Systems, methods, and storage media for a vehicle

Abstract

The invention relates to systems, methods, and storage media for a vehicle. Additionally, the following techniques are described: receiving, from at least one sensor of the vehicle, sensor data associated with a surface along a path to be traveled by the vehicle; determining, using a surface classifier, a classification of the surface based on the sensor data; determining, based on the classification of the surface, a drivability property of the surface; planning, based on the drivability property of the surface, a behavior of the vehicle in a situation of driving in proximity to the surface or on the surface; and controlling the vehicle based on the planned behavior.

Description

[0001] This application is a divisional application of Chinese Patent Application No. 202110841873.5, filed on July 26, 2021, entitled "System, Method and Storage Medium for Vehicle". Technical Field

[0002] This application relates to decision-making and prediction guided by road conditions. Background Technology

[0003] The surfaces on which a vehicle travels can vary along its path. For example, the road surface along the vehicle's path can include asphalt, concrete, rock, etc. These surfaces can also change dynamically under different conditions such as weather conditions (e.g., rain, snow, sleet, etc.). Summary of the Invention

[0004] According to one aspect of the invention, a system for a vehicle includes: at least one sensor; at least one computer-readable medium storing computer-executable instructions; and at least one processor configured to communicate with the at least one sensor and execute the computer-executable instructions, the execution of which includes: receiving sensor data from the at least one sensor associated with a surface along a path to be traveled by the vehicle; determining a classification of the surface based on the sensor data using a surface classifier; determining a drivability attribute of the surface based on the surface classification; planning the behavior of the vehicle in the vicinity of or on the surface of the surface based on the drivability attribute of the surface; and controlling the vehicle based on the planned behavior.

[0005] According to another aspect of the invention, a method for a vehicle includes: receiving sensor data from at least one sensor of the vehicle associated with a surface along a path to be traveled by the vehicle; determining a classification of the surface based on the sensor data using a surface classifier; determining a drivability attribute of the surface based on the classification of the surface; planning the behavior of the vehicle in the vicinity of or on the surface when driving based on the drivability attribute of the surface; and controlling the vehicle based on the planned behavior.

[0006] According to another aspect of the invention, a non-transitory computer-readable storage medium includes at least one program for execution by at least one processor of a first device, the at least one program including instructions that, when executed by the at least one processor, cause the first device to perform the method described above. Attached Figure Description

[0007] Figure 1An example of an autonomous vehicle with autonomous capabilities is shown.

[0008] Figure 2 An example "cloud" computing environment is shown.

[0009] Figure 3 The computer system is shown.

[0010] Figure 4 An example architecture for an autonomous vehicle is shown.

[0011] Figure 5 Examples of inputs and outputs that the perception module can use are shown.

[0012] Figure 6 An example of a LiDAR system is shown.

[0013] Figure 7 The image shows a LiDAR system in operation.

[0014] Figure 8 Additional details on the operation of the LiDAR system are shown.

[0015] Figure 9 A block diagram showing the relationship between the inputs and outputs of the planning module.

[0016] Figure 10 This shows the directed graph used in path planning.

[0017] Figure 11 A block diagram showing the inputs and outputs of the control module is provided.

[0018] Figure 12 A block diagram showing the controller's inputs, outputs, and components is provided.

[0019] Figure 13A , Figure 13B and Figure 13C A block diagram of an example system for surface-guided decision making is shown.

[0020] Figure 14 A flowchart of an example method is shown. Detailed Implementation

[0021] In the following description, numerous specific details are set forth for purposes of explanation in order to provide a thorough understanding of the invention. However, it will be apparent that the invention may be practiced without these specific details. In other instances, well-known constructions and apparatuses are shown in block diagram form to avoid unnecessarily obscuring the invention.

[0022] In the accompanying drawings, for ease of description, a specific arrangement or order of schematic elements (such as those representing devices, modules, instruction blocks, and data elements) is shown. However, those skilled in the art will understand that the specific order or arrangement of the schematic elements in the drawings is not intended to imply a requirement for a particular processing order or sequence, or a separation of processing procedures. Furthermore, the inclusion of schematic elements in the drawings is not intended to imply that such elements are required in all embodiments, nor is it intended to imply that features represented by such elements cannot be included in some embodiments or cannot be combined with other elements in some embodiments.

[0023] Furthermore, in the accompanying drawings, connecting elements, such as solid or dashed lines or arrows, are used to illustrate connections, relationships, or associations between two or more other schematic elements. The absence of any such connecting element does not imply that connections, relationships, or associations cannot exist. In other words, connections, relationships, or associations between some elements are not shown in the drawings so as not to obscure the content of this disclosure. Additionally, for ease of illustration, a single connecting element is used to represent multiple connections, relationships, or associations between elements. For example, if a connecting element represents communication of signals, data, or instructions, those skilled in the art will understand that such an element represents one or more signal paths (e.g., a bus) that may be necessary to influence the communication.

[0024] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. Numerous specific details are set forth in the following detailed description in order to provide a thorough understanding of the various embodiments described. However, it will be apparent to those skilled in the art that the various embodiments described can be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0025] The features described below can each be used independently of each other or in any combination with other features. However, any individual feature may not solve any of the problems discussed above, or may only solve one of the problems discussed above. Some of the problems discussed above may not be adequately solved by any of the features described herein. Although headings are provided, information relating to specific headings but not found in the sections bearing those headings can be found elsewhere in this specification. Embodiments are described herein based on the following summary:

[0026] 1. General Overview

[0027] 2. System Overview

[0028] 3. Autonomous Vehicle Architecture

[0029] 4. Autonomous Vehicle Input

[0030] 5. Autonomous Vehicle Planning

[0031] 6. Autonomous Vehicle Control

[0032] 7. Surface-guided decision making

[0033] General Overview

[0034] Vehicle behavior is adaptively adjusted based on dynamically changing road surface and conditions that affect safety and drivability. For example, sensor measurements are used to identify and classify road surfaces. Based on the road surface category, the vehicle can determine the drivability attributes of that surface and make appropriate planning decisions. Additionally, based on the surface's drivability attributes, the vehicle can predict the behavior of other vehicles driving on that surface and proactively adjust its behavior accordingly. In this way, the vehicle can exhibit behavior similar to that of a human driver under hazardous conditions, such as following existing tracks on the road, avoiding icy areas, reducing speed, veer within the lane, or changing lanes to avoid obstacles on the road during snow or rain.

[0035] Adapting vehicle behavior to dynamically changing road conditions and road surfaces improves vehicle safety and reliability, especially when driving in hazardous environments. Furthermore, recognizing that the behavior of other vehicles changes based on these dynamics improves the accuracy of predicting their behavior. This, in turn, reduces the likelihood of collisions and enhances vehicle reliability and safety.

[0036] System Overview

[0037] Figure 1 An example of an autonomous vehicle 100 with autonomous capabilities is shown.

[0038] As used herein, the term “autonomy” refers to a function, feature, or facility that enables a vehicle to operate partially or fully without real-time human intervention, including but not limited to fully autonomous vehicles, highly autonomous vehicles, and conditionally autonomous vehicles.

[0039] As used in this article, an autonomous vehicle (AV) is a vehicle with autonomous capabilities.

[0040] As used in this article, "vehicle" includes any mode of transport for goods or people. Examples include cars, buses, trains, airplanes, drones, trucks, ships, vessels, submersibles, and spacecraft. Driverless cars are an example of vehicles.

[0041] As used herein, a “track” refers to a path or route that navigates an AV from a first spatiotemporal location to a second spatiotemporal location. In embodiments, the first spatiotemporal location is referred to as the initial location or starting point, and the second spatiotemporal location is referred to as the destination, final location, target, target location, or target position. In some examples, a track consists of one or more segments (e.g., segments of a road), and each segment consists of one or more blocks (e.g., a lane or part of an intersection). In embodiments, spatiotemporal locations correspond to real-world locations. For example, a spatiotemporal location is a pick-up or drop-off point for people or goods to board or alight.

[0042] As used herein, “(one or more) sensors” includes one or more hardware components for detecting information relating to the environment surrounding the sensor. Some hardware components may include sensing components (e.g., image sensors, biometric sensors), transmission and / or receiving components (e.g., laser or radio frequency wave transmitters and receivers), electronic components (such as analog-to-digital converters), data storage devices (such as RAM and / or non-volatile memory), software or firmware components, and data processing components (such as application-specific integrated circuits), microprocessors, and / or microcontrollers.

[0043] As used herein, a “scene description” is a data structure (e.g., a list) or data stream that includes one or more classified or labeled objects detected by one or more sensors on an AV vehicle, or one or more classified or labeled objects provided by a source outside the AV.

[0044] As used in this article, a "road" is a physical area that can be traversed by vehicles and can correspond to a named passageway (e.g., a city street, an interstate highway, etc.) or an unnamed passageway (e.g., a driveway within a house or office building, a section of a parking lot, a section of an vacant parking lot, a waste disposal area in a rural area, etc.). Because some vehicles (e.g., four-wheel drive pickup trucks, SUVs, etc.) can traverse a variety of physical areas that are not particularly suitable for vehicle travel, a "road" can be any physical area that is not formally defined as a passageway by any municipality or other government or administrative agency.

