Vehicle, method for vehicle and storage medium

Abstract

The present invention relates to a vehicle, a method for a vehicle, and a storage medium. Multiple trajectories of the vehicle are generated based on a road segment. Sensor data is received from at least one sensor. The vehicle is traveling on the road segment according to a first trajectory of the multiple trajectories. A potential collision between the vehicle and an object is predicted based on the sensor data and the first trajectory. A constraint set is determined to avoid the potential collision. The constraint set is determined based on the sensor data. A maneuver of the vehicle is determined by superimposing each constraint in the constraint set on each other constraint in the constraint set. The maneuver includes a second trajectory independent of the multiple trajectories. Instructions are transmitted to a control circuit of the vehicle to overturn the first trajectory and traverse the road segment according to the second trajectory to perform a maneuver.

Description

Technical Field

[0001] The present description relates generally to vehicle operation and, in particular, to vehicle operation using maneuver generation. Background Art

[0002] The operation of a vehicle from an initial location to a final destination typically requires a user or a decision-making system of the vehicle to select a route through a road network from the initial location to the final destination. The route may involve meeting objectives such as not exceeding a maximum driving time. Complex routes may require many decisions, making traditional algorithms used for autonomous driving impractical. Summary of the invention

[0003] Methods, devices and systems for vehicle operations using maneuver generation are disclosed. In an embodiment, at least one processor of a vehicle generates a plurality of trajectories of the vehicle based on a road segment on which the vehicle is traveling. The at least one processor receives sensor data from at least one sensor of the vehicle. The vehicle travels on the road segment according to a first trajectory of the plurality of trajectories. The at least one processor predicts a potential collision between the vehicle and an object moving on the road segment based on the sensor data and the first trajectory. The at least one processor determines a set of constraints for the vehicle to avoid the potential collision. The set of constraints is determined based on the sensor data. The at least one processor determines a maneuver of the vehicle by superimposing each constraint in the set of constraints on each other constraint in the set of constraints. The maneuver includes a second trajectory independent of the plurality of trajectories. The at least one processor transmits instructions to a control circuit of the vehicle to: overturn the first trajectory; and to traverse the road segment according to the second trajectory to perform the maneuver.

[0004] In an embodiment, the set of constraints comprises an environmental constraint indicating at least one of a drivable area of ​​the road segment and a lane marking of the road segment.

[0005] In an embodiment, the constraint set includes hard logic constraints that the vehicle must comply with in order to avoid the potential collision. The vehicle can violate soft logic constraints in order to avoid the potential collision.

[0006] In an embodiment, the set of constraints comprises site-based constraints parameterized by the at least one processor over time using the sensor data.The space-based constraints are parameterized by the at least one processor over site and time using the sensor data.

[0007] In an embodiment, determining the maneuver comprises generating, with the at least one processor, a union of the site-based constraints and the space-based constraints to provide the maneuver.

[0008] In an embodiment, determining the set of constraints is performed at a first frequency, and determining the maneuver to generate the second trajectory is performed at a second frequency that is higher than the first frequency.

[0009] In an embodiment, performing the maneuver comprises operating the vehicle to a specific location relative to the object.

[0010] In an embodiment, the at least one processor determines a plurality of homotopies. Each homotopy in the plurality of homotopies includes a different respective combination of the set of constraints. Determining the maneuver is based on at least some of the plurality of homotopies.

[0011] In an embodiment, the at least one processor predicts the movement of the vehicle on the road segment according to an accuracy. The at least one processor determines that the vehicle can traverse the road segment according to a subset of the plurality of homotopies based on the predicted movement. Determining the maneuver is also based on the subset of the plurality of homotopies.

[0012] In an embodiment, determining that the vehicle is capable of traversing the road segment according to the plurality of homotopic subsets comprises: generating, using the at least one processor, a decision graph based on the plurality of homotopic subsets. The graph comprises a plurality of nodes. Each node corresponds to a different maneuver.

[0013] In an embodiment, determining the maneuver to generate the second trajectory comprises: assigning, with the at least one processor, a respective quality metric to each of the plurality of homotopies. The at least one processor selects the second trajectory based on the respective quality metric.

[0014] In an embodiment, the corresponding quality metric is determined based on at least one of: the predicted time taken to traverse the road segment according to each homology; the predicted safety of the occupants of the vehicle while traversing the road segment according to each homology; and the predicted comfort of the occupants while traversing the road segment according to each homology.

[0015] In an embodiment, performing the maneuver includes positioning, using the control circuit, the vehicle in front of two moving objects while traversing the road segment.

[0016] In an embodiment, performing the maneuver includes positioning, using the control circuit, the vehicle behind two moving objects while traversing the road segment.

[0017] In an embodiment, performing the maneuver comprises positioning, using the control circuit, the vehicle between two moving objects while traversing the road segment.

[0018] In an embodiment, superimposing each constraint in the constraint set on each other constraint in the constraint set includes: utilizing the at least one processor to sample each constraint in the constraint set relative to time based on changes in the sensor data to provide the maneuver.

[0019] These and other aspects, features, and implementations may be represented as methods, apparatus, systems, components, program products, methods or steps for performing the functions, and other means.

[0020] These and other aspects, features and implementations will be apparent from the following description including the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a block diagram illustrating an example of an autonomous vehicle (AV) having autonomous capabilities according to one or more embodiments.

[0022] Figure 2 is a block diagram illustrating an example "cloud" computing environment in accordance with one or more embodiments.

[0023] Figure 3 is a block diagram illustrating a computer system in accordance with one or more embodiments.

[0024] Figure 4A is a block diagram illustrating an example architecture of an AV in accordance with one or more embodiments.

[0025] Figure 4B is a block diagram of a planning module according to one or more embodiments.

[0026] Figure 5 is a block diagram illustrating examples of inputs and outputs that may be used by a perception module in accordance with one or more embodiments.

[0027] Figure 6 is a block diagram illustrating an example of a LiDAR system in accordance with one or more embodiments.

[0028] Figure 7 is a block diagram illustrating a LiDAR system in operation according to one or more embodiments.

[0029] Figure 8 is a block diagram illustrating additional details of the operation of a LiDAR system according to one or more embodiments.

[0030] Fig. 9 is a block diagram illustrating the relationship between inputs and outputs of a planning module in accordance with one or more embodiments.

[0031] Fig.10 Illustrate a directed graph used in path planning according to one or more embodiments.

[0032] Fig.11 is a block diagram illustrating inputs and outputs of a control module according to one or more embodiments.

[0033] Fig.12 is a block diagram illustrating the inputs, outputs, and components of a controller according to one or more embodiments.

[0034] Fig.13 is a flow chart illustrating an example process for determining a maneuver in accordance with one or more embodiments.

[0035] Fig.14 An example decision diagram is illustrated in accordance with one or more embodiments.

[0036] Fig.15 is a block diagram illustrating an example maneuver in accordance with one or more embodiments.

[0037] Fig.16 is a flow chart illustrating a process for operation of a vehicle according to one or more embodiments. DETAILED DESCRIPTION

[0038] 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 will be 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.

[0039] In the accompanying drawings, for ease of description, the specific arrangement or order of schematic elements (such as those representing devices, modules, instruction blocks, and data elements) is shown. However, it should be understood by those skilled in the art that the specific order or arrangement of the schematic elements in the accompanying drawings is not intended to mean that a specific processing order or sequence, or separation of processing processes is required. In addition, the inclusion of schematic elements in the accompanying drawings is not intended to mean that such elements are required in all embodiments, nor is it intended to mean that the features represented by such elements cannot be included in the embodiments or cannot be combined with other elements in the embodiments.

[0040] 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 there is no such connecting element and is not intended to 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 make the present disclosure vague. In addition, for ease of illustration, a single connecting element is used to represent multiple connections, relationships or associations between elements. For example, if the connecting element represents the communication of signal, data or instruction, it will be understood by those skilled in the art that this element represents one or more than one signal path (for example, bus) that may be needed to affect the communication.

[0041] Reference will now be made in detail to the embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, 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.

