Corridor / homotopy scoring and verification

Abstract

Methods and systems for corridor / homotopy scoring and verification are provided. A method for operating an autonomous vehicle is provided. The method includes obtaining sensor data associated with an environment in which the autonomous vehicle is operating; and determining, with the at least one processor, a set of agents in the environment based on the sensor data. The method includes determining, with at least one processor, a plurality of sets of navigation options for the autonomous vehicle, where each set of navigation options includes a first navigation option and a second navigation option, and where at least one set of navigation options is associated with at least one agent of a set of agents. The method comprises the following steps: selecting a plurality of navigation options from a plurality of navigation option sets; and generating a corridor for the autonomous vehicle based on the selected plurality of navigation options.

Description

Background Art

[0001] Autonomous vehicles can use a variety of methods and systems to determine the trajectory used by the autonomous vehicle. However, these methods and systems may require high computing power, which may lead to inefficient calculations. In addition, these methods and systems may slow down the reaction time of the autonomous vehicle, which may lead to real-world complexities. BRIEF DESCRIPTION OF THE DRAWINGS

[0002] Figure 1 is an example environment in which a vehicle including one or more components of an autonomous system may be implemented;

[0003] Figure 2 is a diagram of one or more systems of a vehicle including an autonomous system;

[0004] Figure 3 yes Figure 1 and Figure 2 a diagram of one or more devices and / or components of one or more systems;

[0005] Figure 4A is a diagram of some components of an autonomous system;

[0006] Figure 4B is a graph of the implementation of a neural network;

[0007] Figure 4C and Figure 4D is a diagram illustrating an example operation of a CNN;

[0008] Figure 5 is a diagram of an implementation of the process for homotopy / navigation option selection;

[0009] Figure 6 is a flow chart of the process for homotopy / navigation selection, scoring, and validation;

[0010] Fig. 7A and Figure 7B is a diagram of an implementation of a process for determining a corridor; and

[0011] Figure 8 is a flow chart of the process used for corridor selection, scoring, and validation. DETAILED DESCRIPTION

[0012] 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 disclosure. However, it will be apparent that the embodiments described in the present disclosure can be implemented without these specific details. In some instances, well-known configurations and devices are illustrated in block diagram form to avoid unnecessarily obscuring aspects of the present disclosure.

[0013] In the accompanying drawings, for ease of description, the specific arrangement or order of schematic elements (such as those representing systems, devices, modules, instruction blocks and / or data elements, etc.) is illustrated. However, those skilled in the art will understand that, unless explicitly described, the specific order or arrangement of schematic elements in the accompanying drawings is not intended to mean that a specific processing order or sequence, or separation of processing is required. In addition, unless explicitly described, 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 some embodiments or cannot be combined with other elements in some embodiments.

[0014] In addition, in the accompanying drawings, connecting elements (such as solid or dotted lines or arrows, etc.) are used to illustrate the connection, relationship or association between or among two or more other schematic elements, and the absence of any such connecting elements is not intended to mean that there can be no connection, relationship or association. In other words, some connections, relationships or associations between elements are not illustrated in the accompanying drawings so as not to obscure the present disclosure. In addition, for ease of illustration, a single connecting element can be used to represent multiple connections, relationships or associations between elements. For example, if the connecting element represents the communication of a signal, data or instruction (e.g., "software instruction"), it will be understood by those skilled in the art that such an element can represent one or more than one signal path (e.g., bus) that may be needed to affect the communication.

[0015] Although the terms "first", "second" and / or "third", etc. are used to describe various elements, these elements should not be limited by these terms. The terms "first", "second" and / or "third" are only used to distinguish one element from another. For example, a first contact may be referred to as a second contact, and similarly, a second contact may be referred to as a first contact without departing from the scope of the described embodiments. Both the first contact and the second contact are contacts, but they are not the same contacts.

[0016] The terms used in the specification of the various embodiments described herein are included only 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 and can be used interchangeably with "one or more than one" or "at least one", unless the context clearly indicates otherwise. It will also be understood that the term "and / or" as used herein refers to and includes any and all possible combinations of one or more than one of the associated listed items. It will also be understood that when the terms "include", "comprise", "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.

[0017] As used herein, the terms "communication" and "communicating" refer to at least one of receiving, receiving, transmitting, transmitting and / or providing information (or information represented by, for example, data, signals, messages, instructions and / or commands, etc.). For a unit (e.g., a device, a system, a component of a device or system, and / or a combination thereof) to communicate with another unit, this means that the unit is able to directly or indirectly receive information from the other unit and / or send (e.g., transmit) information to the other unit. This can refer to a direct or indirect connection that is wired and / or wireless in nature. In addition, even if the transmitted information can be modified, processed, relayed and / or routed between the first unit and the second unit, the two units can communicate with each other. For example, even if the first unit passively receives information and does not actively transmit information to the second unit, the first unit can communicate with the second unit. As another example, if at least one intermediary unit (e.g., a third unit located between the first unit and the second unit) processes the information received from the first unit and transmits the processed information to the second unit, the first unit can communicate with the second unit. In some embodiments, a message may refer to a network packet (eg, a data packet, etc.) that includes data.

[0018] As used herein, the term "if" is optionally interpreted to mean "when," "at," "in response to being determined to be," and / or "in response to being detected," etc., depending on the context. Similarly, the phrases "if it is determined" or "if [the stated condition or event] is detected" are optionally interpreted to mean "when determining," "in response to being determined to be" or "when [the stated condition or event] is detected," and / or "in response to being detected," etc., depending on the context. In addition, as used herein, the terms "have," "have," or "possess," etc. are intended to be open-ended terms. Furthermore, unless expressly stated otherwise, the phrase "based on" is intended to mean "based at least in part on."

[0019] Reference will now be made in detail to 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, processes, components, circuits, and networks have not yet been described in detail in order not to unnecessarily obscure aspects of the embodiments.

[0020] General Overview

[0021] In some aspects and / or embodiments, the systems, methods, and computer program products described herein include and / or implement corridor / homotopy scoring and verification. In vehicle path planning, there may be many potential corridors through which a vehicle can travel through a scene. Considering the computing resources and time constraints of the vehicle, it is impossible or inefficient to analyze all paths in all potential corridors in some cases. These problems can be solved by a system that verifies, scores, and prioritizes corridors. In some cases, the system can verify or reject corridors based on spatial constraints (e.g., corridors include insufficient space for driving off roads or vehicles) and / or time constraints (e.g., limited vehicle acceleration capabilities). In order to verify, score, and prioritize corridors, the system can generate various homotopy / navigation options about objects encountered within a time window. For example, the system can simulate a scene based on a vehicle driving before or after different objects in a scene. Depending on the vehicle's reaction to an object, the system can simulate options for the next object that is simulated as interacting with the vehicle. Using these navigation options, the system can generate potential corridors through which a vehicle can travel. The system can continue to generate corridors based on additional navigation options, and score corridors based on comfort, safety, or other criteria, and select corridors based on these scores. In some cases, the system can analyze various paths within the selected corridor to identify a path used by the vehicle, and then control the vehicle based on the selected path.

[0022] Each homology / navigation option may be evaluated based on corridor validation criteria. Corridor validation criteria may include temporal criteria (e.g., acceleration or deceleration limits of the autonomous vehicle) and / or spatial criteria (e.g., taking the autonomous vehicle off the road or insufficient space for the vehicle in a particular location). For example, corridor validation criteria may include whether a homology / navigation option is physically possible given the spatiotemporal parameters and capabilities of the vehicle.

[0023] By implementation of the systems, methods, and computer program products described herein, techniques for corridor / homotopy scoring and verification provide for more efficient path planning and reduced computational resources to determine paths.

[0024] Reference now Figure 1, illustrates an example environment 100 in which vehicles including autonomous systems and vehicles not including autonomous systems operate. As illustrated, the environment 100 includes vehicles 102a-102n, objects 104a-104n, routes 106a-106n, areas 108, vehicle-to-infrastructure (V2I) devices 110, a network 112, a remote autonomous vehicle (AV) system 114, a fleet management system 116, and a V2I system 118. The vehicles 102a-102n, the vehicle-to-infrastructure (V2I) devices 110, the network 112, the autonomous vehicle (AV) system 114, the fleet management system 116, and the V2I system 118 are interconnected (e.g., establish connections for communication, etc.) via wired connections, wireless connections, or a combination of wired or wireless connections. In some embodiments, objects 104a-104n are interconnected with at least one of vehicles 102a-102n, vehicle-to-infrastructure (V2I) devices 110, networks 112, autonomous vehicle (AV) systems 114, fleet management systems 116, and V2I systems 118 via wired connections, wireless connections, or a combination of wired or wireless connections.

[0025] Vehicles 102a-102n (individually referred to as vehicles 102 and collectively referred to as vehicles 102) include at least one device configured to transport goods and / or people. In some embodiments, vehicles 102 are configured to communicate with V2I devices 110, remote AV systems 114, fleet management systems 116, and / or V2I systems 118 via network 112. In some embodiments, vehicles 102 include cars, buses, trucks, and / or trains, etc. In some embodiments, vehicles 102 are similar to vehicles 200 described herein (see Figure 2 ). In some embodiments, vehicles 200 in the set of vehicles 200 are associated with an autonomous queue manager. In some embodiments, vehicles 102 travel along respective routes 106a-106n (individually referred to as routes 106 and collectively referred to as routes 106) as described herein. In some embodiments, 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).

[0026] Objects 104a-104n (individually referred to as objects 104 and collectively referred to as objects 104) include, for example, at least one vehicle, at least one pedestrian, at least one cyclist, and / or at least one structure (e.g., a building, a sign, a fire hydrant, etc.), etc. Each object 104 is stationary (e.g., located at a fixed location and over a period of time) or moves (e.g., has a speed and is associated with at least one trajectory). In some embodiments, objects 104 are associated with corresponding locations in area 108.

[0027] Routes 106a-106n (individually referred to as routes 106 and collectively referred to as routes 106) are each associated with (e.g., specify a series of actions (also referred to as trajectories) connecting states along which the AV can navigate. Each route 106 begins at an initial state (e.g., a state corresponding to a first spatiotemporal location and / or speed, etc.) and ends at a final target state (e.g., a state corresponding to a second spatiotemporal location different from the first spatiotemporal location) or a target zone (e.g., a subspace of acceptable states (e.g., terminal states)). In some embodiments, the first state includes a location where one or more individuals will board the AV, and the second state or zone includes one or more locations where one or more individuals boarding the AV will disembark. In some embodiments, routes 106 include multiple acceptable state sequences (e.g., multiple spatiotemporal location sequences) that are associated with (e.g., define multiple trajectories). In an example, routes 106 include only high-level actions or imprecise state locations, such as a series of connecting roads indicating a change of direction at a roadway intersection, etc. Additionally or alternatively, the route 106 may include more precise actions or states, such as, for example, specific target lanes or precise locations within lane regions and target speeds at those locations, etc. In an example, the route 106 includes a plurality of precise state sequences along at least one high-level action with a limited look-ahead horizon to an intermediate target, wherein a combination of consecutive iterations of the limited horizon state sequences cumulatively correspond to a plurality of trajectories that collectively form a high-level route terminating at a final target state or region.

[0028] The area 108 includes a physical area (e.g., a geographic region) in which the vehicle 102 can navigate. In an example, the area 108 includes at least one state (e.g., a country, a province, a separate state of a plurality of states included in a country, etc.), at least a portion of a state, at least one city, at least a portion of a city, etc. In some embodiments, the 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. Additionally or alternatively, in some examples, the area 108 includes at least one unnamed road, such as a driveway, a section of a parking lot, a section of an open space and / or undeveloped area, a dirt road, etc. In some embodiments, the road includes at least one lane (e.g., a portion of the road that the vehicle 102 can traverse). In an example, the road includes at least one lane associated with (e.g., identified based on) at least one lane marking line.

[0029] The vehicle-to-infrastructure (V2I) device 110 (sometimes referred to as a vehicle-to-infrastructure or vehicle-to-everything (V2X) device) includes at least one device configured to communicate with the vehicle 102 and / or the V2I system 118. In some embodiments, the V2I device 110 is configured to communicate with the vehicle 102, the remote AV system 114, the fleet management system 116, and / or the V2I system 118 via the network 112. In some embodiments, the V2I device 110 includes a radio frequency identification (RFID) device, a sign, a camera (e.g., a two-dimensional (2D) and / or three-dimensional (3D) camera), lane markings, street lights, parking meters, etc. In some embodiments, the V2I device 110 is configured to communicate directly with the vehicle 102. Additionally or alternatively, in some embodiments, the V2I device 110 is configured to communicate with the vehicle 102, the remote AV system 114, and / or the fleet management system 116 via the V2I system 118. In some embodiments, the V2I device 110 is configured to communicate with the V2I system 118 via the network 112.