[0045] As used herein, a “lane” is the portion of a road that can be traversed by vehicles. Sometimes lanes are identified based on lane markings. For example, a lane may correspond to most or all of the space between lane markings, or only a portion of the space between lane markings (e.g., less than 50%). For instance, a road with widely spaced lane markings may accommodate two or more vehicles, allowing one vehicle to overtake another without crossing the lane markings; therefore, this could be interpreted as a lane being narrower than the space between lane markings, or as having two lanes. Lanes can also be interpreted in the absence of lane markings. For example, a lane may be defined based on the physical characteristics of the environment (e.g., rocks and trees along a main road in a rural area, or natural obstacles that should be avoided, for example, in underdeveloped areas). Lanes can also be interpreted independently of lane markings or physical characteristics. For example, a lane may be interpreted based on any unobstructed path in an area that would otherwise lack features that would be interpreted as lane boundaries. In the example scenario, an AV could interpret a lane as a lane traversing an unobstructed portion of a field or open space. In another example scenario, an AV can interpret lanes that pass through a wide road (e.g., wide enough for two or more lanes) without lane markings. In this scenario, an AV can communicate lane-related information to other AVs, allowing them to coordinate route planning using the same lane information.

[0046] The term “over-the-air (OTA) client” includes any AV, or any electronic device embedded in, coupled to, or communicating with an AV (e.g., computer, controller, IoT device, electronic control unit (ECU)).

[0047] The term “over-the-air (OTA) update” means any update, alteration, deletion, or addition to software, firmware, data, or configuration settings, or any combination thereof, delivered to an OTA client using proprietary and / or standardized wireless communication technologies, including but not limited to: cellular mobile communications (e.g., 2G, 3G, 4G, 5G), radio local area networks (e.g., WiFi), and / or satellite Internet.

[0048] The term "edge node" refers to one or more edge devices coupled to a network that provide a portal for communicating with AV and can communicate with other edge nodes and cloud-based computing platforms to schedule OTA updates and deliver OTA updates to OTA clients.

[0049] The term "edge device" refers to a device that implements an edge node and provides a physical wireless access point (AP) to the core network of an enterprise or service provider (such as Verizon or AT&T). Examples of edge devices include, but are not limited to: computers, controllers, transmitters, routers, routing switches, integrated access devices (IADs), multiplexers, metropolitan area network (MAN) and wide area network (WAN) access devices.

[0050] "One or more" includes functions performed by a single element, functions performed by multiple elements, such as in a distributed manner, several functions performed by a single element, several functions performed by several elements, or any combination of the foregoing.

[0051] It will also be understood that, although in some cases the terms “first,” “second,” etc., are used herein to describe various elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, without departing from the scope of the various described embodiments, a first contact may be referred to as a second contact, and similarly, a second contact may be referred to as a first contact. Both the first contact and the second contact are contacts, but they are not the same contact.

[0052] The terminology used in the description of the various embodiments described herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various embodiments described and the appended claims, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It will also be understood that “and / or” as used herein refers to and includes any and all possible combinations of one or more of the relevant list items. It will also be understood that when the terms “comprising,” “including,” “possessing,” and / or “having” are used in this specification, they specifically indicate the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0053] As used herein, depending on the context, the term "if" may optionally be understood as meaning "when" or "at that time" or "in response to being determined" or "in response to being detected." Similarly, depending on the context, the phrase "if determined" or "if [the stated condition or event] has been detected" may optionally be understood as meaning "when determined" or "in response to being determined" or "when [the stated condition or event] is detected" or "in response to being detected."

[0054] As used herein, an AV system refers to an AV and an array of hardware, software, stored data, and real-time generated data that support AV operation. In embodiments, the AV system is incorporated within an AV. In embodiments, the AV system is distributed across several locations. For example, some of the software of the AV system is similar to that described below. Figure 2 The cloud computing environment described is implemented on the cloud computing environment 200.

[0055] Generally, this document describes techniques applicable to any vehicle with one or more autonomous capabilities, including fully autonomous vehicles, highly autonomous vehicles, and conditionally autonomous vehicles, such as so-called Level 5, Level 4, and Level 3 vehicles, respectively (see SAE International Standard J3016: Classification and Definition of Terms Related to Automated Driving Systems for Motor Vehicles on Roads, the entire contents of which are incorporated herein by reference for further details on vehicle autonomy levels). The techniques described in this document are also applicable to partially autonomous vehicles and driver-assisted vehicles, such as so-called Level 2 and Level 1 vehicles (see SAE International Standard J3016: Classification and Definition of Terms Related to Automated Driving Systems for Motor Vehicles on Roads). In embodiments, one or more Level 1, Level 2, Level 3, Level 4, and Level 5 vehicle systems may automatically perform certain vehicle operations (e.g., steering, braking, and map usage) under certain operating conditions based on the processing of sensor inputs. The techniques described in this document can benefit vehicles of any level, ranging from fully autonomous vehicles to human-operated vehicles.

[0056] Autonomous vehicles offer advantages over those requiring human drivers. One such advantage is safety. For example, in 2016, the U.S. experienced 6 million car accidents, 2.4 million injuries, 40,000 deaths, and 13 million vehicle collisions, with an estimated social cost of over $910 billion. From 1965 to 2015, the number of traffic fatalities per 100 million miles driven in the U.S. decreased from approximately 6 to approximately 1, partly due to additional safety features deployed in vehicles. For example, an extra half-second of warning associated with an impending collision is believed to mitigate 60% of front and rear collisions. However, passive safety features (such as seat belts and airbags) may have reached their limits in improving these figures. Therefore, active safety measures, such as automated vehicle controls, are a likely next step in improving these statistics. Since human drivers are considered to be responsible for serious pre-collision events in 95% of collisions, autonomous driving systems could potentially achieve better safety outcomes by: identifying and avoiding emergencies more reliably than humans; making better decisions, obeying traffic regulations better than humans, and predicting future events better than humans; and controlling vehicles more reliably than humans.

[0057] refer to Figure 1 The AV system 120 enables the vehicle 100 to operate along a trajectory 198, traversing the environment 190 to the destination 199 (sometimes referred to as the final location), while avoiding objects (e.g., natural obstacles 191, vehicles 193, pedestrians 192, cyclists and other obstacles) and complying with road rules (e.g., operating rules or driving preferences).

[0058] In an embodiment, the AV system 120 includes means 101 for receiving and operating operation commands from and on a computer processor 146. The term "operation command" is used to refer to executable instructions (or a set of instructions) that cause a vehicle to perform actions (e.g., driving maneuvers). Operation commands may, without limitation, include instructions for causing the vehicle to begin moving forward, stop moving forward, begin moving backward, stop moving backward, accelerate, decelerate, make a left turn, and make a right turn. In an embodiment, the computer processor 146 is referenced below. Figure 3 The processor 304 described is similar. Examples of the device 101 include a steering controller 102, a brake 103, a gear, an accelerator pedal or other acceleration control mechanism, a windshield wiper, a side door lock, a window controller, and a turn indicator.

[0059] In an embodiment, the AV system 120 includes sensors 121 for measuring or inferring attributes of the state or condition of the vehicle 100, such as the AV's position, linear velocity and angular velocity, linear acceleration and angular acceleration, and heading (e.g., the direction of the front end of the vehicle 100). Examples of sensors 121 are GPS, inertial measurement units (IMUs) that measure both linear acceleration and angular rate of the vehicle, wheel rate sensors for measuring or estimating wheel slip ratio, wheel braking pressure or braking torque sensors, engine torque or wheel torque sensors, and steering angle and angular rate sensors.

[0060] In an embodiment, sensor 121 also includes sensors for sensing or measuring properties of the AV's environment. Examples include a monocular or stereo camera 122 with visible, infrared, or thermal (or both) spectra, a LiDAR 123, a RADAR, an ultrasonic sensor, a time-of-flight (TOF) depth sensor, a rate sensor, a temperature sensor, a humidity sensor, and a precipitation sensor.

[0061] In one embodiment, the AV system 120 includes a data storage unit 142 and a memory 144 for storing machine instructions associated with a computer processor 146 or data collected by the sensor 121. In another embodiment, the data storage unit 142 is associated with the following... Figure 3The described ROM 308 or storage device 310 is similar. In this embodiment, memory 144 is similar to main memory 306 described below. In this embodiment, data storage unit 142 and memory 144 store historical, real-time, and / or predictive information about environment 190. In this embodiment, the stored information includes maps, driving performance, traffic congestion updates, or weather conditions. In this embodiment, data related to environment 190 is transmitted from remote database 134 to vehicle 100 via a communication channel.