[0042] Several features described below can each be used independently of one another or in 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 fully solved by any one of the features described herein. Although a title is provided, information related to a specific title but not found in the section with that title may also be found elsewhere in this specification. This article describes an embodiment according to the following summary:

[0043] 1. General Overview

[0044] 2. System Overview

[0045] 3. Autonomous Vehicle Architecture

[0046] 4. Autonomous Vehicle Input

[0047] 5. Autonomous vehicle planning

[0048] 6. Autonomous Vehicle Control

[0049] 7. Autonomous Vehicle Operations Using Maneuver Generation

[0050] General Overview

[0051] Methods, systems, and apparatus for operating an autonomous vehicle (AV) using maneuver generation are described herein. A planning module of the AV generates a graphical representation of the environment and sensor signal measurements (e.g., velocity and position) of the AV. The nodes of the graphical representation represent samples of the AV's decision space (e.g., a set of maneuvers for the AV relative to other vehicles and environmental constraints (such as a drivable area or lane markings, etc.)). The edges of the graphical representation represent different possible trajectories that the AV can follow to perform different maneuvers.

[0052] Advantages and benefits of AV operations using maneuver generation include reduced computational complexity in determining the trajectory of the AV. The planning module is not required to determine the trajectory in a brute force manner by generating a large number of candidate trajectories and evaluating each candidate trajectory. Using the embodiments disclosed herein, the planning module can reduce the search space by identifying a subset of the search space corresponding to feasible homotopies (e.g., combinations of constraints that the AV can safely comply with when traversing the route) and generating candidate trajectories only for that subset.

[0053] System Overview

[0054] Figure 1 is a block diagram illustrating an example of an autonomous vehicle 100 having autonomous capabilities according to one or more embodiments.

[0055] 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.

[0056] As used herein, an autonomous vehicle (AV) is a vehicle with autonomous capabilities.

[0057] 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.

[0058] As used herein, a "trajectory" refers to a path or route for operating 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 trajectory 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.

[0059] 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.

[0060] 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 the AV vehicle, or one or more classified or labeled objects provided by a source external to the AV.

[0061] As used herein, a "road" is a physical area that can be traversed by a vehicle and may correspond to a named thoroughfare (e.g., a city street, an interstate highway, etc.) or may correspond to an unnamed thoroughfare (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 road 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 any physical area that is not formally defined as a thoroughfare by a municipality or other governmental or administrative agency.

[0062] As used herein, a "lane" is a portion of a road that can be traversed by vehicles and 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 example, a road with lane markings that are far apart may accommodate two or more vehicles, allowing one vehicle to pass another without crossing the lane markings and thus may be interpreted as having a lane narrower than the space between lane markings, or as having two lanes between 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 in a rural area and trees along an avenue.

[0063] As used herein, "homotopy" refers to a subset of a set of constraints on the trajectory of the AV that the AV can obey while traversing a particular route.

[0064] “One or more than one” 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.

[0065] 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.

[0066] 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 forms "a", "an" and "the" are also intended to include 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 than one related list items. It will also be understood that when the terms "include", "comprises", "have" and / or "have" are used in this specification, the stated features, integers, steps, operations, elements and / or components are specifically stated, but the presence or addition of one or more than one other features, integers, steps, operations, elements, components, and / or groups thereof are not excluded.

[0067] 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.

[0068] 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 on the cloud computing environment.

[0069] 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 use of 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.

[0070] 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).

[0071] In an embodiment, the AV system 120 includes means 101 for receiving and operating commands from a computer processor 146. In an embodiment, the computer processor 146 is associated with 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.

[0072] 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 velocity and acceleration, 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 GNSS, an inertial measurement unit (IMU) that measures both linear acceleration and angular rate of the vehicle, wheel 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.

[0073] 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.

[0074] 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 3 190. 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.

[0075] 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 an embodiment, 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.

[0076] In an embodiment, the communication device 140 includes a communication interface. For example, a wired, wireless, WiMAX, Wi-Fi, 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 2The 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 an embodiment, the AV 100 communicates with other remote (e.g., "cloud") servers 136.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] Sample cloud computing environment

[0082] Figure 2 is a block diagram illustrating an example "cloud" computing environment according to one or more embodiments. 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.

[0083] 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.

[0084] 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 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 ground 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 an embodiment where the network represents a combination of multiple subnetworks, different network layer protocols are used on each underlying subnetwork. In an embodiment, the network represents one or more interconnected internetworks (such as the public Internet, etc.).

[0085] 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.

[0086] Computer Systems

[0087] Figure 3 300 is a block diagram illustrating a computer system 300 according to one or more embodiments. In implementation, the 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 may also combine customized hard-wired logic, ASICs or FPGAs with customized programming to accomplish 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 includes hard-wired and / or program logic to implement these techniques.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] Autonomous Vehicle Architecture

[0099] Figure 4A is an example of a method for an autonomous vehicle (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 4 is a portion of the AV system 120 shown. In an embodiment, 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 a combination of any or all of these).

[0100] In use, planning module 404 receives data representing destination 412 and determines data representing a trajectory 414 (sometimes referred to as a route) that AV 100 may travel in order to reach (e.g., arrive at) destination 412. In order for planning module 404 to determine data representing trajectory 414, planning module 404 receives data from perception module 402, positioning module 408, and database module 410.

[0101] 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.

[0102] 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 global navigation satellite system (GNSS) unit 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.).

[0103] 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.

[0104] Figure 4B 4 is a block diagram of a planning module 404 according to one or more embodiments. The planning module 404 includes a path planner 451, logical constraints 452, a homotopy extractor 453, a sample-based maneuver enabler 454, a trajectory score generator 455, a tracking controller 456, and an AV 457.

[0105] In an embodiment, the route planner 451 performs the following operations: 1) receives an initial and terminal state; 2) plans a desired roadblock / lane sequence using a lane route selector; 3) splits the route into segments based on lane changes, such that a segment does not contain a lane change; 4) selects the segment that the AV is on based on the state of the AV (from the dynamic world model 458) projected onto the roadblock; 5) extracts a baseline path for the selected segment (which may be marked as an "expected" baseline path in the case where a lane change is desired); and 6) prunes the baseline path based on a maximum / minimum length. In the case where a lane change is not required, an adjacent baseline path is extracted and marked as a soft logic constraint, which means that the AV can use that lane to avoid a collision if needed. Reference Fig.13 To illustrate and explain soft logic constraints in more detail.

[0106] In an embodiment, the route planner 451 generates a graphical representation of the AV's operating environment, the physical state of the AV based on sensor data (e.g., speed, location), and possible outcomes. In an embodiment, the graphical representation is a directed graph or decision graph (described below) including multiple nodes, where each node represents a sample of the AV's decision space for a particular driving scenario, such as multiple maneuvers related to other vehicles and objects and environmental constraints (e.g., drivable area, lane markings 1524), etc. Fig.15 The edges of the directed graph represent different trajectories that can be used by the AV 100 for a particular driving scenario (see Fig.15 ).

[0107] In an embodiment, the logical constraints 452 include "hard" constraints and "soft" constraints. A hard constraint is a logical constraint that cannot be violated because, if violated, the AV will collide with another object (such as a pedestrian who may be "jaywalking" across the road, etc.). Note that a hard constraint does not mean "no collision." Instead, a hard constraint can be a combination of, for example, spatial and velocity constraints that can result in a collision. For example, a hard constraint may be expressed verbally as: "If the AV is traveling at 30 mph in lane A or accelerates at 2 mph / s in lane B, the AV will collide with a pedestrian." Therefore, the hard constraints formally expressed are "do not travel at 30 mph in lane A" and "do not exceed 25 mph in lane A."

[0108] Soft constraints are constraints that the AV should follow but can violate to, for example, complete the trip to the destination or avoid a collision. Some examples of "soft" constraints include, but are not limited to: occupant comfort constraints; and a minimum threshold for lateral clearance from pedestrians crossing the street (violation) to provide comfort to pedestrians and AV occupants. In an embodiment, soft constraints are embodied in one or more rulebooks. Soft constraints may include spatial constraints that change over time. A spatial constraint may be a drivable area.

[0109] In an embodiment, the homotopy extractor 453 generates a set of potential maneuvers for the AV. Instead of assuming a goal and then selecting the best performing goal, the homotopy extractor 453 assumes a set of active constraints called "homopy" (defined below) and then selects the set of constraints that results in a lower cost. The homotopy extractor 453 receives a route plan from the route planner 451 that includes a baseline path (also called an "anchor path"). The "anchor path" is the best estimate of the lane the AV is in, as well as soft logic constraints (e.g., potential desired paths) that the AV can use when making lane changes. In an embodiment, the route planner 451 also includes rate squared and spatial constraints calculated along the anchor path (e.g., calculated using a binding generator).