[0030] The network 112 includes one or more wired and / or wireless networks. In an example, the 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., a public switched telephone network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber-based network, a cloud computing network, etc., and / or a combination of some or all of these networks, etc.

[0031] The remote AV system 114 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the network 112, the fleet management system 116, and / or the V2I system 118 via the network 112. In an example, the remote AV system 114 includes a server, a server group, and / or other similar devices. In some embodiments, the remote AV system 114 is co-located with the fleet management system 116. In some embodiments, the remote AV system 114 participates in the installation of some or all of the components of the vehicle (including autonomous systems, autonomous vehicle computing, and / or software implemented by autonomous vehicle computing, etc.). In some embodiments, the remote AV system 114 maintains (e.g., updates and / or replaces) these components and / or software during the life of the vehicle.

[0032] The queue management system 116 includes at least one device configured to communicate with the vehicles 102, the V2I devices 110, the remote AV system 114, and / or the V2I system 118. In an example, the queue management system 116 includes a server, a server group, and / or other similar devices. In some embodiments, the queue management system 116 is associated with a ridesharing company (e.g., an organization for controlling the operation of multiple vehicles (e.g., vehicles including autonomous systems and / or vehicles not including autonomous systems)).

[0033] In some embodiments, the V2I system 118 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the remote AV system 114, and / or the fleet management system 116 via the network 112. In some examples, the V2I system 118 is configured to communicate with the V2I device 110 via a connection other than the network 112. In some embodiments, the V2I system 118 includes a server, a server group, and / or other similar devices. In some embodiments, the V2I system 118 is associated with a municipality or a private agency (e.g., a private agency for maintaining the V2I device 110, etc.).

[0034] supply Figure 1 The number and arrangement of elements illustrated are examples. Figure 1 There may be additional elements, fewer elements, different elements, and / or differently arranged elements than those illustrated. Additionally or alternatively, at least one element of environment 100 may be described as being Figure 1 Additionally or alternatively, at least one set of elements of environment 100 may perform one or more functions described as being performed by at least one different set of elements of environment 100.

[0035] Reference now Figure 2 , vehicle 200 (which can be Figure 1 102) includes, or is associated with, autonomous system 202, powertrain control system 204, steering control system 206, and braking system 208. In some embodiments, vehicle 200 is similar to vehicle 102 (see Figure 1 ) is the same or similar. In some embodiments, the autonomous system 202 is configured to give the vehicle 200 autonomous driving capabilities (e.g., implementing at least one of the following driving automatic or maneuver-based functions, features and / or devices, etc., which enable the vehicle 200 to operate partially or completely without human intervention, including but not limited to fully autonomous vehicles (e.g., vehicles that abandon reliance on human intervention, such as Level 5 ADS-operated vehicles, etc.), highly autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in certain situations, such as Level 4 ADS-operated vehicles, etc.), and / or conditionally autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in limited situations, such as Level 3 ADS-operated vehicles, etc.), etc.). In one embodiment, the autonomous system 202 includes the operational or tactical functionality required to enable the vehicle 200 to operate in traffic on the road and continuously perform part or all of the dynamic driving task (DDT). In another embodiment, the autonomous system 202 includes an advanced driver assistance system (ADAS) including driver support features. The autonomous system 202 supports various levels of driving automation ranging from no driving automation (e.g., level 0) to full driving automation (e.g., level 5). For a detailed description of fully autonomous vehicles and highly autonomous vehicles, reference may be made to SAE International's standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, the entire contents of which are incorporated by reference. In some embodiments, the vehicle 200 is associated with an autonomous queue manager and / or a ride-sharing company.

[0036] Autonomous system 202 includes a sensor suite that includes one or more devices such as camera 202a, LiDAR sensor 202b, Radar sensor 202c, and microphone 202d. In some embodiments, autonomous system 202 may include more or fewer devices and / or different devices (e.g., ultrasonic sensors, inertial sensors, GPS receivers (discussed below), and / or odometer sensors for generating data associated with an indication of the distance that vehicle 200 has traveled, etc.). In some embodiments, autonomous system 202 uses one or more devices included in autonomous system 202 to generate data associated with environment 100 described herein. Data generated by one or more devices of autonomous system 202 can be used by one or more systems described herein to observe the environment (e.g., environment 100) in which vehicle 200 is located. In some embodiments, autonomous system 202 includes communication device 202e, autonomous vehicle computing 202f, drive-by-wire (DBW) system 202h, and safety controller 202g.

[0037] The camera 202a includes a communication device 202e, an autonomous vehicle computer 202f, and / or a safety controller 202g configured to communicate with the communication device 202e via a bus (e.g., Figure 3 The camera 202a includes at least one device for communicating with the autonomous vehicle computing 202f (e.g., an image processing unit 202a, a bus ... Figure 1In some embodiments, the autonomous vehicle computing 202f determines a depth to one or more objects in a field of view of at least two of the plurality of cameras based on image data from the at least two cameras. In some embodiments, the camera 202a is configured to capture images of objects within a distance relative to the camera 202a (e.g., up to 100 meters and / or up to 1 kilometer, etc.). Thus, the camera 202a includes features such as sensors and lenses that are optimized for sensing objects at one or more distances relative to the camera 202a.

[0038] In an embodiment, the 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. In some embodiments, the camera 202a generates traffic light data associated with the one or more images. In some examples, the camera 202a generates TLD (traffic light detection) data associated with one or more images including a format (e.g., RAW, JPEG, and / or PNG, etc.). In some embodiments, the camera 202a that generates TLD data differs from other systems incorporating cameras described herein in that the camera 202a may include one or more cameras with a wide field of view (e.g., a wide-angle lens, a fisheye lens, and / or a lens with a viewing angle of approximately 120 degrees or greater, etc.) to generate images related to as many physical objects as possible.

[0039] The light detection and ranging (LiDAR) sensor 202b includes a communication device 202e, an autonomous vehicle computing device 202f, and / or a safety controller 202g via a bus (e.g., Figure 3The LiDAR sensor 202b includes at least one device that communicates with a bus (the same or similar bus as the bus 302 of the embodiment of the present invention). The LiDAR sensor 202b includes a system configured to emit light from a light emitter (e.g., a laser emitter). The light emitted by the LiDAR sensor 202b includes light outside the visible spectrum (e.g., infrared light, etc.). In some embodiments, during operation, the light emitted by the LiDAR sensor 202b encounters a physical object (e.g., a vehicle) and is reflected back to the LiDAR sensor 202b. In some embodiments, the light emitted by the LiDAR sensor 202b does not penetrate the physical object encountered by the light. The LiDAR sensor 202b also includes at least one light detector that detects the light emitted from the light emitter after encountering the physical object. In some embodiments, at least one data processing system associated with the LiDAR sensor 202b generates an image (e.g., a point cloud and / or a combined point cloud, etc.) representing objects included in the field of view of the LiDAR sensor 202b. In some examples, at least one data processing system associated with the LiDAR sensor 202b generates an image representing the boundaries of the physical object and / or the surface of the physical object (e.g., the topology of the surface), etc. In such examples, the image is used to determine the boundaries of the physical object in the field of view of the LiDAR sensor 202b.

[0040] The radio detection and ranging (Radar) sensor 202c includes a sensor configured to communicate with the communication device 202e, the autonomous vehicle computing 202f, and / or the safety controller 202g via a bus (e.g., Figure 3 At least one device for communicating with a bus (same or similar bus as bus 302 of the embodiment of the present invention). Radar sensor 202c includes a system configured to transmit (pulsed or continuous) radio waves. The radio waves transmitted by Radar sensor 202c include radio waves within a predetermined spectrum. In some embodiments, during operation, the radio waves transmitted by Radar sensor 202c encounter physical objects and are reflected back to Radar sensor 202c. In some embodiments, the radio waves transmitted by Radar sensor 202c are not reflected by some objects. In some embodiments, at least one data processing system associated with Radar sensor 202c generates a signal representing an object included in the field of view of Radar sensor 202c. For example, at least one data processing system associated with Radar sensor 202c generates an image representing the boundary of a physical object and / or the surface of a physical object (e.g., the topology of the surface), etc. In some examples, the image is used to determine the boundary of a physical object in the field of view of Radar sensor 202c.

[0041] The microphone 202d includes a microphone configured to communicate with the communication device 202e, the autonomous vehicle computing device 202f, and / or the safety controller 202g via a bus (e.g., Figure 3 At least one device for communicating with the vehicle 200 (the same or similar bus as bus 302 of FIG. 1 ). Microphone 202d includes one or more microphones (e.g., an array microphone and / or an external microphone, etc.) that capture an audio signal and generate data associated with (e.g., representing) the audio signal. In some examples, microphone 202d includes a transducer device and / or the like. In some embodiments, one or more systems described herein can receive the data generated by microphone 202d and determine the position (e.g., distance, etc.) of an object relative to vehicle 200 based on an audio signal associated with the data.

[0042] The communication device 202e includes at least one device configured to communicate with the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, the microphone 202d, the autonomous vehicle computing 202f, the safety controller 202g, and / or the DBW (drive-by-wire) system 202h. For example, the communication device 202e may include at least one device configured to communicate with the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, the microphone 202d, the autonomous vehicle computing 202f, the safety controller 202g, and / or the DBW (drive-by-wire) system 202h. Figure 3 The communication device 202e may be a device that is the same as or similar to the communication interface 314 of the vehicle. In some embodiments, the communication device 202e includes a vehicle-to-vehicle (V2V) communication device (eg, a device for enabling wireless communication of data between vehicles).

[0043] Autonomous vehicle computing 202f includes at least one device configured to communicate with camera 202a, LiDAR sensor 202b, Radar sensor 202c, microphone 202d, communication device 202e, safety controller 202g, and / or DBW system 202h. In some examples, autonomous vehicle computing 202f includes devices such as client devices, mobile devices (e.g., cellular phones and / or tablet computers, etc.), and / or servers (e.g., computing devices including one or more central processing units and / or graphics processing units, etc.). In some embodiments, autonomous vehicle computing 202f is the same or similar to autonomous vehicle computing 400 described herein. Additionally or alternatively, in some embodiments, autonomous vehicle computing 202f is configured to communicate with an autonomous vehicle system (e.g., with Figure 1 remote AV system 114 of the same or similar autonomous vehicle system), a fleet management system (e.g., Figure 1 of the same or similar queue management system as the queue management system 116), V2I devices (e.g., Figure 1 V2I device 110 that is the same as or similar to V2I device 110) and / or V2I system (e.g., Figure 1The V2I system 118 may communicate with the same or similar V2I system.

[0044] Safety controller 202g includes at least one device configured to communicate with camera 202a, LiDAR sensor 202b, Radar sensor 202c, microphone 202d, communication device 202e, autonomous vehicle computing 202f, and / or DBW system 202h. In some examples, safety controller 202g includes one or more controllers (electrical controllers and / or electromechanical controllers, etc.) 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, and / or braking system 208, etc.). In some embodiments, safety controller 202g is configured to generate control signals that take precedence over (e.g., override) control signals generated and / or transmitted by autonomous vehicle computing 202f.

[0045] The DBW system 202h includes at least one device configured to communicate with the communication device 202e and / or the autonomous vehicle computing 202f. In some examples, the DBW system 202h includes one or more controllers (e.g., electrical controllers and / or electromechanical controllers, etc.) configured to generate and / or transmit control signals to operate one or more devices of the vehicle 200 (e.g., powertrain control system 204, steering control system 206, and / or braking system 208, etc.). Additionally or alternatively, one or more controllers of the DBW system 202h are configured to generate and / or transmit control signals to operate at least one different device of the vehicle 200 (e.g., turn signal lights, headlights, door locks, and / or windshield wipers, etc.).

[0046] The powertrain control system 204 includes at least one device configured to communicate with the DBW system 202h. In some examples, the powertrain control system 204 includes at least one controller and / or actuator, etc. In some embodiments, the powertrain control system 204 receives a control signal from the DBW system 202h, and the powertrain control system 204 causes the vehicle 200 to perform longitudinal vehicle movement (such as starting to move forward, stopping to move forward, starting to move backward, stopping to move backward, accelerating in a certain direction, decelerating in a certain direction, etc.), or to perform lateral vehicle movement (such as turning left and / or turning right, etc.). In an example, the powertrain control system 204 increases, maintains the same, or decreases the energy (e.g., fuel and / or electricity, etc.) provided to the motor of the vehicle, thereby rotating or not rotating at least one wheel of the vehicle 200.

[0047] The steering control system 206 includes at least one device configured to rotate one or more wheels of the vehicle 200. In some examples, the steering control system 206 includes at least one controller and / or actuator, etc. In some embodiments, the steering control system 206 rotates the two front wheels and / or the two rear wheels of the vehicle 200 to the left or right to turn the vehicle 200 left or right. In other words, the steering control system 206 causes the activity required to regulate the y-axis component of the vehicle's motion.