[0062] In an embodiment, the AV system 120 includes communication devices 140 for transmitting measured or inferred attributes of the state and conditions of other vehicles, such as position, linear velocity and angular velocity, linear acceleration and angular acceleration, and linear heading and angular heading, to vehicle 100. These devices include vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication devices, as well as devices for wireless communication via point-to-point or ad hoc networks, or both. In an embodiment, communication device 140 communicates across the electromagnetic spectrum (including radio and optical communications) or other media (e.g., air and acoustic media). The combination of vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) communication (and in some embodiments, one or more other types of communication) is sometimes referred to as vehicle-to-all-things (V2X) communication. V2X communication typically conforms to one or more communication standards for communication with and between autonomous vehicles.

[0063] In an embodiment, the communication device 140 includes a communication interface. For example, this may be a wired, wireless, WiMAX, Wi-Fi, Bluetooth, satellite, cellular, optical, near-field, infrared, or radio interface. The communication interface transmits data from a remote database 134 to the AV system 120. In an embodiment, the remote database 134 is embedded in, for example... Figure 2 In the cloud computing environment 200 described herein, communication device 140 transmits data collected from sensor 121 or other data related to the operation of vehicle 100 to remote database 134. In some embodiments, communication device 140 transmits information related to teleoperation to vehicle 100. In some embodiments, vehicle 100 communicates with other remote (e.g., "cloud") servers 136.

[0064] In this embodiment, the remote database 134 also stores and transmits digital data (e.g., data such as road and street locations). This data is stored in memory 144 on the vehicle 100 or transmitted from the remote database 134 to the vehicle 100 via a communication channel.

[0065] In one embodiment, the remote database 134 stores and transmits historical information (e.g., rate and acceleration distribution) related to driving attributes of vehicles that previously traveled along trajectory 198 at similar times of day. In one implementation, such data can be stored in memory 144 on vehicle 100 or transmitted from the remote database 134 to vehicle 100 via a communication channel.

[0066] The computer processor 146 located on the vehicle 100 generates control actions in an algorithmic manner based on both real-time sensor data and prior information, allowing the AV system 120 to perform its autonomous driving capabilities.

[0067] In one embodiment, the AV system 120 includes a computer peripheral device 132 coupled to a computer processor 146 for providing information and alerts to a user of the vehicle 100 (e.g., a passenger or a remote user) and receiving input from that user. In another embodiment, the peripheral device 132 is similar to the one described in the following reference. Figure 3 The discussed display 312, input device 314, and cursor controller 316 are coupled wirelessly or wiredly. Any two or more interface devices can be integrated into a single device.

[0068] In one embodiment, the AV system 120 receives and enforces a privacy level for an occupant, such as one specified by the occupant or stored in a profile associated with the occupant. The occupant's privacy level determines how access is permitted to specific occupant-related information (e.g., occupant comfort data, biometric data, etc.) stored in the occupant profile and / or stored on cloud server 136 and associated with the occupant profile. In one embodiment, the privacy level specifies specific occupant-related information that is deleted once the ride is complete. In another embodiment, the privacy level specifies specific occupant-related information and identifies one or more entities authorized to access that information. Examples of the specified entities authorized to access the information may include other AV systems, third-party AV systems, or any entity that could potentially access the information.

[0069] An occupant's privacy level can be specified at one or more granular levels. In one embodiment, the privacy level identifies specific information to be stored or shared. In another embodiment, the privacy level applies to all information associated with the occupant, allowing the occupant to specify that her personal information should not be stored or shared. The designation of entities authorized to access specific information can also be specified at various granular levels. The various sets of entities authorized to access specific information may include, for example, other AVs, cloud server 136, specific third-party AV systems, etc.

[0070] In an embodiment, AV system 120 or cloud server 136 determines whether AV 100 or another entity can access certain information associated with an occupant. For example, a third-party AV system attempting to access occupant input related to a specific time and place must, for example, obtain authorization from AV system 120 or cloud server 136 to access occupant-related information. For example, AV system 120 uses a specified privacy level for the occupant to determine whether location- and time-related occupant input can be presented to a third-party AV system, AV 100, or another AV. This allows the occupant's privacy level to specify which other entities are allowed to receive data related to the occupant's actions or other data associated with the occupant.

[0071] Figure 2 This illustrates an example "cloud" computing environment. Cloud computing is a service delivery model that enables convenient, on-demand access over a network to a shared pool of configurable computing resources, such as networks, network bandwidth, servers, processing power, memory, storage, applications, virtual machines, and services. In a typical cloud computing system, one or more large cloud data centers house the machines used to deliver the services provided by the cloud. Now refer to... Figure 2 The cloud computing environment 200 includes cloud data centers 204a, 204b, and 204c interconnected via cloud 202. Data centers 204a, 204b, and 204c provide cloud computing services to computer systems 206a, 206b, 206c, 206d, 206e, and 206f connected to cloud 202.

[0072] A cloud computing environment 200 includes one or more cloud data centers. Generally, a cloud data center (e.g.) Figure 2 The cloud data center 204a shown refers to the cloud (e.g., Figure 2 The physical arrangement of servers in cloud 202 (or a specific portion of the cloud) is illustrated. For example, servers are physically arranged in rooms, groups, rows, and racks within a cloud data center. A cloud data center has one or more regions, each containing one or more server rooms. Each room has one or more rows of servers, and each row includes one or more racks. Each rack includes one or more individual server nodes. In some implementations, servers in regions, rooms, racks, and / or rows are arranged into groups based on the physical infrastructure requirements of the data center facility, including power, energy, heat, heat sources, and / or other requirements. In this embodiment, server nodes are similar to... Figure 3 The computer system described herein. Data center 204a has many computing systems distributed across multiple racks.

[0073] Cloud 202 includes cloud data centers 204a, 204b, and 204c, and networks and network resources (e.g., network devices, nodes, routers, switches, and network cables) for connecting cloud data centers 204a, 204b, and 204c and facilitating access to cloud computing services by computing systems 206a-f. In embodiments, the network represents one or more local area networks, wide area networks, or any combination of wired or wireless networks coupled using terrestrial or satellite connections. Data exchanged over the network is transmitted using various network layer protocols, such as Internet Protocol (IP), Multiprotocol Label Switching (MPLS), Asynchronous Transfer Mode (ATM), Frame Relay, etc. Furthermore, in embodiments where the network represents a combination of multiple subnetworks, different network layer protocols are used on each underlying subnetwork. In some embodiments, the network represents one or more interconnected internetworks (such as the public Internet).

[0074] The computing system 206a-f or cloud computing service consumer connects to the cloud 202 via a network link and a network adapter. In embodiments, the computing system 206a-f is implemented as various computing devices, such as servers, desktops, laptops, tablets, smartphones, Internet of Things (IoT) devices, autonomous vehicles (including cars, drones, space shuttles, trains, buses, etc.), and consumer electronics. In embodiments, the computing system 206a-f is implemented in other systems or as part of other systems.

[0075] Figure 3 A computer system 300 is illustrated. In an implementation, the computer system 300 is a dedicated computing device. The dedicated computing device is hardwired to perform these technologies, or includes a digital electronic device such as one or more application-specific integrated circuits (ASICs) or field-programmable gate arrays (FPGAs) that is persistently programmed to perform the aforementioned technologies, or is capable of including one or more general-purpose hardware processors programmed to perform these technologies according to program instructions in firmware, memory, other memory, or a combination thereof. Such a dedicated computing device is also capable of combining custom hardwired logic, ASICs, or FPGAs with custom programming to accomplish these technologies. In various embodiments, the dedicated computing device is a desktop computer system, a portable computer system, a handheld device, a network device, or any other device that includes hardwired and / or program logic to implement these technologies.

[0076] In one embodiment, the computer system 300 includes a bus 302 or other communication mechanism for conveying information, and a processor 304 coupled to the bus 302 to process information. The processor 304 is, for example, a general-purpose microprocessor. The computer system 300 also includes a main memory 306, such as random access memory (RAM) or other dynamic storage device, coupled to the bus 302 to store information and instructions executed by the processor 304. In one implementation, the main memory 306 is used to store temporary variables or other intermediate information during the execution of instructions to be executed by the processor 304. When these instructions are stored in a non-transitory storage medium accessible to the processor 304, the computer system 300 becomes a dedicated machine customized to perform the operations specified in the instructions.

[0077] In an embodiment, the computer system 300 further includes a read-only memory (ROM) 308 or other static storage device coupled to the bus 302 for storing static information and instructions of the processor 304. A storage device 310, such as a disk, optical disk, solid-state drive, or three-dimensional cross-point memory, is provided and coupled to the bus 302 to store information and instructions.

[0078] In this embodiment, the computer system 300 is coupled via a bus 302 to a display 312, such as a cathode ray tube (CRT), liquid crystal display (LCD), plasma display, light-emitting diode (LED) display, or an organic light-emitting diode (OLED) display for displaying information to a computer user. An input device 314, including alphanumeric keys and other keys, is coupled to the bus 302 for transmitting information and command selections to the processor 304. Another type of user input device is a cursor controller 316, such as a mouse, trackball, touchscreen, or cursor arrow keys, for transmitting directional information and command selections to the processor 304 and for controlling the movement of the cursor on the display 312. Such input devices typically have two degrees of freedom on two axes (a first axis (e.g., the x-axis) and a second axis (e.g., the y-axis)), which allow the device to specify a position in a plane.

