Autonomous navigation of road intersections

Abstract

Provided are methods for autonomously navigating a road intersection by a vehicle, which can include determining a candidate trajectory for an autonomous vehicle to traverse a road intersection; identifying one or more additional vehicles in an environment of the autonomous vehicle; determining one or more predicted trajectories of the one or more additional vehicles through the road intersection; selecting at least some of the predicted trajectories based on a proximity between the candidate trajectory and each of the one or more prediction trajectories; determining, a precedence order based on the candidate trajectory and the selected trajectories; and causing the autonomous vehicle to traverse through the road intersection based on the candidate trajectory and the precedence order.

Description

Autonomous Navigation of Road IntersectionsCROSS-REFERENCE TO RELATED APPLICATION[1] This application claims the benefit of U.S. Provisional Application No. 63 / 533,117 filed August 16, 2023, the disclosure of which is incorporated herein by reference in its entirety.BACKGROUND[2] In general, autonomous vehicles obtain sensor data to determine characteristics of the environment and control vehicle operations. As an example, an autonomous vehicle obtains sensor data to determine the location of an obstacle in the environment, and navigate around the obstacle.BRIEF DESCRIPTION OF THE FIGURES[3] FIG. 1 is an example environment in which a vehicle including one or more components of an autonomous system can be implemented;[4] FIG. 2 is a diagram of one or more systems of a vehicle including an autonomous system;[5] FIG. 3 is a diagram of components of one or more devices and / or one or more systems of FIGS. 1 and 2;[6] FIG. 4 is a diagram of certain components of an autonomous system;[7] FIGS. 5A is a diagram of an example process for autonomously navigating a road intersection;[8] FIGS. 5B-5D are diagrams of example aspects of a process for autonomously navigating a road intersection;[9] FIG. 6A is a diagram of an implementation of a neural network;

[0010] FIG. 6B and 6C are a diagram illustrating example operation of a CNN;

[0011] FIG. 7 is a flowchart of a process for autonomously navigating a road intersection.DETAILED DESCRIPTION

[0012] In the following description numerous specific details are set forth in order to provide a thorough understanding of the present disclosure for the purposes of explanation. It will be apparent, however, that the embodiments described by the present disclosure can be practiced without these specific details. In some instances, well-known structures and devices are illustrated in block diagram form in order to avoid unnecessarily obscuring aspects of the present disclosure.

[0013] Specific arrangements or orderings of schematic elements, such as those representing systems, devices, modules, instruction blocks, data elements, and / or the like are illustrated in the drawings for ease of description. However, it will be understood by those skilled in the art that the specific ordering or arrangement of the schematic elements in the drawings is not meant to imply that a particular order or sequence of processing, or separation of processes, is required unless explicitly described as such. Further, the inclusion of a schematic element in a drawing is not meant to imply that such element is requ6Cired in all embodiments or that the features represented by such element may not be included in or combined with other elements in some embodiments unless explicitly described as such.

[0014] Further, where connecting elements such as solid or dashed lines or arrows are used in the drawings to illustrate a connection, relationship, or association between or among two or more other schematic elements, the absence of any such connecting elements is not meant to imply that no connection, relationship, or association can exist. In other words, some connections, relationships, or associations between elements are not illustrated in the drawings so as not to obscure the 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, where a connecting element represents communication of signals, data, or instructions (e.g., “software instructions”), it should be understood by those skilled in the art that such element can represent one or multiple signal paths (e.g., a bus), as may be needed, to affect the communication.

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

[0016] The terminology used in the description of the various described embodiments herein is included for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well and can be used interchangeably with “one or more” 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 encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and / or “comprising,” when used in this description specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

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

[0018] As used herein, the term “if” is, optionally, construed to mean “when”, “upon”, “in response to determining,” “in response to detecting,” and / or the like, depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” is, optionally, construed to mean “upon determining,” “in response to determining,” “upon detecting [the stated condition or event],” “in response to detecting [the stated condition or event],” and / or the like, depending on the context. Also, as used herein, the terms “has”, “have”, “having”, or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based at least partially on” unless explicitly stated otherwise.

[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 described embodiments. However, it will be apparent to one of ordinary skill in the art that the various described embodiments can be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0020] General Overview

[0021] In some aspects and / or embodiments, systems, methods, and computer program products described herein include and / or implement autonomous navigation of a road intersection by a vehicle.