[0048] Braking system 208 includes at least one device configured to actuate one or more brakes to decelerate and / or hold vehicle 200 stationary. In some examples, braking system 208 includes at least one controller and / or actuator configured to cause one or more calipers associated with one or more wheels of vehicle 200 to close on respective rotors of vehicle 200. Additionally or alternatively, in some examples, braking system 208 includes an automatic emergency braking (AEB) system and / or a regenerative braking system, etc.

[0049] In some embodiments, vehicle 200 includes at least one platform sensor (not explicitly illustrated) for measuring or inferring a property of a state or condition of vehicle 200. In some examples, vehicle 200 includes platform sensors such as a global positioning system (GPS) receiver, an inertial measurement unit (IMU), wheel rate sensors, wheel brake pressure sensors, wheel torque sensors, engine torque sensors, and / or steering angle sensors. Although brake system 208 is illustrated as being located at Figure 2 The braking system 208 is located on the proximal side of the vehicle 200 , but the braking system 208 can be located anywhere in the vehicle 200 .

[0050] Reference now Figure 3 , a schematic diagram of an exemplary device 300. As illustrated, device 300 includes a processor 304, a memory 306, a storage component 308, an input interface 310, an output interface 312, a communication interface 314, and a bus 302. In some embodiments, device 300 corresponds to: at least one device of vehicle 102 (e.g., at least one device of a system of vehicle 102); vehicle 200; and / or one or more devices of network 112 (e.g., one or more devices of a system of network 112). In some embodiments, one or more devices of vehicle 102 (e.g., one or more devices of a system of vehicle 102), vehicle 200, and / or one or more devices of network 112 (e.g., one or more devices of a system of network 112) include at least one device 300 and / or at least one component of device 300. As Figure 3As shown, apparatus 300 includes a bus 302 , a processor 304 , a memory 306 , a storage component 308 , an input interface 310 , an output interface 312 , and a communication interface 314 .

[0051] The bus 302 includes components that permit communication between components of the device 300. In some cases, the processor 304 includes a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), and / or an accelerated processing unit (APU), etc.), a microphone, a digital signal processor (DSP), and / or any processing component that can be programmed to perform at least one function (e.g., a field programmable gate array (FPGA) and / or an application specific integrated circuit (ASIC), etc.). The memory 306 includes a random access memory (RAM), a read-only memory (ROM), and / or another type of dynamic and / or static storage device (e.g., flash memory, magnetic memory, and / or optical memory, etc.) that stores data and / or instructions for use by the processor 304.

[0052] Storage component 308 stores data and / or software related to the operation and use of device 300. In some examples, storage component 308 includes a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optical disk, and / or a solid-state disk, etc.), a compact disk (CD), a digital versatile disk (DVD), a floppy disk, a cassette, a tape, a CD-ROM, a RAM, a PROM, an EPROM, a FLASH-EPROM, an NV-RAM, and / or another type of computer-readable medium, and a corresponding drive.

[0053] The input interface 310 includes components that permit the device 300 to receive information, such as via user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, and / or a camera, etc.). Additionally or alternatively, in some embodiments, the input interface 310 includes a sensor for sensing information (e.g., a global positioning system (GPS) receiver, an accelerometer, a gyroscope, and / or an actuator, etc.). The output interface 312 includes components for providing output information from the device 300 (e.g., a display, a speaker, and / or one or more light emitting diodes (LEDs), etc.).

[0054] In some embodiments, communication interface 314 includes a transceiver-like component (e.g., a transceiver and / or a separate receiver and transmitter, etc.) that permits device 300 to communicate with other devices via a wired connection, a wireless connection, or a combination of a wired connection and a wireless connection. In some examples, communication interface 314 permits device 300 to receive information from another device and / or provide information to another device. In some examples, 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, interface and / or cellular network interface, etc.

[0055] In some embodiments, the device 300 performs one or more processes described herein. The device 300 performs these processes based on the processor 304 executing software instructions stored by a computer-readable medium such as a memory 306 and / or a storage component 308. Computer-readable media (e.g., non-transitory computer-readable media) are defined herein as non-transitory memory devices. Non-transitory memory devices include storage space located within a single physical storage device or storage space distributed across multiple physical storage devices.

[0056] In some embodiments, the software instructions are read into the memory 306 and / or storage component 308 from another computer-readable medium or from another device via the communication interface 314. The software instructions stored in the memory 306 and / or storage component 308, when executed, cause the processor 304 to perform one or more processes described herein. Additionally or alternatively, hardwired circuitry is used in place of or in combination with the software instructions to perform one or more processes described herein. Therefore, unless expressly stated otherwise, the embodiments described herein are not limited to any specific combination of hardware circuitry and software.

[0057] The memory 306 and / or the storage component 308 include a data storage unit or at least one data structure (e.g., a database, etc.). The device 300 can receive information from the data storage unit or at least one data structure in the memory 306 or the storage component 308, store information in the data storage unit or at least one data structure, communicate information to the data storage unit or at least one data structure, or search for information stored in the data storage unit or at least one data structure. In some examples, the information includes network data, input data, output data, or any combination thereof.

[0058] In some embodiments, the device 300 is configured to execute software instructions stored in the memory 306 and / or the memory of another device (e.g., another device that is the same as or similar to the device 300). As used herein, the term "module" refers to at least one instruction stored in the memory 306 and / or the memory of another device, which, when executed by the processor 304 and / or the processor of another device (e.g., another device that is the same as or similar to the device 300), causes the device 300 (e.g., at least one component of the device 300) to perform one or more processes described herein. In some embodiments, the module is implemented in software, firmware, and / or hardware, etc.

[0059] supply Figure 3 The number and arrangement of components illustrated are examples. Figure 3The device 300 may include additional components, fewer components, different components, or differently arranged components than the components illustrated. Additionally or alternatively, a set of components (e.g., one or more components) of the device 300 may perform one or more functions described as being performed by another component or another set of components of the device 300.

[0060] Reference now Figure 4A , illustrates an example block diagram of an autonomous vehicle computing 400 (sometimes referred to as an "AV stack"). As illustrated, the autonomous vehicle computing 400 includes a perception system 402 (sometimes referred to as a perception module), a planning system 404 (sometimes referred to as a planning module), a positioning system 406 (sometimes referred to as a positioning module), a control system 408 (sometimes referred to as a control module), and a database 410. In some embodiments, the perception system 402, the planning system 404, the positioning system 406, the control system 408, and the database 410 are included in and / or implemented in an automatic navigation system of a vehicle (e.g., the autonomous vehicle computing 202f of the vehicle 200). Additionally or alternatively, in some embodiments, the perception system 402, the planning system 404, the positioning system 406, the control system 408, and the database 410 are included in one or more independent systems (e.g., one or more systems that are the same or similar to the autonomous vehicle computing 400, etc.). In some examples, the perception system 402, planning system 404, positioning system 406, control system 408, and database 410 are included in one or more independent systems located in the vehicle and / or at least one remote system as described herein. In some embodiments, any and / or all of the systems included in the autonomous vehicle computing 400 are implemented in software (e.g., software instructions stored in a memory), computer hardware (e.g., by a microprocessor, microcontroller, application specific integrated circuit (ASIC) and / or field programmable gate array (FPGA), etc.), or a combination of computer software and computer hardware. It will also be understood that in some embodiments, the autonomous vehicle computing 400 is configured to communicate with a remote system (e.g., an autonomous vehicle system that is the same or similar to the remote AV system 114, a fleet management system 116 that is the same or similar to the fleet management system 116, and / or a V2I system that is the same or similar to the V2I system 118, etc.).

[0061] In some embodiments, the perception system 402 receives data associated with at least one physical object in the environment (e.g., data used by the perception system 402 to detect at least one physical object) and classifies the at least one physical object. In some examples, the perception system 402 receives image data captured by at least one camera (e.g., camera 202a), the image being associated with (e.g., representing) one or more physical objects within the field of view of the at least one camera. In such examples, the perception system 402 classifies the at least one physical object based on one or more groups of physical objects (e.g., bicycles, vehicles, traffic signs, and / or pedestrians, etc.). In some embodiments, based on the classification of the physical object by the perception system 402, the perception system 402 transmits data associated with the classification of the physical object to the planning system 404.

[0062] In some embodiments, the planning system 404 receives data associated with a destination and generates data associated with at least one route (e.g., route 106) along which a vehicle (e.g., vehicle 102) can travel toward the destination. In some embodiments, the planning system 404 periodically or continuously receives data (e.g., data associated with the classification of physical objects described above) from the perception system 402, and the planning system 404 updates at least one trajectory or generates at least one different trajectory based on the data generated by the perception system 402. In other words, the planning system 404 can perform tasks related to tactical functions required to operate the vehicle 102 in traffic on the road. Tactical efforts involve maneuvering the vehicle in traffic during the journey, which includes but is not limited to deciding whether and when to overtake another vehicle, change lanes, or select an appropriate rate, acceleration, deceleration, etc. In some embodiments, the planning system 404 receives data associated with an updated position of the vehicle (e.g., vehicle 102) from the positioning system 406, and the planning system 404 updates at least one trajectory or generates at least one different trajectory based on the data generated by the positioning system 406.

[0063] In some embodiments, the positioning system 406 receives data associated with (e.g., representing) a location of a vehicle (e.g., vehicle 102) in an area. In some examples, the positioning system 406 receives LiDAR data associated with at least one point cloud generated by at least one LiDAR sensor (e.g., LiDAR sensor 202b). In some examples, the positioning system 406 receives data associated with at least one point cloud from multiple LiDAR sensors, and the positioning system 406 generates a combined point cloud based on each point cloud. In these examples, the positioning system 406 compares the at least one point cloud or the combined point cloud with a two-dimensional (2D) and / or three-dimensional (3D) map of the area stored in the database 410. Then, based on the positioning system 406 comparing the at least one point cloud or the combined point cloud with the map, the positioning system 406 determines the position of the vehicle in the area. In some embodiments, the map includes a combined point cloud of the area generated before the navigation of the vehicle. In some embodiments, the map includes, but is not limited to, a high-precision map of roadway geometry, a map describing the road network connectivity, a map describing the physical properties of the roadway (such as traffic speed, traffic volume, the number of vehicle and bicycle traffic lanes, lane width, lane traffic direction or the type and location of lane markings, or a combination thereof, etc.), and a map describing the spatial location of road features (such as crosswalks, traffic signs, or various types of other driving signals, etc.). In some embodiments, the map is generated in real time based on data received by the perception system 402.

[0064] In another example, positioning system 406 receives global navigation satellite system (GNSS) data generated by a global positioning system (GPS) receiver. In some examples, positioning system 406 receives GNSS data associated with a location of a vehicle in an area, and positioning system 406 determines the latitude and longitude of the vehicle in the area. In such an example, positioning system 406 determines the position of the vehicle in the area based on the latitude and longitude of the vehicle. In some embodiments, positioning system 406 generates data associated with the position of the vehicle. In some examples, based on the location of the vehicle determined by positioning system 406, positioning system 406 generates data associated with the position of the vehicle. In such an example, the data associated with the position of the vehicle include data associated with one or more semantic properties corresponding to the position of the vehicle.

[0065] In some embodiments, the control system 408 receives data associated with at least one trajectory from the planning system 404, and the control system 408 controls the operation of the vehicle. In some examples, the control system 408 receives data associated with at least one trajectory from the planning system 404, and the control system 408 controls the operation of the vehicle by generating and transmitting control signals to operate the powertrain control system (e.g., DBW system 202h and / or powertrain control system 204, etc.), the steering control system (e.g., steering control system 206) and / or the braking system (e.g., braking system 208). For example, the control system 408 is configured to perform operational functions such as lateral vehicle motion control or longitudinal vehicle motion control. Lateral vehicle motion control causes the activity required to regulate the y-axis component of the vehicle motion. Longitudinal vehicle motion control causes the activity required to regulate the x-axis component of the vehicle motion. In the example, in the case where the trajectory includes a left turn, the control system 408 transmits a control signal to cause the steering control system 206 to adjust the steering angle of the vehicle 200, thereby causing the vehicle 200 to turn left. Additionally or alternatively, the control system 408 generates and transmits control signals to cause other devices of the vehicle 200 (eg, headlights, turn signal lights, door locks, and / or windshield wipers, etc.) to change states.

[0066] In some embodiments, perception system 402, planning system 404, positioning system 406, and / or control system 408 implement at least one machine learning model (e.g., at least one multi-layer perceptron (MLP), at least one convolutional neural network (CNN), at least one recurrent neural network (RNN), at least one autoencoder, and / or at least one transformer, etc.). In some examples, perception system 402, planning system 404, positioning system 406, and / or control system 408 implement at least one machine learning model alone or in combination with one or more of the above systems. In some examples, perception system 402, planning system 404, positioning system 406, and / or control system 408 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, etc.).

