one processor 2315 may also be programmed to transmit the navigation information from the vehicle 1205 to the server 1230. In some embodiments, the navigation information may be transmitted to the server 1230 along with the road information. The road location information may include at least one of the GPS signal received by the GPS unit 2310, landmark information, road geometry, lane information, etc. The at least one processor 2315 may receive the autonomous vehicle's road navigation model or a portion of the model from the server 1230. The autonomous vehicle's 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 server 1230 to vehicle 1205 may include an updated portion of the model. The at least one processor 2315 may cause at least one navigation maneuver (e.g., steering, such as turning, braking, accelerating, passing another vehicle, etc.) by vehicle 1205 based on the received autonomous vehicle road navigation model or the updated portion of the model.

[0257] 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 various sensors and components and transmit the information or data to the server 1230 via the communication unit 2305. Alternatively or additionally, 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.

[0258] In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 may communicate with each other and may share navigation information with each other, such that at least one of vehicles 1205, 1210, 1215, 1220, and 1225 may generate the road navigation model of the autonomous vehicle using crowdsourcing, e.g., based on information shared by other vehicles. In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 may share navigation information with each other, and each vehicle may update its own road navigation model of the autonomous vehicle provided in the vehicle. In some embodiments, at least one of vehicles 1205, 1210, 1215, 1220, and 1225 (e.g., vehicle 1205) may act as a hub vehicle. The at least one processor 2315 of the hub vehicle (e.g., vehicle 1205) may perform some or all of the functions performed by server 1230.For example, the at least one processor 2315 of the hub vehicle may communicate with other vehicles and receive navigation information from other vehicles. The at least one processor 2315 of the hub vehicle may generate the autonomous vehicle's road navigation model or an update to the model based on the shared information received from other vehicles. The at least one processor 2315 of the hub vehicle may transmit the autonomous vehicle's road navigation model or the update to the model to other vehicles to provide autonomous navigation guidance. Navigation based on sparse maps

[0259] As previously discussed, the autonomous vehicle's road navigation model, including sparse map 800, may include a plurality of mapped lane markings and a plurality of mapped objects / features associated with a road segment. As discussed in more detail below, these mapped lane markings, objects, and features may be used when the autonomous vehicle navigates. For example, in some embodiments, the mapped objects and features may be used to locate a host vehicle relative to the map (e.g., relative to a mapped target trajectory). The mapped lane markings may be used to determine a lateral position and / or orientation relative to a planned or target trajectory.With this position information, the autonomous vehicle may be able to adjust a heading to match a direction of a target trajectory at the given position.

[0260] The vehicle 200 may be configured to detect lane markings in a given road segment. The road segment may include any markings on a roadway for guiding vehicular traffic on a roadway. For example, the lane markings may be solid or dashed lines that mark the edge of a lane. The lane markings may also include double lines, such as double solid lines, double dashed lines, or a combination of solid and dashed lines, indicating, for example, whether passing is permitted in an adjacent lane. The lane markings may also include highway entrance and exit markings that indicate, for example, a deceleration lane for an exit ramp, or dotted lines that indicate that a lane is turning only or that the lane ends.The markings may further indicate a work zone, a temporary lane shift, a path through an intersection, a median, a special lane (e.g., a bicycle lane, an HOV lane, etc.), or other miscellaneous markings (e.g., a pedestrian crossing, a speed ramp, a railroad crossing, a stop line, etc.).

[0261] The vehicle 200 may use cameras, such as image capture devices 122 and 124 included in the image capture unit 120, to capture images of the surrounding lane markings. The vehicle 200 may analyze the images to capture point locations associated with the lane markings based on features identified within one or more of the captured images. These point locations may be uploaded to a server to represent the lane markings in the sparse map 800. Depending on the camera's position and field of view, lane markings for both sides of the vehicle may be captured simultaneously from a single image. In other embodiments, different cameras may be used to capture images on multiple sides of the vehicle.Instead of uploading actual images of the lane markings, the markings may be stored in the sparse map 800 as a spline or a series of points, thereby reducing the size of the sparse map 800 and / or the data that must be uploaded remotely by the vehicle.

[0262] The Fig. 24A-24D illustrate example point locations that may be detected by vehicle 200 to represent particular lane markings. 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 detect a series of edge points, corner points, or various other point locations associated with a particular lane marking. Fig. 24A shows a solid lane marking 2410 that can be detected by the vehicle 200. The lane marking 2410 may represent the outer edge of a roadway, represented by a solid white line. As in Fig. 24A, the vehicle 200 may be configured to detect a plurality of edge location points 2411 along the lane marking. The location points 2411 may be collected to represent the lane marking at any intervals sufficient to create a mapped lane marking in the sparse map. For example, the lane marking may be represented by one point per meter of the detected edge, one point per every five meters of the detected edge, or at other suitable intervals. In some embodiments, the spacing may be determined by other factors rather than at fixed intervals, such as, for example, based on points at which the vehicle 200 has a highest confidence rating of the location of the detected points. Although Fig. 24A shows edge location points at an inner edge of the lane marking 2410, points can be collected at the outer edge of the line or along both edges. Furthermore, while a single line in Fig. 24A, similar edge points are detected for a double continuous line. For example, points 2411 may be detected along an edge of one or both of the continuous lines.

[0263] The vehicle 200 may also display lane markings differently depending on the type or shape of the lane marking. Fig. 24B shows an exemplary dashed lane marking 2420 that can be detected by the vehicle 200. Instead of identifying edge points, as in Fig. 24A, the vehicle can detect a series of vertices 2421, representing corners of the lane lines, to define the complete boundary of the line. Although Fig. 24B shows that every corner of a given tally mark is located, the vehicle 200 may detect or record a subset of the points shown in the figure. For example, the vehicle 200 may detect the leading edge or corner of a given tally mark, or may detect the two vertices closest to the interior of the lane. Further, not every tally mark may be detected; for example, the vehicle 200 may detect and / or record points representing a sample of tally marks (e.g., every second, every third, every fifth, etc.) or tally marks at a predefined spacing (e.g., every meter, every five meters, every 10 meters, etc.).Corner points can also be detected for similar lane markings, such as markings indicating that a lane is for an exit ramp, that a particular lane ends, or other miscellaneous lane markings that may have detectable corner points. Corner points can also be detected for lane markings consisting of double-dashed lines or a combination of solid and dashed lines.

