WO2024081191A1 - Sélection de trajet pour assistance à distance de véhicule - Google Patents

Sélection de trajet pour assistance à distance de véhicule Download PDF

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Publication number
WO2024081191A1
WO2024081191A1 PCT/US2023/034741 US2023034741W WO2024081191A1 WO 2024081191 A1 WO2024081191 A1 WO 2024081191A1 US 2023034741 W US2023034741 W US 2023034741W WO 2024081191 A1 WO2024081191 A1 WO 2024081191A1
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WIPO (PCT)
Prior art keywords
vehicle
candidate paths
path
candidate
user interface
Prior art date
Application number
PCT/US2023/034741
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English (en)
Inventor
Rohit SRIDHARAN
Gregory Scott BUTRON
Andrew Shacklock
Kelvin ROBLES
Original Assignee
Motional Ad Llc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Priority claimed from US18/162,256 external-priority patent/US20240126254A1/en
Application filed by Motional Ad Llc filed Critical Motional Ad Llc
Publication of WO2024081191A1 publication Critical patent/WO2024081191A1/fr

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3407—Route searching; Route guidance specially adapted for specific applications
    • G01C21/3415—Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3484—Personalized, e.g. from learned user behaviour or user-defined profiles

Definitions

  • An autonomous vehicle may be capable of sensing its surrounding environment and navigating to a goal location with minimal to no human input. In some situations, the autonomous vehicle may become stuck and unable to navigate autonomously to the goal location.
  • Remote vehicle assistance systems can determine and provide alternate navigable paths to assist the autonomous vehicle as it operates from the stuck location toward the goal location. Determining acceptable alternate paths that the autonomous vehicle can rapidly implement can be challenging.
  • FIG. 1 is an example environment in which a vehicle including one or more components of an autonomous system can be implemented
  • FIG. 2 is a diagram of one or more systems of a vehicle including an autonomous system
  • FIG. 3 is a diagram of components of one or more devices and/or one or more systems of FIGS. 1 and 2;
  • FIG. 4A is a diagram of certain components of an autonomous system
  • FIG. 4B is a diagram of an implementation of a neural network
  • FIG. 5 is a diagram of an implementation of a process for remote vehicle assistance by a remote vehicle assistance system
  • FIG. 6 is a diagram of a detailed implementation of a remote vehicle assistance system
  • FIG. 7 is a flowchart of a process for remote vehicle assistance path selection
  • FIG. 8 is a diagram of an example dataflow executed by the remote vehicle assistance system described herein;
  • FIGS. 9A and 9B illustrate embodiments of user interfaces of the remote vehicle assistance system described herein;
  • FIG. 10 illustrates an embodiment of a user interface of the remote vehicle assistance system described herein showing one or more detected objects
  • FIG. 11 illustrates another embodiment of a user interface of the remote vehicle assistance system described herein showing one or more costs associated with the detected objects of FIG. 10;
  • FIG. 12 illustrates another embodiment of a user interface of the remote vehicle assistance system described herein showing another embodiment of one or more costs associated with the detected objects of FIG. 10;
  • FIG. 13 illustrates another embodiment of a user interface of the remote vehicle assistance system showing a path determined and provided to avoid a detected object as described herein;
  • FIG. 14 illustrates another embodiment of a user interface of the remote vehicle assistance system showing sensor data and a vehicle path as described herein;
  • FIG. 15 illustrates another embodiment of a user interface of the remote vehicle assistance system showing sensor data, RVA operator data, and path selection as described herein.
  • connecting elements such as solid or dashed lines or arrows are used in the drawings to illustrate a connection, relationship, or association between or among two or more other schematic elements
  • the absence of any such connecting elements is not meant to imply that no connection, relationship, or association can exist.
  • some connections, relationships, or associations between elements are not illustrated in the drawings so as not to obscure the disclosure.
  • a single connecting element can be used to represent multiple connections, relationships or associations between elements.
  • a connecting element represents communication of signals, data, or instructions (e.g., “software instructions”)
  • signal paths e.g., a bus
  • first, second, third, and/or the like are used to describe various elements, these elements should not be limited by these terms.
  • the terms first, second, third, and/or the like are used only to distinguish one element from another.
  • a first contact could be termed a second contact and, similarly, a second contact could be termed a first contact without departing from the scope of the described embodiments.
  • the first contact and the second contact are both contacts, but they are not the same contact.
  • the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this description specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
  • the terms “communication” and “communicate” refer to at least one of the reception, receipt, transmission, transfer, provision, and/or the like of information (or information represented by, for example, data, signals, messages, instructions, commands, and/or the like).
  • one unit e.g., a device, a system, a component of a device or system, combinations thereof, and/or the like
  • another unit means that the one unit is able to directly or indirectly receive information from and/or send (e.g., transmit) information to the other unit.
  • This may refer to a direct or indirect connection that is wired and/or wireless in nature.
  • two units may be in communication with each other even though the information transmitted may be modified, processed, relayed, and/or routed between the first and second unit.
  • a first unit may be in communication with a second unit even though the first unit passively receives information and does not actively transmit information to the second unit.
  • a first unit may be in communication with a second unit if at least one intermediary unit (e.g., a third unit located between the first unit and the second unit) processes information received from the first unit and transmits the processed information to the second unit.
  • a message may refer to a network packet (e.g., a data packet and/or the like) that includes data.
  • the term “if” is, optionally, construed to mean “when”, “upon”, “in response to determining,” “in response to detecting,” and/or the like, depending on the context.
  • the phrase “if it is determined” or “if [a stated condition or event] is detected” is, optionally, construed to mean “upon determining,” “in response to determining,” “upon detecting [the stated condition or event],” “in response to detecting [the stated condition or event],” and/or the like, depending on the context.
  • the terms “has”, “have”, “having”, or the like are intended to be open- ended terms.
  • the phrase “based on” is intended to mean “based at least partially on” unless explicitly stated otherwise.
  • a remote vehicle assistance (RVA) system can enable a remote human operator (RHO) to provide optimized paths to a vehicle that is stuck and no longer able to autonomously navigate.
  • the system can include tools to allow the RHO to create, visualize, and manipulate existing or new candidate paths that are optimized based on costs and/or constraints associated with a candidate path.
  • An candidate path can be verified as an acceptable trajectory and implemented as a navigable path by the vehicle.
  • Some of the advantages of the RVA system include providing a dynamic graphical user interface (HMI) for an RHO to plan, select, and provide optimized paths to the vehicle during RVA.
  • HMI dynamic graphical user interface
  • the RVA system can reduce the amount of time for generating and implementing a candidate path so that the vehicle can continue autonomous navigation more quickly, thus enhancing safety, as well as the overall user experience.
  • the techniques and methods implemented by the RVA system can reduce the rate at which a vehicle may reject a path provided by the RHO by improving the accuracy of the selected path.
  • environment 100 illustrated is example environment 100 in which vehicles that include autonomous systems, as well as vehicles that do not, are operated.
  • environment 100 includes vehicles 102a-102n, objects 104a- 104n, routes 106a-106n, area 108, vehicle-to-infrastructure (V2I) device 110, network 112, remote autonomous vehicle (AV) system 114, fleet management system 116, and V2I system 118.
  • V2I vehicle-to-infrastructure
  • AV remote autonomous vehicle
  • V2I system 118 illustrated is example environment 100 in which vehicles that include autonomous systems, as well as vehicles that do not, are operated.
  • environment 100 includes vehicles 102a-102n, objects 104a- 104n, routes 106a-106n, area 108, vehicle-to-infrastructure (V2I) device 110, network 112, remote autonomous vehicle (AV) system 114, fleet management system 116, and V2I system 118.
  • V2I vehicle-to-infrastructure
  • Vehicles 102a-102n include at least one device configured to transport goods and/or people.
  • vehicles 102 are configured to be in communication with V2I device 110, remote AV system 114, fleet management system 116, and/or V2I system 118 via network 112.
