Vehicle, method for vehicle, and storage medium

Abstract

The present invention relates to a vehicle, a method for a vehicle, and a storage medium. Techniques for calibrating sensors of a vehicle are described. One technique involves identifying a trigger for calibrating at least one sensor of the vehicle. In response to identifying the trigger, the technique then involves initiating a calibration path planning mode of the vehicle. In the calibration path planning mode, the technique includes: generating a plurality of calibration cognitive paths, each calibration cognitive path including at least one calibration trajectory along at least one road; and selecting a first calibration cognitive path of the vehicle from the plurality of paths, wherein the at least one sensor is calibrated while the vehicle is traveling along the selected path.

Description

Technical Field

[0001] The present application relates to vehicle path planning utilizing calibration information. Background Art

[0002] The calibration of vehicle sensors deteriorates over time or mileage for a variety of reasons. As a result, the vehicle may become less safe due to deterioration in performance in mapping, localization, planning, control, and perception. Therefore, vehicle sensors need to be recalibrated to avoid errors in sensor functionality. Summary of the invention

[0003] According to one aspect, a vehicle is provided, comprising: at least one sensor; at least one computer; and a memory storing instructions, wherein the instructions, when executed by the at least one computer, cause the at least one computer to perform operations, the operations comprising: identifying a trigger for calibrating the at least one sensor; in response to identifying the trigger, starting a calibration path planning mode of the vehicle; and in the calibration path planning mode: generating a plurality of calibration cognitive paths, each of the plurality of calibration cognitive paths comprising at least one calibration trajectory along at least one road; and selecting a first calibration cognitive path for the vehicle from the plurality of calibration cognitive paths, wherein the at least one sensor is calibrated while the vehicle is traveling along the first calibration cognitive path.

[0004] According to another aspect, a method in a vehicle is provided, comprising: identifying a trigger for calibrating the at least one sensor; in response to identifying the trigger, starting a calibration path planning mode of the vehicle; and in the calibration path planning mode: generating a plurality of calibration cognitive paths, each of the plurality of calibration cognitive paths comprising at least one of at least one calibration target along at least one road and at least one calibration trajectory along at least one road; and selecting a first calibration cognitive path for the vehicle from the plurality of calibration cognitive paths, wherein the at least one sensor is calibrated while the vehicle is traveling along the first calibration cognitive path.

[0005] According to another aspect, a non-transitory computer-readable storage medium is provided, which includes one or more programs for execution by one or more processors of a first device, the one or more programs including instructions that, when executed by the one or more processors, cause the first device to perform the above method. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 An example of an autonomous vehicle with autonomous capabilities is shown.

[0007] Figure 2An example "cloud" computing environment is illustrated.

[0008] Figure 3 A computer system is illustrated.

[0009] Figure 4 An example architecture of an autonomous vehicle is shown.

[0010] Figure 5 Shows examples of inputs and outputs that a perception module can use.

[0011] Figure 6 An example of a LiDAR system is shown.

[0012] Figure 7 The LiDAR system is shown in operation.

[0013] Figure 8 The operation of the LiDAR system is shown in additional detail.

[0014] Fig. 9 Block diagram showing the relationship between the inputs and outputs of the planning module.

[0015] Fig.10 Shows the directed graph used in path planning.

[0016] Fig.11 A block diagram showing the inputs and outputs of the control module.

[0017] Fig.12 A block diagram showing the inputs, outputs, and components of the controller.

[0018] Fig.13A and Fig. 13B A comparison between an unconstrained calibration path and a constrained calibration path is shown.

[0019] Fig.14 A block diagram of the calibration system is shown.

[0020] Fig.15 An exemplary calibration learning path for a vehicle is shown.

[0021] Fig.16 A flow chart showing a process for generating a calibrated learning path for a vehicle. DETAILED DESCRIPTION

[0022] In the following description, for the purpose of explanation, many specific details are set forth in order to provide a thorough understanding of the present invention. However, it is apparent that the present invention can be implemented without these specific details. In other examples, well-known configurations and devices are shown in block diagram form to avoid unnecessarily obscuring the present invention.

[0023] In the accompanying drawings, for ease of description, the specific arrangement or order of the schematic elements is shown, such as those elements representing devices, modules, instruction blocks, and data elements. However, it should be understood by those skilled in the art that the specific ordering or arrangement of the schematic elements in the accompanying drawings does not mean that a specific processing order or sequence or separation of the processing process is required. In addition, the inclusion of schematic elements in the accompanying drawings does not mean that such elements are required in all embodiments, nor does it mean that the features represented by such elements cannot be included in some embodiments or cannot be combined with other elements in some embodiments.

[0024] In addition, in the accompanying drawings, connecting elements, such as solid or dotted lines or arrows, are used to illustrate the connection, relationship or association between two or more other schematic elements, and the absence of any such connecting elements does not mean that there can be no connection, relationship or association. In other words, the connection, relationship or association between some elements are not shown in the accompanying drawings so as not to cover up the present invention. In addition, for ease of explanation, a single connecting element is used to represent multiple connections, relationships or associations between elements. For example, if a connecting element represents the communication of a signal, data or instruction, it will be understood by those skilled in the art that the element represents one or more signal paths (e.g., buses) that may be needed to affect the communication.

[0025] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the detailed description that follows, numerous specific details are set forth in order to provide a thorough understanding of the various embodiments described. However, it will be apparent to one of ordinary skill in the art that the various embodiments described may be implemented without these specific details. In other cases, well-known methods, procedures, components, circuits, and networks are not described in detail in order not to unnecessarily obscure aspects of the embodiments.

[0026] Several features described below may be used independently of each other or in combination with other features. However, any individual feature may not solve any of the above problems, or may only solve one of the above problems. Some of the problems discussed above may not be fully solved by any one of the features described herein. Although headings are provided, information related to a heading but not found in that heading may be found elsewhere in this description. Embodiments are described herein according to the following summary:

[0027] 1. General Overview

[0028] 2. System Overview

[0029] 3. Autonomous Vehicle Architecture

[0030] 4. Autonomous Vehicle Input

[0031] 5. Autonomous vehicle planning

[0032] 6. Autonomous Vehicle Control

[0033] 7. Calibrate the system

[0034] General Overview

[0035] An unsupervised calibration system performs sensor calibration and validation by utilizing a vehicle's path planning (e.g., by selecting a path that includes features used in calibration). Typically, sensor calibration requires exploration of a parameter space, where the parameter space is all possible combinations of values ​​associated with a set of sensor parameters. The calibration system utilizes a road network that inherently provides a variety of vehicle trajectories that enable sensors to effectively explore the parameter space. In addition, the structure along the road network provides calibration targets that also allow the sensor to explore the parameter space. When operating in calibration mode, the calibration system generates calibration-aware paths along the road network, where each calibration-aware path includes vehicle trajectories and structures that contribute to sensor calibration. The system scores each path based on driving factors and / or calibration factors, and selects one of the paths based on the score. Then, as the vehicle travels along the selected path, the sensor is calibrated because the various vehicle trajectories and structures along the path allow the sensor to explore the parameter space.

[0036] Some advantages of the calibration system include the ability to perform sensor calibration without the need for dedicated calibration facilities. In addition, the calibration system improves the efficiency of the services provided by the vehicle because sensor calibration of the vehicle can be performed without removing the vehicle from deployment. In addition, the ability to perform sensor calibration while the vehicle is deployed avoids any unnecessary delays or postponements in sensor calibration. This continuous updating of parameters improves vehicle mapping, positioning, planning, control and perception, and in turn improves passenger safety.

[0037] System Overview

[0038] Figure 1 An example of an autonomous vehicle 100 having autonomous capabilities is shown.

[0039] As used herein, the term "autonomous capability" 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.

[0040] As used herein, an autonomous vehicle (AV) is a vehicle with autonomous capabilities.

[0041] As used herein, "vehicle" includes a mode of transport of goods or people. For example, a car, bus, train, airplane, drone, truck, boat, ship, submersible, spacecraft, etc. An unmanned car is an example of a vehicle.