[0067] Database 410 stores data transmitted to, received from, and / or updated by perception system 402, planning system 404, positioning system 406, and / or control system 408. In some examples, database 410 includes a storage component (e.g., a storage component) for storing data and / or software related to operations and using at least one system of autonomous vehicle computing 400. Figure 3In some embodiments, database 410 stores data associated with a 2D and / or 3D map of at least one area. In some examples, database 410 stores data associated with a 2D and / or 3D map of a portion of a city, portions of multiple cities, multiple cities, a county, a state, and / or a state (e.g., a country), etc. In such an example, a vehicle (e.g., a vehicle that is the same or similar to vehicle 102 and / or vehicle 200) can be driven along one or more drivable areas (e.g., a single lane road, a multi-lane road, a highway, a remote road, and / or an off-road trail, etc.) and cause at least one LiDAR sensor (e.g., a LiDAR sensor that is the same or similar to LiDAR sensor 202b) to generate data associated with an image representing an object included in the field of view of the at least one LiDAR sensor.

[0068] In some embodiments, database 410 can be implemented across multiple devices. In some examples, database 410 includes a vehicle (e.g., a vehicle that is the same or similar to vehicle 102 and / or vehicle 200), an autonomous vehicle system (e.g., an autonomous vehicle system that is the same or similar to remote AV system 114), a fleet management system (e.g., a vehicle that is the same or similar to remote AV system 114), and a fleet management system (e.g., a vehicle that is the same or similar to remote AV system 114). Figure 1 The same or similar queue management system as the queue management system 116 of FIG. 1 and / or the V2I system (e.g., Figure 1 The V2I system 118 is the same as or similar to the V2I system 118).

[0069] Reference now Figure 4B , a diagram illustrating an implementation of a machine learning model. More specifically, a diagram illustrating an implementation of a convolutional neural network (CNN) 420. For purposes of illustration, the following description of CNN 420 will be with respect to implementing CNN 420 by perception system 402. However, it will be understood that in some examples, CNN 420 (e.g., one or more components of CNN 420) is implemented by other systems (such as planning system 404, positioning system 406, and / or control system 408, etc.) other than or in addition to perception system 402. Although CNN 420 includes certain features as described herein, these features are provided for purposes of illustration and are not intended to limit the present disclosure.

[0070] CNN 420 includes a plurality of convolutional layers including a first convolutional layer 422, a second convolutional layer 424, and a convolutional layer 426. In some embodiments, CNN 420 includes a subsampling layer 428 (sometimes referred to as a pooling layer). In some embodiments, subsampling layer 428 and / or other subsampling layers have a dimension that is smaller than the dimension of the upstream system (i.e., the number of nodes). With the subsampling layer 428 having a dimension that is smaller than the dimension of the upstream layer, CNN 420 merges the amount of data associated with the initial input and / or output of the upstream layer, thereby reducing the amount of computation required for CNN 420 to perform downstream convolution operations. Additionally or alternatively, with the subsampling layer 428 being associated with (e.g., configured to perform) at least one subsampling function (as described below with respect to Figure 4C and Figure 4D As described above, CNN 420 incorporates the amount of data associated with the initial input.

[0071] The perception system 402 performs the convolution operation based on the perception system 402 providing respective inputs and / or outputs associated with each of the first convolution layer 422, the second convolution layer 424, and the convolution layer 426 to generate respective outputs. In some examples, the perception system 402 implements the CNN 420 based on the perception system 402 providing data as input to the first convolution layer 422, the second convolution layer 424, and the convolution layer 426. In such examples, the perception system 402 provides data as input to the first convolution layer 422, the second convolution layer 424, and the convolution layer 426 based on the perception system 402 receiving data from one or more different systems (e.g., one or more systems of a vehicle that is the same or similar to the vehicle 102, a remote AV system that is the same or similar to the remote AV system 114, a queue management system that is the same or similar to the queue management system 116, and / or a V2I system that is the same or similar to the V2I system 118, etc.). The following is about Figure 4C Includes a detailed description of the convolution operation.

[0072] In some embodiments, the perception system 402 provides data associated with the input (referred to as the initial input) to the first convolutional layer 422, and the perception system 402 generates data associated with the output using the first convolutional layer 422. In some embodiments, the perception system 402 provides the output generated by the convolutional layer as input to a different convolutional layer. For example, the perception system 402 provides the output of the first convolutional layer 422 as input to the subsampling layer 428, the second convolutional layer 424, and / or the convolutional layer 426. In such an example, the first convolutional layer 422 is referred to as an upstream layer, and the subsampling layer 428, the second convolutional layer 424, and / or the convolutional layer 426 are referred to as downstream layers. Similarly, in some embodiments, the perception system 402 provides the output of the subsampling layer 428 to the second convolutional layer 424 and / or the convolutional layer 426, and in this example, the subsampling layer 428 will be referred to as the upstream layer, and the second convolutional layer 424 and / or the convolutional layer 426 will be referred to as the downstream layer.

[0073] In some embodiments, before the perception system 402 provides the input to the CNN 420, the perception system 402 processes the data associated with the input provided to the CNN 420. For example, the perception system 402 processes the data associated with the input provided to the CNN 420 based on the perception system 402 normalizing the sensor data (e.g., image data, LiDAR data, and / or Radar data, etc.).

[0074] In some embodiments, CNN 420 generates an output based on perception system 402 performing convolution operations associated with each convolution layer. In some examples, CNN 420 generates an output based on perception system 402 performing convolution operations associated with each convolution layer and the initial input. In some embodiments, perception system 402 generates an output and provides the output to fully connected layer 430. In some examples, perception system 402 provides the output of convolution layer 426 to fully connected layer 430, wherein fully connected layer 430 includes data associated with multiple feature values ​​referred to as F1, F2, ..., FN. In this example, the output of convolution layer 426 includes data associated with multiple output feature values ​​representing predictions.

[0075] In some embodiments, perception system 402 identifies a prediction from the plurality of predictions based on perception system 402 identifying a feature value associated with a highest likelihood of being a correct prediction from the plurality of predictions. For example, where fully connected layer 430 includes feature values ​​F1, F2, ..., FN and F1 is the largest feature value, perception system 402 identifies the prediction associated with F1 as the correct prediction from the plurality of predictions. In some embodiments, perception system 402 trains CNN 420 to generate the predictions. In some examples, perception system 402 trains CNN 420 to generate the predictions based on perception system 402 providing training data associated with the predictions to CNN 420.

[0076] Reference now Figure 4C and Figure 4D , a diagram illustrating an example operation of CNN 440 utilizing perception system 402. In some embodiments, CNN 440 (e.g., one or more components of CNN 440) is coupled to CNN 420 (e.g., one or more components of CNN 420) (see Figure 4B ) are the same or similar.

[0077] At step 450, the perception system 402 provides data associated with the image as input to the CNN 440 (step 450). For example, as illustrated, the perception system 402 provides data associated with the image to the CNN 440, where the image is a grayscale image represented as values ​​stored in a two-dimensional (2D) array. In some embodiments, the data associated with the image may include data associated with a color image represented as values ​​stored in a three-dimensional (3D) array. Additionally or alternatively, the data associated with the image may include data associated with an infrared image and / or a Radar image, etc.

[0078] At step 455, CNN 440 performs a first convolution function. For example, CNN 440 performs a first convolution function based on CNN 440 providing a value representing an image as an input to one or more neurons (not explicitly illustrated) included in first convolution layer 442. In this example, the value representing the image may correspond to a value of a region (sometimes referred to as a receptive field) representing the image. In some embodiments, each neuron is associated with a filter (not explicitly illustrated). The filter (sometimes referred to as a kernel) may be represented as an array of values ​​corresponding in size to the value provided as input to the neuron. In one example, the filter may be configured to identify edges (e.g., horizontal lines, vertical lines, and / or straight lines, etc.). In successive convolution layers, the filters associated with the neurons may be configured to continuously identify more complex patterns (e.g., arcs and / or objects, etc.).

[0079] In some embodiments, CNN 440 performs a first convolution function based on CNN 440 multiplying the values ​​of each neuron provided as input to one or more neurons included in the first convolution layer 442 by the values ​​of the filters corresponding to each neuron in the same or more neurons. For example, CNN 440 may multiply the values ​​of each neuron provided as input to one or more neurons included in the first convolution layer 442 by the values ​​of the filters corresponding to each neuron in the one or more neurons to generate a single value or an array of values ​​as output. In some embodiments, the collective output of the neurons of the first convolution layer 442 is referred to as a convolution output. In some embodiments, when each neuron has the same filter, the convolution output is referred to as a feature map.

[0080] In some embodiments, CNN 440 provides the output of each neuron of the first convolutional layer 442 to the neurons of the downstream layer. For clarity, the upstream layer may be a layer that transmits data to a different layer (referred to as the downstream layer). For example, CNN 440 may provide the output of each neuron of the first convolutional layer 442 to the corresponding neurons of the subsampling layer. In the example, CNN 440 provides the output of each neuron of the first convolutional layer 442 to the corresponding neurons of the first subsampling layer 444. In some embodiments, CNN 440 adds a bias value to the aggregate set of all values ​​provided to each neuron of the downstream layer. For example, CNN 440 adds a bias value to the aggregate set of all values ​​provided to each neuron of the first subsampling layer 444. In such an example, CNN 440 determines the final value to be provided to each neuron of the first subsampling layer 444 based on the aggregate set of all values ​​provided to each neuron and the activation function associated with each neuron of the first subsampling layer 444.

[0081] At step 460, CNN 440 performs a first subsampling function. For example, based on CNN 440 providing the values ​​output by first convolutional layer 442 to the corresponding neurons of first subsampling layer 444, CNN 440 may perform the first subsampling function. In some embodiments, CNN 440 performs the first subsampling function based on an aggregation function. In an example, CNN 440 performs the first subsampling function based on CNN 440 determining the maximum input (referred to as a maximum pooling function) among the values ​​provided to a given neuron. In another example, CNN 440 performs the first subsampling function based on CNN 440 determining the average input (referred to as an average pooling function) among the values ​​provided to a given neuron. In some embodiments, based on CNN 440 providing values ​​to the respective neurons of first subsampling layer 444, CNN 440 generates an output, which is sometimes referred to as a subsampled convolution output.

[0082] At step 465, CNN 440 performs a second convolution function. In some embodiments, CNN 440 performs the second convolution function in a manner similar to how CNN 440 performs the first convolution function described above. In some embodiments, CNN 440 performs the second convolution function based on CNN 440 providing the value output by first subsampling layer 444 as input to one or more neurons (not explicitly illustrated) included in second convolution layer 446. In some embodiments, as described above, each neuron of second convolution layer 446 is associated with a filter. As described above, the filter (one or more) associated with second convolution layer 446 can be configured to recognize more complex patterns than the filter associated with first convolution layer 442.

[0083] In some embodiments, the CNN 440 performs a second convolution function based on the CNN 440 multiplying the value of each neuron provided as input to the one or more neurons included in the second convolution layer 446 by the value of the filter corresponding to each neuron of the one or more neurons. For example, the CNN 440 may multiply the value of each neuron provided as input to the one or more neurons included in the second convolution layer 446 by the value of the filter corresponding to each neuron of the one or more neurons to generate a single value or a value array as an output.

[0084] In some embodiments, the CNN 440 provides the output of each neuron of the second convolutional layer 446 to the neurons of the downstream layer. For example, the CNN 440 may provide the output of each neuron of the first convolutional layer 442 to the corresponding neurons of the subsampling layer. In an example, the CNN 440 provides the output of each neuron of the first convolutional layer 442 to the corresponding neurons of the second subsampling layer 448. In some embodiments, the CNN 440 adds a bias value to the aggregate set of all values ​​provided to each neuron of the downstream layer. For example, the CNN 440 adds a bias value to the aggregate set of all values ​​provided to each neuron of the second subsampling layer 448. In such an example, the CNN 440 determines the final value provided to each neuron of the second subsampling layer 448 based on the aggregate set of all values ​​provided to each neuron and the activation function associated with each neuron of the second subsampling layer 448.

[0085] At step 470, CNN 440 performs a second subsampling function. For example, based on CNN 440 providing the values ​​output by second convolutional layer 446 to corresponding neurons of second subsampling layer 448, CNN 440 may perform a second subsampling function. In some embodiments, based on CNN 440 using an aggregation function, CNN 440 performs a second subsampling function. In an example, as described above, based on CNN 440 determining the maximum input or average input among the values ​​provided to a given neuron, CNN 440 performs a first subsampling function. In some embodiments, based on CNN 440 providing values ​​to respective neurons of second subsampling layer 448, CNN 440 generates an output.