[0022] In an example implementation, an autonomous vehicle approaches a road intersection, and detects other vehicles in and / or around the intersection. Based on the detection, the autonomous vehicle (i) determines “tracks” representing predicted trajectories of the other vehicles through the intersection, (ii) identifies a subset of the tracks that could result in a collision between the autonomous vehicle and another vehicle, and (iii) determines a precedence of travel by the autonomous vehicle and the other vehicles based on the identified tracks. The autonomous vehicle proceeds throughthe intersection according to the determined precedence. In some implementations, the autonomous vehicle also determines a stop station for temporarily stopping the autonomous vehicle.

[0023] Further, in some implementations, the autonomous vehicle performs at least some of these operations without the aid of manually identified stop stations and / or precedence areas.

[0024] The embodiments described herein can provide various technical benefits.

[0025] At least in some embodiments, these systems and techniques enable an autonomous vehicle to navigate an intersection in a safe manner (e.g., by minimizing or otherwise reducing the likelihood that the autonomous vehicle collides with other vehicles in the intersection).

[0026] Further, these systems and techniques reduce an autonomous vehicle’s reliance on manually provided information in performing autonomous operations. For example, these systems and techniques enable autonomous vehicle to automatically determine precedence of travel through an intersection, without requiring that users manually identify precedence areas with respect to the intersection (e.g., areas that, when occupied by another vehicle, signify that the other vehicle takes precedence over the autonomous vehicle). As another example, these systems and techniques enable autonomous vehicle to automatically identify safe locations at which to stop, without requiring that users manually identify stop stations with respect to the intersection. Accordingly, the autonomous vehicle can operate more safely in a variety of different environments, while reducing its reliance on pre-determined information regarding those environments.

[0027] Referring now to FIG. 1 , illustrated is example environment 100 in which vehicles that include autonomous systems, as well as vehicles that do not, are operated. As illustrated, environment 100 includes vehicles 102a-102n, objects 104a-104n, routes 106a-106n, area 108, vehicle-to-infrastructure (V2I) device 110, network 112, remote autonomous vehicle (AV) system 114, fleet management system 116, and V2I system 118. Vehicles 102a-102n, vehicle-to-infrastructure (V2I) device 110, network 112, autonomous vehicle (AV) system 114, fleet management system 116, and V2I system 118 interconnect (e.g., establish a connection to communicate and / or the like) via wired connections, wireless connections, or a combination of wired or wireless connections. Insome embodiments, objects 104a-104n interconnect with at least one of vehicles 102a- 102n, vehicle-to-infrastructure (V2I) device 110, network 112, autonomous vehicle (AV) system 114, fleet management system 116, and V2I system 118 via wired connections, wireless connections, or a combination of wired or wireless connections.

[0028] Vehicles 102a-102n (referred to individually as vehicle 102 and collectively as vehicles 102) include at least one device configured to transport goods and / or people. In some embodiments, vehicles 102 are configured to be in communication with V2I device 110, remote AV system 114, fleet management system 116, and / or V2I system 118 via network 112. In some embodiments, vehicles 102 include cars, buses, trucks, trains, and / or the like. In some embodiments, vehicles 102 are the same as, or similar to, vehicles 200, described herein (see FIG. 2). In some embodiments, a vehicle 200 of a set of vehicles 200 is associated with an autonomous fleet manager. In some embodiments, vehicles 102 travel along respective routes 106a-106n (referred to individually as route 106 and collectively 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).

[0029] Objects 104a-104n (referred to individually as object 104 and collectively as objects 104) include, for example, at least one vehicle, at least one pedestrian, at least one cyclist, at least one structure (e.g., a building, a sign, a fire hydrant, etc.), and / or the like. Each object 104 is stationary (e.g., located at a fixed location for a period of time) or mobile (e.g., having a velocity and associated with at least one trajectory). In some embodiments, objects 104 are associated with corresponding locations in area 108.