[0042] 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 an initial location or starting location, and the second spatiotemporal location is referred to as a destination, final location, target, target location, or target location. In some examples, a track consists of one or more road segments (e.g., several sections of a road), and each road segment consists of one or more blocks (e.g., a portion of a lane or intersection). In embodiments, a spatiotemporal location corresponds to a real-world location. For example, a spatiotemporal location is a pickup or drop-off location to get people or cargo on or off the vehicle.

[0043] As used herein, "sensor(s)" includes one or more hardware components for detecting information about the environment surrounding the sensor. Some hardware components may include sensing components (e.g., image sensors, biometric sensors), transmission and / or reception 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.

[0044] 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 external to the AV.

[0045] As used herein, a "road" is a physical area that can be traversed by a vehicle and may correspond to a named passage (e.g., a city street, an interstate highway, etc.) or may correspond to an unnamed passage (e.g., a driveway within a house or office building, a section of a parking lot, a section of a vacant parking lot, a dirt passage in a rural area, etc.). Because some vehicles (e.g., four-wheel drive pickup trucks, off-road vehicles (SUVs), etc.) are able to traverse a variety of physical areas that are not particularly suitable for vehicle travel, a "road" may be a physical area that is not formally defined as a passage.

[0046] As used herein, a "lane" is a portion of a road that can be traversed by a vehicle. Lanes are sometimes identified based on lane markings. For example, a lane may correspond to most or all of the space between lane markings, or only some of the space between lane markings (e.g., less than 50%). For example, a road with lane markings that are far apart may accommodate two or more vehicles between the markings, so that one vehicle can pass another vehicle without crossing the lane markings, and thus may be interpreted as having a lane that is narrower than the space between the lane markings, or having two lanes between the lanes. Lanes may also be interpreted in the absence of lane markings. For example, lanes may be defined based on physical features of the environment, such as rocks and trees along a pathway in a rural area, or natural obstacles to be avoided in an undeveloped area, for example. Lanes may also be interpreted in a manner that is independent of lane markings or physical features. For example, in an area lacking features that would be interpreted as lane boundaries, a lane may be interpreted as an arbitrary path without obstacles. In an example scenario, an AV may interpret a lane as an unobstructed portion through a field or open space. In another example scenario, an AV may interpret a lane through a wide road (e.g., up to two or more lanes wide) without lane markings. In this scenario, the AV can transmit information about the lane to other AVs so that the other AVs can use the same lane information to coordinate path planning between them.

[0047] The term “Over-the-Air (OTA) Client” includes any AV or any electronic device (e.g., computer, controller, IoT device, Electronic Control Unit (ECU)) embedded in, coupled to, or in communication with an AV.

[0048] The term "Over-the-Air (OTA) Update" refers to any update, change, 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 wireless area networks (e.g., WiFi), and / or satellite Internet.

[0049] The term “edge node” refers to one or more edge devices coupled to a network that provide a portal for communicating with AVs and can communicate with other edge nodes and cloud-based computing platforms for scheduling and delivering OTA updates to OTA clients.

[0050] The term "edge device" refers to a device that implements an edge node and provides a physical wireless access point (AP) to an enterprise or service provider (e.g., VERIZON, AT&T) core network. Examples of edge devices include, but are not limited to: computers, controllers, transmitters, routers, routing switches, integrated access devices (IADs), multiplexers, metropolitan area networks (MANs), and wide area network (WAN) access devices.

[0051] “One or more” includes a function performed by one element, a function performed by multiple elements, such as in a distributed manner, several functions performed by one element, several functions performed by several elements, or any combination of the above.

[0052] 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 only used 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.

[0053] The terms used in the specification of the various embodiments described herein are only used for the purpose of describing specific embodiments, and are not intended to be limiting. As used in the specification of the various embodiments described and the appended claims, the singular form is also intended to include the plural form, 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 related list items. It will also be understood that when the terms "include", "comprise", "have" and / or "have" are used in this specification, it is specifically stated that there are stated features, integers, steps, operations, elements and / or components, but it does not exclude the existence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0054] As used herein, the term "if" is alternatively understood to mean "when" or "at the time" or "in response to determining that" or "in response to detecting," depending on the context. Similarly, the phrases "if it has been determined" or "if [the stated condition or event] has been detected" are alternatively understood to mean "upon determination" or "in response to determining that" or "upon detecting [the stated condition or event]" or "in response to detecting [the stated condition or event]," depending on the context.

[0055] As used herein, an AV system refers to an AV and the array of hardware, software, stored data, and real-time generated data that supports the operation of the AV. In an embodiment, the AV system is incorporated into the AV. In an embodiment, the AV system is distributed across several locations. For example, some software of the AV system is in a system similar to the following about Figure 2 The described cloud computing environment 200 is implemented in a cloud computing environment.

[0056] In general, this document describes technologies applicable to any vehicle having 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 Definitions of Terms Relating to Automated Driving Systems for On-Road Motor Vehicles, which is incorporated by reference in its entirety into this document for more details on vehicle autonomy levels). The technologies 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 Definitions of Terms Relating to Automated Driving Systems for On-Road Motor Vehicles). 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 using maps) under certain operating conditions based on processing of sensor inputs. The technologies described in this document may benefit any level of vehicle ranging from fully autonomous vehicles to human-operated vehicles.

[0057] Autonomous vehicles have advantages over vehicles that require human drivers. One advantage is safety. From 1965 to 2015, the number of traffic deaths per 100 million miles in the United States has decreased, in part due to the additional safety measures deployed in vehicles. For example, an extra half-second of warning that a collision is about to occur is believed to mitigate front-to-rear collisions. However, passive safety features (e.g., seat belts, air bags) may have reached their limits in improving this number. Therefore, active safety measures such as automatic control of vehicles may be the next step to improve these statistics. Because human drivers are believed to be responsible for severe pre-crash accidents in the majority of collisions, autonomous driving systems may achieve better safety outcomes, such as identifying and avoiding critical situations more reliably than humans; making better decisions, obeying traffic rules, and predicting future events better than humans; and controlling vehicles more reliably than humans.

[0058] refer to Figure 1, the AV system 120 causes the AV 100 to operate along a trajectory 198, through an environment 190 to a destination 199 (sometimes referred to as a 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).

[0059] In an embodiment, the AV system 120 includes means 101 for receiving and operating commands from a computer processor 146. The term "operating command" is used to refer to an executable instruction (or set of instructions) that causes a vehicle to perform an action (e.g., a driving maneuver). The operating command may include, but is not limited to, instructions for the vehicle to start moving forward, stop moving forward, start moving backward, stop moving backward, speed up, slow down, make a left turn, and make a right turn. In an embodiment, the computing processor 146 and the following reference Figure 3 The processor 304 is similarly described. Examples of devices 101 include steering controls 102, brakes 103, gears, an accelerator pedal or other acceleration control mechanism, windshield wipers, side door locks, window controls, and turn indicators.

[0060] In an embodiment, the AV system 120 includes sensors 121 for measuring or inferring attributes of the state or condition of the AV 100, such as the position, linear and angular velocity and acceleration, and heading (e.g., the direction of the front end of the AV 100) of the AV. Examples of sensors 121 are GPS, an inertial measurement unit (IMU) that measures both linear acceleration and angular rate of the vehicle, wheel rate sensors for measuring or estimating wheel slip, wheel brake pressure or brake torque sensors, engine torque or wheel torque sensors, and steering angle and angular rate sensors.

[0061] In an embodiment, the sensors 121 also include sensors for sensing or measuring properties of the environment of the AV, such as monocular or stereo cameras 122 in the visible, infrared, or thermal (or both) spectrum, LiDAR 123, RADAR, ultrasonic sensors, time-of-flight (TOF) depth sensors, velocity sensors, temperature sensors, humidity sensors, and precipitation sensors.