[0110] The homotopy extractor 453 finds all "approximately" feasible maneuvers that the AV can perform, taking into account the initial state of the AV, the final state of the AV, the map representation and predictions of other agents in the scene. Note that in this context, the maneuvers so obtained may not be dynamically feasible, but the homotopy extractor 453 ensures that the set of constraints describing the maneuvers so obtained is not an empty set (also taking into account the AV's occupation of space). AV maneuvers are described by homotopies. As described above, a homotopy is a subset of a set of constraints on the trajectory of the AV that the AV can comply with when traversing a specific route. When there are multiple homotopies, a subset of multiple homotopies including a subset of constraints can be selected. In some implementations, a homotopy can be a unique space where any path starting at a starting position (AV state) and ending at a final state can be continuously deformed. In order to find these maneuvers, the homotopy extractor 453 iterates all possible decisions that the AV can make with respect to other agents (e.g., passing on the left / right, passing in front or behind, or just staying behind). In short, the output of the homotopy extractor 453 describes the spatiotemporal location of the AV relative to the agents. Although this may be a computationally expensive search, all infeasible combinations can be eliminated due to a set of simple checks. The homotopy extractor 452 is further described in detail in the co-pending application entitled “Homotopic-Based Planner for Autonomous Vehicles” (Attorney Docket No. 46154-0261001), filed on December 7, 2021, which is incorporated herein by reference in its entirety.

[0111] In order to be able to describe constraints that represent where other agents are located and what a collision of the AV with these agents means, each agent is converted into a station-based and a space-based obstacle. The station-based constraints are parameterized over time, while the space-based constraints are parameterized over both station and time. Figure 13-16 Further details regarding the homotopy extractor 503 are described below.

[0112] In an embodiment, the realization search 454a...454n is performed by a sample-based maneuver realizer 454 to generate a set of trajectories 1...N for all extracted homologs. The sample-based maneuver realizer 454 is further described in detail in the co-pending application entitled "Sampling-Based Maneuver Realizer" filed on December 7, 2021 (attorney docket number 46154-0310001), which is incorporated herein by reference in its entirety.

[0113] In an embodiment, trajectory score generator 455 uses one or more rulebooks, one or more machine learning models 459, and / or one or more safe maneuver models 460 to score trajectories 1...N, and uses the scores to select the trajectory that best complies with the rules in the one or more rulebooks. In an embodiment, a predefined cost function is used to generate trajectory scores.

[0114] In an embodiment using a cost function, a total or partial order hierarchical cost function may be used to score trajectories. The cost function is applied to a metric (e.g., Boolean) associated with a hierarchy of violations and / or satisfaction of rules in one or more rulebooks based on priority or relative importance. An example hierarchy of priority-based rules is as follows (from top to bottom): collision avoidance (Boolean), blocking (Boolean), terminal state in desired lane (Boolean), lane change (Boolean), and comfort (double float). In this example, each non-zero priority rule is defined as a Boolean to avoid over-optimization of high priority costs. The most important or highest priority rule is collision avoidance, followed by blocking avoidance, followed by avoiding terminal states in desired lanes, followed by lane changes, and then comfort rules (e.g., maximum acceleration or deceleration). These example rules are described more fully as follows:

[0115] 1. Collision: Set to TRUE if there is a state along the scored trajectory where the AV vehicle's footprint collides with the footprint of any other agent / object (eg, if the two polygons intersect, then the two are considered to be colliding).

[0116] 2. Blocked: A trajectory is considered blocked if the terminal homotopy does not contain the desired goal state and the terminal velocity of the trajectory is below a specified threshold (e.g., 2 m / s).

[0117] 3. Final state in desired lane: Set to true if the trajectory's final state is found in a lane that is a desired lane change, and set to true if the AV's occupancy space crosses a lane divider at any time during the trajectory.

[0118] 4. Comfort: The maximum values ​​of acceleration / deceleration, braking distance, and lateral clearance can be considered.

[0119] For each trajectory, the rules are checked and a metric is determined. The metric is used to formulate a cost function, which is then minimized using, for example, a least squares formula or any other suitable solver. The trajectory with the lowest cost is the selected trajectory, i.e., the trajectory with the fewest rule violations or the most compliance. In an embodiment, as described in further detail below, minimizing the cost function can be used to score trajectories. Note that the above rules are examples only. One of ordinary skill will recognize that any suitable cost function and rulebook (including rulebooks with more or fewer rules) can be used for trajectory scoring.

[0120] For machine learning embodiments, trajectory score generator 455 may implement one or more machine learning models 509 and / or safe maneuver models 510 to score trajectories. For example, a neural network may be used to predict the score of a trajectory.

[0121] Tracking controller 456 is used to improve the robustness of planning module 404 to unexpected spikes in computational demand. Tracking controller 456 is a fast-performing tracking controller that provides stable and smooth control inputs and enables scale module 404 to react faster to disturbances. In an embodiment, tracking controller 506 runs at 40 Hz. The input to tracking controller 456 is the selected trajectory provided by trajectory score generator 455 that has been time-parameterized so that tracking controller 506 can query the exact desired position of the AV at a given time.

[0122] In an embodiment, the tracking controller 456 is formulated as a model predictive control (MPC) problem with constraints on control inputs and states. However, any suitable multivariable control algorithm may be used. The MPC type formulation uses an internal dynamic model of the process, a cost function J in the receding horizon, and an optimization algorithm for minimizing the cost function J using the control input u. An example of a cost function used for optimization is a quadratic cost function.

[0123] In an embodiment, the dynamic model is a model of a moving vehicle in Cartesian coordinates or any other suitable reference coordinate system. For example, the moving vehicle model may be a bicycle model that enables the sideslip angle to be defined geometrically to represent the yaw rate in terms of a variable expressed relative to the center of gravity of the AV. In an embodiment, the cost function J follows the profile error formula (orthogonal deviation from the anchor path), where the goal is to minimize the lateral and longitudinal errors.

[0124] Autonomous Vehicle Input

[0125] Figure 5 is an example of a sensing module 402 ( Figures 4A-4B ) 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.

[0126] 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.

[0127] 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., up to 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.

[0128] 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 operational information. The TLD system produces TLD data as an output 504d. The TLD data often takes the form of image data (e.g., data in an image data format 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 operational information as possible, so that the AV 100 can access all relevant operational information provided by these objects. For example, the viewing angle of the TLD system can be about 120 degrees or greater.

[0129] In an embodiment, the outputs 504a-504d are combined using sensor fusion techniques. Thus, the individual outputs 504a-504d are provided to other systems of the AV 100 (e.g., to Figures 4A-4B 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 an embodiment, 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 an embodiment, 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.

[0130] Figure 6 is an example of a LiDAR system 602 (eg, Figure 5 6a-604c). 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 solid form of a physical object.) 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. As such, the image 612 is used to determine the boundary 616 of one or more physical objects near the AV.

[0131] Figure 7 is a block diagram illustrating a LiDAR system 602 in operation according to one or more embodiments. In the scenario shown in the figure, the AV 100 receives both camera system output 504c in the form of an image 702 and 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.

[0132] Figure 8 is a block diagram illustrating additional details of the operation of the LiDAR system 602. As described above, the AV 100 detects the boundaries of a physical object based on the characteristics of the data points detected by the LiDAR system 602. Figure 8As 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.

[0133] Path Planning

[0134] Fig. 9 is an example of a method according to one or more embodiments (e.g., Figures 4A-4B 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.

[0135] 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.

[0136] In an embodiment, inputs to the planning module 404 include (e.g., Figures 4A-4B ) database data 914, current location data 916 (e.g., Figures 4A-4B AV position 418 shown), (e.g., for Figures 4A-4B Destination data 918 and object data 920 (e.g., such as the destination 412 shown) Figures 4A-4B 4 (a) and (b) a schematic diagram of ...

[0137] Fig.10 In path planning (eg, by planning module 404 ( Figures 4A-4B )) 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).

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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 .

[0142] 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.

[0143] 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.

[0144] Autonomous Vehicle Control

[0145] Fig.11 is an example of a method according to one or more embodiments (e.g., Figures 4A-4B1 and 1 . The control module 406 is shown in block diagram 1100 of 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 the 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 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.

[0146] 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, Figures 4A-4B 104 . 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.

[0147] 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.

[0148] 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.