  • vehicles 102 include cars, buses, trucks, trains, and/or the like.
  • vehicles 102 are the same as, or similar to, vehicles 200, described herein (see FIG. 2).
  • a vehicle 200 of a set of vehicles 200 is associated with an autonomous fleet manager.
  • vehicles 102 travel along respective routes 106a-106n (referred to individually as route 106 and collectively as routes 106), as described herein.
  • one or more vehicles 102 include an autonomous system (e.g., an autonomous system that is the same as or similar to autonomous system 202).
  • Objects 104a-104n include, for example, at least one vehicle, at least one pedestrian, at least one cyclist, at least one structure (e.g., a building, a sign, a fire hydrant, etc.), and/or the like.
  • Each object 104 is stationary (e.g., located at a fixed location for a period of time) or mobile (e.g., having a velocity and associated with at least one trajectory).
  • objects 104 are associated with corresponding locations in area 108.
  • Routes 106a-106n are each associated with (e.g., prescribe) a sequence of actions (also known as a trajectory) connecting states along which an AV can navigate.
  • Each route 106 starts at an initial state (e.g., a state that corresponds to a first spatiotemporal location, velocity, and/or the like) and a final goal state (e.g., a state that corresponds to a second spatiotemporal location that is different from the first spatiotemporal location) or goal region (e.g. a subspace of acceptable states (e.g., terminal states)).
  • the first state includes a location at which an individual or individuals are to be picked-up by the AV and the second state or region includes a location or locations at which the individual or individuals picked-up by the AV are to be dropped-off.
  • routes 106 include a plurality of acceptable state sequences (e.g., a plurality of spatiotemporal location sequences), the plurality of state sequences associated with (e.g., defining) a plurality of trajectories.
  • routes 106 include only high level actions or imprecise state locations, such as a series of connected roads dictating turning directions at roadway intersections.
  • routes 106 may include more precise actions or states such as, for example, specific target lanes or precise locations within the lane areas and targeted speed at those positions.
  • routes 106 include a plurality of precise state sequences along the at least one high level action sequence with a limited lookahead horizon to reach intermediate goals, where the combination of successive iterations of limited horizon state sequences cumulatively correspond to a plurality of trajectories that collectively form the high level route to terminate at the final goal state or region.
  • Area 108 includes a physical area (e.g., a geographic region) within which vehicles 102 can navigate.
  • area 108 includes at least one state (e.g., a country, a province, an individual state of a plurality of states included in a country, etc.), at least one portion of a state, at least one city, at least one portion of a city, etc.
  • area 108 includes at least one named thoroughfare (referred to herein as a “road”) such as a highway, an interstate highway, a parkway, a city street, etc.
  • area 108 includes at least one unnamed road such as a driveway, a section of a parking lot, a section of a vacant and/or undeveloped lot, a dirt path, etc.
  • a road includes at least one lane (e.g., a portion of the road that can be traversed by vehicles 102).
  • a road includes at least one lane associated with (e.g., identified based on) at least one lane marking.
  • Vehicle-to-lnfrastructure (V2I) device 110 (sometimes referred to as a Vehicle- to-lnfrastructure (V2X) device) includes at least one device configured to be in communication with vehicles 102 and/or V2I infrastructure system 118.
  • V2I device 110 is configured to be in communication with vehicles 102, remote AV system 114, fleet management system 116, and/or V2I system 118 via network 112.
  • V2I device 110 includes a radio frequency identification (RFID) device, signage, cameras (e.g., two-dimensional (2D) and/or three-dimensional (3D) cameras), lane markers, streetlights, parking meters, etc.
  • RFID radio frequency identification
  • V2I device 110 is configured to communicate directly with vehicles 102. Additionally, or alternatively, in some embodiments V2I device 110 is configured to communicate with vehicles 102, remote AV system 114, and/or fleet management system 116 via V2I system 118. In some embodiments, V2I device 110 is configured to communicate with V2I system 118 via network 112.
  • Network 112 includes one or more wired and/or wireless networks.
  • network 112 includes a cellular network (e.g., a long term evolution (LTE) network, a third generation (3G) network, a fourth generation (4G) network, a fifth generation (5G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., the public switched telephone network (PSTN), a private network, an ad hoc network, an intranet, the Internet, a fiber optic-based network, a cloud computing network, etc., a combination of some or all of these networks, and/or the like.
  • LTE long term evolution
  • 3G third generation
  • 4G fourth generation
  • 5G fifth generation
  • CDMA code division multiple access
  • PLMN public land mobile network
  • LAN local area network
  • WAN wide area network
  • MAN metropolitan
  • Remote AV system 114 includes at least one device configured to be in communication with vehicles 102, V2I device 110, network 112, remote AV system 114, fleet management system 116, and/or V2I system 118 via network 112.
  • remote AV system 114 includes a server, a group of servers, and/or other like devices.
  • remote AV system 114 is co-located with the fleet management system 116.
  • remote AV system 114 is involved in the installation of some or all of the components of a vehicle, including an autonomous system, an autonomous vehicle compute, software implemented by an autonomous vehicle compute, and/or the like.
  • remote AV system 114 maintains (e.g., updates and/or replaces) such components and/or software during the lifetime of the vehicle.
  • Fleet management system 116 includes at least one device configured to be in communication with vehicles 102, V2I device 110, remote AV system 114, and/or V2I infrastructure system 118.
  • fleet management system 116 includes a server, a group of servers, and/or other like devices.
  • fleet management system 116 is associated with a ridesharing company (e.g., an organization that controls operation of multiple vehicles (e.g., vehicles that include autonomous systems and/or vehicles that do not include autonomous systems) and/or the like).
  • V2I system 118 includes at least one device configured to be in communication with vehicles 102, V2I device 110, remote AV system 114, and/or fleet management system 116 via network 112. In some examples, V2I system 118 is configured to be in communication with V2I device 110 via a connection different from network 112. In some embodiments, V2I system 118 includes a server, a group of servers, and/or other like devices. In some embodiments, V2I system 118 is associated with a municipality or a private institution (e.g., a private institution that maintains V2I device 110 and/or the like). [39] The number and arrangement of elements illustrated in FIG. 1 are provided as an example.
  • a remote vehicle assistance (RVA) system 505 can be included in the environment 100.
  • the remote vehicle assistance system 505 can be configured within a vehicle 102 or external to a vehicle 102.
  • first portions of the remote vehicle assistance system 505 can be configured within a vehicle 102 and second portions of the remote vehicle assistance system 505 can be configured external to a vehicle 102.
  • vehicle 200 includes autonomous system 202, powertrain control system 204, steering control system 206, and brake system 208.
  • vehicle 200 is the same as or similar to vehicle 102 (see FIG. 1 ).
  • vehicle 200 can correspond to any one of vehicles 102.
  • vehicles 102 have autonomous capability (e.g., implement at least one function, feature, device, and/or the like that enable vehicle 200 to be partially or fully operated without human intervention including, without limitation, fully autonomous vehicles (e.g., vehicles that forego reliance on human intervention), highly autonomous vehicles (e.g., vehicles that forego reliance on human intervention in certain situations), and/or the like).
  • vehicle 200 is associated with an autonomous fleet manager and/or a ridesharing company.
  • Autonomous system 202 includes a sensor suite that includes one or more devices such as cameras 202a, LiDAR sensors 202b, radar sensors 202c, and microphones 202d.
  • autonomous system 202 can include more or fewer devices and/or different devices (e.g., ultrasonic sensors, inertial sensors, GPS receivers (discussed below), odometry sensors that generate data associated with an indication of a distance that vehicle 200 has traveled, and/or the like).
  • autonomous system 202 uses the one or more devices included in autonomous system 202 to generate data associated with environment 100, described herein.
  • autonomous system 202 includes communication device 202e, autonomous vehicle compute 202f, and drive-by-wire (DBW) system 202h.