[0079] According to one embodiment, the techniques described herein are executed by computer system 300 in response to processor 304 executing one or more sequences of one or more instructions contained in main memory 306. These instructions are read into main memory 306 from another storage medium, such as storage device 310. Executing the sequence of instructions contained in main memory 306 causes processor 304 to perform the process steps described herein. In alternative embodiments, hardwired circuitry is used instead of or in combination with software instructions.

[0080] As used herein, the term "storage medium" refers to any non-transitory medium that stores data and / or instructions that enable a machine to operate in a particular manner. Such storage media include non-volatile media and / or volatile media. Non-volatile media include, for example, optical discs, magnetic disks, solid-state drives, or three-dimensional cross-point memory such as storage device 310. Volatile media include dynamic memory, such as main memory 306. Common forms of storage media include, for example, floppy disks, floppy disks, hard disks, solid-state drives, magnetic tape or any other magnetic data storage media, CD-ROMs, any other optical data storage media, any physical media with perforations, RAM, PROMs and EPROMs, FLASH-EPROMs, NV-RAMs, or any other memory chips or memory cartridges.

[0081] Storage media differ from transmission media, but can be used in conjunction with them. Transmission media participate in the information transmission between storage media. For example, transmission media include coaxial cables, copper wires, and optical fibers, which include wires with a bus 302. Transmission media can also take the form of sound waves or light waves, such as those generated during radio wave and infrared data communication.

[0082] In embodiments, various forms of media involve carrying one or more sequences of one or more instructions to processor 304 for execution. For example, these instructions may initially be executed on a disk or solid-state drive of a remote computer. The remote computer loads the instructions into its dynamic memory and transmits them over a telephone line using a modem. A local modem of computer system 300 receives data over the telephone line and converts the data into an infrared signal using an infrared transmitter. An infrared detector receives the data carried in the infrared signal, and appropriate circuitry places the data on bus 302. Bus 302 carries the data to main memory 306, from which processor 304 retrieves and executes the instructions. Instructions received in main memory 306 may optionally be stored on storage device 310 before or after execution by processor 304.

[0083] Computer system 300 also includes a communication interface 318 coupled to bus 302. Communication interface 318 provides bidirectional data communication coupled to network link 320 connected to local network 322. For example, communication interface 318 is an Integrated Services Digital Network (ISDN) card, a cable modem, a satellite modem, or a modem used to provide data communication connectivity with a corresponding type of telephone line. As another example, communication interface 318 is a Local Area Network (LAN) card used to provide data communication connectivity with a compatible LAN. In some implementations, a wireless link is also implemented. In any such implementation, communication interface 318 transmits and receives electrical, electromagnetic, or optical signals carrying digital data streams representing various types of information.

[0084] Network link 320 typically provides data communication to other data devices via one or more networks. For example, network link 320 provides connectivity to host computer 324 or to a cloud data center or device operated by Internet Service Provider (ISP) 326 via local network 322. ISP 326, in turn, provides data communication services via a worldwide packet data communication network now commonly referred to as the "Internet" 328. Both local network 322 and Internet 328 use electrical, electromagnetic, or optical signals that carry digital data streams. Signals through various networks and signals on network link 320 via communication interface 318 are example forms of transmission media carrying digital data entering and leaving computer system 300. In embodiments, network 320 includes the aforementioned cloud 202 or a portion of cloud 202.

[0085] Computer system 300 sends messages and receives data including program code through one or more networks, network links 320, and communication interfaces 318. In an embodiment, computer system 300 receives code for processing. The received code is executed by processor 304 upon receipt and / or stored in storage device 310, or in other non-volatile storage devices for later execution.

[0086] Autonomous Vehicle Architecture

[0087] Figure 4 This illustrates the use of autonomous vehicles (e.g., Figure 1 The example architecture 400 of the vehicle 100 shown is illustrated. Architecture 400 includes a sensing module 402 (sometimes called a sensing circuit), a planning module 404 (sometimes called a planning circuit), a control module 406 (sometimes called a control circuit), a positioning module 408 (sometimes called a positioning circuit), and a database module 410 (sometimes called a database circuit). Each module plays a role in the operation of the vehicle 100. Commonly, modules 402, 404, 406, 408, and 410 can be... Figure 1 This is part of the AV system 120 shown. In some embodiments, any of modules 402, 404, 406, 408, and 410 is a combination of computer software (e.g., executable code stored on a computer-readable medium) and computer hardware (e.g., one or more microprocessors, microcontrollers, application-specific integrated circuits (ASICs), hardware memory devices, other types of integrated circuits, other types of computer hardware, or any or all combinations of these hardware). Modules 402, 404, 406, 408, and 410 are each sometimes referred to as processing circuitry (e.g., computer hardware, computer software, or a combination of both). Any or all combinations of modules 402, 404, 406, 408, and 410 are also examples of processing circuitry.

[0088] In use, the planning module 404 receives data representing the destination 412 and determines data representing the trajectory 414 (sometimes called a route) that the vehicle 100 can travel to reach (e.g., arrive at) the destination 412. In order for the planning module 404 to determine the data representing the trajectory 414, the planning module 404 receives data from the sensing module 402, the positioning module 408, and the database module 410.

[0089] The sensing module 402 is used, for example, as follows Figure 1 One or more sensors 121 are shown to identify nearby physical objects. The objects are classified (e.g., grouped into types such as pedestrians, bicycles, cars, traffic signs, etc.), and a scene description including the classified objects 416 is provided to the planning module 404.

[0090] The planning module 404 also receives data representing the location 418 of the AV from the positioning module 408. The positioning module 408 determines the location of the AV by calculating location using data from sensor 121 and data (e.g., geographic data) from database module 410. For example, the positioning module 408 uses data from GNSS (Global Navigation Satellite System) sensors and geographic data to calculate the longitude and latitude of the AV. In embodiments, the data used by the positioning module 408 includes high-precision maps with lane geometry properties, maps describing road network connectivity properties, maps describing lane physical properties (such as traffic speed, traffic volume, number of vehicle and bicycle lanes, lane width, lane traffic direction, or lane marking type and location, or combinations thereof), and maps describing the spatial locations of road features (such as intersections, traffic signs, or various types of other traffic signals). In embodiments, the high-precision map is constructed by adding data to a low-precision map via automatic or manual annotation.

[0091] The control module 406 receives data representing trajectory 414 and data representing AV position 418, and operates the AV control functions 420a-420c (e.g., steering, throttle, braking, ignition) in a manner that will cause the vehicle 100 to travel along trajectory 414 to reach destination 412. For example, if trajectory 414 includes a left turn, the control module 406 will operate the control functions 420a-420c in such a way that the steering angle of the steering function will cause the vehicle 100 to turn left, and the throttle and brake will cause the vehicle 100 to pause before turning and wait for passing pedestrians or vehicles.

[0092] Autonomous Vehicle Input

[0093] Figure 5 The sensing module 402 is shown. Figure 4 The inputs used are 502a-502d (e.g., Figure 1 Examples of sensor 121 and outputs 504a-504d (e.g., sensor data) are shown. One input 502a is a LiDAR (light detection and ranging) system (e.g., Figure 1 The LiDAR system shown is 123. LiDAR is a technique that uses light (e.g., a beam of light such as infrared light) to obtain data related to physical objects in its line of sight. The LiDAR system produces LiDAR data as output 504a. For example, LiDAR data is a collection of 3D or 2D points (also called point clouds) used to construct a representation of environment 190.

[0094] Another input 502b is a RADAR (radar) system. RADAR is a technology that uses radio waves to obtain data related to nearby physical objects. RADAR can obtain data related to objects that are not within the line of sight of a LiDAR system. The RADAR system generates RADAR data as output 504b. For example, RADAR data is one or more radio frequency electromagnetic signals used to construct a representation of environment 190.

[0095] Another input 502c is a camera system. The camera system uses one or more cameras (e.g., a digital camera using a light sensor such as a charge-coupled device [CCD]) to acquire information about nearby physical objects. The camera system produces camera data as output 504c. Camera data is typically in the form of image data (e.g., data in image data formats such as RAW, JPEG, PNG, etc.). In some examples, the camera system has multiple independent cameras, for example, for stereoscopic imaging (stereoscopic vision), which enables the camera system to perceive depth. Although the objects perceived by the camera system are described here as "nearby," this is relative to the AV (view of objects). In some embodiments, the camera system is configured to "see" distant objects (e.g., objects as far as 1 kilometer or more in front of the AV). Therefore, in some embodiments, the camera system has features such as sensors and lenses optimized for perceiving distant objects.