[0149] Fig.12 1 is a block diagram 1200 illustrating inputs, outputs, and components of a controller 1102 according to one or more embodiments. The controller 1102 has a rate analyzer 1204 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.

[0150] 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.

[0151] 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.

[0152] Autonomous vehicle operations using maneuver generation

[0153] Fig.13 is a flow chart illustrating an example process 1300 for determining a maneuver for the AV 100 in accordance with one or more embodiments. Figure 1100 is illustrated and described in more detail. In some implementations, the process 1300 may use, at least in part, the homotopy extractor 453 of the planning module 404 of the AV 100 (e.g., as described for Figure 4A and Figure 4B as described above).

[0154] The AV 100 generates a plurality of initial trajectories for the AV 100 based on the road segment (eg, the road segment 1500 may include lanes 1512, 1516) using at least one processor 146 of the AV 100. Figure 1 The processor 146 is illustrated and described in more detail. Fig.15 The road segment 1500 and lanes 1512, 1516 are illustrated and described in more detail. Fig.10 The processing for trajectory generation is illustrated and described in more detail in FIG. 1 . The AV 100 receives sensor data (eg, LiDAR output data 504 ) from at least one sensor 121 of the AV 100 . Fig.10 The sensor 121 is illustrated and described in more detail. Figure 5 504a. Figure 4B 15. The AV 100 is traveling on a road segment 1500 in a lane 1516 according to a trajectory 198 belonging to a plurality of initial trajectories. Figure 1 198 is further illustrated and described. The AV 100 predicts a potential collision between the AV 100 and an object (eg, object 416) moving in the lane 1516 based on the sensor data 504a and the trajectory 198. Referring to FIGS. 4 and 5 Fig.15 4. The object 416 is illustrated and described in more detail.

[0155] The AV 100 determines a set of constraints 1302 for the AV 100 to avoid a potential collision. The set of constraints is determined based on the sensor data 504a. For example, Fig.13 As shown, the homotopy extractor 453 can generate a set of constraints 1302 associated with the AV 100 traversing the road segment 1500. In some implementations, each constraint of the set of constraints 1302 can include a specific parameter and a corresponding parameter value. For example, one constraint indicates that a specific parameter C_1 should be equal to a specific parameter value X_1. As another example, another constraint indicates that the same parameter C_1 should be equal to a different parameter value X_2. As another example, another constraint can indicate that a different parameter C_2 is equal to a parameter value Y_1.

[0156] In an embodiment, the set of constraints includes hard logic constraints and soft logic constraints. Hard logic constraints are constraints that the AV 100 must comply with, for example, to avoid a potential collision or to reach the destination 199. Figure 1 The destination 199 is illustrated and described in more detail. Figure 4B 199). Thus, a constraint may be required or "hard" (e.g., the AV 100 must comply with the hard constraint when traversing to the destination 199). For example, a hard constraint may relate to the predicted safety of one or more occupants of the AV 100 and / or the safety of the AV 100. The constraints may specify that the AV 100 not make contact with certain objects (e.g., other vehicles 193, pedestrians 192, or obstacles), remain within the boundaries 1528, 1532 of the road segment 1500, travel in the direction of traffic on the road, not accelerate or decelerate in a manner that would injure its occupants, etc. Reference Figure 1 The vehicle 193 and the pedestrian 192 are illustrated and described in more detail. Fig.15 1528, 1532 and road segment 1500 are illustrated and described in more detail. As another example, a constraint may specify that the probability of the AV 100 colliding with an object 416, a vehicle 193, a pedestrian 192, or other object is less than a threshold. Figure 4A , Fig.15 4. The object 416 is illustrated and described in more detail. In some implementations, the likelihood is calculated by the homotopy extractor 453 using one or more computer simulations or dynamic models.

[0157] Soft logic constraints are constraints that the AV 100 should obey but may violate, for example, to avoid a potential collision. Figure 4B 1302. For example, at least a portion of the set of constraints 1302 may be soft logic constraints (e.g., soft logic constraints that the AV 100 should attempt to, but not necessarily need to, comply with when traversing to the destination 199). The soft logic constraints may be related to the predicted comfort of one or more occupants of the AV. For example, the constraints may specify that the AV 100 comply with certain acceleration limits, braking limits, speed limits, turn rate limits, etc., based on the impact of these constraints on the comfort of the occupants of the vehicle.

[0158] The set of constraints 1302 may represent any aspect of the operation of the AV 100 as it traverses to a destination. As an example, at least a portion of the set of constraints 1302 may be related to the performance capabilities of the AV 100. For example, the constraints may specify that the AV 100 will comply with certain map constraints based on the performance capabilities of the AV, including but not limited to acceleration limits, braking limits, speed limits, turn rate limits, inertia limits, etc. As another example, the constraints may specify a range of motion for the AV 100 (e.g., the AV 100 may travel forward or backward while maintaining a straight path or turning, but may not travel left or right).

[0159] In an embodiment, constraint set 1302 includes an environmental constraint indicating at least one of a drivable area of ​​road segment 1500 and lane markings 1524 of road segment 1500. Fig.15 1500 and lane markings 1524 are illustrated in FIG. 1502. For example, at least a portion of the set of constraints 1302 may be map constraints related to the map geometry of one or more roads that the AV 100 may use to traverse to a destination. For example, the constraints may specify that the AV 100 is to be confined to certain lanes of the road and / or confined to certain boundaries 1528, 1532 of the road segment 1500 (e.g., between the left and right edges of the navigable portion of the road). As another example, the constraints may specify the presence and location of obstacles (e.g., object 416) on the road that the AV 100 may not traverse.

[0160] In an embodiment, at least a portion of the set of constraints 1302 may relate to legal constraints regarding the operation of the AV 100. For example, a constraint may specify that the AV 100 is to comply with a particular speed limit for a road and / or a particular traffic flow (e.g., direction of travel) for a road. As another example, a constraint may specify that the AV 100 is to comply with traffic rules or regulations within a particular jurisdiction. Although example constraints 1302 are described herein, these are merely illustrative examples. In practice, the set of constraints 1302 may include additional constraints instead of or in addition to the constraints described herein.

[0161] In an embodiment, the AV 100 determines a plurality of homotopies 1304a-1304n. Each homotopy of the plurality of homotopies 1304a-1304n includes a different corresponding combination of the constraint set 1302. The homotopy extractor 453 generates one or more homotopies 1304a-1304n based on the constraint set 1302. For example, each homotopy can include a different constraint in the constraint set 1302, and / or a different combination of two or more of the constraint set 1302. Determining a maneuver of the AV 100 is based on at least a portion of the plurality of homotopies 1304a-1304n.

[0162] In some implementations, each homotopy includes one or more of the soft logic constraints. In addition, each homotopy may include each hard logic constraint. In practice, whether a particular constraint is a soft logic constraint or a hard logic constraint may vary depending on the implementation. As an example, in some implementations, constraints related to the performance capabilities of the AV 100, map (environmental) constraints of one or more roads that the AV 100 can use to traverse to the destination 199, legal constraints related to the operation of the AV 100, and / or the safety of one or more occupants of the AV may be considered "required". As another example, in some implementations, constraints related to the comfort of one or more occupants of the AV 100 and / or constraints that dictate that the AV 100 perform certain operations or tasks may be considered soft logic constraints.

[0163] exist Fig.13 In the example shown, the first homotopy 1304a ("homotopy 1") includes: (i) a soft logic constraint that parameter C_1 is equal to parameter value X_1; (ii) a soft logic constraint that parameter C_2 is equal to parameter value Y_2; and (iii) various hard logic constraints. In addition, the second homotopy 1304b ("homotopy 2") includes: (i) a soft logic constraint that parameter C_1 is equal to parameter value X_1 (same as homotopy 1); (ii) a soft logic constraint that parameter C_2 is equal to parameter value Y_2 (same as homotopy 1); (iii) an additional soft logic constraint that parameter C_N is equal to parameter value Z_1; and (iv) various hard logic constraints (same as homotopy 1). That is, although homotopy 2 shares the same 1 Same set of constraints, but homotopy 2 includes additional constraints that are not in homotopy 1.

[0164] In addition, the third homotopy 1304n ("homotopy N") includes: (i) a soft logical constraint that parameter C_1 is equal to parameter value X_1 (same as homotopy 1 and homotopy 2); (ii) a soft logical constraint that parameter C_2 is equal to parameter value Y_2 (same as homotopy 1 and homotopy 2); (iii) a soft logical constraint that parameter C_N is equal to parameter value Z_2; (iv) various hard logical constraints (same as homotopy 1 and homotopy 2). That is, although homotopy 3 shares some of the same constraints as homotopy 1 and homotopy 2, homotopy 3 specifies a different parameter value for one of its constraints.