  • DBW drive-by-wire
  • Cameras 202a include at least one device configured to be in communication with communication device 202e, autonomous vehicle compute 202f, and/or safety controller 202g via a bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3).
  • Cameras 202a include at least one camera (e.g., a digital camera using a light sensor such as a charge-coupled device (CCD), a thermal camera, an infrared (IR) camera, an event camera, and/or the like) to capture images including physical objects (e.g., cars, buses, curbs, people, and/or the like).
  • CCD charge-coupled device
  • IR infrared
  • an event camera e.g., IR camera
  • camera 202a generates camera data as output.
  • camera 202a generates camera data that includes image data associated with an image.
  • the image data may specify at least one parameter (e.g., image characteristics such as exposure, brightness, etc., an image timestamp, and/or the like) corresponding to the image.
  • the image may be in a format (e.g., RAW, JPEG, PNG, and/or the like).
  • camera 202a includes a plurality of independent cameras configured on (e.g., positioned on) a vehicle to capture images for the purpose of stereopsis (stereo vision).
  • camera 202a includes a plurality of cameras that generate image data and transmit the image data to autonomous vehicle compute 202f and/or a fleet management system (e.g., a fleet management system that is the same as or similar to fleet management system 116 of FIG. 1 ).
  • autonomous vehicle compute 202f determines depth to one or more objects in a field of view of at least two cameras of the plurality of cameras based on the image data from the at least two cameras.
  • cameras 202a is configured to capture images of objects within a distance from cameras 202a (e.g., up to 100 meters, up to a kilometer, and/or the like). Accordingly, cameras 202a include features such as sensors and lenses that are optimized for perceiving objects that are at one or more distances from cameras 202a.
  • camera 202a includes at least one camera configured to capture one or more images associated with one or more traffic lights, street signs and/or other physical objects that provide visual navigation information.
  • camera 202a generates traffic light data associated with one or more images.
  • camera 202a generates TLD data associated with one or more images that include a format (e.g., RAW, JPEG, PNG, and/or the like).
  • camera 202a that generates TLD data differs from other systems described herein incorporating cameras in that camera 202a can include one or more cameras with a wide field of view (e.g., a wide-angle lens, a fish-eye lens, a lens having a viewing angle of approximately 120 degrees or more, and/or the like) to generate images about as many physical objects as possible.
  • a wide field of view e.g., a wide-angle lens, a fish-eye lens, a lens having a viewing angle of approximately 120 degrees or more, and/or the like
  • Laser Detection and Ranging (LiDAR) sensors 202b include at least one device configured to be in communication with communication device 202e, autonomous vehicle compute 202f, and/or safety controller 202g via a bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3).
  • LiDAR sensors 202b include a system configured to transmit light from a light emitter (e.g., a laser transmitter).
  • Light emitted by LiDAR sensors 202b include light (e.g., infrared light and/or the like) that is outside of the visible spectrum.
  • LiDAR sensors 202b during operation, light emitted by LiDAR sensors 202b encounters a physical object (e.g., a vehicle) and is reflected back to LiDAR sensors 202b. In some embodiments, the light emitted by LiDAR sensors 202b does not penetrate the physical objects that the light encounters. LiDAR sensors 202b also include at least one light detector which detects the light that was emitted from the light emitter after the light encounters a physical object. In some embodiments, at least one data processing system associated with LiDAR sensors 202b generates an image (e.g., a point cloud, a combined point cloud, and/or the like) representing the objects included in a field of view of LiDAR sensors 202b.
  • an image e.g., a point cloud, a combined point cloud, and/or the like
  • the at least one data processing system associated with LiDAR sensor 202b generates an image that represents the boundaries of a physical object, the surfaces (e.g., the topology of the surfaces) of the physical object, and/or the like.
  • the image is used to determine the boundaries of physical objects in the field of view of LiDAR sensors 202b.
  • Radio Detection and Ranging (radar) sensors 202c include at least one device configured to be in communication with communication device 202e, autonomous vehicle compute 202f, and/or safety controller 202g via a bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3).
  • Radar sensors 202c include a system configured to transmit radio waves (either pulsed or continuously).
  • the radio waves transmitted by radar sensors 202c include radio waves that are within a predetermined spectrum
  • radio waves transmitted by radar sensors 202c encounter a physical object and are reflected back to radar sensors 202c.
  • the radio waves transmitted by radar sensors 202c are not reflected by some objects.
  • At least one data processing system associated with radar sensors 202c generates signals representing the objects included in a field of view of radar sensors 202c.
  • the at least one data processing system associated with radar sensor 202c generates an image that represents the boundaries of a physical object, the surfaces (e.g., the topology of the surfaces) of the physical object, and/or the like.
  • the image is used to determine the boundaries of physical objects in the field of view of radar sensors 202c.
  • Microphones 202d includes at least one device configured to be in communication with communication device 202e, autonomous vehicle compute 202f, and/or safety controller 202g via a bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3).
  • Microphones 202d include one or more microphones (e.g., array microphones, external microphones, and/or the like) that capture audio signals and generate data associated with (e.g., representing) the audio signals.
  • microphones 202d include transducer devices and/or like devices.
  • one or more systems described herein can receive the data generated by microphones 202d and determine a position of an object relative to vehicle 200 (e.g., a distance and/or the like) based on the audio signals associated with the data.
  • Communication device 202e include at least one device configured to be in communication with cameras 202a, LiDAR sensors 202b, radar sensors 202c, microphones 202d, autonomous vehicle compute 202f, safety controller 202g, and/or DBW system 202h.
  • communication device 202e may include a device that is the same as or similar to communication interface 314 of FIG. 3.
  • communication device 202e includes a vehicle-to-vehicle (V2V) communication device (e.g., a device that enables wireless communication of data between vehicles).
  • V2V vehicle-to-vehicle
  • Autonomous vehicle compute 202f include at least one device configured to be in communication with cameras 202a, LiDAR sensors 202b, radar sensors 202c, microphones 202d, communication device 202e, safety controller 202g, and/or DBW system 202h.
  • autonomous vehicle compute 202f includes a device such as a client device, a mobile device (e.g., a cellular telephone, a tablet, and/or the like) a server (e.g., a computing device including one or more central processing units, graphical processing units, and/or the like), and/or the like.
  • autonomous vehicle compute 202f is the same as or similar to autonomous vehicle compute 400, described herein.
  • autonomous vehicle compute 202f is configured to be in communication with an autonomous vehicle system (e.g., an autonomous vehicle system that is the same as or similar to remote AV system 114 of FIG. 1 ), a fleet management system (e.g., a fleet management system that is the same as or similar to fleet management system 116 of FIG. 1 ), a V2I device (e.g., a V2I device that is the same as or similar to V2I device 110 of FIG. 1 ), and/or a V2I system (e.g., a V2I system that is the same as or similar to V2I system 118 of FIG. 1 ).
  • an autonomous vehicle system e.g., an autonomous vehicle system that is the same as or similar to remote AV system 114 of FIG. 1
  • a fleet management system e.g., a fleet management system that is the same as or similar to fleet management system 116 of FIG. 1
  • V2I device e.g., a V2I device that is the same as or similar
  • Safety controller 202g includes at least one device configured to be in communication with cameras 202a, LiDAR sensors 202b, radar sensors 202c, microphones 202d, communication device 202e, autonomous vehicle computer 202f, and/or DBW system 202h.
  • safety controller 202g includes one or more controllers (electrical controllers, electromechanical controllers, and/or the like) that are configured to generate and/or transmit control signals to operate one or more devices of vehicle 200 (e.g., powertrain control system 204, steering control system 206, brake system 208, and/or the like).
  • safety controller 202g is configured to generate control signals that take precedence over (e.g., overrides) control signals generated and/or transmitted by autonomous vehicle compute 202f.
  • DBW system 202h includes at least one device configured to be in communication with communication device 202e and/or autonomous vehicle compute 202f.