[0096] Another input 502d is a Traffic Light Detection (TLD) system. The TLD system uses one or more cameras to acquire information related to traffic lights, street signs, and other physical objects that provide visual navigation information. The TLD system produces TLD data as output 504d. TLD data is often in the form of image data (e.g., data in image data formats such as RAW, JPEG, PNG, etc.). The TLD system differs from systems that include cameras in that it uses cameras with a wide field of view (e.g., using a wide-angle lens or fisheye lens) to acquire information related to as many physical objects as possible that provide visual navigation information, enabling the vehicle 100 to access all relevant navigation information provided by these objects. For example, the TLD system has a field of view of approximately 120 degrees or greater.

[0097] In some embodiments, sensor fusion technology is used to combine outputs 504a-504d. Thus, individual outputs 504a-504d are provided to other systems of the vehicle 100 (e.g., to systems such as...). Figure 4 The planning module 404 shown may provide combined outputs to other systems in the form of single or multiple combined outputs of the same type (e.g., using the same combination technique or combining the same outputs or both) or single or multiple combined outputs of different types (e.g., using different individual combination techniques or combining different individual outputs or both). In some embodiments, early fusion techniques are used. Early fusion techniques are characterized by combining the outputs before applying one or more data processing steps to the combined outputs. In some embodiments, late fusion techniques are used. Late fusion techniques are characterized by combining the outputs after applying one or more data processing steps to the individual outputs.

[0098] Figure 6 An example of a LiDAR system 602 is shown (e.g., Figure 5The input 502a is shown. The LiDAR system 602 emits light 604a-604c from a emitter 606 (e.g., a laser emitter). The light emitted by the LiDAR system is typically not in the visible spectrum; for example, infrared light is often used. Some of the emitted light 604b encounters a physical object 608 (e.g., a vehicle) and is reflected back to the LiDAR system 602. (The light emitted from the LiDAR system typically does not penetrate the physical object, e.g., a solid physical object.) The LiDAR system 602 also has one or more photodetectors 610 for detecting the reflected light. In an embodiment, one or more data processing systems associated with the LiDAR system generate an image 612 representing the field of view 614 of the LiDAR system. Image 612 includes information representing the boundary 616 of the physical object 608. Thus, image 612 is used to determine the boundary 616 of one or more physical objects near the AV.

[0099] Figure 7 The diagram illustrates a LiDAR system 602 in operation. In the scenario shown, the vehicle 100 receives both a camera system output 504c in the form of an image 702 and a LiDAR system output 504a in the form of LiDAR data points 704. In use, the vehicle 100's data processing system compares the image 702 with the data points 704. Specifically, physical objects 706 identified in the image 702 are also identified in the data points 704. Thus, the vehicle 100 perceives the boundaries of physical objects based on the contours and density of the data points 704.

[0100] Figure 8 Additional details of the operation of the LiDAR system 602 are shown. As described above, the vehicle 100 detects the boundaries of physical objects based on the characteristics of the data points detected by the LiDAR system 602. Figure 8 As shown, a flat object, such as ground 802, will reflect light 804a-804d emitted from LiDAR system 602 in a consistent manner. In other words, because LiDAR system 602 emits light at a consistent interval, ground 802 will reflect light back to LiDAR system 602 at the same consistent interval. When vehicle 100 travels on ground 802, LiDAR system 602 will continue to detect light reflected by the next effective surface point 806 if nothing obstructs its path. However, if object 808 obstructs its path, the light 804e-804f emitted by LiDAR system 602 will be reflected from points 810a-810b in a manner inconsistent with the expected consistency. Based on this information, vehicle 100 can determine the presence of object 808.

[0101] Path planning

[0102] Figure 9 Show (for example, as) Figure 4 The diagram 900 illustrates the relationship between the inputs and outputs of the planning module 404. Generally, the output of the planning module 404 is a route 902 from a starting point 904 (e.g., a source location or initial location) to an ending point 906 (e.g., a destination or final location). Route 902 is typically defined by one or more road segments. For example, a road segment refers to the distance to be traveled over at least a portion of a street, road, highway, driveway, or other physical area suitable for vehicle travel. In some examples, such as if the vehicle 100 is an off-road capable vehicle such as a four-wheel drive (4WD) or all-wheel drive (AWD) car, SUV, or pickup truck, route 902 includes “off-road” segments such as unpaved paths or open fields.

[0103] In addition to route 902, the planning module also outputs lane-level route planning data 908. Lane-level route planning data 908 is used to navigate segments of route 902 at specific times based on conditions. For example, if route 902 comprises a multi-lane highway, lane-level route planning data 908 includes trajectory planning data 910, which vehicle 100 can use to select a lane from the multiple lanes based on factors such as whether an exit is nearby, whether other vehicles are present in one or more lanes, or other factors that change over a period of minutes or less. Similarly, in some implementations, lane-level route planning data 908 includes a speed constraint 912 specific to a segment of route 902. For example, if the segment includes pedestrians or unexpected traffic, speed constraint 912 can limit vehicle 100 to a slower speed than expected, such as a speed limit based on the segment's speed limit data.

[0104] In this embodiment, the input to the planning module 404 includes (e.g., from...) Figure 4 The database module 410 shown contains database data 914 and current location data 916 (for example, Figure 4 The AV position shown is 418), (for example, for use with Figure 4 The destination data 918 and object data 920 shown for destination 412 (e.g., as shown) Figure 4The perception module 402 shown perceives classified objects 416. In some embodiments, database data 914 includes rules used during planning. The rules are specified using a formal language (e.g., Boolean logic). At least some of these rules will apply to any given situation encountered by vehicle 100. A rule applies to a given situation if it has conditions satisfied based on information available to vehicle 100 (e.g., information about the surrounding environment). Rules can have priorities. For example, a rule "move to the leftmost lane if the road is a highway" can have a lower priority than "move to the rightmost lane if the exit is within a mile."

[0105] Figure 10 This is shown in path planning (e.g., by planning module 404). Figure 4 The directed graph used is 1000. Generally speaking, such as... Figure 10 The directed graph 1000 shown is used to determine any path between a starting point 1002 and an ending point 1004. In the real world, the distance separating the starting point 1002 and the ending point 1004 may be relatively large (e.g., in two different urban areas) or relatively small (e.g., two intersections in adjacent city blocks or two lanes of a multi-lane road).

[0106] In an embodiment, the directed graph 1000 has nodes 1006a-1006d representing different locations that a vehicle 100 may occupy between a starting point 1002 and an ending point 1004. In some examples, for instance, when the starting point 1002 and the ending point 1004 represent different urban areas, nodes 1006a-1006d represent road segments. In some examples, for instance, when the starting point 1002 and the ending point 1004 represent different locations on the same road, nodes 1006a-1006d represent different locations on that road. Thus, the directed graph 1000 includes information at different levels of granularity. In an embodiment, the directed graph with higher granularity is also a subgraph of another directed graph with a larger scale. For example, most of the information in a directed graph where the starting point 1002 and the ending point 1004 are far apart (e.g., many miles apart) is at a low granularity, and the directed graph is based on stored data, but the directed graph also includes some high-granularity information for representing a portion of the physical location in the field of view of the vehicle 100.

[0107] Nodes 1006a-1006d are distinct from objects 1008a-1008b that cannot overlap with nodes. In an embodiment, at a low granularity, objects 1008a-1008b represent areas that vehicles cannot pass through, such as areas without streets or roads. At a high granularity, objects 1008a-1008b represent physical objects within the field of view of vehicle 100, such as other vehicles, pedestrians, or other entities with which vehicle 100 cannot share physical space. In an embodiment, some or all of objects 1008a-1008b are static objects (e.g., objects that do not change position, such as streetlights or utility poles) or dynamic objects (e.g., objects that can change position, such as pedestrians or other cars).

[0108] Nodes 1006a-1006d are connected by edges 1010a-1010c. If two nodes 1006a-1006b are connected by edge 1010a, then vehicle 100 can travel between one node 1006a and the other node 1006b, for example, without having to travel to an intermediate node before reaching the other node 1006b. (When it is mentioned that vehicle 100 travels between nodes, it means that vehicle 100 travels between two physical locations represented by the respective nodes.) Edges 1010a-1010c are typically bidirectional, meaning that vehicle 100 can travel from a first node to a second node, or from a second node to a first node. In an embodiment, edges 1010a-1010c are unidirectional, meaning that vehicle 100 can travel from a first node to a second node, but not from a second node to a first node. When edges 1010a-1010c represent, for example, a one-way street, a single lane of a street, road, or highway, or other features that can only be traversed in one direction due to legal or physical constraints, edges 1010a-1010c are one-way.

[0109] In an embodiment, the planning module 404 uses a directed graph 1000 to identify a path 1012 consisting of nodes and edges between the start point 1002 and the end point 1004.