[0062] In an 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 an embodiment, the data storage unit 142 is associated with the following Figure 3190. In an embodiment, the memory 144 is similar to the main memory 306 described below. In an embodiment, the data storage unit 142 and the memory 144 store historical, real-time, and / or predictive information about the environment 190. In an embodiment, the stored information includes maps, driving performance, traffic congestion updates, or weather conditions. In an embodiment, data related to the environment 190 is transmitted from the remote database 134 to the AV 100 via a communication channel.

[0063] In an embodiment, the AV system 120 includes a communication device 140 for transmitting measured or inferred properties of the state and condition of other vehicles (such as position, linear and angular velocity, linear and angular acceleration, and linear and angular heading) to the AV 100. These devices include vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication devices and devices for wireless communication through point-to-point or ad hoc networks or both. In an embodiment, the 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) communications (and in some embodiments, one or more other types of communications) is sometimes referred to as vehicle-to-everything (V2X) communications. V2X communications typically comply with one or more communication standards for communication with and between autonomous vehicles.

[0064] In an embodiment, the communication device 140 includes a communication interface. For example, a wired, wireless, WiMAX, WiFi, Bluetooth, satellite, cellular, optical, near field, infrared, or radio interface. The communication interface transmits data from the remote database 134 to the AV system 120. In an embodiment, the remote database 134 is embedded in a Figure 2 The communication interface 140 transmits data collected from the sensor 121 or other data related to the operation of the AV 100 to the remote database 134. In an embodiment, the communication interface 140 transmits information related to teleoperation to the AV 100. In some embodiments, the AV 100 communicates with other remote (e.g., "cloud") servers 136.

[0065] In an embodiment, the remote database 134 also stores and transmits digital data (e.g., stores data such as roads and street locations). Such data is stored in the memory 144 on the AV 100 or transmitted from the remote database 134 to the AV 100 via a communication channel.

[0066] In an embodiment, the remote database 134 stores and transmits historical information (e.g., speed and acceleration profiles) related to driving attributes of vehicles that have previously traveled along the trajectory 198 at similar times of day. In one implementation, such data may be stored in the memory 144 on the AV 100 or transmitted from the remote database 134 to the AV 100 via a communication channel.

[0067] The computing device 146 located on the AV 100 algorithmically generates control actions based on both real-time sensor data and a priori information, allowing the AV system 120 to perform its autonomous driving capabilities.

[0068] In an embodiment, the AV system 120 includes a computer peripheral device 132 coupled to the computing device 146 for providing information and reminders to a user of the AV 100 (e.g., an occupant or a remote user) and receiving input from the user. In an embodiment, the peripheral device 132 is similar to the one described below with reference to Figure 3 Display 312, input device 314 and cursor control 316 are discussed. The coupling may be wireless or wired. Any two or more of the interface devices may be integrated into a single device.

[0069] In an embodiment, the AV system 120 receives and enforces a passenger's privacy level (e.g., specified by the passenger or stored in a profile associated with the passenger). The passenger's privacy level determines how certain information associated with the passenger (e.g., passenger comfort data, biometric data, etc.) is allowed to be used, stored in the passenger profile, and / or stored on the cloud server 136 and associated with the passenger profile. In an embodiment, the privacy level specifies certain information associated with the passenger that is deleted once the ride is completed. In an embodiment, the privacy level specifies certain information associated with the passenger and identifies one or more entities that are authorized to access the information. Examples of specified entities that are authorized to access the information may include other AVs, third-party AV systems, or any entity that can potentially access the information.

[0070] The privacy level of a passenger may be specified at one or more levels of granularity. In an embodiment, the privacy level identifies specific information to be stored or shared. In an embodiment, the privacy level applies to all information associated with the passenger, so that the passenger can specify that none of their personal information is to be stored or shared. Specifications for entities that are permitted to access specific information may also be specified at various levels of granularity. The various sets of entities that are permitted to access specific information may include, for example, other AVs, cloud servers 136, specific third-party AV systems, etc.

[0071] In an embodiment, the AV system 120 or cloud server 136 determines whether certain information associated with a passenger is accessible to the AV 100 or other entities. For example, a third-party AV system attempting to access passenger input associated with a particular spatiotemporal location must obtain authorization, for example, from the AV system 120 or cloud server 136 to access information associated with the passenger. For example, the AV system 120 uses the passenger's specified privacy level to determine whether the passenger input associated with the spatiotemporal location can be presented to a third-party AV system, AV 100, or other AV. This enables the passenger's privacy level to specify which other entities are allowed to receive data related to the passenger's actions or other data associated with the passenger.

[0072] Figure 2 An example "cloud" computing environment is illustrated. Cloud computing is a service delivery model for enabling convenient, on-demand access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) over a network. 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 , cloud computing environment 200 includes cloud data centers 204a, 204b, and 204c interconnected by cloud 202. Data centers 204a, 204b, and 204c provide cloud computing services for computer systems 206a, 206b, 206c, 206d, 206e, and 206f connected to cloud 202.

[0073] The cloud computing environment 200 includes one or more cloud data centers. Figure 2 The cloud data center 204a shown in FIG. 2 refers to a cloud (eg, Figure 2 The physical arrangement of servers in a cloud 202 or a specific portion of a cloud (as shown in FIG. 202 or a specific portion of a cloud). For example, servers are physically arranged into rooms, groups, rows, and racks in a cloud data center. A cloud data center has one or more zones, which include 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 zones, 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 an embodiment, server nodes are similar to Figure 3 The data center 204a has many computing systems distributed across multiple racks.

[0074] 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) used to connect cloud data centers 204a, 204b, and 204c and help facilitate access to cloud computing services by computing systems 206a-f. In an embodiment, the network represents any combination of one or more local networks, wide area networks, or internetworks coupled by wired or wireless links deployed using terrestrial or satellite connections. Data exchanged through the network is transmitted using a variety of network layer protocols (such as Internet Protocol (IP), Multi-Protocol Label Switching (MPLS), Asynchronous Transfer Mode (ATM), Frame Relay, etc.). In addition, 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, etc.).

[0075] Computing systems 206a-f or cloud computing service consumers are connected to the cloud 202 through network links and network adapters. In an embodiment, computing systems 206a-f are implemented as various computing devices, such as servers, desktops, laptops, tablets, smart phones, Internet of Things (IoT) devices, autonomous vehicles (including cars, drones, shuttles, trains, buses, etc.) and consumer electronics. In an embodiment, computing systems 206a-f are implemented in other systems or as part of other systems.

[0076] Figure 3 Computer system 300 is illustrated. In implementation, computer system 300 is a special purpose computing device. The special purpose computing device is hardwired to perform these techniques, or includes a digital electronic device such as one or more application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are permanently programmed to perform the above-mentioned techniques, or may include one or more general purpose hardware processors that are programmed to perform these techniques according to program instructions in firmware, memory, other memory, or a combination. Such a special purpose computing device can also combine customized hardwired logic, ASICs or FPGAs with customized programming to complete these techniques. In various embodiments, the special purpose computing device is a desktop computer system, a portable computer system, a handheld device, a network device, or any other device that contains hardwired and / or program logic to implement these techniques.

[0077] In an embodiment, computer system 300 includes a bus 302 or other communication mechanism for communicating information, and a hardware processor 304 coupled to bus 302 to process information. Hardware processor 304 is, for example, a general-purpose microprocessor. Computer system 300 also includes a main memory 306, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 302 to store information and instructions, which are executed by processor 304. In one implementation, main memory 306 is used to store temporary variables or other intermediate information during the execution of instructions to be executed by processor 304. When these instructions are stored in a non-transitory storage medium accessible to processor 304, computer system 300 becomes a special-purpose machine that is customized to perform the operations specified in the instructions.

[0078] In an embodiment, computer system 300 also includes a read only memory (ROM) 308 or other static storage device coupled to bus 302 for storing static information and instructions for processor 304. A storage device 310, such as a magnetic disk, optical disk, solid state drive, or three-dimensional cross point memory, is provided and coupled to bus 302 to store information and instructions.