[0086] At step 475, CNN 440 provides the output of each neuron of the second subsampling layer 448 to the fully connected layer 449. For example, CNN 440 provides the output of each neuron of the second subsampling layer 448 to the fully connected layer 449 so that the fully connected layer 449 generates an output. In some embodiments, the fully connected layer 449 is configured to generate an output associated with a prediction (sometimes referred to as a classification). The prediction may include an indication that the objects included in the image provided as input to CNN 440 include objects and / or sets of objects, etc. In some embodiments, the perception system 402 performs one or more operations and / or provides data associated with the prediction to the various systems described herein.

[0087] Path planning using corridors

[0088] Path planning for an autonomous vehicle is an operation that can use a large amount of computing resources from the autonomous vehicle 200. Excessive path planning processing may adversely affect the performance of the autonomous vehicle. For example, the autonomous vehicle planning system 404 can analyze several possible paths when determining the path to be executed for the vehicle 200. However, analyzing the possible paths used by the vehicle may consume a large amount of computing resources (such as RAM, processor bandwidth, and time, etc.), resulting in the vehicle 200 allocating relatively limited resources for other processing. The high computing resource consumption for analyzing multiple potential paths may also slow down the processing time for determining the path used by the vehicle, and / or the vehicle 200 may not have enough time to analyze the potential paths within a given time constraint to effectively operate the vehicle.

[0089] In addition, some paths generated by planning system 404 may include general areas that cannot or should not be traveled by vehicle 200. For example, an area may include a lane or roadway that is blocked, unreachable, or unsafe to navigate. As another example, certain areas of the environment may include an off-road environment or space between an agent and a boundary (e.g., the edge of a road) that is not large enough to accommodate a vehicle and may therefore be unsafe, physically impossible, or infeasible. As another example, an area in the environment may not be reachable within a certain amount of time, given the physical constraints of the vehicle (e.g., acceleration capabilities). In this way, determining a detailed path leading to or suitable for a blocked or unreachable area or an unsafe lane or roadway may be inefficient or a waste of computing resources.

[0090] Determining a detailed path for a vehicle can be computationally intensive, while eliminating general areas (or even corridors) from consideration before determining the path for the vehicle can be significantly less computationally intensive. Thus, planning system 404 can save computational resources by generating corridors where autonomous vehicles can or should travel (and identifying or excluding areas or corridors where autonomous vehicles cannot or should not travel), and then generating paths within some or all of these corridors. In some cases, corridors can be generated based on temporal or spatial constraints of the environment.

[0091] In some cases, planning system 404 can rank and prioritize corridors and determine paths for vehicles within some or all of these corridors. In some cases, planning system 404 determines that there are no valid corridors for a particular environment. In some such cases, planning system 404 can take appropriate measures to maintain the safety and / or security of the occupants of vehicle 200.

[0092] In some cases, the planning system determines a path for a subset of the corridors based on the ranking of the generated corridors and / or the computing resources of the planning system 404. The paths for a particular corridor or set of corridors may be ranked and selected based on predetermined parameters such as safety, comfort, or efficiency. The selected path may be used to control the vehicle 200.

[0093] In some examples, the planning system 404 may use navigation options (including, but not limited to, homology options, binary options, non-binary options, etc.) to identify or generate available corridors. The planning system 404 may identify navigation options based on objects in the environment of the vehicle 200 detected by the perception system 402. For example, the perception system 402 may identify objects in the environment of the vehicle 200. The planning system 404 may then determine how to interact with one or more of the identified objects. For example, the planning system 404 may determine that for each object, the vehicle will travel left or right, or in front of or behind the object, and so on. In some cases, traveling left / right or traveling in front / behind may be navigation options used by the planning system to generate corridors.

[0094] As vehicle 200 simulates the environment, planning system 404 can make sequential decisions regarding navigation around objects based on the navigation options. The planning system can combine a set of sequential navigation options to form a corridor.

[0095] When grouping navigation options to form corridors, the planning system 404 can use corridor validation criteria. The corridor validation criteria can be used to eliminate navigation options that have less desirable effects (e.g., physically impossible options, actions that would break certain rules (such as violating minimum clearance requirements between the vehicle 200 and pedestrians or leaving the driving surface), actions or states that do not meet comfort or safety thresholds, etc.). For example, if the planning system 404 determines that a navigation option will result in a collision, the planning system 404 eliminates the corridor associated with the navigation option from consideration. Therefore, the planning system 404 can utilize corridor validation criteria to iteratively select navigation options and construct corridors through the environment of the vehicle 200.

[0096] In some cases, corridors can be determined based on a continuous selection of navigation options. The navigation options can provide a simplified determination for the agents in the environment and provide a framework for estimating paths. By selecting and using navigation options that meet corridor validation criteria, the planning system 404 can eliminate corridors or areas in the environment of the vehicle 200 that may cause collisions with the agents or lead to other unsafe or undesirable results. Compared to traditional techniques for path planning, this use of navigation options and elimination of unsafe or undesirable corridors to effectively reduce the number of areas or corridors in the environment for which a path is to be generated can reduce computational costs.

[0097] In some cases, the planning system 404 can group navigation options associated with a particular decision for an object into a navigation option set. For example, a decision to drive in front of or behind a particular agent can be associated (correlated) as a navigation option set. Navigation options in a navigation option set can branch into other navigation option sets to form a navigation option tree, examples of which are at least referenced herein. Figure 5 To show and illustrate.

[0098] In some examples, planning system 404 recursively analyzes navigation options up to an endpoint and groups the analyzed navigation options into corridors. An endpoint may correspond to a stopping point, where planning system 404 does not continue to analyze subsequent navigation options in a branch. In some examples, an endpoint may correspond to an estimated travel time, an estimated travel distance, etc. In some cases, planning system 404 may traverse a navigation option tree to generate multiple corridors. As described herein, a corridor may include overlapping navigation options.

[0099] As described herein, the navigation options available for selection can be selected based on the previous navigation options. For example, a first navigation option set can include a first navigation option and a second navigation option. The first navigation option can result in a subsequent second navigation option set having the first navigation option and the second navigation option. Similarly, a second navigation option in the first navigation option set can result in a subsequent third navigation option set having the first navigation option and the second navigation option that is different from the second navigation option set. Selecting the first navigation option can enable the planning system 404 to simulate the environment and access a subsequent second navigation option set, which includes its own first navigation option and the second navigation option. Selecting the second navigation option (in the first navigation option set) can enable the planning system 404 to simulate the environment in a different manner and access a subsequent third navigation option set, which includes its own first navigation option and the second navigation option. If the first navigation option is selected for a corridor, in some cases, the third navigation option set may not be available for the autonomous system to be assigned to the corridor. In this way, the navigation option set can be linked to the previous navigation option to efficiently construct the corridor.

[0100] As a non-limiting example, consider the scenario that an obstacle may block the path of the vehicle 200 in the roadway. In some such cases, the vehicle 200 may have a navigation option in the navigation option set to move to the left of the obstacle and a second navigation option in the navigation option set to move to the right of the obstacle. After the selection of navigating left or right has been made, the selection can constrain the possible navigation options used by the vehicle 200. For example, if there is an entrance ramp that leaves to the left, the vehicle 200 can only enter the entrance ramp in the continuous navigation option when the vehicle 200 moves to the left instead of to the right. In this way, the decision to enter the entrance ramp will only be available when the vehicle 200 selects the navigation option to move to the left. As another example, other vehicles in the left lane or the right lane may limit the options of the vehicle 200.

[0101] In some cases, when the planning system 404 generates potential corridors, the planning system 404 can filter out certain navigation options (e.g., physically inaccessible options or unsafe options) based on corridor validation criteria. By filtering out certain navigation options, the planning system 404 can reduce the number of corridors that will be used to analyze potential paths, and therefore can reduce the number of vehicle paths generated and analyzed. For example, if the planning system 404 determines that a certain navigation option does not meet a certain corridor validation criterion, the processor planning system 404 can exclude subsequent navigation options branching out from the navigation option that does not meet the criterion. With reference to the above example, if the autonomous vehicle 200 moves to the left lane, the planning system 404 can deem the navigation options used to enter the pass road that is only accessible from the right lane unimportant. In another example, if the autonomous system cannot navigate in front of an obstacle due to obstruction, the planning system 404 can exclude other options that will become available if the vehicle 200 navigates in front of the obstacle. In this way, the planning system 404 considers the options available if the vehicle 200 navigates before the obstacle. Eliminating navigation options and eliminating sets of navigation options that branch off from the eliminated navigation options can have the following benefits: eliminating an area of ​​the environment from consideration for potential paths, avoiding the generation of corridors that may include the eliminated area, and avoiding the generation of paths within the eliminated corridors, thereby reducing the amount of computing resources used to generate the path used by the vehicle.

[0102] In some examples, no navigation option in a given set of navigation options may satisfy the corridor validation criteria. For example, the set of navigation options may include two navigation options that will each result in a collision with an obstacle. In such a case, the planning system 404 may eliminate these two navigation options from consideration as part of the corridor, and may select an alternative navigation option from an earlier set of navigation options. The set of alternative navigation options is separated from the set of navigation options from which no navigation option was selected. In this way, the alternative navigation options may result in navigation options that satisfy the corridor validation criteria (and therefore may be used to generate a corridor).

[0103] In addition, in some examples, the planning system 404 may determine that the alternative navigation option does not meet the corridor validation criteria. In the case where the alternative navigation option does not meet the corridor validation criteria, the planning system 404 may select an earlier navigation option until a navigation option (if any) that meets the corridor validation criteria is identified. For example, a navigation option may result in a navigation option set in which no navigation option meets the corridor validation criteria. Instead, the planning system 404 may revert to a previous alternative navigation option set. The previous alternative navigation option set may produce two navigation options that also do not meet the corridor validation criteria. Then, the planning system 404 may revert to a second previous alternative navigation option set containing at least one navigation option that does meet the corridor validation criteria. Then, the planning system 404 may determine the navigation option based on the second previous navigation option set.

[0104] Figure 5 is a diagram illustrating an example of a navigation option tree for determining corridors that satisfy certain corridor validation criteria. As described herein, navigation options may be based on a description of the vehicle 200 relative to the agents in the environment of the vehicle 200. For example, Figure 5 A set of navigation options is illustrated below, which includes word descriptions such as "before" and "after" that indicate the relationship of vehicle 200 relative to at least one obstacle / agent in the environment. However, in some examples, these word descriptions can indicate any navigation decision that vehicle 200 can make, such as left / right, etc. In addition, the navigation option tree can illustrate spatial or temporal navigation options.

[0105] Figure 5 Nodes 502a through 502q (individually or collectively referred to as (one or more than one) nodes 502) are shown. Each node 502 may represent a navigation option for the vehicle 200 at a particular time and / or location. Thus, the navigation option tree may illustrate selections for different objects or trails for the autonomous vehicle at different times and / or locations within the environment. Figure 5In the example shown, the navigation options are word descriptions such as "before..." and "after...", but in other examples, the navigation options may include "left", "right", "front", "back", or any other identifier that defines the relationship between the vehicle 200 and the obstacle / agent.

[0106] Some navigation options may be grouped into navigation option sets. In addition, some or all of the navigation option sets may have a parent navigation option (e.g., a navigation option from which a navigation option in the navigation option set branches).

[0107] In the illustrated examples, the navigation option sets include one or two navigation options, however, it will be appreciated that any number of navigation options may be included in the navigation option sets. As described herein, each navigation option set may represent the effects of different actions taken by the vehicle 200 as a result of navigation decisions as it moves through an environment. Figure 5 In the illustrated example of , the navigation option set includes an action of "before" or "after" indicating whether the vehicle is to travel before or after an object, however, it will be understood that other actions and / or non-binary selections may be used for the navigation option set.

[0108] In some examples, navigation options in a set of navigation options may be selected based on predetermined factors such as the physical availability of the navigation options. For example, the planning system 404 may select navigation options that are not physically obstructed relative to the vehicle 200 during the time window. The planning system 404 may use various logical processes to construct the evaluation of the navigation options. For example, the planning system 404 may directly and continuously evaluate navigation sets with similar proximity relative to the parent navigation option set. For example, the planning system 404 may directly and continuously evaluate 502b and 502k because 502b and 502k each directly branch out from 502a. In some examples, the planning system 404 may continuously evaluate the navigation options until the logical branch ends. For example, the planning system 404 may directly and continuously evaluate navigation options 502a, 502b, 502c, 502e, and 502f, and then evaluate other branches. However, it will be understood that other structures and logical processes may be used to evaluate and select navigation options and navigation option sets.

[0109] The system may use two or more consecutively selected navigation options to construct a pathway / corridor for the autonomous system to follow. Figure 5 In the example shown, various navigation options form multiple potential corridors for the vehicle 200 based on "before" and "after" relationships between the vehicle 200 and one or more objects (or tracks) in the vehicle scene.