[0264] In some embodiments, the points uploaded to the server to generate the mapped lane markings may represent other points besides the detected edge points or corner points. Fig. 24C illustrates a series of points that may represent a centerline of a given lane marking. For example, the continuous lane 2410 may be represented by centerline points 2441 along a 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 (CNN), scale-invariant feature transform (SIFT), histogram of oriented gradients (HOG) features, or other techniques. Alternatively, the vehicle 200 may detect other points, such as edge points 2411, that are in Fig. 24A, and may calculate centerline points 2441, for example, by detecting points along each edge and determining a midpoint between the edge points. Similarly, the dashed lane marking 2420 may be represented by centerline points 2451 along a centerline 2450 of the lane marking. The centerline points may be located at the edge of a dash, as shown in Fig. 24C, or at various other locations along the centerline. For example, each stroke may be represented by a single point at the geometric center of the stroke. The points may also be spaced at a predetermined interval along the centerline (e.g., every meter, 5 meters, 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 vertices 2421, as shown in Fig. 24B. A centerline may also be used to represent other lane marking types, such as a double line, using similar techniques as above.

[0265] In some embodiments, the vehicle may identify 200 points representing other features, such as a vertex between two intersecting lane markings. Fig. 24D shows example points representing an intersection between two lane markings 2460 and 2465. The vehicle 200 may calculate a vertex 2466 representing an intersection between the two lane markings. For example, one of the lane markings 2460 or 2465 may represent a railroad crossing area or other transition area in the road segment. While the lane markings 2460 and 2465 are shown as crossing perpendicularly to each other, various other configurations may be detected. For example, the lane markings 2460 and 2465 may cross at other angles, or one or both of the lane markings may end at the vertex 2466. Similar techniques may also be applied for intersections between dashed or other lane marking types.In addition to the vertex 2466, various other points 2467 may also be detected, which provide further information about the orientation of the lane markings 2460 and 2465.

[0266] The vehicle 200 may associate real-world coordinates with each detected lane marking point. For example, location identifiers may be generated, including coordinates for each point, to upload to a server for mapping the lane marking. The location identifiers may further include other identifying information about the points, including whether the point represents a corner point, an edge point, a center point, etc. The vehicle 200 may therefore be configured to determine a real-world position of each point based on the analysis of the images. For example, the vehicle 200 may detect other features in the image, such as the various landmarks described above, to locate the real-world position of the lane markings.This may involve determining the location of the lane markings in the image relative to the detected landmark, or determining the position of the vehicle based on the detected landmark and then determining a distance from the vehicle (or the vehicle's target trajectory) to the lane marking. If no landmark is available, the location of the lane marking points may be determined relative to a position of the vehicle determined based on dead reckoning. The real-world coordinates contained in the location identifiers may be represented as absolute coordinates (e.g., latitude / longitude coordinates) or may be relative to other features, such as based on a longitudinal position along a target trajectory and a lateral distance from the target trajectory.The location identifiers can then be uploaded to a server to generate the mapped lane markings in the navigation model (such as sparse map 800). In some embodiments, the server can construct a spline representing the lane markings of a road segment. Alternatively, the vehicle 200 can generate the spline and upload it to the server to be recorded in the navigation model.

[0267] Fig. 24E shows an example navigation model or sparse map for a respective road segment including mapped lane markings. The sparse map may include a target trajectory 2475 for a vehicle to follow along a road segment. As described above, the target trajectory 2475 may represent an ideal path for a vehicle to take when traveling the respective road segment or may be located elsewhere on the road (e.g., a road centerline, etc.). The target trajectory 2475 may be calculated in the various methods 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.

[0268] In some embodiments, the target trajectory may be generated equally for all vehicle types and for all road, vehicle, and / or environmental conditions. However, in other embodiments, various other factors or variables may be considered when generating the target trajectory. A different target trajectory may be generated for different vehicle types (e.g., a private vehicle, a light truck, and a full trailer). For example, a target trajectory with relatively tighter turning radii may be generated for a small private vehicle than a larger semi-trailer truck. In some embodiments, road, vehicle, and environmental conditions may also be considered. For example, a different target trajectory may be generated for different road conditions (e.g., wet, snowy, icy, dry, etc.), vehicle conditions (e.g.,Tire condition or estimated tire condition, brake condition or estimated brake condition, remaining fuel quantity, 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 curves, grade, etc.). In some embodiments, various user settings may also be used to determine the target trajectory, such as a specified driving mode (e.g., desired driving aggressiveness, economy mode, etc.).

[0269] The sparse map may also include mapped lane markings 2470 and 2480, which represent lane markings along the road segment. The mapped lane markings may be represented by a plurality of location identifiers 2471 and 2481. As described above, the location identifiers may include locations in real-world coordinates of points associated with a detected lane marking. Similar to the target trajectory in the model, the lane markings may also include elevation data and may be represented as a curve in three-dimensional space. For example, the curve may be a spline connecting three-dimensional polynomials of appropriate order, where the curve may be calculated based on the location identifiers. The mapped lane markings may also include other information or metadata about the lane marking, such as an identifier of the type of lane marking (e.g.,between two lanes traveling in the same direction, between two lanes traveling in opposite directions, edge of a roadway, etc.) and / or other lane marking properties (e.g., solid, dashed, single line, double line, yellow, white, etc.). In some embodiments, the mapped lane markings may be continuously updated within the model, for example, using crowdsourcing techniques. The same vehicle may upload location identifiers during multiple trips of the same road segment, or data may be selected from a plurality of 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.As the mapped lane markings are updated and refined, the updated road navigation model and / or sparse map can be distributed to a variety of autonomous vehicles.

[0270] Generating the imaged lane markings in the sparse map may also involve detecting and / or mitigating errors based on anomalies in the images or in the actual lane markings themselves. Fig. 24F shows an example anomaly 2495 associated with detecting a lane marking 2490. The anomaly 2495 may appear in the image captured by the vehicle 200, for example, from an object obstructing the camera's view of the lane marking, dirt on the lens, etc. In some cases, the anomaly may be due to the lane marking itself, which may be damaged or worn or partially covered, for example, by dirt, debris, water, snow, or other materials on the road. The anomaly 2495 may result in an erroneous point 2491 being detected by the vehicle 200. The sparse map 800 may provide the correct imaged lane marking and eliminate the error.In some embodiments, the vehicle 200 may detect an erroneous point 2491, for example, by detecting the anomaly 2495 in the image or by identifying the error based on detected lane marker points before and after the anomaly. Based on detecting the anomaly, the vehicle may omit the point 2491 or may adjust it to match other detected points. In other embodiments, the error may be corrected after the point is uploaded, for example, by determining that the point is outside an expected threshold based on other points uploaded during the same trip or based on an aggregation of data from previous trips along the same road segment.