[0030] Routes 106a-106n (referred to individually as route 106 and collectively as routes 106) are each associated with (e.g., prescribe) a sequence of actions (also known as a trajectory) connecting states along which an AV can navigate. Each route 106 starts at an initial state (e.g., a state that corresponds to a first spatiotemporal location, velocity, and / or the like) and ends at a final goal state (e.g., a state that corresponds to a second spatiotemporal location that is different from the first spatiotemporal location) or goal region (e.g. a subspace of acceptable states (e.g., terminal states)). In some embodiments, the first state includes a location at which an individual or individuals are to be picked-up by the AV and the second state or region includes a location or locationsat which the individual or individuals picked-up by the AV are to be dropped-off. In some embodiments, routes 106 include a plurality of acceptable state sequences (e.g., a plurality of spatiotemporal location sequences), the plurality of state sequences associated with (e.g., defining) a plurality of trajectories. In an example, routes 106 include only high level actions or imprecise state locations, such as a series of connected roads dictating turning directions at roadway intersections. Additionally, or alternatively, routes 106 may include more precise actions or states such as, for example, specific target lanes or precise locations within the lane areas and targeted speed at those positions. In an example, routes 106 include a plurality of precise state sequences along the at least one high level action sequence with a limited lookahead horizon to reach intermediate goals, where the combination of successive iterations of limited horizon state sequences cumulatively correspond to a plurality of trajectories that collectively form the high level route to terminate at the final goal state or region.

[0031] Area 108 includes a physical area (e.g. , a geographic region) within which vehicles 102 can navigate. In an example, area 108 includes at least one state (e.g., a country, a province, an individual state of a plurality of states included in a country, etc.), at least one portion of a state, at least one city, at least one portion of a city, etc. In some embodiments, 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 area 108 includes at least one unnamed road such as a driveway, a section of a parking lot, a section of a vacant and / or undeveloped lot, a dirt path, etc. In some embodiments, a road includes at least one lane (e.g., a portion of the road that can be traversed by vehicles 102). In an example, a road includes at least one lane associated with (e.g., identified based on) at least one lane marking.

[0032] Vehicle-to-lnfrastructure (V2I) device 110 (sometimes referred to as a Vehicle-to- Infrastructure or Vehicle-to-Everything (V2X) device) includes at least one device configured to be in communication with vehicles 102 and / or V2I infrastructure system 118. In some embodiments, V2I device 110 is configured to be in communication with vehicles 102, remote AV system 114, fleet management system 116, and / or V2I system 118 via network 112. In some embodiments, V2I device 110 includes a radio frequencyidentification (RFID) device, signage, cameras (e.g., two-dimensional (2D) and / or three- dimensional (3D) cameras), lane markers, streetlights, parking meters, etc. In some embodiments, V2I device 110 is configured to communicate directly with vehicles 102. Additionally, or alternatively, in some embodiments V2I device 110 is configured to communicate with vehicles 102, remote AV system 114, and / or fleet management system 116 via V2I system 118. In some embodiments, V2I device 110 is configured to communicate with V2I system 118 via network 112.

[0033] Network 112 includes one or more wired and / or wireless networks. In an example, network 112 includes a cellular network (e.g., a long term evolution (LTE) network, a third generation (3G) network, a fourth generation (4G) network, a fifth generation (5G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., the public switched telephone network (PSTN), a private network, an ad hoc network, an intranet, the Internet, a fiber opticbased network, a cloud computing network, etc., a combination of some or all of these networks, and / or the like.

[0034] Remote AV system 114 includes at least one device configured to be in communication with vehicles 102, V2I device 110, network 112, fleet management system 116, and / or V2I system 118 via network 112. In an example, remote AV system 114 includes a server, a group of servers, and / or other like devices. In some embodiments, remote AV system 114 is co-located with the fleet management system 116. In some embodiments, remote AV system 114 is involved in the installation of some or all of the components of a vehicle, including an autonomous system, an autonomous vehicle compute, software implemented by an autonomous vehicle compute, and / or the like. In some embodiments, remote AV system 114 maintains (e.g., updates and / or replaces) such components and / or software during the lifetime of the vehicle.

[0035] Fleet management system 116 includes at least one device configured to be in communication with vehicles 102, V2I device 110, remote AV system 114, and / or V21 infrastructure system 118. In an example, fleet management system 116 includes a server, a group of servers, and / or other like devices. In some embodiments, fleet management system 116 is associated with a ridesharing company (e.g., an organizationthat controls operation of multiple vehicles (e.g., vehicles that include autonomous systems and / or vehicles that do not include autonomous systems) and / or the like).