[0165] Despite Fig.131304a-1304n, but these are merely illustrative examples. In practice, homotopy extractor 453 may generate any number of homotopies, each with a different corresponding subset of constraint set 1302. Homotopy extractor 453 determines whether each homotopy 1304a-1304n is "feasible." As an example, for each homotopy, homotopy extractor 453 may determine whether the AV can traverse to destination 199 according to the constraints of the homotopy without colliding with other objects (e.g., object 416) on road segment 1500, without negatively affecting the safety of its occupants, without violating traffic rules or regulations of the jurisdiction, and so on.

[0166] In an embodiment, the AV 100 predicts the motion of the AV 100 on the road segment 1500 according to the accuracy. The AV 100 determines, based on the predicted motion, that the AV 100 can traverse the road segment 1500 according to a subset of the plurality of homotopies (a specific combination of constraints). Determining the maneuver is further based on the subset of the plurality of homotopies. For example, the homotopy extractor 453 can determine whether each of the homotopies 1304a-1304n is feasible by predicting the motion of the AV 100. For each homotopy, the homotopy extractor 453 uses a computer simulation or a dynamic model to perform a simulation of the AV motion to predict how the AV 100 will move as it traverses the road segment 1500 while attempting to comply with the various constraints of the homotopy. If the homotopy extractor 453 determines that the AV 100 cannot traverse the road segment 1500 while complying with the various constraints of the homotopy, then the homotopy extractor 453 determines that the homotopy is "not feasible". If the homotopy extractor 453 determines that the AV 100 is able to traverse the road segment 1500 while complying with the various constraints of the homotopy, then the homotopy extractor 453 determines that the homotopy (and the associated maneuver) is "feasible."

[0167] In an embodiment, the AV 100 determines based on the predicted motion that the AV 100 can traverse the road segment 1500 according to a subset of the plurality of homotopies. Determining the maneuver is further based on the subset of the plurality of homotopies. As an example, the homotopy can include a subset of constraints (a subset of the plurality of homotopies) that stipulate: (i) the AV 100 performs certain operations and tasks at certain times and places; (ii) the AV 100 obeys all traffic rules and regulations in the jurisdiction; (iii) the AV 100 acts in a manner that does not exceed its performance capabilities; and (iv) the AV 100 does not collide with any object (e.g., vehicle 193) or obstacle (e.g., object 416) on the road. The homotopy extractor 453 can simulate the motion of the AV 100 according to these constraints.

[0168] If the homotopy extractor 453 determines that the AV 100 cannot perform the specified operation and task unless the AV 100 violates certain traffic rules or regulations, the homotopy extractor 453 determines that the homotopy is "not feasible". Similarly, if the homotopy extractor 453 determines that the AV 100 cannot perform the specified operation and task without colliding with another object, the homotopy extractor 453 also determines that the homotopy is "not feasible". Similarly, if the homotopy extractor 453 determines that performing the specified operation and task will require performance capabilities that exceed the AV 100, the homotopy extractor 453 also determines that the homotopy is "not feasible". However, if the homotopy extractor 453 determines that the AV 100 can perform the specified operation and task without violating any of the other constraints, the homotopy extractor 453 determines that the homotopy is "feasible". For example, in Fig.13 In the example shown, the homotopy extractor 453 determines that homotopy 1 and homotopy N are “feasible”, and homotopy 2 is “infeasible” (eg, due to violation of one or more of the set of constraints 1302 specified by homotopy 2 ).

[0169] In an embodiment, the AV 100 predicts the motion of the AV 100 on the road segment 1500 according to a first accuracy. The homotopy extractor 453 may determine whether each of the homotopies 1304a-1304n is "feasible" according to the first accuracy. For example, the homotopy extractor 453 generates one or more trajectories (to determine a maneuver) for each homotopy determined to be "feasible" and avoids generating trajectories for each homotopy determined to be "infeasible". For example, for each homotopy determined to be "feasible", the homotopy extractor 453 uses computer simulation and one or more dynamic models, control laws, and equations of motion of the AV 100 to generate a trajectory (e.g., trajectory 1520) for the AV 100 that enables the AV 100 to traverse the road segment 1500 using lane 1512 while complying with each constraint of the homotopy. In some implementations, the simulation and / or dynamic model may be implemented using one or more equations and / or control laws that specify the motion of one or more objects (e.g., vehicle 193 or object 416) in environment 190. Figure 4A , Figure 4B , Fig. 9 and Fig.10 ) describes an example technique for generating one or more trajectories of AV 100. Figure 1 To illustrate and describe environment 190 in more detail.

[0170] For example, in Fig.13In the example shown, the homotopy extractor 453 generates one or more trajectories 1306a corresponding to homotopy 1 (to determine the maneuver) and one or more trajectories 1306b corresponding to homotopy N (both homotopies are determined to be "feasible"). However, the homotopy extractor 453 avoids generating any trajectories corresponding to homotopy 2 (which is determined to be "infeasible").

[0171] In some implementations, the homotopy extractor 453 may generate one or more trajectories for each homotopy determined to be "feasible" according to a second accuracy. The second accuracy may be higher than the first accuracy. For example, the accuracy of predicting the motion of the AV 100 and / or generating the trajectory 1520 of the AV 100 may refer to: (i) the spatial resolution of predicting the motion of the AV 100 and / or generating the trajectory 1520 of the AV 100; or (ii) the temporal resolution of predicting the motion of the AV 100 and / or generating the trajectory 1520 of the AV 100. Reference Fig.15 To illustrate and explain trajectory 1520 in more detail.

[0172] The accuracy of predicting the motion of the AV 100 and / or generating the trajectory 1520 of the AV 100 may refer to: (iii) the complexity of the computer simulation or dynamic model used to predict the motion of the AV 100 and / or generate the trajectory 1520 of the AV 100; or (iv) the amount of computing resources allocated to predicting the motion of the AV 100 and / or generating the trajectory 1520 of the AV 100. In embodiments, the accuracy of predicting the motion of the AV 100 and / or generating the trajectory 1520 of the AV 100 may refer to: (v) the tolerance or error range associated with predicting the motion of the AV 100 and / or generating the trajectory 1520 of the AV 100; and / or other such characteristics that can affect how the motion of the AV 100 may be predicted and / or how the trajectory 1520 of the AV 100 may be generated.

[0173] In an embodiment, the homotopy extractor 453 may first generate predictions for each homotopy according to a first spatial and / or temporal resolution, and then generate one or more trajectories (to determine the maneuver) for each homotopy determined to be "feasible" according to a second, higher spatial and / or temporal resolution. The homotopy extractor 453 may first predict the motion of the AV 100 for each homotopy according to a lower spatial resolution (e.g., in 10-foot increments), and then generate one or more trajectories for each homotopy determined to be "feasible" according to a higher spatial resolution (e.g., in 1-foot increments). The homotopy extractor 453 may first predict the motion of the AV 100 for each homotopy according to a lower temporal resolution (e.g., in 10-second increments), and then generate one or more trajectories for each homotopy determined to be "feasible" according to a higher spatial resolution (e.g., in 1-second increments). Although example spatial and / or temporal resolutions are described above, these are merely illustrative examples. Indeed, other spatial and / or temporal resolutions may be used to predict the motion of the AV 100 and / or generate one or more trajectories for the AV.

[0174] In an embodiment, the homotopy extractor 453 may first generate predictions for each homotopy according to a first computer simulation or a first dynamic model, and then generate one or more trajectories for each homotopy determined to be "feasible" according to a second computer simulation or a second dynamic model that is more complex than the first computer simulation or the first dynamic model (e.g., models more variables and / or parameters). For example, the first computer simulation or dynamic model may require fewer computing resources to generate predictions (but may be less accurate), while the first computer simulation or dynamic model may require more computing resources to generate trajectories (but may be more accurate). As another example, the first computer simulation or dynamic model may require fewer data inputs and / or less comprehensive data inputs to generate predictions (but may be less accurate), while the first computer simulation or dynamic model may require more data inputs and / or more comprehensive data inputs to generate trajectories (but may be more accurate). Example data inputs may include, for example, sensor data 504a, traffic data, weather data, and / or other data related to characteristics of the environment of the AV 100.