[0110] Edges 1010a-1010c have associated costs 1014a-1014b. Costs 1014a-1014b represent the resources that would be spent if vehicle 100 selected that edge. A typical resource is time. For example, if the physical distance represented by one edge 1010a is twice the physical distance represented by another edge 1010b, then the associated cost 1014a of the first edge 1010a can be twice the associated cost 1014b of the second edge 1010b. Other factors affecting time include anticipated traffic, the number of intersections, speed limits, etc. Another typical resource is fuel economy. The two edges 1010a-1010b can represent the same physical distance, but due to factors such as road conditions and anticipated weather, one edge 1010a may require more fuel than the other edge 1010b.

[0111] When the planning module 404 identifies the path 1012 between the starting point 1002 and the ending point 1004, the planning module 404 typically selects the path that is optimized for cost, such as the path that has the minimum total cost when the individual costs of the edges are added together.

[0112] Autonomous Vehicle Control

[0113] Figure 11 Show (for example, as) Figure 4 The block diagram 1100 shows the inputs and outputs of the control module 406. The control module operates according to a controller 1102, which includes, for example, one or more processors similar to processor 304 (e.g., one or more computer processors such as a microprocessor or microcontroller or both); short-term and / or long-term data storage devices similar to main memory 306, ROM 308 and storage device 310 (e.g., memory, random access memory or flash memory or both); and instructions stored in the memory that, when executed (e.g. by one or more processors), perform the operation of controller 1102.

[0114] In one embodiment, controller 1102 receives data representing a desired output 1104. The desired output 1104 typically includes speed, such as rate and heading. The desired output 1104 may be based, for example, from (e.g., as...) Figure 4The planning module 404 receives the data shown. Based on the desired output 1104, the controller 1102 generates data that can be used as throttle input 1106 and steering input 1108. Throttle input 1106 indicates the magnitude of the desired output 1104 by engaging the throttle of the vehicle 100 (e.g., acceleration control), for example, by engaging the steering pedal or engaging another throttle control. In some examples, throttle input 1106 also includes data that can be used to engage the brakes of the vehicle 100 (e.g., deceleration control). Steering input 1108 indicates the steering angle, such as the steering control of the AV (e.g., steering wheel, steering angle actuator, or other function for controlling the steering angle), which should be positioned to achieve the desired output 1104.

[0115] In one embodiment, controller 1102 receives feedback used when adjusting inputs provided to throttle and steering. For example, if vehicle 100 encounters an obstacle 1110 such as a hill, the measured rate 1112 of vehicle 100 drops below the desired output rate. In another embodiment, any measured output 1114 is provided to controller 1102 so that necessary adjustments can be made, for example, based on the difference 1113 between the measured rate and the desired output. The measured output 1114 includes measured position 1116, measured speed 1118 (including rate and heading), measured acceleration 1120, and other sensor-measurable outputs of vehicle 100.

[0116] In one embodiment, information related to interference 1110 is detected in advance, for example, by a sensor such as a camera or LiDAR sensor, and this information is provided to the predictive feedback module 1122. The predictive feedback module 1122 then provides information that the controller 1102 can use to make appropriate adjustments. For example, if the vehicle 100's sensors detect ("see") a hill, the controller 1102 can use this information to prepare to engage the throttle at an appropriate time to avoid significant deceleration.

[0117] Figure 12 A block diagram 1200 shows the inputs, outputs, and components of controller 1102. Controller 1102 has a rate analyzer 1202 that influences the operation of throttle / brake controller 1204. For example, the rate analyzer 1202 instructs throttle / brake controller 1204 to accelerate or decelerate using throttle / brake 1206 based on feedback received by, for example, controller 1102 and processed by the rate analyzer 1202.

[0118] The controller 1102 also has a lateral tracking controller 1208 that affects the operation of the steering wheel controller 1210. For example, the lateral tracking controller 1208 instructs the steering wheel controller 1210 to adjust the position of the steering angle actuator 1212 based on feedback received by the controller 1102 and processed by the lateral tracking controller 1208.

[0119] Controller 1102 receives several inputs for determining how to control the throttle / brake 1206 and the steering angle actuator 1212. Planning module 404 provides controller 1102 with information, for example, for selecting the heading of vehicle 100 at the start of operation and determining which road segment vehicle 100 will cross when it reaches an intersection. Positioning module 408 provides controller 1102 with information describing the current location of vehicle 100, for example, so that controller 1102 can determine whether vehicle 100 is at the expected location based on the positive control of the throttle / brake 1206 and steering angle actuator 1212. In an embodiment, controller 1102 receives information from other inputs 1214, such as information received from a database, computer network, etc.

[0120] Surface-guided decision making

[0121] Figure 13A , Figure 13B and Figure 13C A block diagram of an example system for surface guidance decision-making is shown. These systems are configured to guide decisions along a vehicle (e.g., Figure 1 The surface of the path of the vehicle (100) shown is classified. These systems are also configured to control the vehicle based on surface classification. System components may be located on or away from the vehicle. In some examples, [the system uses...] Figure 3 The computer system 300 described herein is similar to a computer system used to implement one or more components. Additionally or alternatively, one or more components may be implemented in conjunction with... Figure 2 The cloud computing environment described herein is implemented on a similar cloud computing environment 200. Note that the system is shown for illustrative purposes only, as the system may include additional components and / or remove one or more components without departing from the scope of this disclosure. Furthermore, the various components of the system may be arranged and connected in any manner. Although the following discussion describes the system in the case of classifying one surface along a vehicle path, such systems may classify more than one surface along a vehicle path simultaneously or sequentially.

[0122] Figure 13A An example system 1300 is shown, configured to classify surfaces along a vehicle path using known surface information. (Example:) Figure 13AAs shown, system 1300 includes sensor 1302 (e.g., with...). Figure 1 Sensor 121 (same as or similar to sensor), surface classifier 1304, motion planner 1306 (e.g., with sensor 121, surface classifier 1304, motion planner 1306). Figure 4 , Figure 9 and Figure 10 The planning module 404 described herein is the same as or similar to a motion planner, and the controller 1308 (e.g., with...) Figure 4 , Figure 11 and Figure 12 The control module 406 described herein is the same as or similar to the controller described herein.

[0123] In an embodiment, sensor 1302 is configured to capture sensor data associated with a surface along the vehicle path. In some examples, sensor 1302 and the captured sensor data are respectively associated with... Figure 5 The inputs 502a-502d and outputs 504a-504d are identical or similar. Among other examples, possible sensor data includes image data, location data (e.g., GPS coordinates, spatial location, or triangulation data), sensing sensor data, weather data (e.g., temperature, humidity, precipitation), wheel rotation sensor data, IMU (e.g., gyroscope and / or accelerometer) data, geometric data (e.g., shape, elevation angle, size, etc.), and point clouds, etc. The surface can be any part along the vehicle's path. For example, the surface is the portion of the vehicle's path that at least one wheel is expected to travel on. Figure 13A In the example shown, sensor 1302 is configured to send sensor data to surface classifier 1304.

[0124] In an embodiment, surface classifier 1304 is configured to classify surfaces based on captured sensor data using known surface information. Known surface information includes known surface classifications and known attributes of those classifications (e.g., previously generated sensor measurements based on vehicle movement on known surfaces, the extent of the sensor measurements, and / or previously labeled data regarding road surface conditions, etc.). In one example, the known surface information is used to train surface classifier 1304 to classify surfaces. Machine learning algorithms such as supervised learning can be used to train surface classifier 1304. In supervised learning, inputs of interest and corresponding outputs are provided to surface classifier 1304. Surface classifier 1304 adjusts its function (e.g., in the case of a neural network, one or more weights associated with two or more nodes in two or more different layers) based on a comparison of its output with an expected output to provide the desired output when subsequent inputs are provided. Examples of supervised learning algorithms include deep neural networks, similarity learning, linear regression, random forests, k-nearest neighbors, support vector machines, and decision trees.

[0125] In another example, surface classifier 1304 classifies a surface by comparing sensor data with attributes of a known surface classification. In this example, if surface classifier 1304 identifies a threshold similarity between the sensor data and the attributes of a known surface classification, then surface classifier 1304 uses that surface classification to classify the surface. Threshold similarity is a measure of the similarity between a sensor reading and a known attribute that is greater than a predetermined threshold (e.g., the similarity between the sensor reading and the known attribute is greater than 90%). For example, the k-nearest neighbor algorithm can be used to compare sensor data with attributes of a known surface classification.

[0126] In another example, surface classifier 1304 uses regression to quantify the road surface. In this example, after surface classifier 1304 classifies a surface as having a specific attribute, it can quantify the degree to which the surface possesses that attribute. For example, a surface might be classified as "icy," and regression could then be used on a scale of 0-10 to estimate the degree of "ice." Alternatively, regression can be used to directly estimate the coefficient of friction. Regression can be trained in a similar manner to classification. For example, supervised learning can be used. More specifically, the training data consists of road surfaces labeled with true ground attributes (e.g., coefficient of friction, water depth, etc.). Example regression models include (deep) neural networks, linear regression, and support vector machines (SVM).