  • DBW system 202h includes one or more controllers (e.g., electrical controllers, electromechanical controllers, and/or the like) that are configured to generate and/or transmit control signals to operate one or more devices of vehicle 200 (e.g., powertrain control system 204, steering control system 206, brake system 208, and/or the like).
  • controllers e.g., electrical controllers, electromechanical controllers, and/or the like
  • the one or more controllers of DBW system 202h are configured to generate and/or transmit control signals to operate at least one different device (e.g., a turn signal, headlights, door locks, windshield wipers, and/or the like) of vehicle 200.
  • a turn signal e.g., a turn signal, headlights, door locks, windshield wipers, and/or the like
  • Powertrain control system 204 includes at least one device configured to be in communication with DBW system 202h. In some examples, powertrain control system 204 includes at least one controller, actuator, and/or the like. In some embodiments, powertrain control system 204 receives control signals from DBW system 202h and powertrain control system 204 causes vehicle 200 to start moving forward, stop moving forward, start moving backward, stop moving backward, accelerate in a direction, decelerate in a direction, perform a left turn, perform a right turn, and/or the like.
  • powertrain control system 204 causes the energy (e.g., fuel, electricity, and/or the like) provided to a motor of the vehicle to increase, remain the same, or decrease, thereby causing at least one wheel of vehicle 200 to rotate or not rotate.
  • energy e.g., fuel, electricity, and/or the like
  • Steering control system 206 includes at least one device configured to rotate one or more wheels of vehicle 200.
  • steering control system 206 includes at least one controller, actuator, and/or the like.
  • steering control system 206 causes the front two wheels and/or the rear two wheels of vehicle 200 to rotate to the left or right to cause vehicle 200 to turn to the left or right.
  • Brake system 208 includes at least one device configured to actuate one or more brakes to cause vehicle 200 to reduce speed and/or remain stationary.
  • brake system 208 includes at least one controller and/or actuator that is configured to cause one or more calipers associated with one or more wheels of vehicle 200 to close on a corresponding rotor of vehicle 200.
  • brake system 208 includes an automatic emergency braking (AEB) system, a regenerative braking system, and/or the like.
  • AEB automatic emergency braking
  • vehicle 200 includes at least one platform sensor (not explicitly illustrated) that measures or infers properties of a state or a condition of vehicle 200.
  • vehicle 200 includes platform sensors such as a global positioning system (GPS) receiver, an inertial measurement unit (IMU), a wheel speed sensor, a wheel brake pressure sensor, a wheel torque sensor, an engine torque sensor, a steering angle sensor, and/or the like.
  • GPS global positioning system
  • IMU inertial measurement unit
  • wheel speed sensor a wheel brake pressure sensor
  • wheel torque sensor a wheel torque sensor
  • engine torque sensor a steering angle sensor
  • device 300 includes processor 304, memory 306, storage component 308, input interface 310, output interface 312, communication interface 314, and bus 302.
  • device 300 corresponds to at least one device of vehicles 102 (e.g., at least one device of a system of vehicles 102), at least one device of the remote vehicle assistance system 505, and/or one or more devices of network 112 (e.g., one or more devices of a system of network 112).
  • one or more devices of vehicles 102 include at least one device 300 and/or at least one component of device 300.
  • device 300 includes bus 302, processor 304, memory 306, storage component 308, input interface 310, output interface 312, and communication interface 314.
  • Bus 302 includes a component that permits communication among the components of device 300.
  • processor 304 is implemented in hardware, software, or a combination of hardware and software.
  • processor 304 includes a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), and/or the like), a microphone, a digital signal processor (DSP), and/or any processing component (e.g., a field-programmable gate array (FPGA), an application specific integrated circuit (ASIC), and/or the like) that can be programmed to perform at least one function.
  • processor e.g., a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), and/or the like
  • DSP digital signal processor
  • any processing component e.g., a field-programmable gate array (FPGA), an application specific integrated circuit (ASIC), and/or the like
  • Memory 306 includes random access memory (RAM), read-only memory (ROM), and/or another type of dynamic and/or static storage device (e.g., flash memory, magnetic memory, optical memory, and/or the like) that stores data and/or instructions for use by processor 304.
  • RAM random access memory
  • ROM read-only memory
  • static storage device e.g., flash memory, magnetic memory, optical memory, and/or the like
  • Storage component 308 stores data and/or software related to the operation and use of device 300.
  • storage component 308 includes a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk, a solid state disk, and/or the like), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, a CD-ROM, RAM, PROM, EPROM, FLASH-EPROM, NVRAM, and/or another type of computer readable medium, along with a corresponding drive.
  • Input interface 310 includes a component that permits device 300 to receive information, such as via user input (e.g., a touchscreen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, a camera, and/or the like). Additionally or alternatively, in some embodiments input interface 310 includes a sensor that senses information (e.g., a global positioning system (GPS) receiver, an accelerometer, a gyroscope, an actuator, and/or the like). Output interface 312 includes a component that provides output information from device 300 (e.g., a display, a speaker, one or more light-emitting diodes (LEDs), and/or the like).
  • GPS global positioning system
  • LEDs light-emitting diodes
  • communication interface 314 includes a transceiver-like component (e.g., a transceiver, a separate receiver and transmitter, and/or the like) that permits device 300 to communicate with other devices via a wired connection, a wireless connection, or a combination of wired and wireless connections.
  • communication interface 314 permits device 300 to receive information from another device and/or provide information to another device.
  • communication interface 314 includes an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi® interface, a cellular network interface, and/or the like.
  • RF radio frequency
  • USB universal serial bus
  • device 300 performs one or more processes described herein. Device 300 performs these processes based on processor 304 executing software instructions stored by a computer-readable medium, such as memory 305 and/or storage component 308.
  • a computer-readable medium e.g., a non-transitory computer readable medium
  • a non-transitory memory device includes memory space located inside a single physical storage device or memory space spread across multiple physical storage devices.
  • software instructions are read into memory 306 and/or storage component 308 from another computer-readable medium or from another device via communication interface 314.
  • software instructions stored in memory 306 and/or storage component 308 cause processor 304 to perform one or more processes described herein.
  • hardwired circuitry is used in place of or in combination with software instructions to perform one or more processes described herein.
  • Memory 306 and/or storage component 308 includes data storage or at least one data structure (e.g., a database and/or the like).
  • Device 300 is capable of receiving information from, storing information in, communicating information to, or searching information stored in the data storage or the at least one data structure in memory 306 or storage component 308.
  • the information includes network data, input data, output data, or any combination thereof.
  • device 300 is configured to execute software instructions that are either stored in memory 306 and/or in the memory of another device (e.g., another device that is the same as or similar to device 300).
  • module refers to at least one instruction stored in memory 306 and/or in the memory of another device that, when executed by processor 304 and/or by a processor of another device (e.g., another device that is the same as or similar to device 300) cause device 300 (e.g., at least one component of device 300) to perform one or more processes described herein.
  • a module is implemented in software, firmware, hardware, and/or the like.
  • device 300 can include additional components, fewer components, different components, or differently arranged components than those illustrated in FIG. 3. Additionally or alternatively, a set of components (e.g., one or more components) of device 300 can perform one or more functions described as being performed by another component or another set of components of device 300.
  • a set of components e.g., one or more components
  • autonomous vehicle compute 400 includes perception system 402 (sometimes referred to as a perception module), planning system 404 (sometimes referred to as a planning module), localization system 406 (sometimes referred to as a localization module), control system 408 (sometimes referred to as a control module), and database 410.
  • perception system 402, planning system 404, localization system 406, control system 408, and database 410 are included and/or implemented in an autonomous navigation system of a vehicle (e.g., autonomous vehicle compute 202f of vehicle 200).
  • perception system 402, planning system 404, localization system 406, control system 408, and database 410 are included in one or more standalone systems (e.g., one or more systems that are the same as or similar to autonomous vehicle compute 400 and/or the like). In some examples, perception system 402, planning system 404, localization system 406, control system 408, and database 410 are included in one or more standalone systems that are located in a vehicle and/or at least one remote system as described herein.