[0079] In an embodiment, the computer system 300 is coupled to a display 312 such as a cathode ray tube (CRT), a liquid crystal display (LCD), a plasma display, a light emitting diode (LED) display, or an organic light emitting diode (OLED) display for displaying information to a computer user via bus 302. An input device 314 including alphanumeric and other keys is coupled to bus 302 for communicating information and command selections to processor 304. Another type of user input device is a cursor controller 316, such as a mouse, a trackball, a touch display, or cursor direction keys, for communicating direction information and command selections to processor 304 and for controlling movement of a cursor on display 312. Such input devices typically have two degrees of freedom in two axes, a first axis (e.g., an x-axis) and a second axis (e.g., a y-axis) that allow the device to specify a position on a plane.

[0080] According to one embodiment, the techniques herein are performed 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. Execution of the sequences of instructions contained in main memory 306 causes processor 304 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry is used in place of or in combination with software instructions.

[0081] As used herein, the term "storage medium" refers to any non-temporary medium that stores data and / or instructions that cause 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 disks, magnetic disks, solid-state drives, or three-dimensional cross-point memories such as storage device 310. Volatile media include dynamic memories such as main memory 306. Common forms of storage media include, for example, floppy disks, floppy disks, hard disks, solid-state drives, tapes or any other magnetic data storage media, CD-ROMs, any other optical data storage media, any physical media with a hole pattern, RAM, PROM and EPROM, FLASH-EPROM, NV-RAM, or any other memory chip or storage box.

[0082] Storage media are distinct from transmission media, but can be used in conjunction with transmission media. Transmission media participate in the transmission of information between storage media. For example, transmission media include coaxial cables, copper wires, and optical fibers, including wires that provide 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 communications.

[0083] In an embodiment, various forms of media are involved in carrying one or more sequences of one or more instructions to the processor 304 for execution. For example, the instructions are initially executed on a disk or solid-state drive of a remote computer. The remote computer loads the instructions into its dynamic memory and sends the instructions over a telephone line using a modem. The local modem of the computer system 300 receives the data on the telephone line and uses an infrared transmitter to convert the data to an infrared signal. An infrared detector receives the data carried in the infrared signal, and appropriate circuitry places the data on the bus 302. The bus 302 carries the data to the main memory 306, from which the processor 304 retrieves and executes the instructions. The instructions received by the main memory 306 may optionally be stored on the storage device 310 before or after execution by the processor 304.

[0084] Computer system 300 also includes a communication interface 318 coupled to bus 302. Communication interface 318 provides a two-way data communication coupled to a network link 320 connected to a 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 for providing a data communication connection with a corresponding type of telephone line. As another example, communication interface 318 is a local area network (LAN) card for providing a data communication connection with a compatible LAN. In some implementations, a wireless link is also implemented. In any such implementation, communication interface 318 sends and receives an electrical, electromagnetic or optical signal carrying a digital data stream representing various types of information.

[0085] The network link 320 typically provides data communication to other data devices through one or more networks. For example, the network link 320 provides a connection to a host computer 324 or to a cloud data center or device operated by an Internet Service Provider (ISP) 326 through a local network 322. The ISP 326, in turn, provides data communication services through a worldwide packet data communication network now commonly referred to as the "Internet" 328. Both the local network 322 and the Internet 328 use electrical, electromagnetic, or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link 320 and through the communication interface 318 are example forms of transmission media, where these signals carry digital data to and from the computer system 300. In an embodiment, the network 320 includes the cloud 202 described above or a portion of the cloud 202.

[0086] Computer system 300 sends messages and receives data, including program code, through network(s), network link 320, and communication interface 318. In an embodiment, computer system 300 receives code for processing. The received code is executed by processor 304 as it is received, and / or stored in storage device 310, or other non-volatile storage for later execution.

[0087] Autonomous Vehicle Architecture

[0088] Figure 4 An example of a method for autonomous vehicles (e.g., Figure 1 100). The architecture 400 includes a sensing module 402 (sometimes referred to as sensing circuitry), a planning module 404 (sometimes referred to as planning circuitry), a control module 406 (sometimes referred to as control circuitry), a positioning module 408 (sometimes referred to as positioning circuitry), and a database module 410 (sometimes referred to as database circuitry). Each module plays a role in the operation of the AV 100. Collectively, the modules 402, 404, 406, 408, and 410 may be Figure 1 404, 406, 408, and 410 are a portion of the AV system 120 shown. In some embodiments, any of the 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). Individual modules in the modules 402, 404, 406, 408, and 410 are sometimes referred to as processing circuits (e.g., computer hardware, computer software, or a combination of both). The combination of any or all of the modules 402, 404, 406, 408, and 410 is also an example of a processing circuit.

[0089] In use, the planning module 404 receives data representing a destination 412 and determines data representing a trajectory 414 (sometimes referred to as a route) that the AV 100 may travel in order 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 perception module 402, the positioning module 408, and the database module 410.

[0090] The perception module 402 uses, for example, Figure 1 One or more sensors 121 are shown to identify nearby physical objects. 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.

[0091] The planning module 404 also receives data representing the AV position 418 from the positioning module 408. The positioning module 408 determines the AV position by using data from the sensor 121 and data from the database module 410 (e.g., geographic data) to calculate the position. For example, the positioning module 408 uses data from a GNSS (Global Navigation Satellite System) sensor and geographic data to calculate the longitude and latitude of the AV. In an embodiment, the data used by the positioning module 408 includes a high-precision map with lane geometry attributes, a map describing the road network connection attributes, a map describing the physical attributes of the lane (such as traffic speed, traffic volume, the number of vehicles and bicycle lanes, lane width, lane traffic direction, or lane marking type and location, or a combination thereof), and a map describing the spatial location of road features (such as intersections, traffic signs or various types of other driving signals, etc.). In an embodiment, the high-precision map is constructed by adding data to the low-precision map through automatic or manual annotations.

[0092] The control module 406 receives data representing the trajectory 414 and data representing the AV position 418, and operates control functions 420a-420c (e.g., steering, throttle, brakes, ignition) of the AV in a manner that will cause the AV 100 to travel the trajectory 414 to reach the destination 412. For example, if the trajectory 414 includes a left turn, the control module 406 will operate the control functions 420a-420c in the following manner: the steering angle of the steering function will cause the AV 100 to turn left, and the throttle and brakes will cause the AV 100 to pause and wait for a passing pedestrian or vehicle before making the turn.

[0093] Autonomous Vehicle Input

[0094] Figure 5 The perception module 402 ( Figure 4) used by the inputs 502a-502d (e.g., Figure 1 1) and outputs 504a-504d (e.g., sensor data). One input 502a is a LiDAR (Light Detection and Ranging) system (e.g., Figure 1 123). LiDAR is a technology that uses light (e.g., a beam of light such as infrared light) to obtain data about 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 a point cloud) used to construct a representation of the environment 190.

[0095] 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 the LiDAR system. The RADAR system 502b produces RADAR data as output 504b. For example, RADAR data is one or more radio frequency electromagnetic signals used to construct a representation of the environment 190.

[0096] 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 obtain information about nearby physical objects. The camera system generates camera data as output 504c. The camera data is typically in the form of image data (e.g., data in an image data format such as RAW, JPEG, PNG, etc.). In some examples, the camera system has multiple independent cameras, such as for the purpose of 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. In use, the camera system can be configured to "see" distant objects (e.g., as far as 1 km or more in front of the AV). Therefore, the camera system can have features such as sensors and lenses that are optimized for perceiving distant objects.