[0036] In some embodiments, V2I system 118 includes at least one device configured to be in communication with vehicles 102, V2I device 110, remote AV system 114, and / or fleet management system 116 via network 112. In some examples, V2I system 118 is configured to be in communication with V2I device 110 via a connection different from network 112. In some embodiments, V2I system 118 includes a server, a group of servers, and / or other like devices. In some embodiments, V2I system 118 is associated with a municipality or a private institution (e.g., a private institution that maintains V2I device 110 and / or the like).

[0037] The number and arrangement of elements illustrated in FIG. 1 are provided as an example. There can be additional elements, fewer elements, different elements, and / or differently arranged elements, than those illustrated in FIG. 1. Additionally, or alternatively, at least one element of environment 100 can perform one or more functions described as being performed by at least one different element of FIG. 1. Additionally, or alternatively, at least one set of elements of environment 100 can perform one or more functions described as being performed by at least one different set of elements of environment 100.

[0038] Referring now to FIG. 2, vehicle 200 (which may be the same as, or similar to vehicles 102 of FIG. 1 ) includes or is associated with autonomous system 202, powertrain control system 204, steering control system 206, and brake system 208. In some embodiments, vehicle 200 is the same as or similar to vehicle 102 (see FIG. 1 ). In some embodiments, autonomous system 202 is configured to confer vehicle 200 autonomous driving capability (e.g., implement at least one driving automation or maneuver-based function, feature, device, and / or the like that enable vehicle 200 to be partially or fully operated without human intervention including, without limitation, fully autonomous vehicles (e.g., vehicles that forego reliance on human intervention such as Level 5 ADS- operated vehicles), highly autonomous vehicles (e.g., vehicles that forego reliance on human intervention in certain situations such as Level 4 ADS-operated vehicles), conditional autonomous vehicles (e.g., vehicles that forego reliance on human intervention in limited situations such as Level 3 ADS-operated vehicles) and / or the like.In one embodiment, autonomous system 202 includes operational or tactical functionality required to operate vehicle 200 in on-road traffic and perform part or all of Dynamic Driving Task (DDT) on a sustained basis. In another embodiment, autonomous system 202 includes an Advanced Driver Assistance System (ADAS) that includes driver support features. 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, which is incorporated by reference in its entirety. In some embodiments, vehicle 200 is associated with an autonomous fleet manager and / or a ridesharing company.

[0039] Autonomous system 202 includes a sensor suite that includes one or more devices such as cameras 202a, LiDAR sensors 202b, radar sensors 202c, and microphones 202d. In some embodiments, autonomous system 202 can include more or fewer devices and / or different devices (e.g., ultrasonic sensors, inertial sensors, GPS receivers (discussed below), odometry sensors that generate data associated with an indication of a distance that vehicle 200 has traveled, and / or the like). In some embodiments, autonomous system 202 uses the one or more devices included in autonomous system 202 to generate data associated with environment 100, described herein. The data generated by the 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 compute 202f, drive-by-wire (DBW) system 202h, and safety controller 202g.

[0040] Cameras 202a include at least one device configured to be in communication with communication device 202e, autonomous vehicle compute 202f, and / or safety controller 202g via a bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3). Cameras 202a include at least one camera (e.g., a digital camera using a light sensor such as a Charge-Coupled Device (CCD), a thermal camera, an infrared (IR) camera, an event camera, and / or the like) to capture images including physical objects (e.g., cars, buses, curbs, people, and / or the like). In some embodiments, camera 202a generates cameradata as output. In some examples, camera 202a generates camera data that includes image data associated with an image. In this example, the image data may specify at least one parameter (e.g., image characteristics such as exposure, brightness, etc., an image timestamp, and / or the like) corresponding to the image. In such an example, the image may be in a format (e.g., RAW, JPEG, PNG, and / or the like). In some embodiments, camera 202a includes a plurality of independent cameras configured on (e.g., positioned on) a vehicle to capture images for the purpose of stereopsis (stereo vision). In some examples, camera 202a includes a plurality of cameras that generate image data and transmit the image data to autonomous vehicle compute 202f and / or a fleet management system (e.g., a fleet management system that is the same as or similar to fleet management system 116 of FIG. 1 ). In such an example, autonomous vehicle compute 202f determines depth to one or more objects in a field of view of at least two cameras of the plurality of cameras based on the image data from the at least two cameras. In some embodiments, cameras 202a is configured to capture images of objects within a distance from cameras 202a (e.g., up to 100 meters, up to a kilometer, and / or the like). Accordingly, cameras 202a include features such as sensors and lenses that are optimized for perceiving objects that are at one or more distances from cameras 202a.