  • any and/or all of the systems included in autonomous vehicle compute 400 are implemented in software (e.g., in software instructions stored in memory), computer hardware (e.g., by microprocessors, microcontrollers, application-specific integrated circuits [ASICs], Field Programmable Gate Arrays (FPGAs), and/or the like), or combinations of computer software and computer hardware.
  • software e.g., in software instructions stored in memory
  • computer hardware e.g., by microprocessors, microcontrollers, application-specific integrated circuits [ASICs], Field Programmable Gate Arrays (FPGAs), and/or the like
  • ASICs application-specific integrated circuits
  • FPGAs Field Programmable Gate Arrays
  • autonomous vehicle compute 400 is configured to be in communication with a remote system (e.g., an autonomous vehicle system that is the same as or similar to remote AV system 114, a fleet management system 116 that is the same as or similar to fleet management system 116, a V2I system that is the same as or similar to V2I system 118, and/or the like).
  • a remote system e.g., an autonomous vehicle system that is the same as or similar to remote AV system 114, a fleet management system 116 that is the same as or similar to fleet management system 116, a V2I system that is the same as or similar to V2I system 118, and/or the like.
  • perception system 402 receives data associated with at least one physical object (e.g., data that is used by perception system 402 to detect the at least one physical object) in an environment and classifies the at least one physical object.
  • perception system 402 receives image data captured by at least one camera (e.g., cameras 202a), the image associated with (e.g., representing) one or more physical objects within a field of view of the at least one camera.
  • perception system 402 classifies at least one physical object based on one or more groupings of physical objects (e.g., bicycles, vehicles, traffic signs, pedestrians, and/or the like).
  • perception system 402 transmits data associated with the classification of the physical objects to planning system 404 based on perception system 402 classifying the physical objects.
  • planning system 404 receives data associated with a destination and generates data associated with at least one route (e.g., routes 106) along which a vehicle (e.g., vehicles 102) can travel along toward a destination.
  • planning system 404 periodically or continuously receives data from perception system 402 (e.g., data associated with the classification of physical objects, described above) and planning system 404 updates the at least one trajectory or generates at least one different trajectory based on the data generated by perception system 402.
  • planning system 404 receives data associated with an updated position of a vehicle (e.g., vehicles 102) from localization system 406 and planning system 404 updates the at least one trajectory or generates at least one different trajectory based on the data generated by localization system 406.
  • a vehicle e.g., vehicles 102
  • localization system 406 receives data associated with (e.g., representing) a location of a vehicle (e.g., vehicles 102) in an area.
  • localization system 406 receives LiDAR data associated with at least one point cloud generated by at least one LiDAR sensor (e.g., LiDAR sensors 202b).
  • localization system 406 receives data associated with at least one point cloud from multiple LiDAR sensors and localization system 406 generates a combined point cloud based on each of the point clouds.
  • localization system 406 compares the at least one point cloud or the combined point cloud to two-dimensional (2D) and/or a three-dimensional (3D) map of the area stored in database 410.
  • Localization system 406 determines the position of the vehicle in the area based on localization system 406 comparing the at least one point cloud or the combined point cloud to the map.
  • the map includes a combined point cloud of the area generated prior to navigation of the vehicle.
  • maps include, without limitation, high-precision maps of the roadway geometric properties, maps describing road network connectivity properties, maps describing roadway physical properties (such as traffic speed, traffic volume, the number of vehicular and cyclist traffic lanes, lane width, lane traffic directions, or lane marker types and locations, or combinations thereof), and maps describing the spatial locations of road features such as crosswalks, traffic signs or other travel signals of various types.
  • the map is generated in real-time based on the data received by the perception system.
  • localization system 406 receives Global Navigation Satellite System (GNSS) data generated by a global positioning system (GPS) receiver.
  • GNSS Global Navigation Satellite System
  • GPS global positioning system
  • localization system 406 receives GNSS data associated with the location of the vehicle in the area and localization system 406 determines a latitude and longitude of the vehicle in the area. In such an example, localization system 406 determines the position of the vehicle in the area based on the latitude and longitude of the vehicle.
  • localization system 406 generates data associated with the position of the vehicle.
  • localization system 406 generates data associated with the position of the vehicle based on localization system 406 determining the position of the vehicle. In such an example, the data associated with the position of the vehicle includes data associated with one or more semantic properties corresponding to the position of the vehicle.
  • control system 408 receives data associated with at least one trajectory from planning system 404 and control system 408 controls operation of the vehicle.
  • control system 408 receives data associated with at least one trajectory from planning system 404 and control system 408 controls operation of the vehicle by generating and transmitting control signals to cause a powertrain control system (e.g., DBW system 202h, powertrain control system 204, and/or the like), a steering control system (e.g., steering control system 206), and/or a brake system (e.g., brake system 208) to operate.
  • a powertrain control system e.g., DBW system 202h, powertrain control system 204, and/or the like
  • steering control system e.g., steering control system 206
  • brake system e.g., brake system 208
  • control system 408 transmits a control signal to cause steering control system 206 to adjust a steering angle of vehicle 200, thereby causing vehicle 200 to turn left. Additionally, or alternatively, control system 408 generates and transmits control signals to cause other devices (e.g., headlights, turn signal, door locks, windshield wipers, and/or the like) of vehicle 200 to change states.
  • other devices e.g., headlights, turn signal, door locks, windshield wipers, and/or the like
  • perception system 402, planning system 404, localization system 406, and/or control system 408 implement at least one machine learning model (e.g., at least one multilayer perceptron (MLP), at least one convolutional neural network (CNN), at least one recurrent neural network (RNN), at least one autoencoder, at least one transformer, and/or the like).
  • MLP multilayer perceptron
  • CNN convolutional neural network
  • RNN recurrent neural network
  • autoencoder at least one transformer, and/or the like
  • perception system 402, planning system 404, localization system 406, control system 408, and/or the remote vehicle assistance system 505 implement at least one machine learning model alone or in combination with one or more of the above-noted systems.
  • perception system 402, planning system 404, localization system 406, control system 408, and/or the remote vehicle assistance system 505 implement at least one machine learning model as part of a pipeline (e.g., a pipeline for identifying one or more objects located in an environment and/or the like).
  • a machine learning model is included below with respect to FIG. 4B.
  • Database 410 stores data that is transmitted to, received from, and/or updated by perception system 402, planning system 404, localization system 406 and/or control system 408.
  • database 410 includes a storage component (e.g., a storage component that is the same as or similar to storage component 308 of FIG. 3) that stores data and/or software related to the operation and uses at least one system of autonomous vehicle compute 400.
  • database 410 stores data associated with 2D and/or 3D maps of at least one area.
  • database 410 stores data associated with 2D and/or 3D maps of a portion of a city, multiple portions of multiple cities, multiple cities, a county, a state, a State (e.g., a country), and/or the like).
  • a vehicle e.g., a vehicle that is the same as or similar to vehicles 102 and/or vehicle 200
  • vehicle can drive along one or more drivable regions (e.g., single-lane roads, multi-lane roads, highways, back roads, off road trails, and/or the like) and cause at least one LiDAR sensor (e.g., a LiDAR sensor that is the same as or similar to LiDAR sensors 202b) to generate data associated with an image representing the objects included in a field of view of the at least one LiDAR sensor.
  • drivable regions e.g., single-lane roads, multi-lane roads, highways, back roads, off road trails, and/or the like
  • LiDAR sensor e.g., a LiDAR sensor that is the same as or similar to LiDAR sensors 202b
  • database 410 can be implemented across a plurality of devices.
  • database 410 is included in a vehicle (e.g., a vehicle that is the same as or similar to vehicles 102 and/or vehicle 200), an autonomous vehicle system (e.g., an autonomous vehicle system that is the same as or similar to remote AV system 114, a fleet management system (e.g., a fleet management system that is the same as or similar to fleet management system 116 of FIG. 1 , a V2I system (e.g., a V2I system that is the same as or similar to V2I system 118 of FIG. 1 ) and/or the like.