[0097] Another input 502d is a traffic light detection (TLD) system. The TLD system uses one or more cameras to obtain information about traffic lights, street signs, and other physical objects that provide visual navigation information. The TLD system generates TLD data as an output 504d. The TLD data often takes the form of image data (e.g., data in image data formats such as RAW, JPEG, PNG, etc.). The TLD system differs from a system that includes a camera in that the TLD system uses a camera with a wide field of view (e.g., using a wide-angle lens or a fisheye lens) to obtain information about as many physical objects that provide visual navigation information as possible, so that the AV 100 can access all relevant navigation information provided by these objects. For example, the viewing angle of the TLD system can be about 120 degrees or greater.

[0098] In some embodiments, sensor fusion techniques are used to combine the outputs 504a-504d. Thus, the individual outputs 504a-504d are provided to other systems of the AV 100 (e.g., to a Figure 4 The combined output may be provided to other systems in the form of a single combined output or multiple combined outputs of the same type (e.g., using the same combining technique or combining the same outputs, or both) or a single combined output or multiple combined outputs of different types (e.g., using different individual combining techniques or combining different individual outputs, or both). In some embodiments, an early fusion technique is used. An early fusion technique is characterized in that the outputs are combined before one or more data processing steps are applied to the combined output. In some embodiments, a late fusion technique is used. A late fusion technique is characterized in that the outputs are combined after one or more data processing steps are applied to the individual outputs.

[0099] Figure 6 An example of a LiDAR system 602 is shown (eg, Figure 5502a shown). The LiDAR system 602 emits light 604a-604c from a light 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 reflects back to the LiDAR system 602. (The light emitted from the LiDAR system typically does not penetrate a physical object, such as a physical object that is solid in form.) The LiDAR system 602 also has one or more light detectors 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 a field of view 614 of the LiDAR system. The image 612 includes information representing a boundary 616 of the physical object 608. In this way, the image 612 is used to determine the boundaries 616 of one or more physical objects near the AV.

[0100] Figure 7 6. The LiDAR system 602 is shown in operation. In the scenario shown in the figure, the AV 100 receives both the camera system output 504c in the form of an image 702 and the LiDAR system output 504a in the form of LiDAR data points 704. In use, the data processing system of the AV 100 compares the image 702 to the data points 704. In particular, physical objects 706 identified in the image 702 are also identified in the data points 704. In this way, the AV 100 perceives the boundaries of the physical objects based on the contours and density of the data points 704.

[0101] Figure 8 6 shows additional details of the operation of the LiDAR system 602. As described above, the AV 100 detects the boundaries of the physical object based on the characteristics of the data points detected by the LiDAR system 602. Figure 8 As shown, a flat object such as the ground 802 will reflect light 804a-804d emitted from the LiDAR system 602 in a consistent manner. In other words, because the LiDAR system 602 emits light using consistent intervals, the ground 802 will reflect light back to the LiDAR system 602 at the same consistent intervals. As the AV 100 drives over the ground 802, the LiDAR system 602 will continue to detect light reflected by the next valid ground point 806 if nothing is blocking the road. However, if an object 808 is blocking the road, the light 804e-804f emitted by the LiDAR system 602 will be reflected from points 810a-810b in a manner that is inconsistent with the expected consistent manner. Based on this information, the AV 100 can determine that the object 808 is present.

[0102] Path Planning

[0103] Fig. 9 Shown (for example, Figure 4 900 of the relationship between the input and output of the planning module 404 (shown in FIG. 1 ). In general, 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 end point 906 (e.g., a destination or final location). The route 902 is typically defined by one or more road segments. For example, a road segment refers to a distance to be traveled on at least a portion of a street, road, highway, lane, or other physical area suitable for automobile travel. In some examples, for example, if the AV 100 is an off-road capable vehicle such as a four-wheel drive (4WD) or all-wheel drive (AWD) car, SUV, or pickup truck, the route 902 includes "off-road" road segments such as unpaved paths or open fields.

[0104] In addition to the route 902, the planning module also outputs lane-level route planning data 908. The lane-level route planning data 908 is used to drive through the road segments of the route 902 at a specific time based on the conditions of the road segments. For example, if the route 902 includes a multi-lane highway, the lane-level route planning data 908 includes trajectory planning data 910, wherein the AV 100 can use the trajectory planning data 910 to select a lane from the multiple lanes based on, for example, whether an exit is approaching, whether there are other vehicles in one or more of the multiple lanes, or other factors that change over the course of a few minutes or less. Similarly, in some implementations, the lane-level route planning data 908 includes a speed constraint 912 that is specific to a road segment of the route 902. For example, if the road segment includes pedestrians or unexpected traffic, the speed constraint 912 can limit the AV 100 to a travel speed that is slower than the expected speed, such as a speed based on the speed limit data of the road segment.

[0105] In an embodiment, inputs to the planning module 404 include (e.g., Figure 4 ) database data 914, current location data 916 (e.g., Figure 4 AV position 418 shown), (e.g., for Figure 4 Destination data 918 and object data 920 (e.g., such as the destination 412 shown) Figure 44 (e.g., a classified object 416 perceived by the perception module 402 shown in FIG. 4 ). In some embodiments, the database data 914 includes rules used in planning. The rules are specified using a formal language (e.g., using Boolean logic). In any given situation encountered by the AV 100, at least some of these rules will apply to the situation. A rule applies to a given situation if it has a condition that is satisfied based on information available to the AV 100 (e.g., information about the surrounding environment). Rules can have priorities. For example, a rule of "if the road is a highway, move to the leftmost lane" can have a lower priority than "if the exit is within one mile, move to the rightmost lane."

[0106] Fig.10 In path planning (eg, by planning module 404 ( Figure 4 )) uses a directed graph 1000. In general, if Fig.10 A directed graph 1000 such as the one shown is used to determine a path between any origin 1002 and destination 1004. In the real world, the distance separating the origin 1002 and destination 1004 may be relatively large (e.g., in two different metropolitan areas), or may be relatively small (e.g., two intersections adjacent to a city block or two lanes of a multi-lane road).

[0107] In an embodiment, directed graph 1000 has nodes 1006a-1006d representing different locations that AV 100 may occupy between starting point 1002 and end point 1004. In some examples, for example, when starting point 1002 and end point 1004 represent different metropolitan areas, nodes 1006a-1006d represent sections of a road. In some examples, for example, when starting point 1002 and end point 1004 represent different locations on the same road, nodes 1006a-1006d represent different locations on the road. In this way, directed graph 1000 includes information at different granularity levels. In an embodiment, a directed graph with high granularity is also a subgraph of another directed graph with a larger scale. For example, most of the information for a directed graph where the start point 1002 and the end 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 a portion of the directed graph that represents a physical location in the field of view of the AV 100.

[0108] Nodes 1006a-1006d are different from objects 1008a-1008b that cannot overlap with nodes. In an embodiment, at low granularity, objects 1008a-1008b represent areas that cars cannot pass through, such as areas without streets or roads. At high granularity, objects 1008a-1008b represent physical objects in the field of view of AV 100, such as other cars, pedestrians, or other entities with which AV 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 street lights or telephone poles, etc.) or dynamic objects (e.g., objects that can change position, such as pedestrians or other cars, etc.).

[0109] Nodes 1006a-1006d are connected by edges 1010a-1010c. If two nodes 1006a-1006b are connected by edge 1010a, the AV 100 can travel between one node 1006a and another node 1006b, for example, without having to travel to an intermediate node before reaching the other node 1006b. (When referring to the AV 100 traveling between nodes, it means that the AV 100 travels between two physical locations represented by the corresponding nodes.) Edges 1010a-1010c are generally bidirectional, in the sense that the AV 100 travels 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, in the sense that the AV 100 can travel from a first node to a second node, but the AV 100 cannot travel from a second node to the first node. Edges 1010a-1010c are unidirectional where they 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.

[0110] In an embodiment, the planning module 404 uses the directed graph 1000 to identify a path 1012 consisting of nodes and edges between the start point 1002 and the end point 1004 .