[0041] In an embodiment, 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, camera 202a generates traffic light data associated with one or more images. In some examples, camera 202a generates TLD (Traffic Light Detection) data associated with one or more images that include a format (e.g., RAW, JPEG, PNG, and / or the like). In some embodiments, camera 202a that generates TLD data differs from other systems described herein incorporating cameras in that camera 202a can include one or more cameras with a wide field of view (e.g., a wide-angle lens, a fish-eye lens, a lens having a viewing angle of approximately 120 degrees or more, and / or the like) to generate images about as many physical objects as possible.

[0042] Light Detection and Ranging (LiDAR) sensors 202b include at least one device configured to be in communication with communication device 202e, autonomous vehiclecompute 202f, and / or safety controller 202g via a bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3). LiDAR sensors 202b include a system configured to transmit light from a light emitter (e.g., a laser transmitter). Light emitted by LiDAR sensors 202b include light (e.g., infrared light and / or the like) that is outside of the visible spectrum. In some embodiments, during operation, light emitted by LiDAR sensors 202b encounters a physical object (e.g., a vehicle) and is reflected back to LiDAR sensors 202b. In some embodiments, the light emitted by LiDAR sensors 202b does not penetrate the physical objects that the light encounters. LiDAR sensors 202b also include at least one light detector which detects the light that was emitted from the light emitter after the light encounters a physical object. In some embodiments, at least one data processing system associated with LiDAR sensors 202b generates an image (e.g., a point cloud, a combined point cloud, and / or the like) representing the objects included in a field of view of LiDAR sensors 202b. In some examples, the at least one data processing system associated with LiDAR sensor 202b generates an image that represents the boundaries of a physical object, the surfaces (e.g., the topology of the surfaces) of the physical object, and / or the like. In such an example, the image is used to determine the boundaries of physical objects in the field of view of LiDAR sensors 202b.

[0043] Radio Detection and Ranging (radar) sensors 202c include at least one device configured to be in communication with communication device 202e, autonomous vehicle compute 202f, and / or safety controller 202g via a bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3). Radar sensors 202c include a system configured to transmit radio waves (either pulsed or continuously). The radio waves transmitted by radar sensors 202c include radio waves that are within a predetermined spectrum In some embodiments, during operation, radio waves transmitted by radar sensors 202c encounter a physical object and are reflected back to radar sensors 202c. In some embodiments, the radio waves transmitted by radar sensors 202c are not reflected by some objects. In some embodiments, at least one data processing system associated with radar sensors 202c generates signals representing the objects included in a field of view of radar sensors 202c. For example, the at least one data processing system associated with radar sensor 202c generates an image that represents the boundaries of a physical object, the surfaces (e.g., the topology of the surfaces) of the physical object,and / or the like. In some examples, the image is used to determine the boundaries of physical objects in the field of view of radar sensors 202c.

[0044] Microphones 202d includes at least one device configured to be in communication with communication device 202e, autonomous vehicle compute 202f, and / or safety controller 202g via a bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3). Microphones 202d include one or more microphones (e.g., array microphones, external microphones, and / or the like) that capture audio signals and generate data associated with (e.g., representing) the audio signals. In some examples, microphones 202d include transducer devices and / or like devices. In some embodiments, one or more systems described herein can receive the data generated by microphones 202d and determine a position of an object relative to vehicle 200 (e.g., a distance and / or the like) based on the audio signals associated with the data.

[0045] Communication device 202e includes at least one device configured to be in communication with cameras 202a, LiDAR sensors 202b, radar sensors 202c, microphones 202d, autonomous vehicle compute 202f, safety controller 202g, and / or DBW (Drive-By-Wire) system 202h. For example, communication device 202e may include a device that is the same as or similar to communication interface 314 of FIG. 3. In some embodiments, communication device 202e includes a vehicle-to-vehicle (V2V) communication device (e.g., a device that enables wireless communication of data between vehicles).