[0271] The mapped lane markings in the navigation model and / or the sparse map can also be used for navigation by an autonomous vehicle traversing the corresponding roadway. For example, a vehicle navigating along a target trajectory can periodically use the mapped lane markings in the sparse map to align itself with the target trajectory. As mentioned above, the vehicle can navigate between landmarks based on dead reckoning, in which the vehicle uses sensors to determine its own motion and estimate its position relative to the target trajectory. Errors can accumulate over time, and the vehicle's position determinations relative to the target trajectory can become increasingly inaccurate.Accordingly, the vehicle can utilize lane markers appearing in the sparse map 800 (and their known locations) to reduce dead reckoning-induced errors in positioning. In this way, the identified lane markers contained in the sparse map 800 can serve as navigation anchors from which an accurate position of the vehicle relative to a target trajectory can be determined.

[0272] Fig. 25A shows an exemplary image 2500 of a vehicle's surroundings that can be used for navigation based on the depicted lane markings. The image 2500 can be captured, for example, by the vehicle 200 via the image capture devices 122 and 124 included in the image capture unit 120. The image 2500 can include an image of at least one lane marking 2510, as shown in Fig. 25A. The image 2500 may also include one or more landmarks 2521, such as a street sign, used for navigation as described above. Some in Fig. 25A, such as elements 2511, 2530, and 2520, that do not appear in the captured image 2500 but are detected and / or determined by the vehicle 200 are also shown for reference.

[0273] Using the various techniques described above with respect to the Fig. 24A-D and 24F, a vehicle may analyze image 2500 to identify lane marking 2510. Various points 2511 may be detected according to features of the lane marking in the image. For example, points 2511 may correspond to an edge of the lane marking, a corner of the lane marking, a midpoint of the lane marking, a vertex between two intersecting lane markings, or various other features or locations. Points 2511 may be detected to correspond to a location of points stored in a navigation model received from a server. For example, if a sparse map is received that includes points representing a centerline of an imaged lane marking, points 2511 may also be detected based on a centerline of lane marking 2510.

[0274] The vehicle may also determine a longitudinal position, represented by element 2520, located along a target trajectory. The longitudinal position 2520 may be determined from the image 2500, for example, by detecting the landmark 2521 within the image 2500 and comparing a measured location to a known landmark location stored in the road model or sparse map 800. The vehicle's location along a target trajectory may then be determined based on the distance to the landmark and the known location of the landmark. The longitudinal position 2520 may also be determined from images other than those used to determine the position of a lane marking.For example, the longitudinal position 2520 may be determined by detecting landmarks in images from other cameras within the image capture unit 120 that are captured simultaneously or nearly simultaneously with the image 2500. In some cases, the vehicle may not be near any landmarks or other reference points for determining the longitudinal position 2520. In such cases, the vehicle may navigate based on dead reckoning and thus may use sensors to determine its own motion and estimate a longitudinal position 2520 relative to the target trajectory. The vehicle may also determine a distance 2530 that represents the actual distance between the vehicle and the lane marking 2510 observed in the captured image(s).The camera angle, the speed of the vehicle, the width of the vehicle or various other factors can be taken into account when determining the distance 2530.

[0275] Fig. 25B illustrates a lateral location correction of the vehicle based on the mapped lane markings in a road navigation model. As described above, the vehicle 200 may determine a distance 2530 between the vehicle 200 and a lane marking 2510 using one or more images captured by the vehicle 200. The vehicle 200 may also have access to a road navigation model, such as the sparse map 800, which may include a mapped lane marking 2550 and a target trajectory 2555. The mapped lane marking 2550 may be modeled using the techniques described above, for example, using crowdsourced location identifiers captured from a plurality of vehicles. The target trajectory 2555 may also be generated using the 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. Fig. 25A. The vehicle 200 may then determine an expected distance 2540 based on a lateral distance between the target trajectory 2555 and the mapped lane marking 2550, which corresponds to the longitudinal position 2520. The lateral location of the vehicle 200 may be corrected or adjusted by comparing the actual distance 2530, measured using the captured image(s), with the expected distance 2540 from the model.

[0276] The Fig. 25C and Fig. 25D provide illustrations associated with another example of locating a host vehicle during navigation based on mapped landmarks / objects / features in a sparse map. Fig. Figure 25C conceptually illustrates a series of images captured by a vehicle navigating along a road segment 2560. In this example, the road segment 2560 includes a straight section of a two-lane divided highway delineated by the road edges 2561 and 2562 and the center lane marker 2563. As shown, the host vehicle is navigating along a lane 2564 associated with a mapped target trajectory 2565. Therefore, in an ideal situation (and without influencing factors such as the presence of target vehicles or objects in the roadway, etc.), the host vehicle should closely follow the mapped target trajectory 2565 while navigating along the lane 2564 of the road segment 2560. In reality, the host vehicle may experience drift while navigating along the depicted target trajectory 2565.For effective and safe navigation, this drift should be kept within acceptable limits (e.g., + / - 10 cm lateral displacement from the target trajectory 2565 or any other suitable threshold). To periodically account for the drift and make any necessary course corrections to ensure that the host vehicle follows the target trajectory 2565, the disclosed navigation systems may be capable of locating the host vehicle along the target trajectory 2565 (e.g., determining a lateral and longitudinal position of the host vehicle relative to the target trajectory 2565) using one or more mapped features / objects included in the sparse map.

[0277] As a simple example, Fig. 25C illustrates a speed limit sign 2566 as it may appear in five different, sequentially acquired images as the host vehicle navigates along road segment 2560. For example, at a first time, t0, sign 2566 may appear near the horizon in a acquired image. As the host vehicle approaches sign 2566, in subsequent acquired images at times t1, t2, t3, and t4, sign 2566 appears at different 2D XY pixel locations of the acquired images. For example, in the space of the acquired images, sign 2566 moves down and to the right along curve 2567 (e.g., a curve that extends through the center of the sign in each of the five acquired images). Sign 2566 also appears to increase in size as the host vehicle approaches it (i.e., it occupies a large number of pixels in the subsequently acquired images).