[0111] Edges 1010a-1010c have associated costs 1014a-1014b. Costs 1014a-1014b are values ​​representing resources that would be expended if the AV 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, the associated cost 1014a of the first edge 1010a may be twice the associated cost 1014b of the second edge 1010b. Other factors that affect time include expected traffic, number of intersections, speed limits, etc. Another typical resource is fuel economy. Two edges 1010a-1010b may represent the same physical distance, but one edge 1010a requires more fuel than the other edge 1010b, for example, due to road conditions, expected weather, etc.

[0112] When the planning module 404 identifies a path 1012 between the start point 1002 and the end point 1004, the planning module 404 typically selects a path that is optimized for cost, eg, a path that has a minimum total cost when the individual costs of the edges are added together.

[0113] Autonomous Vehicle Control

[0114] Fig.11 Shown (for example, Figure 4 1100 of the inputs and outputs of the control module 406 (shown in FIG. 110). The control module operates according to a controller 1102, which includes, for example: one or more processors similar to the processor 304 (e.g., one or more computer processors such as a microprocessor or a microcontroller or both); short-term and / or long-term data storage devices similar to the main memory 306, ROM 308, and storage devices 310 (e.g., memory, random access memory, or flash memory or both); and instructions stored in the memory that, when executed (e.g., by the one or more processors), perform the operations of the controller 1102.

[0115] In an embodiment, the controller 1102 receives data representing a desired output 1104. The desired output 1104 typically includes speed, such as velocity and heading. The desired output 1104 may be based on, for example, Figure 4104 . Based on the desired output 1104, the controller 1102 generates data that can be used as a throttle input 1106 and a steering input 1108. The throttle input 1106 represents the amount by which the throttle of the AV 100 (e.g., an acceleration control) should be engaged to achieve the desired output 1104, such as by engaging a steering pedal or engaging another throttle control. In some examples, the throttle input 1106 also includes data that can be used to engage the brakes of the AV 100 (e.g., a deceleration control). The steering input 1108 represents a steering angle, such as the angle at which the steering control of the AV (e.g., a steering wheel, a steering angle actuator, or other function for controlling the steering angle) should be positioned to achieve the desired output 1104.

[0116] In an embodiment, the controller 1102 receives feedback used in adjusting the inputs provided to the throttle and steering. For example, if the AV 100 encounters a disturbance 1110, such as a hill, the measured velocity 1112 of the AV 100 drops below the desired output rate. In an embodiment, any measured output 1114 is provided to the controller 1102 so that the required adjustments can be made, for example, based on the difference 1113 between the measured velocity and the desired output. The measured output 1114 includes measured position 1116, measured velocity 1118 (including velocity and heading), measured acceleration 1120, and other outputs that the sensors of the AV 100 can measure.

[0117] In an embodiment, information about the disturbance 1110 is detected in advance, for example, by a sensor such as a camera or LiDAR sensor, and provided to the predictive feedback module 1122. The predictive feedback module 1122 then provides information to the controller 1102 that the controller 1102 can use to adjust accordingly. For example, if the sensors of the AV 100 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.

[0118] Fig.12 A block diagram 1200 is shown of the inputs, outputs, and components of the controller 1102. The controller 1102 has a rate analyzer 1206 that affects the operation of a throttle / brake controller 1204. For example, the rate analyzer 1202 instructs the throttle / brake controller 1204 to use a throttle / brake 1206 to accelerate or decelerate based on, for example, feedback received by the controller 1102 and processed by the rate analyzer 1202.

[0119] Controller 1102 also has a lateral tracking controller 1208 that affects the operation of steering wheel controller 1210. For example, lateral tracking controller 1208 instructs steering wheel controller 1210 to adjust the position of steering angle actuator 1212 based on feedback received by controller 1102 and processed by lateral tracking controller 1208, for example.

[0120] The controller 1102 receives several inputs used to determine how to control the throttle / brake 1206 and the steering angle actuator 1212. The planning module 404 provides information used by the controller 1102, for example, to select a heading for the AV 100 to begin operation and to determine which road segment to cross when the AV 100 reaches an intersection. The positioning module 408 provides information describing the current location of the AV 100 to the controller 1102, for example, so that the controller 1102 can determine whether the AV 100 is in the expected location based on the way the throttle / brake 1206 and the steering angle actuator 1212 are being controlled. In an embodiment, the controller 1102 receives information from other inputs 1214, such as information received from a database, a computer network, etc.

[0121] Calibration system

[0122] Sensor calibration requires exploring the sensor's parameter space, where the parameter space is all possible combinations of values ​​(or portions thereof) associated with a set of sensor parameters. Traditionally, vehicle sensor calibration is performed in a structured environment (e.g., a designated indoor facility) where the vehicle follows an unconstrained path that allows the sensor to explore its parameter space. However, this process is expensive and time consuming, particularly because the vehicle must be removed from the road for calibration.

[0123] In order to more efficiently perform vehicle sensor calibration, the disclosed calibration system generates a constrained calibration path along a road network. The road network inherently provides various vehicle trajectories that enable sensors to effectively explore their parameter space. The constrained path (also referred to as a calibration cognitive path) includes a calibration trajectory that enables sensors to effectively explore their parameter space. The calibration trajectory includes any vehicle movement along the axis of the vehicle coordinate system. Examples of calibration trajectories include vehicle turns (e.g., 90-degree turns or circular turns), height changes (e.g., driving along a sloped road), acceleration, braking, and other examples. In addition, the structure along the road network provides calibration targets that also allow sensors to explore their parameter space. Therefore, as the vehicle travels along the calibration cognitive path, the vehicle's sensors can be calibrated using the calibration trajectory and / or calibration targets. By calibrating along the road network, the disclosed calibration system reduces the need for dedicated calibration facilities and enables more efficient sensor calibration.

[0124] Fig.13A and Fig. 13B A comparison between an unconstrained calibration path 1300 and a constrained calibration path 1302 is shown. Fig.13A As shown, when the vehicle is in an unconstrained environment, the vehicle can travel along the unconstrained calibration path 1300 to calibrate the vehicle's sensors. However, the vehicle cannot follow the unconstrained calibration path 1300 in a constrained environment including infrastructure such as roads and buildings (e.g., building 1304). Fig. 13B As shown, due to the infrastructure and regulations (e.g., driving rules) in the constrained environment, the vehicle cannot follow an unconstrained path on a designated road. In order to achieve the same calibration in the constrained environment, the disclosed calibration system generates a constrained calibration path 1302. Fig. 13B As shown, the constrained calibration path 1302 includes two segments 1306a, 1306b that, in combination, have a similar overall trajectory as the unconstrained calibration path 1300. As such, the constrained calibration path 1302 achieves the same sensor calibration as the unconstrained calibration path 1300, despite being constrained by the environment.

[0125] Fig.14 1 shows a block diagram of a calibration system 1400. The calibration system 1400 is configured to generate a calibration learning path to calibrate at least one sensor of a vehicle. Fig.14 As shown, calibration system 1400 includes controller 1402 and path generator 1404. Fig.14 1400 is depicted as a standalone system, but in some examples, calibration system 1400 is part of other systems, such as planning module 404 (e.g., Figure 4 ), etc. In addition, although the subsequent description describes the calibration system 1400 as generating a calibration cognitive path to calibrate a single sensor, in some examples, the calibration cognitive path is used to calibrate multiple sensors. In these examples, the calibration cognitive path can be used to calibrate multiple sensors at the same time, or different sections of the calibration cognitive path can be used to calibrate different sensors. For example, the calibration system 1400 can generate a single calibration cognitive path based on information associated with multiple sensors, or can generate corresponding calibration cognitive paths for each sensor, which are then combined to form an overall calibration cognitive path.

[0126] In an embodiment, the controller 1402 identifies a trigger for calibrating a sensor of a vehicle. In an example, the trigger is a threshold amount of time that has passed since the last calibration of the sensor. In another example, the trigger is a calibration request received from a sensor or a system that monitors the calibration of the sensor. In response to identifying the trigger, the controller 1402 may determine whether to calibrate the sensor based on factors such as the current time (e.g., morning or evening) and whether the vehicle is currently transporting passengers. In an example, the controller 1402 can be configured to calibrate the sensor at a certain time of day or under certain conditions (e.g., the vehicle is between passenger transport trips).