[0110] exist Figure 5In the illustrated example of , planning system 404 has identified three corridors that include navigation options that satisfy corridor validation criteria. A first corridor 503a includes nodes 502a, 502b, 502c, 502e, and 502f; a second corridor 503b includes nodes 502a, 502b, 502g, 502i, and 502j; and a third corridor 503c includes nodes 502a, 502k, 502m, 502o, and 502q.

[0111] To create different corridors, the planning system 404 can evaluate various navigation options or nodes 502 (e.g., satisfactory options) that meet the corridor validation criteria and link them together. For example, at node 502a, the planning system 404 determines that there are two satisfactory options for a vehicle at a particular time: moving before or after an object. At node 502b, the planning system 404 determines that there are two satisfactory options for a vehicle at different times: moving before or after different objects. At node 502c, the planning system 404 determines that there is one satisfactory option: moving after an object. Node 502d has no branches because the corresponding navigation options do not meet the corridor validation criteria. At node 502e, the planning system 404 determines that there is one satisfactory option: moving after an object. At node 502f, the planning system 404 identifies the endpoint at which the planning system 404 stops branching the navigation options. At node 502g, the planning system 404 determines that there is one satisfactory option: moving after an object. Node 502h has no branches (or is not further analyzed) because it is not a satisfactory option. At node 502i, the planning system 404 determines that there is a satisfactory option: move after the object. At node 502j, the processor has reached a stopping point where the processor will stop determining navigation options. At node 502k, the planning system 404 determines that there is a satisfactory option: move before the object. Node 502l has no branches because it is not a satisfactory option. At node 502m, the planning system 404 determines that there is a satisfactory option: move before the object. Node 502n has no branches because it is not a satisfactory option. At node 502o, the planning system 404 determines that there is a satisfactory option: move before the object. Node 502d has no branches because it is not a satisfactory option. At node 502c, the planning system 404 determines that there is a satisfactory option: move after the object. Node 502p has no branches because it is not a satisfactory option. At node 502q, the processor is at an endpoint where the processor will stop determining navigation options.

[0112] The planning system 404 can evaluate various navigation options and group them into corridors. In some cases, the planning system 404 can use one or more corridor creation criteria to determine whether a group of navigation options can be considered a corridor. The corridor creation criteria can use any one or any combination of a threshold number of navigation options, a threshold distance, and / or a threshold time to determine whether a group of navigation options will be used to create a corridor. For example, in some cases, the planning system 404 can determine that to be a corridor, the group of navigation options must include a threshold number of navigation options (e.g., five consecutive navigation options are required to form a corridor), or the group of navigation options must cover an estimated distance (e.g., the total distance traveled for the navigation options will be at least 200 yards) or an estimated time (e.g., the total time of the navigation options will last six seconds).

[0113] In some cases, the planning system 404 may continue to select navigation options for the corridor until the end state is reached. In some cases, an end state strategy may be used to determine when the end state is reached. The end state strategy may consider any one or any combination of a threshold simulation time period, a threshold number of navigation options, a threshold simulation distance, and a landmark reached or passed to determine when the end state will occur. For example, the end state strategy may indicate that the planning system 404 will select navigation options until six seconds of travel time (or some other threshold time period) have been simulated. As another example, the end state strategy may indicate that the planning system 404 will select navigation options for the vehicle 200 until five navigation options (or some other threshold number of navigation options) have been grouped to form a corridor. In some cases, the end state strategy may indicate that the scene evaluation system 504 will select continuous navigation options for the corridor until a threshold distance has been reached or a landmark has been reached. For example, the planning system 404 may select navigation options for the vehicle 200 and objects in the vehicle scene until 1 / 4 mile (or other threshold distance) has been simulated or until an intersection (or other landmark) has been passed. Thus, an end state may be reached even though additional navigation options may be selected (e.g., the simulation may continue). For example, in some examples, an end state may represent something other than the end of a route or termination of a drive, and an end state may represent a point (in terms of time, place, distance, etc.) at which any generated path will end.

[0114] Once the end state is reached, the simulated trajectory corridors may be evaluated based on a corridor evaluation strategy. The corridor evaluation strategy may indicate criteria by which the corridors will be evaluated. For example, the corridor evaluation strategy may indicate that the corridors will be evaluated based on factors such as shortest distance, safety, comfort, or any other factor that may inform vehicle navigation decisions.

[0115] In the illustrated example, the planning system 404 identifies three corridors 503a, 503b, and 503c (and / or determines that the three corridors 503a, 503b, and 503c are satisfactory corridors). In some cases, the planning system 404 can rank the three corridors (503a to 503c) based on any desired number of factors (such as, but not limited to, shortest distance, safety, comfort, or any other factor that can inform the navigation decision of the vehicle). For example, the planning system 404 can determine that following a certain corridor will cause the vehicle 200 to follow a longer path compared to following a different corridor (which will cause the vehicle 200 to follow a shorter / more direct path). Based on the shortest distance, the planning system 404 can rank the corridor with the shorter / more direct path higher than the corridor with the longer path. In another example, the planning system 404 can evaluate the proximity of the corridors relative to the agents. Based on safety, the planning system 404 can rank corridors that are farther from at least one of the agents higher than corridors that are closer to at least one of the agents. In some examples, planning system 404 may determine that following a certain corridor will cause sudden acceleration and deceleration of vehicle 200 when compared to other corridors. Based on comfort, planning system 404 may rank corridors that cause sudden acceleration and deceleration lower than corridors that cause less sudden acceleration and deceleration. Based on the ranking, planning system 404 may select a subset of corridors for path generation. In some cases, planning system 404 may have a predetermined number of corridors to evaluate and further analyze the highest ranked corridors up to the set number of corridors.

[0116] In some cases, planning system 404 can evaluate corridors based on one or more criteria and eliminate the corridor from consideration for path generation if the corridor does not meet the criteria. For example, planning system 404 can determine to evaluate both the corridor between 502a and 502f and the corridor between 502a and 502j based on safety risk. Planning system 404 can then determine to not consider the corridor between 502a and 502q because it does not meet the safety threshold. In this way, planning system 404 can only expend computing resources to determine the detailed path between 502a and 502f and between 502a and 502j.

[0117] As described herein, planning system 404 analyzes different corridors to identify available paths within each corridor. In addition, planning system 404 can evaluate and rank the available paths. In addition, planning system 404 determines which path to select based at least in part on one or more factors or trained neural networks. Examples of factors that can be considered when determining a preferred vehicle path can include distance, time, safety risk, route accessibility, comfort, and the number of available alternative corridors. Planning system 404 can evaluate these factors to determine which paths to invest additional computing resources in. Planning system 404 can determine a lower threshold for each factor and not consider path options that are not raised to greater than the lower limit. In other examples, planning system 404 can have a predetermined number of paths to evaluate and further analyze the highest ranked paths until the set number of paths.

[0118] Figure 6 6 is a flow chart of an example process 600 for generating a path and controlling a vehicle using corridors. In some examples, one or more of the steps described for process 600 are performed by planning system 404 (e.g., where planning system 404 is implemented by autonomous system 202) (e.g., completely and / or partially, etc.). Additionally or alternatively, in some examples, one or more of the steps described for process 600 are performed by another device or group of devices (e.g., completely and / or partially, etc.) separate from or including planning system 404, such as perception system 402 (e.g., where perception system 402 is implemented by autonomous system 202).

[0119] At block 602, planning system 404 receives semantic image data from perception system 402. The semantic image data is associated with at least one physical object in the environment. Planning system 404 classifies the at least one physical object. The semantic image data may be associated with the environment in which autonomous vehicle 200 is operating. For example, planning system 404 receives semantic image data associated with cars, pedestrians, road features, and traffic lights in the environment around vehicle 200.

[0120] At block 604, planning system 404 identifies agents in the environment based on the semantic image data received from perception system 402. As described herein, in some examples, perception system 402 may implement at least one machine learning model as part of a pipeline for classifying one or more objects located in an environment, etc. Planning system 404 may identify various objects from the semantic image data based on the classification of these objects. For example, planning system 404 may identify one or more cars, pedestrians, road features, or traffic signals, etc., as corresponding objects based on the data received from perception system 402.

[0121] At block 606, planning system 404 determines / generates a set of navigation options based on the identified objects, map elements, and surrounding features in the environment (such as roads, lanes of roads, barriers, etc.). As described herein, the set of navigation options may include a first navigation option and a second navigation option associated with an agent or other identified object. As described herein, the navigation options may form part of one or more potential corridors for vehicle 200.

[0122] In some examples, each navigation option represents a prediction of the interaction between the vehicle 200 and at least one agent or object. For example, the planning system 404 may determine that a potential path to bypass the agent may be on the left side of the place of the vehicle 200 at a given time, and another potential path to bypass the agent is on the right side of the place of the vehicle 200 at a given time. As another example, the planning system 404 may determine the path to adopt the left side of a stationary obstacle or the right side of a stationary obstacle. As another example, the planning system 404 may determine that the path to adopt in front of the bicycle is adopted due to passing in front of a mobile agent (such as a bicycle, etc.). In another example, the navigation option may be to increase the speed of the vehicle 200 to travel through the intersection before another vehicle arrives at the intersection. Alternatively, the navigation option may be to slow down to wait for another vehicle to enter the intersection, and then travel behind the other vehicle. However, the planning system 404 may evaluate any physically possible options for navigating the vehicle 200 in the environment.

[0123] In some examples, one or more of the navigation options may be determined based on other (previous) navigation options. Figure 5 As described, at least one of the navigation options may branch off from a previous navigation option. For example, traveling in front of an object may result in a different set of subsequent navigation options than traveling behind the object. Similarly, traveling to the left of an object may result in a different set of subsequent navigation options than traveling to the right of the object. Thus, some navigation options may be identified by planning system 404 based on other navigation options. However, it will be appreciated that in some cases, navigation options may be independent of previous navigation options.

[0124] At box 608, the planning system 404 selects a navigation option. As part of selecting a navigation option, the planning system 404 evaluates a set of navigation options. In some cases, the planning system 404 selects at least one navigation option from one or more of the set of navigation options. In some cases, the planning system 404 selects a navigation option based on a corridor validation criterion. For example, if the planning system 404 determines that the navigation option is likely to cause a collision or move the vehicle 200 to an unsafe location (such as leaving the road, etc.), the planning system 404 may determine that the navigation option does not meet the corridor validation criterion. If the planning system 404 determines that the navigation option meets the corridor validation criterion, the planning system 404 may select the navigation option.

[0125] In some cases, the planning system 404 may select a navigation option based at least in part on the physical capabilities of the vehicle 200. Additionally or alternatively, the corridor validation criteria may also be based at least in part on the physical capabilities of the vehicle 200. For example, if a navigation option includes a lateral deviation rate that is greater than the turning capabilities of the vehicle 200, the planning system 404 may determine that the navigation option does not meet the corridor validation criteria.

[0126] In some examples, the corridor validation criteria may be selected based on a predetermined set of priorities. For example, the corridor validation criteria may indicate that the planning system 404 will prioritize staying on a single road or not changing lanes (or in some cases, only select navigation options for staying on a single road or not changing lanes). In this way, the planning system 404 may select navigation options that keep the vehicle 200 on a given road or lane. In some examples, the set of priorities may be priorities such as safety, regulatory priority, and comfort.

[0127] As described herein, in some examples, no navigation option in the navigation option set can meet the corridor validation criteria. In this case, planning system 404 eliminates two navigation options for potential use in the corridor and reverts to a previous navigation option set containing a navigation option suitable for selection.

[0128] In some cases, upon selecting a navigation option, the planning system 404 may branch or generate at least one additional set of navigation options and evaluate the navigation options in the generated set of navigation options. In some cases, as described herein, the planning system 404 may recursively generate a set of navigation options, evaluate the navigation options in the set of navigation options, and select at least one of the navigation options from which to generate another set of navigation options until an end state is reached. For example, the planning system 404 may continue to generate sets of navigation options, evaluate the navigation options in the set of navigation options, and select at least one of the navigation options until a threshold time period is satisfied, a threshold simulation time period is satisfied, a threshold number of sets of navigation options are generated, and / or a threshold number of levels of sets of navigation options are generated, etc.

[0129] At block 610, planning system 404 generates a corridor based on the selected navigation options. As described herein, multiple navigation options are grouped together to form or generate a corridor. Thus, one or more corridors are generated using navigation options selected from a set of navigation options. For example, planning system 404 may each select a navigation option from a series of consecutive sets of navigation options to generate a corridor.

[0130] Additionally, planning system 404 may generate multiple corridors that include various combinations of the navigation options selected at block 608. For example, planning system 404 may generate a first corridor for vehicle 200 based on a first set of navigation options selected from the first set of sequential sets of navigation options. Planning system 404 may concurrently generate a second corridor for vehicle 200 based on a second set of navigation options selected from the second set of sequential sets of navigation options.