[0278] These changes in the image-space representations of an object, such as sign 2566, can be exploited to determine the local position of the host vehicle along a target trajectory. For example, as described in the present disclosure, any detectable object or feature, such as a semantic feature like sign 2566 or a detectable non-semantic feature, can be identified by one or more harvesting vehicles that have previously traversed a road segment (e.g., road segment 2560). A mapping server can collect the collected driving information from a plurality of vehicles, aggregate and correlate this information, and generate a sparse map that includes, for example, a target trajectory 2565 for lane 2564 of road segment 2560. The sparse map can also store the location of sign 2566 (along with type information, etc.). During navigation (e.g.Before entering road segment 2560, a host vehicle may be provided with a map tile containing a sparse map for road segment 2560. To navigate lane 2564 of road segment 2560, the host vehicle may follow the mapped target trajectory 2565.

[0279] The imaged representation of the sign 2566 can be used by the host vehicle to locate itself relative to the target trajectory. For example, a camera on the host vehicle captures an image 2570 of the host vehicle's surroundings, and this captured image 2570 can include an image representation of the sign 2566 with a particular size and a particular XY image position, as shown in Fig. 25D. This size and XY image position can be used to determine the position of the host vehicle relative to the target trajectory 2565. For example, a host vehicle's navigation processor may determine, based on the sparse map including a representation of the sign 2566, that in response to the host vehicle traveling along the target trajectory 2565, a representation of the sign 2566 should appear in the captured images such that a center of the sign 2566 (in image space) moves along line 2567. If a captured image, such as image 2570, shows that the center point (or other reference point) is shifted from line 2567 (e.g., the expected image space trajectory), the host vehicle's navigation system may determine that it was not on the target trajectory 2565 at the time the image was captured.However, from the image, the navigation processor can determine an appropriate navigation correction to return the host vehicle to the target trajectory 2565. For example, if the analysis shows an image position of the sign 2566 that is shifted in the image by a distance 2572 to the left from the expected image-space position on the line 2567, the navigation processor can cause the host vehicle to change direction (e.g., by changing the steering angle of the wheels) to move the host vehicle to the left by a distance 2573. In this way, each acquired image can be used as part of a feedback loop so that any difference between an observed image position of the sign 2566 and the expected image trajectory 2567 can be minimized to ensure that the host vehicle continues along the target trajectory 2565 with little or no deviation.Of course, the more imaged objects are available, the more frequently the described localization technique can be used, thereby reducing or eliminating drift-related deviations from the target trajectory 2565.

[0280] The process described above may be useful for detecting a lateral orientation or displacement of the host vehicle relative to a target trajectory. Localizing the host vehicle relative to the target trajectory 2565 may also include determining the longitudinal position of the target vehicle along the target trajectory. For example, the captured image 2570 includes a representation of the sign 2566 with a particular image size (e.g., 2D XY pixel area). This size may be compared to an expected image size of the imaged sign 2566 as it moves along line 2567 through image space (e.g., as the size of the sign progressively increases, as in Fig. 25C). Based on the image size of the sign 2566 in image 2570 and the expected size progression in image space relative to the mapped target trajectory 2565, the host vehicle can determine its longitudinal position (at the time of acquiring image 2570) relative to the target trajectory 2565. This longitudinal position, coupled with any lateral displacement relative to the target trajectory 2565, as described above, enables complete localization of the host vehicle relative to the target trajectory 2565 as the host vehicle navigates along the road 2560.

[0281] The Fig. 25C and Fig. 25D illustrate only one example of the disclosed localization technique using a single mapped object and a single target trajectory. In other examples, there may be many more target trajectories (e.g., one target trajectory for each usable lane of a multi-lane highway, an urban street, a complex intersection, etc.) and there may be many more mapped objects for localization. For example, a sparse map representative of an urban environment may include many objects per meter available for localization.

[0282] Fig. 26A is a flowchart illustrating an exemplary process 2600A for mapping a lane marking for use in autonomous vehicle navigation, in accordance with the disclosed embodiments. At step 2610, process 2600A may include receiving two or more location identifiers associated with a detected lane marking. For example, step 2610 may be performed by server 1230 or one or more processors associated with the server. The location identifiers may include locations in real-world coordinates of points associated with the detected lane marking, 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 marking. Additional data may also be received during step 2610, such as accelerometer data, velocity data, landmark data, road geometry or profile data, vehicle positioning data, self-motion data, or various other forms of data described above. The location identifiers may be generated by a vehicle, such as vehicles 1205, 1210, 1215, 1220, and 1225, based on images captured by the vehicle.For example, the identifiers may be determined based on capturing at least one image representing an environment of the host vehicle from a camera associated with a host vehicle, analyzing the at least one image to detect the lane marking in the environment of the host vehicle, and analyzing the at least one image to determine a position of the detected lane marking relative to a location associated with the host vehicle. As described above, the lane marking may include a variety of different marking types, and the location identifiers may correspond to a variety of points relative to the lane marking. For example, if the detected lane marking is part of a dashed line marking a lane boundary, the points may correspond to detected corners of the lane marking.If the detected lane marking is part of a continuous line marking a lane boundary, the points may correspond to a detected edge of the lane marking at various distances, as described above. In some embodiments, the points may correspond to the centerline of the detected lane marking, as shown in FIG. Fig. 24C, or may correspond to a vertex between two intersecting lane markings and at least two other points associated with the intersecting lane markings, as shown in Fig. 24D shown.

[0283] At step 2612, process 2600A may include associating the detected lane marking with a corresponding road segment. For example, server 1230 may analyze the real-world coordinates or other information received during step 2610 and compare the coordinates or other information with location information stored in a road navigation model of the autonomous vehicle. Server 1230 may determine a road segment in the model that corresponds to the real-world road segment in which the lane marking was detected.

[0284] At step 2614, process 2600A may include updating a road navigation model of the autonomous vehicle relative to the corresponding road segment based on the two or more location identifiers associated with the detected lane marking. For example, the autonomous road navigation model may be a sparse map 800, and server 1230 may update the sparse map to include or adjust a mapped lane marking in the model. Server 1230 may update the model based on the various methods or processes described above with respect to Fig. 24E. In some embodiments, updating the autonomous vehicle's road navigation model may include storing one or more position indicators in real-world coordinates of the detected lane marking. The autonomous vehicle's road navigation model may also include at least one target trajectory for a vehicle to follow along the corresponding road segment, as shown in Fig. 24E shown.

[0285] At step 2616, process 2600A may include distributing the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles. For example, server 1230 may distribute the updated autonomous vehicle road navigation model to vehicles 1205, 1210, 1215, 1220, and 1225, which may use the model for navigation. The autonomous vehicle road navigation model may be distributed over one or more networks (e.g., over a cellular network and / or the Internet, etc.) via wireless communication paths 1235, as shown in Fig. 12 shown.