  • a vehicle e.g., a vehicle that is the same as or similar to vehicles 102 and/or vehicle 200
  • an autonomous vehicle system e.g., an autonomous vehicle system that is the same as or similar to remote AV system 114
  • a fleet management system e.g., a fleet management system that is the same as or similar to fleet management system 116 of FIG. 1
  • CNN 420 convolutional neural network
  • perception system 402. the following description of CNN 420 will be with respect to an implementation of CNN 420 by perception system 402.
  • CNN 420 e.g., one or more components of CNN 420
  • other systems different from, or in addition to, perception system 402 such as planning system 404, localization system 406, control system 408, and/or the remote vehicle assistance system 505.
  • CNN 420 includes certain features as described herein, these features are provided for the purpose of illustration and are not intended to limit the present disclosure.
  • CNN 420 includes a plurality of convolution layers including first convolution layer 422, second convolution layer 424, and convolution layer 426.
  • CNN 420 includes sub-sampling layer 428 (sometimes referred to as a pooling layer).
  • sub-sampling layer 428 and/or other subsampling layers have a dimension (i.e., an amount of nodes) that is less than a dimension of an upstream system.
  • CNN 420 consolidates the amount of data associated with the initial input.
  • Perception system 402 performs convolution operations based on perception system 402 providing respective inputs and/or outputs associated with each of first convolution layer 422, second convolution layer 424, and convolution layer 426 to generate respective outputs.
  • perception system 402 implements CNN 420 based on perception system 402 providing data as input to first convolution layer 422, second convolution layer 424, and convolution layer 426.
  • perception system 402 provides the data as input to first convolution layer 422, second convolution layer 424, and convolution layer 426 based on perception system 402 receiving data from one or more different systems (e.g., one or more systems of a vehicle that is the same as or similar to vehicle 102), a remote AV system that is the same as or similar to remote AV system 114, a fleet management system that is the same as or similar to fleet management system 116, a V2I system that is the same as or similar to V2I system 118, and/or the like).
  • one or more different systems e.g., one or more systems of a vehicle that is the same as or similar to vehicle 102
  • a remote AV system that is the same as or similar to remote AV system 114
  • a fleet management system that is the same as or similar to fleet management system 116
  • V2I system that is the same as or similar to V2I system 118, and/or the like.
  • perception system 402 provides data associated with an input (referred to as an initial input) to first convolution layer 422 and perception system 402 generates data associated with an output using first convolution layer 422.
  • perception system 402 provides an output generated by a convolution layer as input to a different convolution layer.
  • perception system 402 provides the output of first convolution layer 422 as input to sub-sampling layer 428, second convolution layer 424, and/or convolution layer 426.
  • first convolution layer 422 is referred to as an upstream layer and subsampling layer 428, second convolution layer 424, and/or convolution layer 426 are referred to as downstream layers.
  • perception system 402 provides the output of sub-sampling layer 428 to second convolution layer 424 and/or convolution layer 426 and, in this example, sub-sampling layer 428 would be referred to as an upstream layer and second convolution layer 424 and/or convolution layer 426 would be referred to as downstream layers.
  • perception system 402 processes the data associated with the input provided to CNN 420 before perception system 402 provides the input to CNN 420. For example, perception system 402 processes the data associated with the input provided to CNN 420 based on perception system 420 normalizing sensor data (e.g., image data, LiDAR data, radar data, and/or the like). [79] In some embodiments, CNN 420 generates an output based on perception system 420 performing convolution operations associated with each convolution layer. In some examples, CNN 420 generates an output based on perception system 420 performing convolution operations associated with each convolution layer and an initial input. In some embodiments, perception system 402 generates the output and provides the output as fully connected layer 430.
  • sensor data e.g., image data, LiDAR data, radar data, and/or the like.
  • perception system 402 provides the output of convolution layer 426 as fully connected layer 430, where fully connected layer 420 includes data associated with a plurality of feature values referred to as F1 , F2 . . . FN.
  • the output of convolution layer 426 includes data associated with a plurality of output feature values that represent a prediction.
  • perception system 402 identifies a prediction from among a plurality of predictions based on perception system 402 identifying a feature value that is associated with the highest likelihood of being the correct prediction from among the plurality of predictions. For example, where fully connected layer 430 includes feature values F1 , F2, . . . FN, and F1 is the greatest feature value, perception system 402 identifies the prediction associated with F1 as being the correct prediction from among the plurality of predictions. In some embodiments, perception system 402 trains CNN 420 to generate the prediction. In some examples, perception system 402 trains CNN 420 to generate the prediction based on perception system 402 providing training data associated with the prediction to CNN 420.
  • implementation 500 includes remote vehicle assistance system 505, vehicles 102a- 102n and/or vehicles 200, objects 104a-104n, routes 106a-106n, area 108, vehicle- to-infrastructure (V2I) device 110, network 112, remote autonomous vehicle (AV) system 114, fleet management system 116, and/or V2I system 118.
  • V2I vehicle- to-infrastructure
  • AV remote autonomous vehicle
  • remote vehicle assistance system 505 includes, forms a part of, is coupled to, and/or uses vehicles 102a-102n and/or vehicles 200, objects 104a-104n, routes 106a-106n, area 108, vehicle-to-infrastructure (V2I) device 110, network 112, remote autonomous vehicle (AV) system 114, fleet management system 116, and/or V2I system 118.
  • V2I vehicle-to-infrastructure
  • AV remote autonomous vehicle
  • the implementation 500 includes an remote vehicle assistance system 505.
  • the remote vehicle assistance system 505 includes a path planner 510 configured to acquire and process sensor data from one or more sensors 202 affixed to the vehicle.
  • the sensor data is used to determine and provide candidate paths or trajectories for the vehicle to navigate from a current position, such as a position at which it is stuck and has requested remote vehicle assistance, to a goal location that the vehicle is attempting to navigate toward.
  • the path planner 510 determines hypothetical or candidate paths or trajectories 515, which are provided to an operator of the RVA system 505 via a human machine interface (HMI) of the RVA system 505.
  • HMI human machine interface
  • the operator can manipulate, e.g., alter, re-draw, add, or delete, aspects of one or more of the candidate paths generated by the path planner 510.
  • the operator can adjust or alter a candidate path so that it is most likely to be accepted by the vehicle planning system 404 to provide continued autonomous navigation from the stuck location.
  • the operator can then select a path 515 to be provided to the vehicle for navigation through or away from the location at which it is stuck (or has otherwise requested remote vehicle assistance).
  • the operator can determine or otherwise select a path based on sensor data, as well as costs and constraints that can be associated with one or more of the candidate paths provided to the operator via the HMI.
  • the RVA system 505 includes an path planner 510.
  • the path planner 510 receives sensor data 520 from any one of sensors 202.
  • the sensor data 520 is processed by a RVA manager 525 to generate candidate paths in response to a request for remote vehicle assistance received from the autonomous vehicle.
  • the RVA manager 525 can be configured to generate candidate paths that are optimized to reduce the probability of rejection when received by the vehicle planning system 404.
  • the RVA manager 525 can optimize the candidate paths in regard to costs and constraints associated with each path.
  • the RVA manager 525 can also provide the paths, as well as the costs and constraints associated with each path for visualization in the HMI 530.
  • an operator can observe the sensor data and the generated candidate paths to select one of the candidate path to provide to the vehicle planning system 404 as selected path 515.
  • the operator can also adjust or modify a candidate path and provide the adjusted or modified path as the selected path 515 to the vehicle planning system 404.
  • the path planner 510 can be subscribed to or otherwise receive data, such as status updates or RVA requests, from other systems included in the autonomous vehicle computing system 400.
  • the path planner 510 can read static configurations at startup and provide them as data feeds to the HMI 530.