[0046] Autonomous vehicle compute 202f include at least one device configured to be in communication with cameras 202a, LiDAR sensors 202b, radar sensors 202c, microphones 202d, communication device 202e, safety controller 202g, and / or DBW system 202h. In some examples, autonomous vehicle compute 202f includes a device such as a client device, a mobile device (e.g., a cellular telephone, a tablet, and / or the like), a server (e.g., a computing device including one or more central processing units, graphical processing units, and / or the like), and / or the like. In some embodiments, autonomous vehicle compute 202f is the same as or similar to autonomous vehicle compute 400, described herein. Additionally, or alternatively, in some embodiments autonomous vehicle compute 202f is configured to be in communication with an autonomous vehicle system (e.g., an autonomous vehicle system that is the same as orsimilar to remote AV system 114 of FIG. 1 ), a fleet management system (e.g., a fleet management system that is the same as or similar to fleet management system 116 of FIG. 1 ), a V2I device (e.g., a V2I device that is the same as or similar to V2I device 110 of FIG. 1 ), and / or a V2I system (e.g., a V2I system that is the same as or similar to V2I system 118 of FIG. 1 ).

[0047] Safety controller 202g includes at least one device configured to be in communication with cameras 202a, LiDAR sensors 202b, radar sensors 202c, microphones 202d, communication device 202e, autonomous vehicle computer 202f, and / or DBW system 202h. In some examples, safety controller 202g includes one or more controllers (electrical controllers, electromechanical controllers, and / or the like) that are configured to generate and / or transmit control signals to operate one or more devices of vehicle 200 (e.g., powertrain control system 204, steering control system 206, brake system 208, and / or the like). In some embodiments, safety controller 202g is configured to generate control signals that take precedence over (e.g., overrides) control signals generated and / or transmitted by autonomous vehicle compute 202f.

[0048] DBW system 202h includes at least one device configured to be in communication with communication device 202e and / or autonomous vehicle compute 202f. In some examples, DBW system 202h includes one or more controllers (e.g., electrical controllers, electromechanical controllers, and / or the like) that are configured to generate and / or transmit control signals to operate one or more devices of vehicle 200 (e.g., powertrain control system 204, steering control system 206, brake system 208, and / or the like). Additionally, or alternatively, the one or more controllers of DBW system 202h are configured to generate and / or transmit control signals to operate at least one different device (e.g., a turn signal, headlights, door locks, windshield wipers, and / or the like) of vehicle 200.

[0049] Powertrain control system 204 includes at least one device configured to be in communication with DBW system 202h. In some examples, powertrain control system 204 includes at least one controller, actuator, and / or the like. In some embodiments, powertrain control system 204 receives control signals from DBW system 202h and powertrain control system 204 causes vehicle 200 to make longitudinal vehicle motion, such as start moving forward, stop moving forward, start moving backward, stop movingbackward, accelerate in a direction, decelerate in a direction or to make lateral vehicle motion such as performing a left turn, performing a right turn, and / or the like. In an example, powertrain control system 204 causes the energy (e.g., fuel, electricity, and / or the like) provided to a motor of the vehicle to increase, remain the same, or decrease, thereby causing at least one wheel of vehicle 200 to rotate or not rotate.

[0050] Steering control system 206 includes at least one device configured to rotate one or more wheels of vehicle 200. In some examples, steering control system 206 includes at least one controller, actuator, and / or the like. In some embodiments, steering control system 206 causes the front two wheels and / or the rear two wheels of vehicle 200 to rotate to the left or right to cause vehicle 200 to turn to the left or right. In other words, steering control system 206 causes activities necessary for the regulation of the y-axis component of vehicle motion.

[0051] Brake system 208 includes at least one device configured to actuate one or more brakes to cause vehicle 200 to reduce speed and / or remain stationary. In some examples, brake system 208 includes at least one controller and / or actuator that is configured to cause one or more calipers associated with one or more wheels of vehicle 200 to close on a corresponding rotor of vehicle 200. Additionally, or alternatively, in some examples brake system 208 includes an automatic emergency braking (AEB) system, a regenerative braking system, and / or the like.

[0052] In some embodiments, vehicle 200 includes at least one platform sensor (not explicitly illustrated) that measures or infers properties of a state or a condition of vehicle 200. In some examples, vehicle 200 includes platform sensors such as a global positioning system (GPS) receiver, an inertial measurement unit (IMU), a wheel speed sensor, a wheel brake pressure sensor, a wheel torque sensor, an engine torque sensor, a steering angle sensor, and / or the like. Although brake system 208 is illustrated to be located in the near side of vehicle 200 in FIG. 2, brake system 208 may be located anywhere in vehicle 200.