[0175] In an embodiment, AV 100 determines the maneuver by superimposing each constraint in constraint set 1302 on each other constraint in constraint set 1302. The maneuver includes a trajectory (e.g., trajectory 1520) that is independent of a plurality of initial trajectories (e.g., trajectory 198) that are generated using reference Fig.10The process for trajectory generation illustrated and described in more detail is based on the road segment 1500 generated for the AV 100. The homotopy extractor 453 selects one of the generated trajectories 1306a and 1306b for the maneuver (as shown in reference Fig.13 1306a), and instructs the control circuit 406 of the AV 100 to execute the selected trajectory (e.g., trajectory 1306a). Fig.13 In the example shown, homotopy extractor 453 selects trajectory 1306a ("Trajectory 1") over trajectory 1306b ("Trajectory N"). In some implementations, other modules may be used to select trajectory 1520 instead of or in combination with homotopy extractor 453. For example, homotopy extractor 453, sample-based maneuver implementer 454, and / or trajectory score generator 455 (e.g., as described in reference to FIG. 1 ) may be used. Figure 4B as described above) to select the trajectory.

[0176] In an embodiment, superimposing each constraint in constraint set 1302 on each other constraint in constraint set 1302 includes: utilizing at least one processor 146, sampling each constraint in constraint set 1302 relative to time based on changes in sensor data 504a to provide maneuvers. For example, determining constraint set 1302 may be performed at a first frequency, and determining maneuvers to generate trajectory 1520 may be performed at a second frequency that is higher than the first frequency. Thus, different constraints may be sampled in different ways. For example, homotopy extractor 453 may operate at 10 Hz, and implementation search may be performed twice as fast at 20 Hz.

[0177] In an embodiment, determining the maneuver to generate the trajectory 1520 includes: assigning, using at least one processor 146, a corresponding quality metric to each of the plurality of homotopies. Selecting the trajectory 1520 based on the corresponding quality metric. For example, the trajectory 1520 may be generated by (using reference Fig.13 The illustrated and described method) calculates quality scores or other metrics for each generated trajectory, and selects trajectory 1520 based on these quality scores or metrics to select trajectory 1520.

[0178] In an embodiment, a respective quality metric is determined based on at least one of: a predicted time taken to traverse the road segment 1500 according to each homotopy, a predicted safety of an occupant of the AV 100 during traversal of the road segment 1500 according to each homotopy, and a comfort of an occupant of the AV 100 during traversal of the road segment 1500 according to each homotopy. For each trajectory (obtained from the homotopy), a quality score or metric based on factors such as: predicted safety of an occupant of the AV 100, predicted comfort of an occupant of the AV 100, predicted resources (e.g., fuel, battery power, etc.) that the AV 100 will consume, a predicted amount of time that the AV 100 will require to traverse to the destination 199 and / or other factors may be determined.

[0179] Trajectory 1520 may be selected based on the quality score or metric (e.g., the trajectory with the highest quality score or metric). As described above, this selection process may be performed by one or more modules of AV 100 (such as homotopy extractor 453, realization sample-based maneuver implementer 454, and / or trajectory score generator 455, etc.).

[0180] In an embodiment, the AV machine 100 determines a maneuver by superimposing each constraint in the constraint set 1320 on each other constraint in the constraint set 1320. The maneuver includes a trajectory 1520 that is independent of a plurality of trajectories (e.g., trajectory 198). The AV 100 transmits instructions to the control circuit 406 of the AV 100 to override the trajectory 198. The instructions further cause the AV 100 to traverse the road segment 1500 according to the trajectory 1520 to perform the maneuver. For example, the homotopy extractor 453 determines one or more actions 1308 that can override the trajectory 198 based on the homotopies 1304a-1304n. The maneuver may correspond to an evasive action (e.g., a sudden turn, braking, acceleration, lane change, etc.) to avoid an unsafe or otherwise undesirable outcome (e.g., collision with an object 416, driving off the road, etc.). In some implementations, the homotopy extractor 453 can plan the maneuver independently of the trajectory 198, and selectively override the execution of the trajectory 198 with the maneuver based on one or more data inputs. Example data inputs can include, for example, sensor data 504a indicating that an action may be warranted, a command from an occupant of the AV 100 indicating that an action is to be performed, a command from a user who is remotely monitoring or controlling the AV 100 indicating that an action is to be performed, an automated command from a remote computer system indicating that an action is to be performed, and the like.

[0181] Fig.14 An example decision diagram 1400 of the AV 100 is illustrated in accordance with one or more embodiments. Figure 11400. In some implementations, the homotopy extractor 453 determines whether certain homotopies are "feasible" or "infeasible" based on the decision graph 1400. Figure 4B To illustrate and explain the homotopy extractor 453 in more detail. As an illustrative example, in Fig.14 A simplified decision diagram 1400 is shown in FIG.

[0182] In an embodiment, determining that the AV 100 may traverse the road segment 1500 (eg, by performing a maneuver) according to a subset of the plurality of homotopies includes generating, using at least one processor 146, a decision graph 1400 based on the subset of the plurality of homotopies. Fig.15 The road segment 1500 is illustrated and described in more detail. Figure 1 The processor 146 is illustrated and described in more detail. Fig.14 1400 is illustrated and described in more detail. Fig.13 14. The decision graph 1400 includes a plurality of nodes 1402. Each node corresponds to a different maneuver.

[0183] In an embodiment, decision graph 1400 includes a plurality of interconnected nodes 1402, each node 1402 corresponding to a different respective subset of constraint set 1302. The nodes may be arranged hierarchically (e.g., according to different levels) and according to one or more branches, where a "child" node inherits the constraints associated with its "parent" node and additionally includes one or more additional constraints. In some implementations, decision graph 1400 may be similar to Fig.10 The directed graph 1000 is shown. The homotopy extractor 453 can determine the feasibility of the AV 100 traversing to the destination 199 based on the constraints associated with each node (starting with the node with the highest level or layer and proceeding through the nodes of successively lower levels or layers). Figure 1 199 is illustrated and described in greater detail. If homotopy extractor 453 determines that it is not feasible to comply with the constraints associated with a particular node, homotopy extractor 453 may refrain from evaluating the feasibility of the node's child nodes.

[0184] For example, refer to Fig.14, the homotopy extractor 453 determines that it is feasible to traverse to the destination 199 according to the constraints associated with the highest level node 1402a. Based on this determination, the homotopy extractor 453 then evaluates the feasibility of each child node 1402b and 1402c, and determines that it is not feasible to traverse to the destination 199 according to the constraints associated with node 1402b, but it is feasible to traverse to the destination 199 according to the constraints associated with node 1402c. Based on this determination, the homotopy extractor 453 avoids evaluating the feasibility of any nodes that depend on node 1402b, and continues to evaluate the feasibility of nodes that depend on node 1402c. The above process can continue until each node in the decision graph 1400 has been evaluated or omitted from evaluation (due to an "infeasible" parent node).

[0185] In some implementations, the homotopy extractor 453 may generate one or more candidate trajectories (to determine the maneuver) based on the nodes determined to be "feasible". Fig.14 , the homology extractor 453 may generate one or more candidate trajectories for each node 1402a, 1402d, 1402e, 1402f, and / or 1402g. In some implementations, the homology extractor 453 may generate one or more candidate trajectories based on nodes that are determined to be "feasible" and do not have any child nodes. For example, referring to Fig.14 , the homotopy extractor 453 may generate one or more candidate trajectories for each node 1402e and 1402g. As described above, the homotopy extractor 453 may determine the feasibility of each node according to a first accuracy, and generate candidate trajectories according to a higher second accuracy.

[0186] Fig.15 is a block diagram illustrating an example maneuver of the AV 100 according to one or more embodiments. Figure 1 1302 to illustrate and describe the AV 100 in more detail. In an embodiment, at least a portion of the constraint set 1302 specifies that the AV 100 perform certain operations or tasks. Fig.13 1302. For example, a constraint may specify that the AV 100 perform a specific maneuver. In an embodiment, performing the maneuver includes maneuvering the AV 100 to a specific location relative to the object 416 to avoid a potential collision. For example, a constraint may specify that the AV 100 change lanes on a road at a specific time and location. As another example, a constraint may specify that the AV 100 remain at its current location at a specific time and location.