  • the path planner 510 can create visualizations of paths that include costs and constraints associated with each path.
  • the visualizations can be provided via the HMI 530.
  • the path planner 510 can also determine optimized, alternate paths and can provide the alternate paths via the HMI 530.
  • the paths can be modified by an operator via the HMI 530.
  • an operator can adjust a path with respect to a constraint, such as an object or pedestrian, so that the adjusted path avoids the constraint.
  • a constraint such as an object or pedestrian
  • One or more segments of a path can be adjusted via the HMI 530 in regard to a constraint or a cost associated with a path.
  • the RVA operator can interact with the HMI 530 to select a path 515.
  • the selected path 515 can be provided to the vehicle planning system 404 and the vehicle can be operated to navigate to the vehicle in relation to the selected path 515 so as to avoid objects 104.
  • FIG. 7 illustrated is a flowchart of a process 700 for path selection using the RVA system 505 described herein.
  • one or more of the steps described with respect to process 700 are performed (e.g., completely, partially, and/or the like) by the RVA system 505. Additionally, or alternatively, in some embodiments one or more steps described with respect to process 700 are performed (e.g., completely, partially, and/or the like) by another device or group of devices separate from or including the RVA system 505 such as perception system 402, planning system 404, localization system 406, and/or control system 408.
  • the process includes receiving a goal location responsive to an input provided via a graphical user interface.
  • the input can be provided via an HMI of an RVA system, such as HMI 530, in response to a received request for RVA.
  • the request for RVA can be provided by the vehicle at a starting location (e.g., a location at which the vehicle is requesting RVA).
  • the process incudes generating a plurality of candidate paths based on the goal location and a starting location.
  • Each candidate path can represent a corresponding trajectory from the starting location to the goal location.
  • the starting location can be a location at which the vehicle is stuck and/or requesting RVA.
  • Generating the plurality of candidate paths can include generating the plurality of candidate paths based on one or more visualization modes. Each visualization mode can provide a representation of the trajectory from the starting location to the goal location.
  • Generating the plurality of candidate paths can also include generating the plurality of candidate paths based on a plurality of previously generated candidate paths within a predetermined radius of the starting location and/or the goal location.
  • the one or more visualization modes can include a lane graph view, an intersection view, or a wireframe view.
  • Generating the plurality of candidate paths based on the plurality of previously generated candidate within the predetermined radius of the starting location and/or the goal location can also include generating the plurality of candidate paths based on at least one of blockages associated with the plurality of previously generated candidate paths, waypoints associated with the plurality of previously generated candidate paths, trajectory acceptance rates associated with the plurality of previously generated candidate paths, or an environment associated with the plurality of previously generated candidate paths.
  • the process includes providing the plurality of candidate paths via the graphical user interface.
  • Providing the plurality of candidate paths can include providing a representation of at least one corresponding cost and at least one corresponding constraint associated with each corresponding candidate path.
  • the at least one constraint can include at least one of a buffer between a front of the vehicle and an object or the at least one constraint can include a buffer between a side of the vehicle and an object.
  • the constraint can represent a limitation or a safety factor configured to ensure the candidate path can be a safe and navigable path that the vehicle planning system is likely to accept and implement to navigate the vehicle from the starting location.
  • the at least one cost can include a lane change cost or a curvature cost.
  • a cost can represent an event, object, or environmental configuration that can cause the candidate path to be rejected by the vehicle planning system if not accounted for and navigated so as to avoid or reduce the cost.
  • the process includes receiving a selected path identified from the plurality of candidate paths.
  • the selected path can be received via the graphical user interface.
  • the selected path can be adjusted such that a modification can be provided to the selected path via the graphical user interface or HMI 530.
  • the modification can adjust part (e.g., a segment or a way point) of the selected path.
  • Each candidate path (or each selected path) provided in the graphical user interface can include a plurality of editable waypoints located along the trajectory between the starting location and the goal location.
  • the process includes providing data associated with the selected path to a vehicle to implement the corresponding trajectory, wherein implementing the trajectory comprises navigating from the starting location to the goal location along the selected path.
  • the graphical user interface provides a visual representation of features present in an environment in which the vehicle is operating.
  • the visual representation can include features located within a predetermined distance from each candidate path.
  • the graphical user interface can include video data of the environment in which the vehicle is located.
  • a dataflow 800 can be executed between the RVA system 505 and the vehicle 102 to perform the method described in relation to FIG. 7.
  • the RVA process begins when the Assistance Requestor 805 of vehicle 102 generates a RVA request 810.
  • the RVA request 810 can be passed to a Remote Human Operator (RHO) 815 operating the RVA system 505.
  • RHO 815 can draw a trajectory 820 using the RVA HMI 530.
  • a RVA path 825 can be generated by the HMI 530 and the path 825 can be provided to a Propose and Select Task Module (e.g., PnS Task 835).
  • the Propose And Select Task Module 835 is part of the autonomous vehicle compute 400 (sometimes referred to as the “AV stack”) and interfaces with the planning system 404 and the control system 408 of the autonomous vehicle compute 400.
  • the PnS Task 835 can perform operations for determining a planned route for the vehicle 102 and executing the determined route autonomously.
  • the RHO 815 can become aware of a rejected path in response to a RVA status message, a route plan 840, or trajectory checker output 845.
  • the PnS Task 835 can accept or reject the path 825 and can assign a state to any path 825. For example, the PnS Task 835 can determine a path 825 is acceptable (e.g., a path that can be accepted by the vehicle planning system 404 and will not be rejected as a navigable path).
  • the PnS Task 835 can generate an acceptance status 840 associated with the path 825.
  • the acceptance status 840 can be provided to the RVA HMI 530. Since the path 825 was determined to have an acceptance status 840, the PnS Task 835 can compute a route plan 845 based on the accepted path 825.
  • the route plan 845 can be also be provided to the RVA HMI 530.
  • the PnS Task 835 can determine and provide a route plan 845 corresponding to the accepted path 825 to a Trajectory Checker 850.
  • the Trajectory Checker 850 1 can assess constraints and costs associated with the route plan 845 and can accept or reject the route plan 845.
  • the Trajectory Checker 850 can utilize a risk classification scheme, such as ASIL-D, to assess the route plan 845.
  • Automotive Safety Integrity Level (ASIL) is a risk classification scheme defined by the ISO 26262 - Functional Safety for Road Vehicles standard. This standard is an adaptation of the Safety Integrity Level (SIL) used in IEC 61508 for the automotive industry. This classification helps define safety requirements necessary to be in line with the ISO 26262 standard.
  • the ASIL is established by performing a risk analysis of a potential hazard by looking at the Severity, Exposure and Controllability of the vehicle operating scenario.
  • the safety goal for that hazard in turn carries the ASIL requirements.
  • the Trajectory Checker 850 can evaluate the route plan 845 with regard to the ASIL-D requirements and can generate an output 855.
  • the route plan 845 can also be provided to the RHO 815 via the HMI 530.
  • the Trajectory Checker 845 can provide the output 855 to a Controller 860.
  • the controller 860 can be configured to interface with a drive-by-wire actuation system to control one or more actuators in the vehicle 102 associated with acceleration, braking, and/or steering of the vehicle 102.
  • RHO 815 can provide inputs 865 to redraw the path 825. For example, if the PnS Task 835 generates an Unacceptable status 840 for a particular path 825, responsive to receiving that Unacceptable status 840, the RHO 815 can use the HMI 530 to provide inputs 865 modifying or altering the path 825, such as when one or more costs or constraints provided in the path 825 are likely to cause the path 825 to be rejected by the PnS Task 835 and/or the Trajectory Checker 850.
  • FIGS. 9A and 9B illustrate embodiments of user interfaces of the remote vehicle assistance system described herein.
  • the user interface 905 can a lane graph view providing include a visualization of the vehicle 102 with respect to a second vehicle A.
  • the RVA system 505 can determine a path 910 to navigate around vehicle A.