[0286] In some embodiments, the lane markings may be mapped using data received from a plurality of vehicles, such as through a crowdsourcing technique as described above with respect to Fig. 24E. For example, process 2600A may include receiving a first communication from a first host vehicle including location identifiers associated with a detected lane marking, and receiving a second communication from a second host vehicle including additional location identifiers associated with the detected lane marking. For example, the second communication may be received from a following vehicle traveling on the same road segment or from the same vehicle on a subsequent trip along the same road segment. Process 2600A may further include refining a determination of at least one position associated with the detected lane marking based on the location identifiers received in the first communication and based on the additional location identifiers received in the second communication.This may involve taking an average of the multiple location identifiers and / or filtering out “ghost” identifiers that may not reflect the real position of the lane marking.

[0287] Fig. 26B is a flowchart illustrating an example process 2600B for autonomously navigating a host vehicle along a road segment using mapped lane markings. Process 2600B may be performed, for example, by processing unit 110 of autonomous vehicle 200. At step 2620, process 2600B may include receiving a road navigation model of the autonomous vehicle from a server-based system. In some embodiments, the road navigation model of the autonomous vehicle may include a target trajectory for the host vehicle along the road segment and location identifiers associated with one or more lane markings associated with the road segment. For example, vehicle 200 may receive a sparse map 800 or other road navigation model developed using process 2600A.In some embodiments, the target trajectory may be represented as a three-dimensional spline, such as in . Fig. 9B. As described above with respect to the Fig. 24A-F, the location identifiers may include locations in real-world coordinates of points associated with the lane marking (e.g., vertices of a dashed lane marking, edge points of a solid lane marking, a vertex between two intersecting lane markings and other points associated with the intersecting lane markings, a centerline associated with the lane marking, etc.).

[0288] At step 2621, process 2600B may include receiving at least one image representing an environment of the vehicle. The image may be received from an image capture device of the vehicle, such as image capture devices 122 and 124 included in image capture unit 120. The image may include an image of one or more lane markings, similar to image 2500 described above.

[0289] At step 2622, process 2600B may include determining a longitudinal position of the host vehicle along the target trajectory. As described above with respect to Fig. 25A, this may be based on other information in the captured image (e.g., landmarks, etc.) or by dead reckoning of the vehicle between detected landmarks.

[0290] At step 2623, process 2600B may include determining an expected lateral distance to the lane marker based on the determined longitudinal position of the host vehicle along the target trajectory and based on the two or more location identifiers associated with the at least one lane marker. For example, vehicle 200 may use sparse map 800 to determine an expected lateral distance to the lane marker. As in Fig. 25B, the longitudinal position 2520 along a target trajectory 2555 may be determined in step 2622. Using the surrogate map 800, the vehicle 200 may determine an expected distance 2540 to the mapped lane marking 2550 corresponding to the longitudinal position 2520.

[0291] At step 2624, process 2600B may include analyzing the at least one image to identify the at least one lane marking. For example, vehicle 200 may use various image recognition techniques or algorithms to identify the lane marking within the image, as described above. For example, lane marking 2510 may be detected by image analysis of image 2500, as shown in Fig. 25A shown.

[0292] At step 2625, process 2600B may include determining an actual lateral distance to the at least one lane marking based on the analysis of the at least one image. For example, the vehicle may determine a distance 2530, as shown in Fig. 25A, which represents the actual distance between the vehicle and the lane marking 2510. The camera angle, the speed of the vehicle, the width of the vehicle, the position of the camera relative to the vehicle, or various other factors may be taken into account when determining the distance 2530.

[0293] At step 2626, process 2600B may include determining an autonomous steering action for the host vehicle based on a difference between the expected lateral distance to the at least one lane marking and the determined actual lateral distance to the at least one lane marking. For example, as described above with respect to Fig. 25B, the vehicle 200 may compare the actual distance 2530 with an expected distance 2540. The difference between the actual and expected distances may indicate an error (and its magnitude) between the actual position of the vehicle and the target trajectory to be followed by the vehicle. Accordingly, the vehicle may determine an autonomous steering action or other autonomous action based on the difference. For example, if the actual distance 2530 is less than the expected distance 2540, as in Fig. As shown in Figure 25B, the vehicle may determine an autonomous steering action to steer the vehicle to the left, away from lane marking 2510. Thus, the vehicle's position relative to the target trajectory may be corrected. Process 2600B may be used, for example, to improve the vehicle's navigation between landmarks.

[0294] Processes 2600A and 2600B provide only examples of techniques that may be used to navigate a host vehicle using the disclosed sparse maps. In other examples, the techniques may also be used with respect to the Fig. 25C and Fig. Consistent processes described in 25D are used. Augmented Reality with Multiple Views

[0295] As described herein, navigation systems, including those for autonomous or semi-autonomous vehicles, may navigate along a road segment based on images and other data acquired from a surrounding area of the vehicle by various sensors. In some cases, when an autonomous or semi-autonomous vehicle is navigating, it may be beneficial to augment images or videos acquired from a surrounding area of the vehicle to include one or more augmented reality objects. For example, augmenting acquired images or videos may help vehicle users identify or otherwise perceive objects that are obscured by other objects (e.g., a pedestrian obscured by another vehicle). As another example, augmenting acquired images or videos may also provide vehicle users with information (e.g., location information, a description, etc.)) regarding a specific object in the vehicle's environment that might otherwise be unavailable. Other applications of augmented reality in autonomous or semi-autonomous vehicles may include providing advertising information to the vehicle's users. Furthermore, augmented images or videos captured during navigation may be useful as training data for various networks, such as neural networks. For example, captured images or videos may be augmented with computer-generated objects that are unusual (e.g., a bear in the middle of a road) to train one or more systems to detect these objects during navigation.

[0296] The disclosed systems and methods may use one or more images or videos captured by an autonomous or semi-autonomous vehicle. The systems and methods may then generate a segmentation mask to segment the image into a plurality of image portions. For example, the segmentation mask may be used to separate a road depicted in an image from a representation of the sky in the image. Additionally or alternatively, segmenting the image may include painting, shading, coloring, or highlighting certain objects or portions of an image in one way and painting, shading, coloring, or highlighting other objects or portions of the image in a different color. The systems and methods may further use a LIDAR sensor to collect data for a point cloud and highlight a relevant object or image portion in the LIDAR point cloud.For example, in some embodiments, a relatively high-mounted LIDAR sensor (e.g., on the roof of a vehicle) may have a field of view that includes objects or locations typically not visible to passengers. The disclosed systems and methods may further render an object (e.g., an augmented reality object) and project the rendered augmented reality object into the image or video. In some embodiments, if a portion of an object is occluded by the augmented reality object (e.g., if the augmented reality object is a gate), the disclosed systems and methods may remove the data for at least a portion of an object (e.g., a truck) that is occluded by the augmented reality object.