[0127] If the controller 1402 determines not to calibrate the sensor, the controller 1402 may reschedule the calibration at a later time. Conversely, if the controller 1402 determines to calibrate, the controller 1402 sends a request to the path generator 1404 to generate a calibrated cognitive path for the vehicle. The request may include trip information (e.g., current vehicle location, trip destination, and preferred travel direction) and / or calibration information (e.g., the type of sensor to be calibrated and the current sensor offset).

[0128] In an embodiment, path generator 1404 receives a request from controller 1402 as input and generates candidate calibration learning paths as output. Fig.14 As shown, the path generator 1404 includes a wide area map 1410 and a calibration cognitive path database 1412. The path generator 1404 uses the wide area map 1410 and the calibration cognitive path database 1412 to generate candidate calibration cognitive paths. Figure 4 The planning module 404 may work together or may be Figure 4 The wide area map 1410 includes information associated with the geographic area, such as information identifying the shape, geographic location coordinates, and altitude of various objects, such as roads, features used to define driving lanes, intersections, crossroads, traffic lights, buildings, signs, vegetation, or other such objects and information. The calibration cognitive path database 1412 includes previously generated calibration cognitive paths (e.g., generated by the calibration system 1400 or other systems). Each calibration cognitive path may include identification information, such as the coordinates of the road associated with the path, the type of sensor that the path is used to calibrate, the calibration trajectory and / or calibration target along the path, and any other details of the calibration performed using the path.

[0129] In an embodiment, the path generator 1404 generates candidate calibrated cognitive paths using one of two processes. In the first process, the path generator 1404 uses the trip information of the vehicle and the wide area map 1410 to determine possible paths from the current location of the vehicle. If the trip information includes a destination, the path generator 1404 uses the wide area map 1410 to determine possible paths to the destination. And if the request does not include a destination, the path generator 1404 detects all possible paths for the vehicle, which may be within a threshold distance or subject to certain constraints (e.g., a preferred direction of travel). In some scenarios, the path generator 1404 generates a path that includes a section that is not along a road. For example, the section may pass through a parking space, a parking structure, or other publicly accessible area.

[0130] Once the path generator 1404 generates the possible paths, the path generator 1404 evaluates the paths to determine which paths include calibration tracks and / or calibration targets that can be used to calibrate the sensor. If the request from the controller 1402 includes calibration information, the path generator 1404 uses the calibration information to evaluate the possible paths. And if the request does not include calibration information, the path generator 1404 evaluates the possible paths based on a predetermined reference value. Based on the evaluation of the calibration tracks, the path generator 1404 selects a candidate calibration cognitive path. For example, the selected path includes all possible paths that can be used to calibrate the sensor.

[0131] In some embodiments, after generating possible paths, path generator 1404 determines whether calibration cognitive path database 1412 includes a previously generated calibration cognitive path that overlaps with the possible path. As described above, the stored calibration cognitive paths indicate calibration information for previous calibrations performed along the paths. Therefore, path generator 1404 can use this information to determine whether a particular stored calibration cognitive path is suitable for calibration. If path generator 1404 identifies a suitable calibration cognitive path, path generator 1404 selects the path among the candidate calibration cognitive paths.

[0132] In a second process of generating candidate calibration learning paths, if the request includes calibration information, path generator 1404 uses the calibration information to generate a calibration trajectory that can be used to perform the desired calibration. Path generator 1404 then uses wide area map 1410 to identify possible paths that include the generated calibration trajectory. In some examples, path generator 1404 adjusts the generated calibration trajectory to fit the possible paths (e.g., fit the Fig. 13B Possible paths that include the generated calibration trajectory are designated as candidate calibration cognitive paths.

[0133] In an embodiment, after the path generator 1404 generates the candidate calibration cognitive path, the path generator 1404 provides the path to the controller 1402. The controller 1402 uses the calibration factor 1406 and / or the driving factor 1408 to evaluate the path. Examples of calibration factors include the number of calibration targets along the path, the road surface conditions along the path (e.g., bumps, potholes, speed bumps, uneven roads), the number of calibration tracks along the path, or the degree of parameter space explored along the path. Examples of driving factors include the duration of travel along the path, the distance of the path, the degree of traffic along the path, or the driving rules along the path. Based on the evaluation of the candidate paths, the controller 1402 assigns corresponding scores to each path. The controller 1402 selects the calibration cognitive path for the vehicle based on the score. In the example, the controller 1402 selects the calibration cognitive path with the highest overall score. In some examples, the controller 1402 can give different categories of scores to each path, such as distance score, driver comfort score, calibration score, and time score. The controller 1402 can then (e.g., based on predetermined instructions) select the path with the highest score in any of these categories. In addition, the controller 1402 can select a path optimized for resource cost, e.g., the path with the smallest total cost when the individual costs of the path are added together. A typical resource is time. Other factors that affect time include expected traffic, the number of intersections, speed limits, etc. Another typical resource is fuel economy. Two paths can represent the same physical distance, but one path may require more fuel than another path, e.g., due to road conditions, expected weather, etc.

[0134] In a scenario where a vehicle transports passengers, the controller 1402 may additionally and / or optionally evaluate candidate paths based on user feedback. For example, the controller 1402 may provide information indicating candidate calibration cognitive paths and trip information associated with each path to the passenger, for example, via a passenger device. The trip information may include an indication of the trip length, trip cost, and passenger comfort. In some examples, the controller 1402 uses commercial incentives (such as discounts and rewards, etc.) to motivate passengers to select a calibration cognitive path (e.g., a calibration cognitive path with the highest overall score). The controller 1402 uses the path of the vehicle selected by the user.

[0135] Fig.15 An example calibration path generated in scenario 1500 is shown. In scenario 1500, a vehicle ( Fig.15 1502 and a destination 1504. Fig.15 As shown, the journey is in an environment including roads, buildings and other structures. Shaded squares (such as squares 1506, 1508) are used to represent structures.

[0136] In an embodiment, a calibration system of a vehicle identifies a trigger for calibrating a sensor of the vehicle. In response, the calibration system generates a plurality of calibration learning paths for the vehicle. Fig.15 As shown, the calibration system generates three paths identified as path 1506 (also referred to as "path 1"), path 1508 (also referred to as "path 2"), and path 1510 (also referred to as "path 3"). The calibration system evaluates the generated paths based on calibration factors and / or driving factors. For example, the calibration system evaluates the paths based on the number of strong calibration targets along the paths, the road surface conditions along the paths (e.g., bumps, potholes, speed bumps, uneven roads), the number of calibration trajectories along the paths (e.g., 90 degree turns and elevation changes), or the extent of parameter space explored along the paths. In some examples, the calibration system distinguishes between strong calibration targets (e.g., buildings) and weak calibration targets (e.g., vegetation). The calibration system assigns a corresponding score to each path and selects a path for the vehicle based on the score. In this scenario, the controller selects path 3, which is the path with the highest overall score of the three paths.

[0137] Fig.16 A flow chart of a process 1600 for generating a calibrated learning path for use with a vehicle including at least one sensor is shown. For example, the process may be performed by Fig.14 The calibration system 1400 is performed. At 1602, a trigger (e.g., time elapsed since last calibration, or an indication from the sensor or a system monitoring the sensor) for calibrating at least one sensor (e.g., monocular or stereo camera, infrared, thermal spectrum, ultrasonic sensor, time of flight (TOF) depth sensor, camera, radar, LiDAR, infrared, accelerometer) is identified.

[0138] In response to identifying the trigger, a calibration path planning mode of the vehicle is initiated at 1604. At 1606, a plurality of calibration cognitive paths are generated, each including at least one calibration trajectory (e.g., a 90 degree turn, a circular turn, a slope trajectory) along at least one road (e.g., an urban road in a city). The calibration cognitive paths may also include calibration targets (e.g., structures such as buildings, billboards, and specially deployed calibration targets). At 1608, a first calibration cognitive path for the vehicle is selected from the plurality of calibration cognitive paths. As the vehicle travels along the first calibration cognitive path, at least one sensor is calibrated.