[0187] In an embodiment, the constraint set 1302 includes station-based constraints parameterized over time by the at least one processor 146 using the sensor data 504. Objects 416 and vehicles 193, 1504 are sometimes referred to as "agents." In order to be able to describe constraints that represent where agents are located and what a collision of the AV 100 with these agents represents, the agents are converted into station-based and space-based obstacles. The station-based constraints are parameterized over time. Reference Figure 13-14 503 and its extraction and constraint grouping. The constraint set 1302 also includes space-based constraints parameterized by at least one processor 146 over site and time using sensor data 504. The parameterization of the constraints is referenced by Fig.13 The sampling is carried out as described in more detail.

[0188] In an embodiment, determining the maneuver includes: generating, using at least one processor 146, a union of the site-based constraints and the space-based constraints to provide the maneuver. For example, the union of the site-based constraints and the space-based constraints is an operation in which the site-based constraints and the space-based constraints are combined and related to each other using the operation. In an embodiment, each of the site-based constraints and the space-based constraints is stored and manipulated as a metric. The union of two such metrics is obtained by applying the operation to the entries of each pair of elements to obtain a corresponding matrix union. The union of two such matrices is obtained by applying the operation to the entries of each pair of elements to obtain a corresponding matrix union.

[0189] In an embodiment, performing the maneuver includes positioning the AV 100, using the control circuit 406, in front of the two moving vehicles 193, 1504 during the traversal of the road segment 1500. The control circuit 406 is illustrated and described in more detail with reference to FIG. 4. For example, the constraint may specify that the AV 100 overtakes the vehicle 1504 at a specific time and location. In an embodiment, performing the maneuver includes positioning the AV 100, using the control circuit 406, behind the two moving objects (e.g., the two moving vehicles 193, 1504) during the traversal of the road segment 1500. For example, the constraint may specify that the AV 100 remains behind the vehicle 193 at a specific time and location. As another example, the constraint may specify that the AV 100 waits for the vehicle 193 or pedestrian 192 to leave the path of the AV 100 before proceeding further along the trajectory 1520.

[0190] In an embodiment, performing the maneuver includes positioning the AV 100 between two moving objects (two moving vehicles 193, 1504) during traversal of the road segment 1500 using the control circuit 406. For example, a constraint may dictate that the AV 100 proceed along the trajectory 1520 before the vehicle 193 or pedestrian enters the lane 1512.

[0191] Fig.16 is a flow chart illustrating a process for operation of a vehicle according to one or more embodiments. Figure 1 100 is illustrated and described in more detail. In an embodiment, Fig.16 The processing is done by reference Figure 4B The AV 100 is illustrated and described in more detail by the homotopy extractor 453 or the sample-based motor implementer 454. In other embodiments, other entities (e.g., the server 136) perform some or all of the steps of this process. Figure 1 1 and 2 to illustrate and describe the server 136 in more detail. Likewise, embodiments may include different and / or additional steps, or perform the steps in a different order.

[0192] The AV 100 uses at least one processor 146 of the AV 100 to generate (1604) a plurality of initial trajectories based on the road segment 1500 (including lanes 1512, 1516) on which the AV 100 is traveling. Figure 1 The processor 146 is illustrated and described in more detail. Fig.15 1512, 1516 are illustrated and described in more detail. Fig.10 The process for trajectory generation is illustrated and described in more detail.

[0193] The AV 100 uses the processor 146 to receive (1608) sensor data 504a from at least one sensor 121 of the AV 100. Fig.10 The sensor 121 is illustrated and described in more detail. Figure 5 504a. Figure 4B 15. The AV 100 is traveling on a road segment (lane 1516) according to a trajectory 198 in a plurality of trajectories. Figure 1 The two illustrate and explain trajectory 198 in more detail.

[0194] The AV 100 uses the processor 146 to predict (1612) a potential collision between the AV 100 and an object 416 moving on the road segment 1500 based on the sensor data 504a and the trajectory 198. Fig.154. The object 416 is illustrated and described in more detail.

[0195] The AV 100 uses the processor 146 to determine (1616) a set of constraints 1302 for the AV 100 to avoid potential collisions. Fig.13 1302 is illustrated and described in more detail. The constraint set 1302 is determined based on the sensor data 504a. In an embodiment, the set of constraints includes hard logic constraints and soft logic constraints. Hard logic constraints are constraints that the AV 100 must comply with, for example, to avoid a potential collision or reach the destination 199. Figure 1 Soft logic constraints are constraints that the AV 100 should obey but may violate, for example, to avoid a potential collision. Figure 4B For example, at least a portion of the constraint set 1302 may be soft logic constraints (eg, soft logic constraints that the AV 100 should attempt to but not necessarily comply with when traversing to the destination 199 ).

[0196] AV 100 uses processor 146 to determine (1620) a maneuver of AV 100 by superimposing each constraint of constraint set 1302 on each other constraint of constraint set 1302. Maneuver includes trajectory 1520 independent of the plurality of initial trajectories. Fig.15 1520 is illustrated and described in more detail. In an embodiment, determining the maneuver includes: generating, using at least one processor 146, a union of the site-based constraints and the space-based constraints to provide the maneuver. For example, the union of the site-based constraints and the space-based constraints is an operation in which the site-based constraints and the space-based constraints are combined and related to each other using the operation.

[0197] The AV 100 uses the processor 146 to transmit (1624) instructions to the control circuit 406 of the AV 100. Figure 1 1520 to illustrate and describe the control circuit 406 in more detail. The instructions instruct the control circuit 406 to cover the initial trajectory 198. The instructions further instruct the control circuit 406 to traverse the road segment 1500 according to the trajectory 1520 to perform the maneuver.

[0198] 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.

[0199] Additional Examples

[0200] Example implementations of the features described herein are provided below.

[0201] Example 1: A method comprises: using at least one processor of a vehicle to generate multiple trajectories for the vehicle based on a road segment on which the vehicle is traveling; using the at least one processor to receive sensor data from at least one sensor of the vehicle, the vehicle traveling on the road segment according to a first trajectory of the multiple trajectories; using the at least one processor to predict a potential collision between the vehicle and an object moving on the road segment based on the sensor data and the first trajectory; using the at least one processor to determine a set of constraints for the vehicle to avoid the potential collision, the set of constraints being determined based on the sensor data; using the at least one processor to determine a maneuver of the vehicle by superimposing each constraint in the set of constraints on each other constraint in the set of constraints, the maneuver including a second trajectory independent of the multiple trajectories; and using the at least one processor to transmit instructions to a control circuit of the vehicle to: overturn the first trajectory; and traverse the road segment according to the second trajectory to perform the maneuver.

[0202] Example 2: The method according to Example 1, wherein the constraint set includes an environmental constraint indicating at least one of a drivable area of ​​the road segment and a lane marking of the road segment.

[0203] Example 3: A method according to any of the preceding examples, wherein the constraint set includes: hard logic constraints that the vehicle must comply with in order to avoid the potential collision; and soft logic constraints that the vehicle can violate in order to avoid the potential collision.

[0204] Example 4: A method according to any of the preceding examples, wherein the constraint set includes: site-based constraints parameterized over time by the at least one processor using the sensor data; and space-based constraints parameterized over site and time by the at least one processor using the sensor data.

[0205] Example 5: A method according to any of the preceding examples, wherein determining the maneuver comprises: utilizing the at least one processor to generate a union of the site-based constraints and the space-based constraints to provide the maneuver.

[0206] Example 6: A method according to any of the preceding examples, wherein determining the set of constraints is performed at a first frequency, and determining the maneuver to generate the second trajectory is performed at a second frequency that is higher than the first frequency.

[0207] Example 7: A method according to any of the preceding examples, wherein performing the maneuver includes: operating the vehicle to a specific location relative to the object.

[0208] Example 8: According to the method described in any of the preceding examples, the method further includes determining a plurality of homologs using the at least one processor, wherein each homology in the plurality of homologs includes a different corresponding combination of the constraint set, wherein determining the maneuver is based on at least some of the plurality of homologs.

[0209] Example 9: According to the method described in any of the preceding examples, the method further includes using the at least one processor to predict the movement of the vehicle on the road segment based on accuracy; and using the at least one processor to determine, based on the predicted movement, that the vehicle is able to traverse the road segment based on a subset of the multiple homology, wherein determining the maneuver is also based on the subset of the multiple homology.