[0297] As discussed above, the disclosed techniques may include capturing one or more images from the surroundings of a vehicle, which may be augmented with additional information. Fig. 27A illustrates an example image 2700 depicting an environment of a host vehicle in accordance with the disclosed embodiments. The image 2700 may be captured by a camera of a host vehicle, such as image capture devices 122, 124, and / or 126. In the Fig. 27, the image may be captured by a forward-facing camera of the host vehicle while the vehicle is traveling on a road segment. The image 2700 may include representations of various objects within the environment of the host vehicle. For example, as shown in Fig. 27, the image 2700 may include a representation of a vehicle 2710, which may be referred to as a “target vehicle,” traveling on a road segment. Fig. 27 may also include representations of a road 2720 along which the host vehicle and the vehicle 2710 are traveling, a pedestrian 2730, a traffic light 2740, and various other objects as described herein. As in Fig. As shown in Figure 27, various objects may be at least partially obscured. For example, pedestrian 2730 may be partially obscured by vehicle 2710. Furthermore, some of the lights of traffic signal 2740 may also be obscured.

[0298] The disclosed techniques may include segmenting the image 2700 to identify one or more regions of the image. Fig. 27B illustrates an example of a segmented image 2702 that may be generated based on image 2700, in accordance with the disclosed embodiments. In this example, image 2700 may be segmented into a region 2750 representing vehicle 2710, a region 2752 representing road 2720, a region 2754 representing pedestrian 2730, and a region 2756 representing traffic light 2740. As used herein, image segmentation may refer to a process of dividing an image into different segments or regions based on features represented in the image. Image segmentation may involve assigning a label or value to different pixels within the image so that pixels that share the same (or similar) labels or values may represent a common feature in the image. Various techniques for segmenting image 2700 may be implemented.For example, image segmentation can be performed using various thresholding techniques, clustering techniques, compression-based methods, histogram-based methods, edge detection methods, or any other computer vision techniques or combinations thereof.

[0299] In some embodiments, image segmentation may be performed using a trained model. For example, a neural network may be trained to classify pixels into one or more regions of the image. The neural network may be trained, for example, by inputting a set of training images along with labels indicating regions of the images (i.e., at a pixel-by-pixel level) into a training algorithm. As a result, a trained model (or trained system) may be configured to receive an input image and segment pixels of the input image into regions. In some embodiments, a trained system may include multiple trained models. For example, a first model may be applied to extract simple features, such as edges, from the image, and an additional model may then combine these features to label regions of the image.Various other machine learning algorithms may be used, including a pulse-coupled neural network (PCNN), a U-Net convolutional neural network, logistic regression, linear regression, regression, a random forest, a K-Nearest Neighbor (KNN) model (for example, as described above), a K-Means model, a decision tree, a Cox proportional hazards regression model, a Naive Bayes model, a Support Vector Machine (SVM) model, a gradient boosting algorithm, or any other form of machine learning model or algorithm. Although various domains are shown as examples, the present disclosure is not limited to any particular type or form of domains.

[0300] The disclosed techniques may include acquiring a point cloud generated based on an output of a lidar and determining a location for an augmented reality object relative to the at least one image based on the segmented image and the point cloud information. The disclosed techniques may further include selecting or generating an augmented reality object and augmenting one or more images to include a representation of the augmented reality object.

[0301] Fig. 28 illustrates example techniques for positioning an augmented reality object within an environment, in accordance with the disclosed embodiments. As described above, the image 2700 may be segmented into multiple regions, including a region 2752 corresponding to a surface of a road 2720 depicted in the image. For example, the image 2700 may be captured using a camera 2812 onboard the host vehicle 2810, as shown. In accordance with the disclosed embodiments, the host vehicle 2810 may correspond to the vehicle 200 discussed above. Accordingly, any of the features or embodiments described herein with respect to the vehicle 200 may equally apply to the host vehicle 2810. The camera 2812 may therefore correspond to one or more of the image capture devices 122, 124, and 126 described above.Furthermore, the host vehicle 2810 may include at least one processing device, which may correspond to the processing unit 110. The host vehicle 2810 may include various other sensors as described herein.

[0302] In accordance with the disclosed embodiments, various additional data representing the environment of the host vehicle 2810 may be collected. For example, the host vehicle 2810 may be equipped with one or more LIDAR sensors for collecting three-dimensional data from the environment. This three-dimensional data may be represented as (or used to generate) a point cloud representing the position of various surfaces in a three-dimensional space. For example, the point cloud may include points 2820A representing a surface of the vehicle 2710, points 2820B representing a surface of the road 2720, points 2820C representing a surface of the pedestrian 2730, and points 2820D representing a surface of the traffic light 2740.The various points can be analyzed along with the segmented image to determine a location for an augmented reality object relative to an image. For example, image 2700 can be analyzed to determine a position in the image for the augmented reality object. In the example shown in . Fig. 28, an augmented reality object representing pedestrian 2730 may be generated. Image 2700 may be analyzed to determine a location 2814 for the augmented reality object. Based on points 2820B that may be associated with region 2752, a location 2830 for the augmented reality object in three-dimensional space may be determined. In this example, where an object is partially occluded, points 2820C associated with region 2754 may also be analyzed. As another example, points 2820D that may be associated with region 2756 may be analyzed to determine a location 2832 for an augmented reality traffic signal object, as shown.

[0303] In accordance with the disclosed embodiments, an augmented reality object may then be rendered at the determined location in three-dimensional space. For example, an augmented reality representation of pedestrian 2730 may be rendered at location 2830. Similarly, an augmented reality representation of a traffic signal may be rendered at location 2830. In some embodiments, the augmented reality object may be generated based on the analysis of image 2700. For example, generating the augmented reality object may include determining a set of vertices representing the augmented reality object in three-dimensional space, generating a surface based on the vertices, generating texture, color, shading, reflectivity, luminescence, absorption, or various other visual properties for the object.Alternatively or additionally, part or all of the augmented reality object may be selected from a library of object renderings. For example, the disclosed techniques may include accessing a database 2840 containing various three-dimensional object models and positioning them in a virtual three-dimensional environment. In this example, a traffic signal model 2844 may be selected and placed at location 2832.