[0131] In some cases, a first set of navigation options used to generate a first corridor and a second set of navigation options used to generate a second corridor can overlap (e.g., have at least one navigation option in common between the first set of navigation options and the second set of navigation options). In some cases, the first set of navigation options includes at least one (or only one) navigation option that is different from the navigation options in the second set of navigation options.

[0132] Similarly, the sets of navigation options in the continuous set of navigation options of the second set may overlap with the sets of navigation options in the continuous set of navigation options of the first set (e.g., have at least one set of navigation options in common between the continuous set of navigation options of the first set and the continuous set of navigation options of the second set). In some cases, the continuous set of navigation options of the first set includes at least one set of navigation options that is different from the sets of navigation options in the continuous set of navigation options of the second set. In some cases, the continuous set of navigation options of the first set is the same as the continuous set of navigation options of the second set, but the first set of navigation options for the first corridor is different from the second set of navigation options for the second corridor (e.g., a different navigation option is selected for at least one of the sets of navigation options).

[0133] At box 612, the planning system 404 determines a path based on the one or more corridors generated. In some cases, the planning system 404 can select a corridor from a set of corridors and determine a path based on the selected corridor. In some such cases, the planning system 404 can score or rank the generated corridors based on a corridor assessment strategy, and analyze the corridors based on the score or ranking of the corridors. For example, the planning system 404 can determine to further evaluate corridors that fall within a predetermined score threshold for indicating compliance with the corridor assessment strategy. In some cases, corridors can be scored based on safety (e.g., no collision), efficiency (e.g., deviation from the route), etc.

[0134] In some cases, planning system 404 may simulate multiple paths through each corridor being analyzed and determine which path is the most favorable, taking into account a set of priorities or according to a path planning strategy. For example, planning system 404 may determine multiple potential paths for vehicle 200 for a particular corridor (or corridors) and compare multiple potential paths for vehicle 200, each of which is a subspace of the corridor (or corresponding corridors). Planning system 404 may then determine which path is the most favorable, based on the above-described path planning strategy. Figure 5 The planning system 404 may select the desired detailed path based on the criteria or evaluation described above. In some examples, the planning system 404 may select the desired detailed path based on a safety priority, a regulatory priority, or a comfort priority. For example, the planning system 404 may run a safety simulation, rank the safety of the detailed paths, and select the detailed path with the highest safety ranking.

[0135] At block 614, the planning system 404, as implemented by the autonomous system 202, transmits instructions to the drive-by-wire system 202h, which transmits instructions to the powertrain control system 204, the steering control system 206, and the braking system 208 to cause the vehicle 200 to follow the path. For example, the planning system 404 may transmit instructions to cause the steering control system 206 to cause the vehicle 200 to navigate around the agent according to the determined path. The planning system 404 may further transmit instructions to cause the braking system 208 to stop the vehicle 200 before the vehicle 200 collides with the agent.

[0136] Fig. 7A is a diagram illustrating an example of multiple corridors for a vehicle. In the illustrated example, the corridors are generated based on homotopic options of "travel left" or "travel right". As described herein, planning system 404 receives semantic image data associated with a semantic image of a scene. Thus, Fig. 7A An example representation of an environment of a vehicle generated based on semantic image data received by perception system 402 can be illustrated. In the illustrated example, the environment includes self-vehicle 702 (which is an autonomous vehicle (as described herein at least with reference to Figure 2 The vehicle 200 described above is similar)), vehicles 704a, 704b, 704c (individually or collectively referred to as (one or more than one) vehicles 704). Fig. 7A Also shown are a first corridor 706a and a second corridor 706b. As described herein, the planning system 404 can generate the corridors 706a, 706b based on one or more navigation options from the same or different sets of navigation options.

[0137] The first corridor 706a and the second corridor 706b extend from the location of the self-carrier 702 at a certain point in time. The first corridor 706a represents the result of selecting the navigation option of "driving right" relative to the vehicle 704a. The second corridor 706b represents the result of the navigation option of "driving left" relative to the vehicle 704a. As previously described, the selection of driving left and driving right presents different subsequent options to the self-carrier 702. For example, driving right places the self-carrier 702 behind the vehicle 704b, thereby limiting the ability of the self-carrier 702 to perform certain actions (such as accelerating or possibly maintaining the current speed, etc.). The inability to accelerate or maintain its current speed may also limit the ability of the self-carrier 702 to drive to the left and pass the vehicle 704a. In addition, by driving to the right, the self-carrier 702 may not be able to drive to the right again, because this may cause the self-carrier 702 to leave the road. Similarly, driving to the left can limit the ability of the self-carrier 702 to drive to the left again. Additionally, traveling left may place self-vehicle 702 in an unsafe or unpreferable position relative to vehicle 704c (eg, traveling left may stop vehicle 704c).

[0138] In the illustrated example, the navigation option set includes homotopic options of driving to the right or driving to the left, however, it will be understood that other homotopic options may also be used, such as driving in front of or behind the agent (e.g., driving in front of or behind vehicle 704c). In addition, the self-vehicle 702 can change between different homotopic options depending on the scenario or environment. For example, after generating a corridor and selecting a path based on the homotopic options of driving to the left and driving to the right, the self-vehicle 702 can switch to generating a corridor based on the homotopic option of driving in front of or behind the agent. As a non-limiting example, after moving to the right lane behind vehicle 704b, the self-vehicle 702 can determine the corridor based on driving in front of or behind vehicle 704a.

[0139] Additionally, in the illustrated example, going left and going right correspond to the self-vehicle 702 changing lanes, however, it will be understood that going left or going right may correspond to any type of movement to the left or right, such as moving to the left portion of the lane (without leaving the lane) or moving to the right portion of the lane, etc.

[0140] The planning system 404 can score each corridor 706a, 706b based on predetermined criteria and / or corridor assessment strategies. In the illustrated example, if the predetermined criteria prioritize safety, the planning system 404 can prioritize the first corridor 706a because following the first corridor 706a will not cause the self-vehicle 702 to cross in front of the vehicle 704c. If the planning system 404 prioritizes speed or route flexibility, the planning system 404 can prioritize the second corridor 706b because less traffic is expected in the second corridor 706b.

[0141] Figure 7B is a diagram illustrating an example of generating paths within a selected corridor. As described herein, planning system 404 can evaluate and rank the generated corridors and select one or more corridors for additional evaluation. In some cases, the additional evaluation can include generating paths within the corridors and evaluating those paths. Figure 7B In the illustrated example, the planning system 404 has selected the second corridor 706b for further evaluation and has generated various paths 708a, 708b, 708c, 708d within the second corridor 706b.

[0142] like Figure 7B As illustrated, the planning system 404 can simulate and evaluate at least one path through the corridor 706b selected for further evaluation. In some examples, the planning system 404 can evaluate multiple paths through each generated corridor, and further rank and evaluate each path through each corridor. The planning system 404 can then rank the paths through each selected corridor based on factors such as shortest distance, safety, or any other factor that can inform navigation decisions through the selected corridor. Once the preferred path is selected, the processor can send instructions to the control system 408 to cause the vehicle 200 to follow the selected detailed path.

[0143] In this example, the planning system 404 may score the first path 708a and the second path 708b higher than the third path 708c and the fourth path 708d because the third path 708c and the fourth path 708d may be determined to be less safe or more likely to result in a collision with at least the vehicle 704c. The planning system 404 may also prioritize the first path 708a or the third path 708c according to the system priority as described above. The planning system 404 may then select the first path 708a or the third path 708c for the self-vehicle 702 to take.

[0144] Figure 88 is a flow chart of an example process 800 for corridor scoring and verification. In some examples, one or more of the steps described for process 800 are performed by planning system 404 (e.g., where planning system 404 is implemented by autonomous system 202) (e.g., completely and / or partially, etc.). Additionally or alternatively, in some examples, one or more of the steps described for process 800 are performed by another device or group of devices (e.g., completely and / or partially, etc.) separate from or including planning system 404, such as perception system 402 (e.g., where perception system 402 is implemented by autonomous system 202).

[0145] At block 802, as described above with respect to Figure 6 As described, planning system 404 receives data from perception system 402 which obtains sensor data.

[0146] At block 804, as described above for Figure 6 As described, planning system 404 identifies agents in the environment based on sensor data received from perception system 402 .

[0147] At block 806, as described above for Figure 5 and Figure 6 As described, the planning system 404 generates a corridor using navigation options. In some examples, the corridor is generated at least in part by path simulation or other methods of determining a path through an environment. For example, the processor can use techniques such as physics-based trajectory calculation and swept path analysis to simulate the trajectory of the vehicle from a first point to a second point. In some examples, the corridor is generated at least in part by a remote processor or a trained neural network.

[0148] At block 808, planning system 404 generates a detailed path within one or more of the corridors. As described herein, in some cases, planning system 404 can rank the generated corridors and select one or more of the generated corridors for further evaluation and / or processing. In some cases, planning system 404 ranks and / or selects corridors based on a speed or schedule associated with the corridor, a spatial or temporal window associated with the corridor, or a corridor cost based on a determination of computing resources to be used to evaluate a particular corridor.

[0149] The planning system 404 can generate multiple detailed paths for one or more of the selected corridors. The planning system 404 can determine the detailed paths by calculating a specific trajectory for the vehicle based on the vehicle capabilities and the surrounding environment. For example, the detailed paths can be determined based on the braking and traction capabilities of the vehicle 200 and the swept path analysis. In some examples, the detailed paths can be generated to follow road markings or adapt to changing road surfaces such as potholes and unfinished roads.

[0150] At block 810, the planning system 404 selects a path from the generated paths based on system priorities and / or path planning strategies. The system processor may select a path from a plurality of paths determined for the selected corridor. The selected path may be a path that provides an optimized trajectory for the vehicle 200. For example, the selected path may be a path that provides the smoothest road surface or avoids obstacles that other detailed paths in the corridor do not avoid.

[0151] At block 812, the planning system 404, as implemented by the autonomous system 202, transmits instructions to the drive-by-wire system 202h, which transmits instructions to at least one of the powertrain control system 204, the steering control system 206, and the braking system 208 to cause the vehicle 200 to follow the selected path. For example, the planning system 404 may send instructions to cause the powertrain control system 204 to accelerate and cause the vehicle 200 to avoid a collision with the agent. The planning system 404 may send instructions to cause the steering control system 206 to cause the vehicle 200 to navigate around the agent. The planning system 404 may send instructions to cause the braking system 208 to stop the vehicle 200 before the vehicle 200 collides with the agent.

[0152] Figure 6 and Figure 8 The flowchart illustrated in FIG. 1 is provided for illustration purposes only. It will be understood that the Figure 6 and Figure 8 One or more than one of the steps of the illustrated routine may be used, or the order of the steps may be changed. In addition, it will be understood that Figure 6 One or more steps may be combined with Figure 8 In addition, for the purpose of illustrating clear examples, one or more specific system components are described in the context of performing various operations during various data flow stages. However, other system arrangements and distributions of processing steps across system components may be used.

[0153] In some examples, one or more of the steps described for processes 600 and 800 are performed by planning system 404 or other systems of vehicle 200 (e.g., completely and / or partially, etc.). Additionally or alternatively, in some embodiments, one or more of the steps described for processes 600 and 800 are performed by another device or group of devices (e.g., completely and / or partially, etc.) separate from or including planning system 404 or other systems of vehicle 200 (such as a processor remote from planning system 404 or other systems of vehicle 200, etc.).

[0154] In the previous description, aspects and embodiments of the present disclosure have been described with reference to many specific details, which may vary depending on the implementation. Therefore, the description and the accompanying drawings should be regarded as illustrative, not restrictive. The only and exclusive indication of the scope of the invention, and the applicant's expectation that the content of the scope of the invention is the literal and equivalent scope of the claims issued from this application in the specific form of the claims, including any subsequent amendments. Any definition of the terms used to be included in such claims that are 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 may be an additional step or entity, or a sub-step / sub-entity of the previously described step or entity.

[0155] Various additional example embodiments of the present disclosure may be illustrated by the following clauses.

[0156] Clause 1: A method for operating an autonomous vehicle, the method comprising:

[0157] obtaining, with at least one processor, sensor data associated with an environment in which the autonomous vehicle is operating;

[0158] determining, with the at least one processor, a set of agents in the environment based on the sensor data;

[0159] determining, with the at least one processor, a plurality of sets of navigation options for the autonomous vehicle, wherein each set of navigation options comprises a first navigation option and a second navigation option, and wherein at least one set of navigation options is associated with at least one agent from the set of agents;

[0160] selecting, with the at least one processor, a plurality of navigation options from the plurality of sets of navigation options; and

[0161] A corridor is generated for the autonomous vehicle based on the selected plurality of navigation options using the at least one processor.

[0162] Clause 2. The method of clause 1, wherein selecting, with the at least one processor, a navigation option from the plurality of navigation options comprises selecting a navigation option from each of the plurality of navigation option sets.