[0210] Example 10: A method according to any of the preceding examples, wherein determining that the vehicle is capable of traversing the road segment according to the plurality of homologous subsets comprises: utilizing the at least one processor to generate a decision graph based on the plurality of homologous subsets, the decision graph comprising a plurality of nodes, each node corresponding to a different maneuver.

[0211] Example 11: A method according to any of the preceding examples, wherein determining the maneuver to generate the second trajectory comprises: assigning, using the at least one processor, a corresponding quality metric to each of the plurality of homologs; and selecting, using the at least one processor, the second trajectory based on the corresponding quality metric.

[0212] Example 12: A method according to any of the preceding examples, wherein the corresponding quality metric is determined based on at least one of: a predicted time taken to traverse the road segment according to the respective homologs; a predicted safety of the occupants of the vehicle while traversing the road segment according to the respective homologs; and a predicted comfort of the occupants while traversing the road segment according to the respective homologs.

[0213] Example 13: A method according to any of the preceding examples, wherein performing the maneuver includes: using the control circuit to position the vehicle in front of two moving objects when crossing the road segment.

[0214] Example 14: A method according to any of the preceding examples, wherein performing the maneuver includes: using the control circuit to position the vehicle behind two moving objects when crossing the road segment.

[0215] Example 15: A method according to any of the preceding examples, wherein performing the maneuver includes: using the control circuit to position the vehicle between two moving objects when crossing the road segment.

[0216] Example 16: A method according to any of the preceding examples, wherein superimposing each constraint in the constraint set on each other constraint in the constraint set includes: utilizing the at least one processor to sample each constraint in the constraint set relative to time based on changes in the sensor data to provide the maneuver.

[0217] Example 17: A vehicle comprising: one or more computer processors; and one or more non-temporary storage media storing instructions, wherein the instructions, when executed by the one or more computer processors, cause the one or more computer processors to perform a method according to any one of Examples 1 to 16.

[0218] Example 18: One or more non-transitory storage media storing instructions, which, when executed by one or more computer processors, cause the one or more computer processors to perform the method of any one of Examples 1 to 16.

[0219] CROSS-REFERENCE TO RELATED APPLICATIONS

[0220] This application claims priority to U.S. Provisional Application Serial No. 63 / 142,882, filed on January 28, 2021, the entire contents of which are incorporated herein by reference.

Claims

1. A method for a vehicle, include: generating, using at least one processor of the vehicle, a plurality of trajectories for the vehicle based on road segments traveled by the vehicle; receiving, with the at least one processor, sensor data from at least one sensor of the vehicle, the vehicle traveling on the road segment according to a first trajectory of the plurality of trajectories; predicting, with the at least one processor, a potential collision between the vehicle and an object moving on the road segment based on the sensor data and the first trajectory; determining, with the at least one processor, a set of constraints for the vehicle to avoid the potential collision, the set of constraints determined based on the sensor data; determining, with the at least one processor, a maneuver for the vehicle by superimposing each constraint in the set of constraints on each other constraint in the set of constraints, the maneuver comprising a second trajectory independent of the plurality of trajectories; as well as transmitting, with the at least one processor, instructions to control circuitry of the vehicle to: overturning the first trajectory; and The road segment is traversed according to the second trajectory to perform the maneuver.

2. The method according to claim 1, in, The set of constraints includes an environmental constraint indicating at least one of a drivable area of ​​the road segment and a lane marking of the road segment.

3. The method according to claim 1, in, The constraint set includes: hard logic constraints that the vehicle must comply with in order to avoid the potential collision; and Soft logic constraints that the vehicle can violate in order to avoid the potential collision.

4. The method according to claim 1, in, The constraint set includes: site-based constraints parameterized over time by the at least one processor using the sensor data; and Spatially based constraints parameterized by the at least one processor over site and time using the sensor data.

5. The method according to claim 4, in, Determining the maneuver comprises: A union of the site-based constraints and the space-based constraints is generated, utilizing the at least one processor, to provide the maneuver.

6. The method according to claim 1, in, Determining the set of constraints is performed at a first frequency, and determining the maneuver to generate the second trajectory is performed at a second frequency that is higher than the first frequency.

7. The method according to claim 1, in, Performing the maneuver includes: The vehicle is maneuvered to a specific location relative to the object.

8. The method according to claim 1, further comprising: include: A plurality of homotopies is determined with the at least one processor, wherein each homotopy in the plurality of homotopies includes a different respective combination of the set of constraints, wherein determining the maneuver is based on at least some of the plurality of homotopies.

9. The method according to claim 8, further comprising: include: predicting, using the at least one processor, movement of the vehicle on the road segment based on an accuracy; as well as Utilizing the at least one processor, it is determined that the vehicle is capable of traversing the road segment according to a subset of the plurality of homotopies based on the predicted motion, wherein determining the maneuver is further based on the subset of the plurality of homotopies.

10. The method according to claim 9, in, Determining that the vehicle is capable of traversing the road segment according to a subset of the plurality of homotopies includes: A decision graph is generated, using the at least one processor, based on the subset of the plurality of homotopies, the decision graph comprising a plurality of nodes, each node corresponding to a different maneuver.

11. The method according to claim 8, in, Determining the maneuver to generate the second trajectory includes: assigning, with the at least one processor, a respective quality metric to each homotopy in the plurality of homotopies; and The second trajectory is selected, with the at least one processor, based on the corresponding quality metric.

12. The method according to claim 11, in, The corresponding quality metric is determined based on at least one of: A predicted time taken to traverse the road segment according to each of the homotopies; predicted safety of an occupant of the vehicle while traversing the road segment according to the homotopies; as well as A predicted comfort level for the occupant while traversing the road segment according to the respective homotopies.

13. The method according to claim 1, in, Performing the maneuver includes: The control circuit is used to position the vehicle in front of two moving objects when traversing the road segment.

14. The method according to claim 1, in, Performing the maneuver includes: The control circuit is used to position the vehicle behind two moving objects when traversing the road segment.

15. The method according to claim 1, in, Performing the maneuver includes: The control circuit is used to position the vehicle between two moving objects while traversing the road segment.

16. The method according to claim 1, in, Superimposing each constraint in the constraint set on each other constraint in the constraint set includes: Constraints in the set of constraints are sampled with respect to time based on changes in the sensor data to provide the maneuver.

17. A vehicle, include: one or more computer processors; as well as One or more non-transitory storage media storing instructions that, when executed by the one or more computer processors, cause the one or more computer processors to: generating a plurality of trajectories of the vehicle based on the road segments traveled by the vehicle; receiving sensor data from at least one sensor of the vehicle, the vehicle traveling on the road segment according to a first trajectory of the plurality of trajectories; predicting a potential collision between the vehicle and an object moving on the road segment based on the sensor data and the first trajectory; determining a set of constraints for the vehicle to avoid the potential collision, the set of constraints determined based on the sensor data; determining a maneuver for the vehicle by superimposing each constraint in the set of constraints on each other constraint in the set of constraints, the maneuver comprising a second trajectory independent of the plurality of trajectories; as well as transmitting instructions to the control circuit of the vehicle to: overturning the first trajectory; and The road segment is traversed according to the second trajectory to perform the maneuver.

18. The vehicle according to claim 17, in, The set of constraints includes an environmental constraint indicating at least one of a drivable area of ​​the road segment and a lane marking of the road segment.

19. One or more non-transitory storage media storing instructions that, when executed by one or more computer processors, cause the one or more computer processors to: generating a plurality of trajectories of the vehicle based on the road segments traveled by the vehicle; receiving sensor data from at least one sensor of the vehicle, the vehicle traveling on the road segment according to a first trajectory of the plurality of trajectories; predicting a potential collision between the vehicle and an object moving on the road segment based on the sensor data and the first trajectory; determining a set of constraints for the vehicle to avoid the potential collision, the set of constraints determined based on the sensor data; determining a maneuver for the vehicle by superimposing each constraint in the set of constraints on each other constraint in the set of constraints, the maneuver comprising a second trajectory independent of the plurality of trajectories; as well as transmitting instructions to the control circuit of the vehicle to: overturning the first trajectory; and The road segment is traversed according to the second trajectory to perform the maneuver.

20. The non-transitory storage medium according to claim 19, in, The set of constraints includes an environmental constraint indicating at least one of a drivable area of ​​the road segment and a lane marking of the road segment.

21. A computer program product comprising a computer program which, when run by a processor, performs the method for a vehicle according to any one of claims 1 to 16.

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