  • the path 910 can include editable waypoints 915A and 915B.
  • the user interface 905 can also include visual representations of costs and constraints.
  • a side width constraint or side buffer constraint 920 can be visually represented in relation to the proximity of the path 910 to a boundary of the lane that is opposite the intended travel lane and is the lane in which the proposed path 910 is planned.
  • the user interface 905 can include a visual representation of a cost 925 that may be present in regard to waypoint 915B.
  • the cost 925 can correspond to a lane change cost associated with the proximity of the path 925 to vehicle A as vehicle 102 navigates to pass vehicle A and return to the original travel lane.
  • the visual representations can be provided using color, point clouds, shading, patterns, animations, icons, indicators, or the like.
  • the user interface 905 showing the lane graph view can include a path 930 including a plurality of segments, shown as segments 1 -8.
  • the segments can be color coded (or include a similar graphical affordance or visual representation) to indicate the viability of the segment as a navigable path segment.
  • segment 4 can be identified with a side buffer constraint using a first color as the path 930 routes vehicle 102 around vehicle A.
  • segment 6 can provided in a second color to highlight a possible lane change cost associate with the path 930 in relation to vehicle B.
  • Each of the segments 108 can be independently adjusted or redrawn to provide a path 930 that is most likely to be accepted by the vehicle planning system 404.
  • FIG. 10 illustrates an embodiment of a user interface 1005 of the RVA system 505 described herein showing one or more detected objects.
  • the user interface 1005 can be provided as an intersection view depicting an intersection that the vehicle 102 is approaching.
  • the intersection view can be provided with visual representations of other vehicles 1010, as well as pedestrians 1015.
  • the intersection view can also include a visual representation of a hazard 1020.
  • a constraint 1025 can be generated with respect to the hazard 1020 so that route planning inputs can be provided to avoid the hazard 1020.
  • FIG. 11 illustrates another embodiment of a user interface 1100 of the RVA system 505 described herein showing one or more costs associated with the detected objects of FIG. 10.
  • the user interface 1100 includes costs 1105 visually depicted with shading or point clouds for each of the detected objects (e.g., the vehicles 1010, pedestrians 1015, and hazard 1020).
  • the size of the shading or point clouds can be configured to allow the RHO 815 to draw a path with sufficient buffer so as to enable the vehicle to safely navigate with respect to the detected objects the first time and without needing to recalculate the path with respect to any one of the detected objects.
  • FIG. 11 illustrates another embodiment of a user interface 1100 of the RVA system 505 described herein showing one or more costs associated with the detected objects of FIG. 10.
  • the user interface 1100 includes costs 1105 visually depicted with shading or point clouds for each of the detected objects (e.g., the vehicles 1010, pedestrians 1015, and hazard 1020).
  • the size of the shading or point clouds can
  • FIG. 12 illustrates another embodiment of a user interface 1200 of the RVA system 505 described herein showing another embodiment of one or more costs associated with the detected objects of FIG. 10.
  • a heat map can be used with various color patterns used to depict the detected objects and the costs associated with the detected objects.
  • Each color in the colored patterns can be associated with a level of risk of a path (and thus the vehicle 102) contacting the detected object.
  • the RHO 815 can provide a path that is most likely to be accepted by the vehicle planning system 404.
  • FIG. 13 illustrates another embodiment of a user interface 1300 of the RVA system 505 showing a path determined and provided to avoid a detected object as described herein.
  • the user interface 1300 includes an intersection view of an intersection vehicle 102 is approaching. Due to an object (e.g. , a vehicle 1305) obstructing its path 1310, the RVA system 505 can determine a suggested path 1315 to aid vehicle 102 to navigate around vehicle 1305. The path 1315 can replace the original segment 1320 of the path 1310 and allow vehicle 102 to continue navigating to segment 1325.
  • the suggested path 1315 can be determined by the RVA system 505 or can be drawn by the RHO 815.
  • FIG. 14 illustrates another embodiment of a user interface 1400 of the RVA system 505 showing sensor data and a vehicle path as described herein.
  • the user interface 1400 can include an intersection view 1405 and the path 1410 therein.
  • the user interface 1400 can also include sensor data 1415, such as live streamed video image data of the environment surrounding the vehicle.
  • the sensor data can capture a vehicle 1420 positioned in front of the vehicle using the RVA system 505 and response to sending an RVA request, an RHO 815 can view the sensor data associated with the vehicle 1420 and a corresponding visualization 1425 in the user interface 1400.
  • FIG. 15 illustrates another embodiment of a user interface 1500 of the RVA system showing sensor data, RVA operator data, and path selection as described herein.
  • the user interface 1500 can include sensor data 1505, such as video data of the environment surrounding the vehicle 102.
  • the user interface 1500 can include a path 1510 determined by the RVA system 505.
  • the sensor data can include a vehicle 1515 in front of vehicle 102.
  • the RVA system 505 can account for vehicle 1515 and generate a corresponding visual representation 1520 within the path 1510.
  • a RHO 815 can review a generated path 1525 or can draw the path 1525 to aid the vehicle 102 to navigate around vehicle 1515.
  • the techniques for remote vehicle assistance described herein can provide technical solutions, which can provide technical advantages over existing remote vehicle assistance systems.
  • the advantages can include, but are not limited to, increased response times and path accuracy for remotely assisted navigation path provision in autonomous vehicle operating environments.
  • the remote vehicle assistance systems described herein can also reduce the rate at which a vehicle planning system may reject a candidate path by improving the accuracy of a selected path, thereby allowing the vehicle to return to normal navigation safely and more quickly. As a result, more accurate navigation trajectory or path data can be provided to the planning system of the vehicle and the vehicle can be operate more safely in a larger variety of operating conditions.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
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Abstract

Sont proposés des procédés servant à dessiner des trajets à des fins d'assistance à distance de véhicule. Les procédés peuvent consister à recevoir un emplacement de but en réponse à une entrée fournie par l'intermédiaire d'une interface graphique utilisateur et à générer une pluralité de trajets candidats sur la base de l'emplacement de but et d'un emplacement de départ d'un véhicule. Chaque trajet candidat peut représenter une trajectoire correspondante de l'emplacement de départ à l'emplacement de but. La pluralité de trajets candidats peut être fournie par l'intermédiaire de l'interface graphique utilisateur en tant que représentation d'au moins un coût correspondant et d'au moins une contrainte correspondante associée à chaque trajet candidat correspondant. Une sélection de trajet peut être reçue par l'intermédiaire de l'interface graphique utilisateur et des données associées au trajet sélectionné peuvent être fournies au véhicule pour mettre en œuvre la trajectoire correspondante. Des systèmes et des produits programmes d'ordinateur sont également proposés.
PCT/US2023/034741 2022-10-14 2023-10-09 Sélection de trajet pour assistance à distance de véhicule WO2024081191A1 (fr)

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US202263379600P 2022-10-14 2022-10-14
US63/379,600 2022-10-14
US18/162,256 US20240126254A1 (en) 2022-10-14 2023-01-31 Path selection for remote vehicle assistance
US18/162,256 2023-01-31

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130103313A1 (en) * 2011-06-03 2013-04-25 Apple Inc. Devices and methods for comparing and selecting alternative navigation routes
US20180196437A1 (en) * 2013-03-15 2018-07-12 Waymo Llc Trajectory Assistance for Autonomous Vehicles
US20210269056A1 (en) * 2020-02-27 2021-09-02 Baidu Usa Llc Lane based routing system for autonomous driving vehicles

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130103313A1 (en) * 2011-06-03 2013-04-25 Apple Inc. Devices and methods for comparing and selecting alternative navigation routes
US20180196437A1 (en) * 2013-03-15 2018-07-12 Waymo Llc Trajectory Assistance for Autonomous Vehicles
US20210269056A1 (en) * 2020-02-27 2021-09-02 Baidu Usa Llc Lane based routing system for autonomous driving vehicles

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