[0127] In this embodiment, surface classification is based on surface composition or surface properties. Among other examples, surface composition is a manufacturing material (e.g., asphalt, concrete, tar, brick) or a naturally occurring element (e.g., rain, snow, sand, rock). Thus, possible surface classifications include asphalt surfaces, concrete surfaces, tar surfaces, brick surfaces, rain surfaces, snow surfaces, sand surfaces, rock surfaces, etc. Surface properties are any attributes of the surface, such as its shape, whether a vehicle can traverse the surface (e.g., an obstacle), coefficient of friction, or a value relative to a threshold. Thus, surfaces can be classified based on their shape, whether they are obstacles, or whether they have an attribute value greater than, equal to, or less than a threshold. Surface classification of temporary surfaces (e.g., temporary natural elements such as snow or rain) includes a temporal description. For example, a snow surface is classified based on the length of time it has existed (e.g., recent snowfall, one day's snowfall, etc.).

[0128] In some scenarios, surface classifier 1304 is determined to be unable to determine the surface classification based on known surface information (e.g., surface classifier 1304 cannot identify a known surface classification using attributes similar to the surface). In these scenarios, surface classifier 1304 classifies the surface as an unknown surface. Figure 13A As shown, the surface classifier 1304 provides surface classifications to the motion planner 1306. In some examples, the surface classifier 1304 also provides sensor data associated with the surface to the motion planner 1306.

[0129] In an embodiment, motion planner 1306 is configured to determine vehicle behavior based on surface classification. In an example, motion planner 1306 first determines the drivability attributes of a surface based on the surface classification. Drivability attributes may include physical characteristics that affect how a vehicle traverses a surface. Among other attributes, example drivability attributes include friction, traction, road grip, drag, rolling resistance, and obstacles. If the surface classification is known to system 1300, motion planner 1306 obtains the drivability attributes from a database of drivability attributes associated with that known surface classification. In some examples, motion planner 1306 also generates a surface map that includes (e.g., based on sensor data) a list of geometric descriptions of the surface and / or a distribution of the surface's drivability attributes.

[0130] In an embodiment, motion planner 1306 determines vehicle behavior based on the drivability properties of a surface. In one example, motion planner 1306 determines vehicle behavior based on known vehicle behavior (e.g., historical vehicle behavior). More specifically, motion planner 1306 determines vehicle behavior based on known vehicle behavior associated with surfaces of known surface classifications or with similar drivability properties. Example vehicle behaviors include: following an existing track (e.g., on a rainy or snowy surface), avoiding certain surfaces (e.g., avoiding areas of drift ice), adjusting vehicle speed or torque, veer within a lane, changing lanes, and defining a new center lane (e.g., to increase friction on surfaces partially covered by snow or rain). In examples where the surface classification is unknown, motion planner 1306 determines preventative vehicle behavior (e.g., reducing speed and avoiding surfaces if possible). Once motion planner 1306 has determined vehicle behavior, it provides the determined vehicle behavior to controller 1308. Then, the controller 1308 controls the vehicle based on the determined vehicle behavior.

[0131] Figure 13B Example system 1310 is shown, which is configured to classify surfaces along a vehicle path using known surface information and feedback from a vehicle controller. Figure 13B As shown, similar to system 1300, system 1310 includes sensor 1302, motion planner 1306, and controller 1308. However, unlike surface classifier 1304, surface classifier 1312 of system 1310 receives feedback from controller 1308.

[0132] In this embodiment, in addition to using known surface information to classify the surface, the surface classifier 1312 also uses feedback from the controller 1308. The feedback is used to generate new surface classifications or refine known surface classifications. This feedback includes sensor measurements captured when a vehicle is near the surface (e.g., within a threshold distance) or driving on the surface. In an example where the surface has a known classification, the surface classifier 1312 uses the feedback to update the classification attributes (i.e., update the classification output). Additionally, in an example where the surface has an unknown classification, the surface classifier 1312 uses the feedback to generate a new surface classification. The surface classifier 1312 includes feedback as an attribute of the new surface classification. For example, the new surface classification includes feedback as a marker for identifying the new surface classification. The surface classifier 1312 uses the new surface classification and / or the updated surface classification to classify the surface (e.g., using the techniques described above with respect to surface classifier 1304).

[0133] Figure 13CAn example system 1320 is shown, configured to classify surfaces along a vehicle's path using known surface information, feedback from a vehicle controller, shared data between vehicle queues, and data from external sources. System 1320 is also configured to predict vehicle behavior of one or more other vehicles driving near or on the surface based on the surface classification. The system is further configured to use the captured behavior of other vehicles to estimate the road surface (e.g., a skidding vehicle can indicate a slippery surface) and determine appropriate driving behavior. Furthermore, system 1320 is configured to control vehicle behavior based on surface classification and / or the predicted behavior of one or more other vehicles. Figure 13C As shown, with Figure 13A System 1300 and Figure 13B Similar to system 1310, system 1320 includes sensor 1302, motion planner 1306, and controller 1308. System 1320 also includes surface classifier 1322, shared dynamic surface map 1324, external source 1326, and motion predictor 1328.

[0134] In an embodiment, in addition to capturing data associated with the surface along the vehicle's path, sensor 1302 is also configured to capture sensor data indicating the behavior of other vehicles driving near or on the surface. Figure 13C As shown, the captured behavior of other vehicles is provided to the motion predictor 1328. As described below, the captured behavior of other vehicles is used to train the motion predictor 1328 to predict the behavior of one or more other vehicles driving near or on the surface along the vehicle's path.

[0135] In an embodiment, the shared dynamic surface map 1324 is a database (e.g., a database including maps) shared among queues of vehicles. The shared dynamic surface map 1324 receives information from vehicles in the queue and shares that information in the database. For example, the shared dynamic surface map 1324 receives surface property feedback and vehicle behavior feedback from a vehicle controller, such as a vehicle controller 1308. Surface property feedback includes sensor measurements captured when a vehicle is near or driving on a surface. Vehicle behavior feedback includes information indicating the vehicle's trajectory and / or vehicle driving settings (e.g., rate or torque) when the vehicle is near or driving on a surface. In some examples, the shared dynamic surface map 1324 receives information associated with temporary surfaces (e.g., a metal plate temporarily placed on an opening in the road surface, and / or a grating surface, etc.). In such examples, the shared dynamic surface map 1324 schedules the information to expire after a specified amount of time. The amount of time elapsed for information to expire can be associated with the type of surface (e.g., depending on the type of surface) (e.g., information associated with a first temporary surface (e.g., a metal sheet) may expire within days, while information associated with a second temporary surface (e.g., a grating pavement) may expire within days or weeks). Figure 13C As shown, surface classifier 1322 receives known surface information (e.g., known surface attributes and surface classification) from shared dynamic surface map 1324. Surface classifier 1322 uses the known surface information to classify surfaces. Furthermore, motion predictor 1328 receives vehicle behavior feedback from shared dynamic surface map 1324. Motion predictor 1328 uses the vehicle behavior feedback to predict the behavior of other vehicles.

[0136] In an embodiment, external source 1326 includes a database for providing information associated with surfaces along the vehicle path. For example, external source 1326 includes a weather database and / or a structure database for providing weather and structure information for areas along the vehicle path. Figure 13C As shown, surface classifier 1322 receives data from an external source. Surface classifier 1322 uses this data to classify surfaces.

[0137] In an embodiment, surface classifier 1322 is configured to receive sensor data from sensor 1302, feedback from controller 1308, shared data from shared dynamic surface map 1324, and / or external data from external source 1326. In this example, surface classifier 1322 is trained using feedback from controller 1308, shared data from shared dynamic surface map 1324, and / or external data from external source 1326 to classify surfaces (e.g., using the techniques described above regarding surface classifiers 1304 and 1312). For example, the data is used to generate new surface classifications or refine known surface classifications. Surface classifications are used to classify surfaces based on sensor data received from sensor 1302.

[0138] In one embodiment, surface classifier 1322 receives sensor data indicating the behavior of another vehicle traveling near or on the surface. In this embodiment, surface classifier 1322 uses vehicle behavior to classify the surface on which the other vehicle is driving. For example, if the vehicle is skidding or sliding, surface classifier 1322 determines that the surface is a slippery surface. As described below, surface classification can be used to determine vehicle behavior, for example, to determine the maximum speed based on surface classification. Figure 13C As shown, the surface classifier 1322 provides surface classifications to the motion planner 1306 and the motion predictor 1328.

[0139] In an embodiment, motion predictor 1328 is configured to predict the behavior of another vehicle near or traveling on a surface based on a surface classification received from surface classifier 1322. In one example, motion predictor 1328 is trained to predict the behavior of other vehicles using known vehicle behavior, feedback on other vehicle behavior received from shared dynamic surface map 1324, and / or captured vehicle behavior of other vehicles received from sensor 1302. More specifically, motion predictor 1328 may implement one or more machine learning algorithms such as supervised learning and reinforcement learning. In such an example, motion predictor 1328 can be trained using known vehicle behavior, feedback on other vehicle behavior, and / or captured vehicle behavior of other vehicles. In another example, motion predictor 1328 predicts the behavior of another vehicle by comparing how the observed vehicle behaves near or traveling on the surface with how the vehicle has historically behaved. Figure 13C As shown, the motion predictor 1328 provides the predicted vehicle behavior to the motion planner 1306.