[0304] In some embodiments, a scale and orientation for the augmented reality object may also be determined. For example, a scale of the Fig.29A based on images and / or LIDAR data captured by host vehicle 2810. Database 2840 may further include dimension or scaling data for the various models. For example, traffic signal model 2844 may be associated with a predetermined size that scales with the virtual three-dimensional environment. An orientation of the augmented reality object may be determined based on various contextual information. In this example, an orientation of traffic signal model 2844 may be determined based on the traffic signals associated with traffic light 2740 that appear in image 2700. Further, an orientation of a pedestrian augmented reality object may be determined based on the appearance of pedestrian 2730 in image 2700.

[0305] The system may then cause the at least one augmented image to be output to a display device. For example, the at least one augmented image may include a representation of the host vehicle's surroundings and the representation of the augmented reality object. Fig. 29A illustrates an example of an augmented image 2900 depicting an environment of a host vehicle, in accordance with the disclosed embodiments. The augmented image 2900 may be an augmented version of the image 2700, including representations of the augmented reality objects 2910 and 2920. In this example, the augmented image 2900 may include a representation 2910 of a traffic signal as it would appear if positioned at location 2832. Further, the augmented image 2900 may include a representation 2920 of a pedestrian as they would appear if positioned at location 2830. In some embodiments, depicting an augmented reality object may include overlaying the representation onto the image 2700.Accordingly, generating the augmented image may include removing data associated with a portion of the image that is occluded by the augmented reality object. In some embodiments, the representation of the augmented reality object may include a partial representation of the augmented reality object. For example, representation 2920 may include a representation of a portion of pedestrian 2730 that is occluded by vehicle 2710, as shown. Accordingly, representation 2920 may include only a portion of the augmented reality object included in region 2750. Alternatively, the representation may include a full representation of the partially occluded object.For example, although the pedestrian 2730 is partially visible, the augmented reality object 2900 may include a full representation of the pedestrian, which may replace the partial representation of the pedestrian 2730 in the image.

[0306] In some embodiments or examples, the augmented image 2900 may be generated to represent a realistic view of the environment as it would appear if the augmented reality object were included at the particular location, as discussed below. Fig. 30A and Fig. 30B. Alternatively or additionally, the augmented image 2900 may be generated to present information, regardless of whether it reflects a realistic appearance. For example, this may include the display of advertisements, traffic information (e.g., speed limits, route information, etc.), potential hazards, markings, status information, or the like. In the Fig. In the example shown in Figure 29A, representations 2910 of a traffic signal and representation 2920 of pedestrian 2730 may be displayed to present important information to an occupant of the vehicle. Accordingly, although these occupant objects may not otherwise be visible, they may be overlaid to present potentially important safety information. In some embodiments, whether an augmented reality object is displayed in front of objects that would otherwise obscure it may depend on a type of augmented reality object. For example, pedestrians, traffic signals, vehicles, or other objects may be considered important for safety reasons and may therefore be overlaid on obscuring objects.Other augmented reality objects, such as advertisements, entertainment or educational objects, or the like, may be considered less important and therefore may be displayed in a way that obscures them. Various other properties may be considered, such as a speed of the augmented reality object (e.g., fast-moving objects displayed in front of obscuring objects), a proximity of the augmented reality object (e.g., closer objects displayed in front of obscuring objects), a direction of movement of the augmented reality object (e.g., approaching objects displayed in front of obscuring objects), or the like.

[0307] In some embodiments, representations of occluded or partially occluded objects, such as representations 2910 and 2920, may be visually distinguished from the rest of image 2700 or other augmented reality objects. For example, representations 2910 and 2920 may be highlighted or outlined in a specific color (e.g., red), they may be surrounded by a border (e.g., a white border) to distinguish the objects from the surrounding image, they may be displayed in a flashing or blinking manner, or the like.

[0308] In accordance with the disclosed embodiments, the augmented image 2900 may be displayed on a visual display, such as a liquid crystal display (LCD) or other display device. In some embodiments, the display device may be a device within the host vehicle 2810. Accordingly, an occupant of the vehicle 2810 may view the augmented image 2900 while driving and / or riding in the host vehicle 2810. Alternatively or additionally, the display device may be located in a device external to the host vehicle 2810, such as another vehicle, a computing device, an augmented reality display device, or the like. In some embodiments, the display device may be a transparent display, such as a transparent OLED (TOLED).Accordingly, a user can view real objects in the environment through the display along with virtual representations of objects through the display. Fig. Figure 29B illustrates an example of a transparent display 2970 for displaying an augmented image, in accordance with the disclosed embodiments. In this example, the transparent display 2970 may be presented to the occupant of a vehicle, such as the vehicle 2810, as shown. In some embodiments, the transparent display 2970 may be positioned in front of at least a portion of a window, such as the windshield 2960. Accordingly, an occupant of the host vehicle 2810 may view a representation 2972 of a vehicle displayed on the transparent display 2970 as if it were present in the environment in front of the vehicle. In some embodiments, various sensors may be used to position the representation so that it appears in an appropriate location.For example, the host vehicle 2810 may include a camera to determine the position of an occupant's eye, which may be used to determine where the representation 2972 should be displayed within the transparent display 2970.

[0309] In accordance with the disclosed embodiments, various forms of transparent displays may be implemented. For example, as noted above, the transparent display 2970 may be a transparent screen, such as a transparent OLED. As another example, the transparent display 2970 may include a projection device for presenting a display 2972 to the occupant. For example, the host vehicle 2810 may include a projection device 2950 that can transmit light reflected from the windshield 2960 to an occupant's eye. In some embodiments, the windshield 2960 may include a coating or other treatment to enhance reflective properties for use with the projection device 2950.Although the windshield 2960 is shown as an example, the transparent display 2970 may also be included on one or more other windows of the host vehicle 2810, such as a side window, a rear window, a sunroof window, or the like.

[0310] The disclosed embodiments can be implemented in a wide variety of applications. As an example, while navigating a foliage-lined road, one or more animals can be projected onto the transparent display to simulate a zoo- or park-like environment. Furthermore, the one or more rendered augmented reality objects (e.g., the animals in this example) can be further animated to move so that they do not appear as static objects. The scene can be further enhanced by projecting text labels or descriptions near or adjacent to the augmented reality objects (e.g., the labels can identify the animals and / or provide information about the animals). Furthermore, the disclosed embodiments can depict fantastical animals (e.g., fictional animals such as dragons or unicorns) or extinct animals (e.g.,Dinosaurs) to create an entertainment and / or educational experience.