[0163] Clause 3. The method of clause 1, wherein determining, using the at least one processor, a plurality of sets of navigation options comprises determining at least one set of navigation options corresponding to at least one agent in the set of agents.

[0164] Clause 4. The method of Clause 1, wherein determining a first set of navigation options in the plurality of sets of navigation options is dependent upon selecting a particular navigation option from a second set of navigation options in the plurality of sets of navigation options.

[0165] Clause 5. The method of clause 1, further comprising: scoring the corridor based on predetermined criteria.

[0166] Clause 6. The method of clause 5, wherein selecting, using the at least one processor, a plurality of navigation options from the plurality of navigation options comprises: selecting a first plurality of navigation options, and

[0167] Wherein, generating, using the at least one processor, a corridor for the autonomous vehicle comprises: generating a first corridor;

[0168] The method further comprises:

[0169] determining at least one set of additional navigation options, wherein the at least one set of additional navigation options includes a first additional navigation option and a second additional navigation option;

[0170] selecting a second plurality of navigation options from the plurality of sets of navigation options and the at least one additional set of navigation options; and

[0171] A second corridor is generated for the autonomous vehicle based on the selected plurality of navigation options.

[0172] Clause 7. The method according to clause 6, further comprising:

[0173] scoring the second corridor based on the predetermined criteria;

[0174] determining a vehicle path using one of the first corridor and the second corridor based on scoring the first corridor and scoring the second corridor; and

[0175] The autonomous vehicle is moved along the determined vehicle path.

[0176] Clause 8. The method of clause 1, further comprising: determining, using the at least one processor, at least one map element, wherein at least one navigation option of the plurality of navigation options is associated with the at least one map element.

[0177] Clause 9. The method of clause 8, wherein determining, using the at least one processor, at least one map element comprises: determining, using the at least one processor, a lane of a road.

[0178] Clause 10. The method of clause 1, wherein selecting a plurality of navigation options from the plurality of sets of navigation options further comprises selecting a plurality of navigation options associated with a predetermined priority.

[0179] Clause 11. The method of clause 10, wherein selecting a plurality of navigation options associated with predetermined priorities comprises selecting a plurality of navigation options associated with at least one of a safety priority, a regulatory priority, and a comfort priority.

[0180] Clause 12. A method according to clause 1, wherein utilizing the at least one processor to select multiple navigation options from the multiple navigation option sets includes: selecting multiple navigation options associated with predictions of interactions between the autonomous vehicle and at least one agent in the set of agents.

[0181] Clause 13. The method of clause 1, wherein selecting a plurality of navigation options comprises comparing a lateral deviation rate of the corridor to a turning capability of the autonomous vehicle.

[0182] Clause 14. The method of clause 1, further comprising: transmitting the selected navigation option to a control system, the control system being configured to transmit the selected navigation option to a drive-by-wire system.

[0183] Clause 15. The method of clause 1, further comprising: determining at least one vehicle trajectory, wherein the vehicle trajectory is a subspace of the corridor.

[0184] Clause 16. A method according to clause 15, wherein determining the at least one vehicle trajectory includes: determining a plurality of vehicle trajectories, and wherein the method further includes: comparing and scoring the plurality of vehicle trajectories, and controlling the autonomous vehicle based on the vehicle trajectories.

[0185] Clause 17. The method of clause 16, further comprising: selecting a vehicle trajectory from the plurality of vehicle trajectories based on a predetermined priority.

[0186] Clause 18. The method of clause 17, wherein selecting the vehicle trajectory based on a predetermined priority comprises selecting a vehicle trajectory associated with at least one of a safety priority, a regulatory priority, and a comfort priority.

[0187] Clause 19. The method of clause 1, further comprising: abandoning selection of a navigation option in the set of navigation options.

[0188] Clause 20. The method of Clause 19, further comprising: selecting an alternative navigation option from a corridor not associated with the set of navigation options in which no navigation option was selected.

[0189] Clause 21: A method for operating an autonomous vehicle, the method comprising:

[0190] obtaining, with at least one processor, sensor data associated with an environment in which the autonomous vehicle is operating;

[0191] determining, with the at least one processor, a set of agents in the environment based on the sensor data;

[0192] generating, with the at least one processor, a plurality of corridors, wherein each corridor is generated based at least in part on a plurality of binary decisions associated with the set of agents;

[0193] generating, using the at least one processor, at least one path for the generated at least one corridor;

[0194] selecting, with the at least one processor, a path among the at least one path; and

[0195] The autonomous vehicle is controlled, using the at least one processor, based on the at least one path.

[0196] Clause 22. The method of clause 21, wherein generating, with the at least one processor, a plurality of corridors further comprises generating each corridor based at least in part on a plurality of non-binary decisions.

[0197] Clause 23. The method of clause 21, further comprising: scoring the plurality of corridors based on a predetermined set of priorities.

[0198] Clause 24. The method of clause 23, wherein scoring the plurality of corridors based on a predetermined set of priorities further comprises scoring the plurality of corridors based on at least one of a safety priority, a regulatory priority, and a comfort priority.

[0199] Clause 25. The method of clause 21, wherein generating one or more paths further comprises determining at least one vehicle trajectory, wherein the vehicle trajectory is associated with a subspace of one of the corridors.

[0200] Clause 26. The method of clause 21, wherein determining at least one vehicle trajectory comprises determining a plurality of vehicle trajectories, and wherein the method further comprises comparing and scoring the plurality of vehicle trajectories.

[0201] Clause 27. The method of clause 21, further comprising: selecting the at least one vehicle trajectory from the plurality of vehicle trajectories based on a predetermined priority.

[0202] Clause 28. A method according to clause 27, wherein selecting the at least one vehicle trajectory from the plurality of vehicle trajectories based on a predetermined priority comprises selecting the at least one vehicle trajectory based on at least one of a safety priority, a regulatory priority, and a comfort priority.

[0203] Clause 29. A method according to clause 21, wherein utilizing the at least one processor to generate multiple corridors based at least in part on multiple binary decisions related to the set of agents includes: generating each corridor based at least in part on a decision to travel before or after the agent.

[0204] Clause 30. A method according to clause 21, wherein utilizing the at least one processor to generate multiple corridors based at least in part on multiple binary decisions related to a set of agents includes generating each corridor based at least in part on a decision to exceed a first side or a second side of an agent in the set of agents.

[0205] Clause 31. A system for operating an autonomous vehicle, the system comprising:

[0206] at least one processor; and

[0207] at least one non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to:

[0208] obtaining, with at least one processor, sensor data associated with an environment in which the autonomous vehicle is operating;

[0209] determining, with the at least one processor, a set of agents in the environment based on the sensor data;

[0210] generating a plurality of corridors, wherein each corridor is generated based at least in part on a plurality of binary decisions associated with the set of agents;

[0211] generating at least one path for each generated corridor; and

[0212] A path among the at least one path is selected.

[0213] Clause 32. The system of clause 31, wherein each corridor is generated based at least in part on a plurality of non-binary decisions.

[0214] Clause 33. The system of clause 31, further comprising: scoring the corridor based on a predetermined set of priorities.

[0215] Clause 34. A system according to clause 31, wherein at least one binary decision is a decision to drive ahead of the agent or behind the agent.

[0216] Clause 35. A system according to clause 31, wherein at least one binary decision is a decision that a first side or a second side of agents in the set of agents outweighs.

[0217] Clause 36. At least one non-transitory storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to:

[0218] obtaining, with at least one processor, sensor data associated with an environment in which the autonomous vehicle is operating;

[0219] determining, with the at least one processor, a set of agents in the environment based on the sensor data;

[0220] generating a plurality of corridors, wherein each corridor is generated based at least in part on a plurality of binary decisions associated with the set of agents;

[0221] generating at least one path for each generated corridor; and

[0222] A path among the at least one path is selected.

[0223] Clause 37. The at least one non-transitory storage medium of Clause 36, wherein each corridor is generated based at least in part on a plurality of non-binary decisions.

[0224] Clause 38. The at least one non-transitory storage medium of Clause 36, further comprising: scoring the corridor based on a predetermined set of priorities.

[0225] Clause 39. At least one non-transitory storage medium as described in clause 36, wherein at least one binary decision is a decision to drive ahead of the agent or behind the agent.

[0226] Clause 40. At least one non-transitory storage medium of clause 36, wherein at least one binary decision is a decision to outweigh a first side or a second side of agents in the set of agents.

Claims

1. A method for operating an autonomous vehicle, the method comprising: obtaining, with at least one processor, sensor data associated with an environment in which the autonomous vehicle is operating; determining, with the at least one processor, a set of agents in the environment based on the sensor data; determining, with the at least one processor, a plurality of sets of navigation options for the autonomous vehicle, wherein each set of navigation options comprises a first navigation option and a second navigation option, and wherein at least one set of navigation options is associated with at least one agent from the set of agents; selecting, with the at least one processor, a plurality of navigation options from the plurality of sets of navigation options; as well as A corridor is generated for the autonomous vehicle based on the selected plurality of navigation options using the at least one processor.

2. The method according to claim 1, wherein: Selecting, with the at least one processor, a navigation option from the plurality of navigation options includes selecting a navigation option from each of the plurality of sets of navigation options.

3. The method according to claim 1 or 2, wherein: Determining, with the at least one processor, a plurality of sets of navigation options includes determining at least one set of navigation options corresponding to at least one agent in the set of agents.

4. The method according to any one of claims 1 to 3, wherein: Determining a first set of navigation options in the plurality of sets of navigation options is dependent upon selecting a particular navigation option from a second set of navigation options in the plurality of sets of navigation options.

5. The method according to any one of claims 1 to 4, further comprising: The corridors are scored based on predetermined criteria.

6. The method according to claim 5, wherein: Selecting, using the at least one processor, a plurality of navigation options from the plurality of navigation options set comprises: selecting a first plurality of navigation options, and Wherein, generating, using the at least one processor, a corridor for the autonomous vehicle comprises: generating a first corridor; The method further comprises: determining at least one set of additional navigation options, wherein the at least one set of additional navigation options includes a first additional navigation option and a second additional navigation option; selecting a second plurality of navigation options from the plurality of sets of navigation options and the at least one additional set of navigation options; and A second corridor is generated for the autonomous vehicle based on the selected plurality of navigation options.

7. The method according to claim 6, further comprising: scoring the second corridor based on the predetermined criteria; determining a vehicle path using one of the first corridor and the second corridor based on scoring the first corridor and scoring the second corridor; and The autonomous vehicle is moved along the determined vehicle path.

8. The method according to any one of claims 1 to 7, further comprising: At least one map element is determined, utilizing the at least one processor, wherein at least one navigation option of the plurality of navigation options is associated with the at least one map element.

9. The method according to claim 8, wherein: Determining, using the at least one processor, at least one map element includes determining, using the at least one processor, a lane of a road.

10. The method according to any one of claims 1 to 9, wherein: Selecting a plurality of navigation options from the plurality of sets of navigation options further comprises selecting a plurality of navigation options associated with predetermined priorities.

11. The method according to claim 10, wherein: Selecting the plurality of navigation options associated with the predetermined priority includes selecting the plurality of navigation options associated with at least one of a safety priority, a regulatory priority, and a comfort priority.

12. The method according to any one of claims 1 to 11, wherein: Selecting, with the at least one processor, a plurality of navigation options from the plurality of sets of navigation options includes selecting a plurality of navigation options associated with a prediction of an interaction between the autonomous vehicle and at least one agent from the set of agents.

13. The method according to any one of claims 1 to 12, wherein: Selecting a plurality of navigation options includes comparing a lateral deviation rate of the corridor to a turning capability of the autonomous vehicle.

14. The method according to any one of claims 1 to 13, further comprising: The selected navigation option is communicated to a control system for communicating the selected navigation option to a drive-by-wire system.

15. The method according to any one of claims 1 to 14, further comprising: At least one vehicle trajectory is determined, wherein the vehicle trajectory is a subspace of the corridor.

16. The method according to claim 15, wherein: Determining the at least one vehicle trajectory includes determining a plurality of vehicle trajectories, and wherein the method further includes comparing and scoring the plurality of vehicle trajectories, and controlling the autonomous vehicle based on the vehicle trajectories.

17. The method according to claim 16, further comprising: A vehicle trajectory is selected from the plurality of vehicle trajectories based on a predetermined priority.

18. The method according to claim 17, wherein: Selecting the vehicle trajectory based on the predetermined priority includes selecting a vehicle trajectory associated with at least one of a safety priority, a regulatory priority, and a comfort priority.

19. The method according to any one of claims 1 to 18, further comprising: Discards the selection of a navigation option in the navigation options collection.

20. The method according to claim 19, further comprising: An alternate navigation option is selected from a corridor that is not associated with the set of navigation options for which no navigation option was selected.

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