[0053] Referring now to FIG. 3, illustrated is a schematic diagram of a device 300. As illustrated, device 300 includes processor 304, memory 306, storage component 308, input interface 310, output interface 312, communication interface 314, and bus 302. In some embodiments, device 300 corresponds to at least one device of vehicles 102 (e.g.,at least one device of a system of vehicles 102), one or more devices of network 112 (e.g., one or more devices of a system of network 112), and / or any other device of the environment 100. In some embodiments, one or more devices of vehicles 102 (e.g., one or more devices of a system of vehicles 102), one or more devices of network 112 (e.g., one or more devices of a system of network 112), and / or any other device of the environment 100 include at least one device 300 and / or at least one component of device 300. As shown in FIG. 3, device 300 includes bus 302, processor 304, memory 306, storage component 308, input interface 310, output interface 312, and communication interface 314.

[0054] Bus 302 includes a component that permits communication among the components of device 300. In some cases, processor 304 includes a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), and / or the like), a microphone, a digital signal processor (DSP), and / or any processing component (e.g., a field-programmable gate array (FPGA), an application specific integrated circuit (ASIC), and / or the like) that can be programmed to perform at least one function. Memory 306 includes random access memory (RAM), readonly memory (ROM), and / or another type of dynamic and / or static storage device (e.g., flash memory, magnetic memory, optical memory, and / or the like) that stores data and / or instructions for use by processor 304.

[0055] 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-optic disk, a solid state disk, and / or the like), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, a CD-ROM, RAM, PROM, EPROM, FLASH-EPROM, NV-RAM, and / or another type of computer readable medium, along with a corresponding drive.

[0056] Input interface 310 includes a component that permits device 300 to receive information, such as via user input (e.g., a touchscreen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, a camera, and / or the like). Additionally or alternatively, in some embodiments input interface 310 includes a sensor that senses information (e.g., a global positioning system (GPS) receiver, an accelerometer, a gyroscope, an actuator, and / or the like). Output interface 312 includes a component thatprovides output information from device 300 (e.g., a display, a speaker, one or more lightemitting diodes (LEDs), and / or the like).

[0057] In some embodiments, communication interface 314 includes a transceiver-like component (e.g., a transceiver, a separate receiver and transmitter, and / or the like) that permits device 300 to communicate with other devices via a wired connection, a wireless connection, or a combination of wired and wireless connections. 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, a Wi-Fi® interface, a cellular network interface, and / or the like.

[0058] In some embodiments, device 300 performs one or more processes described herein. Device 300 performs these processes based on processor 304 executing software instructions stored by a computer-readable medium, such as memory 305 and / or storage component 308. A computer-readable medium (e.g., a non-transitory computer readable medium) is defined herein as a non-transitory memory device. A non-transitory memory device includes memory space located inside a single physical storage device or memory space spread across multiple physical storage devices.

[0059] In some embodiments, software instructions are read into memory 306 and / or storage component 308 from another computer-readable medium or from another device via communication interface 314. When executed, software instructions stored in memory 306 and / or storage component 308 cause processor 304 to perform one or more processes described herein. Additionally or alternatively, hardwired circuitry is used in place of or in combination with software instructions to perform one or more processes described herein. Thus, embodiments described herein are not limited to any specific combination of hardware circuitry and software unless explicitly stated otherwise.

[0060] Memory 306 and / or storage component 308 includes data storage or at least one data structure (e.g., a database and / or the like). Device 300 is capable of receiving information from, storing information in, communicating information to, or searching information stored in the data storage or the at least one data structure in memory 306 orstorage component 308. In some examples, the information includes network data, input data, output data, or any combination thereof.

[0061] In some embodiments, device 300 is configured to execute software instructions that are either stored in memory 306 and / or in the memory of another device (e.g., another device that is the same as or similar to device 300). As used herein, the term “module” refers to at least one instruction stored in memory 306 and / or in the memory of another device that, when executed by processor 304 and / or by a processor of another device (e.g., another device that is the same as or similar to device 300) cause device 300 (e.g., at least one component of device 300) to perform one or more processes described herein. In some embodiments, a module is implemented in software, firmware, hardware, and / or the like.

[0062] The number and arrangement of components illustrated in FIG. 3 are provided as an example. In some embodiments, device 300 can include additional components, fewer components, different components, or differently arranged components than those illustrated in FIG. 3. Additionally or alternatively, a set of components (e.g., one or more components) of device 300 can perform one or more functions described as being performed by another component or another set of components of device 300.