[0140] In an embodiment, motion planner 1306 is configured to determine vehicle behavior based on surface classification received from surface classifier 1322 and / or predicted vehicle behavior received from motion predictor 1328. More specifically, motion planner 1306 determines vehicle behavior based on surface classification and surface drivability properties. Motion planner 1306 then determines vehicle behavior based on the surface drivability properties and / or the predicted vehicle behavior of another vehicle driving near or on the surface. As an example, motion planner 1306 determines vehicle behavior that causes the vehicle to drive on the tracks of other vehicles during snowfall (e.g., maximizing friction and minimizing the risk of loss of control). As another example, motion planner 1306 determines vehicle behavior based on historical vehicle behavior on surfaces with the same or similar drivability properties. As yet another example, motion planner 1306 determines vehicle behavior that follows or avoids another vehicle acting on the surface. In some examples, motion planner 1306 also determines vehicle behavior associated with a safety or performance value greater than the current safety or performance value associated with the current vehicle behavior. Motion planner 1306 provides vehicle motion to controller 1308.

[0141] In one embodiment, controller 1308 then controls the vehicle based on the determined vehicle behavior. For example... Figure 13C As shown, controller 1308 also sends feedback to surface classifier 1322. Furthermore, controller 1308 sends surface attribute feedback and / or vehicle behavior feedback to shared dynamic surface map 1324. In some examples, controller 1308 sends vehicle behavior feedback to motion planner 1306.

[0142] Figure 14 A flowchart of a process 1400 for surface-guided decision-making is shown. For example, this process can be performed by... Figure 13A System 1300 Figure 13B System 1310 or Figure 13C The system 1320 executes this. From at least one sensor of the vehicle (e.g., a camera, [...] Figure 6 , Figure 7 and Figure 8 The LiDAR, radar, or location sensor receives (1402) sensor data (e.g., an image of the surface, a scan of the surface, or the location of the surface) associated with the surface along the path the vehicle is to travel.

[0143] A surface classifier is used to determine (1404) the classification of a surface based on sensor data (e.g., the type of surface or the material on the surface, such as snow, ice, sand, chemicals [e.g., oil or paint], pebbles, rocks, soil, or any other material that alters the drivability of the surface). Based on the surface classification, (1406) the drivability properties of the surface are determined (e.g., friction, traction, road grip, drag, rolling resistance, obstacles, etc.).

[0144] At 1408, the behavior of a vehicle driving near or on a surface is planned based on the surface's drivability properties. Examples of this behavior include: determining motion considering drivability properties; determining motion based on the motion of a previous vehicle (or another vehicle) on a surface with the same or similar drivability properties; determining motion based on the predicted motion of another vehicle driving on the surface; following existing tracks on the road in snow or rain; avoiding icy areas; reducing speed; veer within the lane; changing lanes to avoid obstacles; defining a new center lane (baseline) path; increasing friction on partially snow-covered surfaces; reducing speed / torque on damaged sections of road; and following or avoiding another vehicle acting directly on the surface. At 1410, the vehicle is controlled based on its behavior.

[0145] In some implementations, determining the drivability attributes of a surface based on surface classification involves generating a surface map that includes at least one of the following: a list of geometric descriptions of the surface and a distribution of drivability attributes along the path of a vehicle.

[0146] In some implementations, surface classification includes known surfaces, and determining the drivability attributes of a surface based on the surface classification involves obtaining the drivability attributes associated with the known surfaces from a database.

[0147] In some implementations, the surface classification is an unknown surface, and determining the drivability attributes of a surface based on the surface classification involves: determining sensor measurements included in the labels of the unknown surface from a database, where the sensor measurements are historical sensor measurements associated with the unknown surface; and determining the drivability attributes of the unknown surface based on the sensor measurements.

[0148] In some implementations, historical sensor measurements are taken by the vehicle or received from another vehicle.

[0149] In some implementations, planning the behavior of a vehicle driving near or on a surface based on the drivability properties of the surface involves determining, based on the drivability properties, the vehicle movement associated with a safety or performance value greater than the current safety or performance value associated with the current vehicle movement.

[0150] In some implementations, the surface is a first surface, and planning the behavior of a vehicle driving near or on the surface based on the drivability properties of the surface involves: determining historical vehicle movements on a second surface having properties similar to those of the first surface.

[0151] In some implementations, the vehicle is a first vehicle, and planning the behavior of a vehicle driving near or on the surface based on the drivability properties of the surface involves: detecting a second vehicle near the first vehicle; determining the expected movement of the second vehicle based on the drivability properties of the surface; and determining the behavior of the first vehicle based on the expected movement of the second vehicle.

[0152] In some implementations, the surface classifier receives sensor measurements from at least one sensor as a vehicle passes over the surface.

[0153] In some implementations, the surface classification is a known surface classification, and processing 1400 also involves updating the classifier associated with the surface classification based on sensor measurements.

[0154] In some implementations, the surface classification is an unknown surface, and the processing 1400 also involves adding sensor measurements to a label associated with the unknown surface.

[0155] In some implementations, processing 1400 also involves receiving at least one of pavement classification information and known surface attribute information from a shared dynamic database.

[0156] In some implementations, the vehicle is the first vehicle, and method 1400 also involves using at least one sensor to capture the motion of a second vehicle driving on the surface.

[0157] In some implementations, processing 1400 also involves sending at least one of the following to a shared dynamic database: surface property feedback and vehicle motion feedback in the case of a vehicle driving on a surface.

[0158] In the preceding description, embodiments of the invention have been described with reference to numerous specific details, which may vary from implementation to implementation. Therefore, the specification and drawings should be considered illustrative rather than restrictive. The sole and exclusive indication of the scope of the invention, and what the applicant expects to be the scope of the invention, is the literal and equivalent scope of the claims published from this application in the specific form of the grant notice claims, including any subsequent amendments. Any definitions of terms expressly set forth herein for inclusion in such claims should be taken as meaning as such terms are used in the claims. Furthermore, when the term “comprising” is used in the preceding specification or appended claims, what follows that phrase may be an additional step or entity, or a sub-step / sub-entity of a previously stated step or entity.

Claims

1. A system for a vehicle, comprising: At least one computer-readable medium storing computer-executable instructions; At least one processor is configured to communicate with at least one sensor of the vehicle and execute the computer-executable instructions, the execution of which includes: Receive sensor data from the at least one sensor that is associated with the surface along the path the vehicle is to travel; A surface classifier is used to determine the surface classification based on the sensor data; The drivability properties of the surface are determined based on the surface classification. The behavior of the vehicle is planned based on the drivability properties of the surface, in situations where it is driven near or on the surface; and The vehicle is controlled based on the planned behavior. Wherein, the surface classification is an unknown surface, and wherein determining the drivability attributes of the surface based on the surface classification includes: Determine from the database the sensor measurement results included in the tags of the unknown surface, wherein the sensor measurement results are historical sensor measurement results associated with the unknown surface; and The drivability properties of the unknown surface are determined based on the sensor measurements.

2. The system according to claim 1, wherein, Determining the drivability attributes of a surface based on the surface classification includes: Generate a surface map that includes at least one of the following: a list of geometric descriptions of the surface and a distribution of drivability attributes along the path of the vehicle.

3. The system according to claim 1, wherein, In response to the surface classification being an unknown surface classification, a new surface classification is generated using surface property feedback.

4. The system according to claim 1, wherein, The historical sensor measurements were taken by the vehicle or received from another vehicle.

5. The system according to claim 1, wherein, Planning the behavior of the vehicle for driving near or on the surface based on the drivability properties of the surface includes: Based on the drivability attribute, determine the vehicle movement associated with a safety or performance value that is greater than the current safety or performance value associated with the current vehicle movement.

6. The system according to claim 1, wherein, The surface is a first surface, and wherein planning the behavior of the vehicle in the vicinity of or on the surface when driving based on the drivability properties of the surface includes: Historical vehicle movement was determined on a second surface having properties similar to the drivability properties of the first surface.

7. The system according to claim 1, wherein, The vehicle is a first vehicle, and the behavior of the vehicle in planning driving near or on the surface based on the drivability properties of the surface includes: Detect a second vehicle near the first vehicle; The expected movement of the second vehicle is determined based on the drivability properties of the surface; and The behavior of the first vehicle is determined based on the expected movement of the second vehicle.

8. The system according to claim 1, wherein, The surface classifier receives sensor measurement results from the at least one sensor as the vehicle passes over the surface.

9. The system according to claim 8, wherein, The surface classification is the unknown surface, and the operation further includes: The sensor measurement results are added to a tag associated with the unknown surface.

10. The system according to claim 1, wherein, The operation also includes: Receive at least one of the following from a shared dynamic database: pavement classification information and known surface attribute information.

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