[0311] Fig. 30A illustrates another example of an augmented image 3000 dep...

Claims

[1] A system for a host vehicle, the system comprising: at least one processor comprising a circuit and a memory, the memory including instructions that, when executed by the circuit, cause the at least one processor to: Receiving at least one image captured by a camera of the host vehicle from an environment of the host vehicle; Segmenting the at least one image, wherein segmenting the image includes identifying a first portion of the at least one image and a second portion of the at least one image, wherein the first portion is different from the second portion; Receiving a point cloud generated based on an output of a LIDAR; Determining a location for an augmented reality object relative to the at least one image based on the first portion of the segmented image and at least one portion of the point cloud; Selecting or creating an augmented reality object; and Augmenting the at least one image to include a representation of the augmented reality object, wherein the at least one augmented image includes a representation of the environment of the host vehicle and the representation of the augmented reality object. [2] The system of claim 1, wherein the first portion of the at least one image includes a representation of an object and the second portion of the at least one image includes a representation of a road surface. [3] The system of claim 2, wherein the object includes a target vehicle. [4] The system of claim 3, wherein the target vehicle is at least partially obscured by another object. [5] The system of claim 2, wherein the object includes a pedestrian. [6] The system of claim 5, wherein the pedestrian is at least partially obscured by another object. [7] The system of claim 1, wherein the at least one image includes a video stream. [8] The system of claim 1, wherein the memory includes instructions that, when executed by the circuitry, cause the at least one processor to render the representation of the augmented reality object. [9] The system of claim 8, wherein the memory includes instructions that, when executed by the circuit, cause the at least one processor to select the representation of the augmented reality object from an image library. [10] The system of claim 1, wherein the memory includes instructions that, when executed by the circuitry, cause the at least one processor to remove data associated with a portion of the at least one image that is obscured by the augmented reality object. [11] The system of claim 1, wherein the memory includes instructions that, when executed by the circuit, cause the at least one processor to input the at least one augmented image to a trained system. [12] The system of claim 11, wherein the trained system includes one or more neural networks. [13] The system of claim 12, wherein the trained system includes one or more machine learning models. [14] The system of claim 1, wherein the memory includes instructions that, when executed by the circuit, cause the at least one processor to output the at least one enhanced image to a display device. [15] The system of claim 14, wherein the display device is a transparent display. [16] The system of claim 15, wherein the transparent display is a transparent organic light-emitting diode display. [17] The system of claim 15, wherein the transparent display is positioned in front of at least a portion of a window of the host vehicle. [18] The system of claim 17, wherein the window of the host vehicle is a front windshield. [19] The system of claim 17, wherein the window of the host vehicle is a side window of the host vehicle. [20] The system of claim 17, wherein the window of the host vehicle is a rear window of the host vehicle. [21] The system of claim 1, wherein the augmented reality object is a representation of a gate. [22] The system of claim 1, wherein the augmented reality object is a representation of a target vehicle. [23] The system of claim 22, wherein at least a portion of the target vehicle is obscured by another object in the vicinity of the host vehicle, and the augmented reality object appears in front of at least a portion of the object that obscures at least a portion of the target vehicle. [24] The system of claim 1, wherein the augmented reality object is a representation of an animal. [25] The system of claim 1, wherein the augmented reality object is a representation of an advertisement. [26] The system of claim 1, wherein the augmented reality object is a representation of a pedestrian. [27] The system of claim 26, wherein at least a portion of the pedestrian is obscured by a target object in the environment of the host vehicle and the augmented reality object appears in front of at least a portion of the target object that obscures at least a portion of the pedestrian. [28] The system of claim 1, wherein the augmented reality object is a representation of a traffic light. [29] The system of claim 28, wherein at least a portion of the traffic light is obscured by a target object in the environment of the host vehicle and the augmented reality object appears in front of at least a portion of the target object that obscures at least a portion of the traffic light. [30] The system of claim 1, wherein the at least one augmented image includes at least one augmented text description associated with the augmented reality object. [31] The system of claim 30, wherein the at least one augmented text description includes a label for the augmented reality object. [32] A non-transitory computer-readable medium storing program instructions executable by at least one processor to perform a method for generating enhanced image data for vehicle navigation, the method comprising: Receiving at least one image captured by a camera of the host vehicle from an environment of a host vehicle; Segmenting the at least one image, wherein segmenting the image includes identifying a first portion of the at least one image and a second portion of the at least one image, wherein the first portion is different from the second portion; Receiving a point cloud generated based on an output of a LIDAR; Determining a location for an augmented reality object relative to the at least one image based on the first portion of the segmented image and at least one portion of the point cloud; Selecting or creating an augmented reality object; and Augmenting the at least one image to include a representation of the augmented reality object, wherein the at least one augmented image includes a representation of the environment of the host vehicle and the representation of the augmented reality object. [33] The non-transitory computer-readable medium of claim 32, wherein the method further includes rendering the representation of the augmented reality object. [34] The non-transitory computer-readable medium of claim 33, wherein the method further includes selecting the representation of the augmented reality object from an image library. [35] The non-transitory computer-readable medium of claim 32, wherein the method further includes inputting the augmented at least one image to a trained system. [36] A computer-implemented method for generating enhanced image data for vehicle navigation, the method comprising: Receiving at least one image captured by a camera of a host vehicle from an environment of the host vehicle; Segmenting the at least one image, wherein segmenting the image includes identifying a first portion of the at least one image and a second portion of the at least one image, wherein the first portion is different from the second portion; Receiving a point cloud generated based on an output of a LIDAR; Determining a location for an augmented reality object relative to the at least one image based on the first portion of the segmented image and at least one portion of the point cloud; Selecting or creating an augmented reality object; and Augmenting the at least one image to include a representation of the augmented reality object, wherein the at least one augmented image includes a representation of the environment of the host vehicle and the representation of the augmented reality object. [37] The method of claim 36, further comprising removing data associated with a portion of the at least one image that is obscured by the augmented reality object. [38] The method of claim 36, wherein at least a portion of the augmented reality object is obscured by another object in the environment of the host vehicle and the augmented reality object appears in front of at least a portion of the object that obscures the at least one portion of the augmented reality object. [39] The method of claim 36, wherein the at least one augmented image includes at least one augmented text description associated with the augmented reality object.

Images