[0139] In some implementations, process 1600 also involves causing the vehicle to travel along the first calibrated cognitive path (eg, by sending instructions to a controller of the vehicle).

[0140] In some embodiments, the plurality of calibration cognitive paths is a first plurality of calibration cognitive paths, and wherein processing 1600 further involves: after the vehicle has arrived at the destination, determining that at least one sensor has not completed calibration (e.g., by examining details of calibration during the first trip, by testing the sensor); in response to the determination, generating a second plurality of calibration cognitive paths; and causing the vehicle to travel along one of the second plurality of calibration cognitive paths.

[0141] In some implementations, selecting a first calibration cognitive path from a plurality of calibration cognitive paths involves: scoring the calibration cognitive path based on at least one of the calibration factors or driving factors associated with each of the calibration cognitive paths (e.g., a weighted score of the factors); and selecting the calibration cognitive path with the highest score.

[0142] In some implementations, the driving factors include at least one of travel duration along the route, distance of the route, traffic levels along the route, or driving regulations (eg, speed limits, road rules) along the route.

[0143] In some implementations, the calibration factors include at least one of the number of calibration targets along the path, the road surface conditions along the path (e.g., potholes, gentle slopes), the number of calibration vehicle trajectories along the path, and the extent of the parameter space explored along the path.

[0144] In some implementations, scoring individual calibration learning paths in the plurality of calibration learning paths is further based on the type of the at least one sensor (eg, scoring the paths by determining which paths best calibrate the desired sensor).

[0145] In some implementations, process 1600 also involves storing the first calibrated learning path in a database of calibrated learning paths (eg, a map database).

[0146] In some implementations, generating the plurality of calibration cognitive paths involves generating at least one of the plurality of calibration cognitive paths using a database of calibration cognitive paths.

[0147] In some implementations, generating multiple calibration cognitive paths involves: analyzing a road network between a starting point and a destination of a vehicle using a wide-area map and based on trip information of the vehicle; and determining a path including a threshold number (e.g., a predetermined threshold) of calibration targets and at least one of vehicle calibration trajectories based on the analysis.

[0148] In some implementations, generating the plurality of calibration learning paths involves: determining a vehicle trajectory for calibrating the at least one sensor; and generating at least one of the plurality of calibration learning paths based on the vehicle trajectory.

[0149] In some implementations, vehicles are scheduled to travel from an origin to a destination (eg, during a passenger trip, between passenger trips).

[0150] In some implementations, the origin and destination are associated with a passenger transportation trip, and wherein processing 1600 further involves outputting to a passenger device (e.g., a user mobile device or a device within a vehicle) (i) information indicating a plurality of calibrated cognitive paths (e.g., a visual representation of the routes); and (ii) trip information associated with each of the plurality of calibrated cognitive paths, wherein the trip information includes passenger comfort and trip cost (e.g., a discount for taking the calibrated cognitive paths).

[0151] In some implementations, selecting a first calibrated cognitive path for the vehicle from the plurality of calibrated cognitive paths involves receiving input from the passenger via a passenger device indicating the first calibrated cognitive path.

[0152] In the previous description, embodiments of the present invention have been described with reference to many specific details, which may be different due to implementation. Therefore, the description and the accompanying drawings should be regarded as illustrative, rather than restrictive. The only and exclusive indication of the scope of the present invention, and the applicant's expectation that the content of the scope of the present invention is the literal and equivalent scope of the claims issued from this application in the specific form of issuing the claims, including any subsequent amendments. Any definition of the terms used to be included in such claims clearly set forth herein should be based on the meaning of such terms as used in the claims. In addition, when the term "also includes" is used in the previous description or the attached claims, the following of the phrase can be an additional step or entity, or a sub-step / sub-entity of the previously described step or entity.

Claims

1. A vehicle, comprising: at least one sensor; at least one computer; as well as a memory storing instructions that, when executed by the at least one computer, cause the at least one computer to perform operations comprising: identifying a trigger for calibrating the at least one sensor of the vehicle; In response to identifying the trigger, a calibration path planning mode is initiated, the mode comprising: generating a plurality of calibrated learning paths from a first location to a second location along at least one road, wherein the plurality of calibrated learning paths each include at least one calibration trajectory along the at least one road; scoring each calibrated cognitive path based at least on a number of calibrated targets along the path or a number of calibrated trajectories along the path; selecting a first calibrated cognitive path for the vehicle from the plurality of calibrated cognitive paths based on the score and one or more discounts or rewards offered to the passenger; The vehicle is driven along the first calibration learning path, wherein the at least one sensor is calibrated while the vehicle is driven along the first calibration learning path.

2. The vehicle according to claim 1, wherein: The plurality of calibration learning paths is a first plurality of calibration learning paths, and wherein the operations further comprise: After the vehicle arrives at the destination, determining that the at least one sensor has not completed calibration; Responsive to the determining, generating a second plurality of calibrated cognitive pathways; and One of the second plurality of calibrated learning paths is selected and the vehicle is driven along the selected path of the second plurality of calibrated learning paths.

3. The vehicle according to claim 1, wherein: Scoring each calibration learning pathway in the plurality of calibration learning pathways is also based on a type of the at least one sensor.

4. The vehicle of claim 1, wherein the operations further comprise: The first calibrated learning path is stored in a database of calibrated learning paths.

5. The vehicle according to claim 1, wherein: Generating the plurality of calibrated cognitive pathways comprises: At least one of the plurality of calibrated learning paths is generated using a database of calibrated learning paths.

6. The vehicle according to claim 1, wherein: Generating the plurality of calibrated cognitive pathways comprises: Analyzing a road network between a starting point and a destination of the vehicle using a wide area map and based on the trip information of the vehicle; and Based on the analysis, a path is determined that includes a threshold number of vehicle calibration trajectories.

7. The vehicle according to claim 1, wherein: Generating the plurality of calibrated cognitive pathways comprises: determining a vehicle trajectory for calibrating the at least one sensor; and At least one of the plurality of calibrated learning paths is generated based on the vehicle trajectory.

8. The vehicle according to claim 1, wherein: The first location and the second location are associated with a passenger transportation trip, and wherein the operations further comprise: Outputting to a passenger device (i) information indicative of the plurality of calibrated cognitive paths and (ii) trip information associated with each of the plurality of calibrated cognitive paths, wherein the trip information includes passenger comfort and trip cost.

9. The vehicle according to claim 8, wherein: Selecting a first calibration learning path for the vehicle from the plurality of calibration learning paths comprises: Input is received from a passenger via the passenger device indicating the first calibrated cognitive path.

10. A method for a vehicle, comprising: identifying a trigger for calibrating at least one sensor of the vehicle; In response to identifying the trigger, a calibration path planning mode is initiated, the mode comprising: generating a plurality of calibration learning paths from a first location to a second location along at least one road, wherein the plurality of calibration learning paths each include at least one of a calibration target along the at least one road and a calibration trajectory along the at least one road; scoring each calibrated cognitive path based at least on a number of calibrated targets along the path or a number of calibrated trajectories along the path; selecting a first calibrated cognitive path for the vehicle from the plurality of calibrated cognitive paths based on the score and one or more discounts or rewards offered to the passenger; The vehicle is driven along the first calibration learning path, wherein the at least one sensor is calibrated while the vehicle is driven along the first calibration learning path.

11. A non-transitory computer-readable storage medium storing one or more programs for execution by one or more processors of a first device, the one or more programs comprising instructions that, when executed by the one or more processors, cause the first device to perform the method according to claim 10.

12. A computer program product comprising one or more programs for execution by one or more processors of a first device, the one or more programs comprising instructions which, when executed by the one or more processors, cause the first device to perform the method according to claim 10.

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