[0063] Referring now to FIG. 4, illustrated is an example block diagram of an autonomous vehicle compute 400 (sometimes referred to as an “AV stack”). As illustrated, autonomous vehicle compute 400 includes perception system 402 (sometimes referred to as a perception module), planning system 404 (sometimes referred to as a planning module), localization system 406 (sometimes referred to as a localization module), control system 408 (sometimes referred to as a control module), and database 410. In some embodiments, perception system 402, planning system 404, localization system 406, control system 408, and database 410 are included and / or implemented in an autonomous navigation system of a vehicle (e.g., autonomous vehicle compute 202f of vehicle 200). Additionally, or alternatively, in some embodiments perception system 402, planning system 404, localization system 406, control system 408, and database 410 are included in one or more standalone systems (e.g., one or more systems that are the same as or similar to autonomous vehicle compute 400 and / or the like). In some examples, perception system 402, planning system 404, localization system 406, control system408, and database 410 are included in one or more standalone systems that are located in a vehicle and / or at least one remote system as described herein. In some embodiments, any and / or all of the systems included in autonomous vehicle compute 400 are implemented in software (e.g., in software instructions stored in memory), computer hardware (e.g., by microprocessors, microcontrollers, application-specific integrated circuits (ASICs), Field Programmable Gate Arrays (FPGAs), and / or the like), or combinations of computer software and computer hardware. It will also be understood that, in some embodiments, autonomous vehicle compute 400 is configured to be in communication with a remote system (e.g., an autonomous vehicle system that is the same as or similar to remote AV system 114, a fleet management system 116 that is the same as or similar to fleet management system 116, a V2I system that is the same as or similar to V2I system 118, and / or the like).

[0064] In some embodiments, perception system 402 receives data associated with at least one physical object (e.g., data that is used by perception system 402 to detect the at least one physical object) in an environment and classifies the at least one physical object. In some examples, perception system 402 receives image data captured by at least one camera (e.g., cameras 202a), the image associated with (e.g., representing) one or more physical objects within a field of view of the at least one camera. In such an example, perception system 402 classifies at least one physical object based on one or more groupings of physical objects (e.g., bicycles, vehicles, traffic signs, pedestrians, and / or the like). In some embodiments, perception system 402 transmits data associated with the classification of the physical objects to planning system 404 based on perception system 402 classifying the physical objects.

[0065] In some embodiments, planning system 404 receives data associated with a destination and generates data associated with at least one route (e.g., routes 106) along which a vehicle (e.g., vehicles 102) can travel along toward a destination. In some embodiments, planning system 404 periodically or continuously receives data from perception system 402 (e.g., data associated with the classification of physical objects, described above) and planning system 404 updates the at least one trajectory or generates at least one different trajectory based on the data generated by perception system 402. In other words, planning system 404 may perform tactical function-relatedtasks that are required to operate vehicle 102 in on-road traffic. Tactical efforts involve maneuvering the vehicle in traffic during a trip, including but not limited to deciding whether and when to overtake another vehicle, change lanes, or selecting an appropriate speed, acceleration, deacceleration, etc. In some embodiments, planning system 404 receives data associated with an updated position of a vehicle (e.g., vehicles 102) from localization system 406 and planning system 404 updates the at least one trajectory or generates at least one different trajectory based on the data generated by localization system 406.

[0066] In some embodiments, localization system 406 receives data associated with (e.g., representing) a location of a vehicle (e.g., vehicles 102) in an area. In some examples, localization system 406 receives LiDAR data associated with at least one point cloud generated by at least one LiDAR sensor (e.g., LiDAR sensors 202b). In certain examples, localization system 406 receives data associated with at least one point cloud from multiple LiDAR sensors and localization system 406 generates a combined point cloud based on each of the point clouds. In these examples, localization system 406 compares the at least one point cloud or the combined point cloud to two-dimensional (2D) and / or a three-dimensional (3D) map of the area stored in database 410. Localization system 406 then determines the position of the vehicle in the area based on localization system 406 comparing the at least one point cloud or the combined point cloud to the map. In some embodiments, the map includes a combined point cloud of the area generated prior to navigation of the vehicle. In some embodiments, maps include, without limitation, high- precision maps of the roadway geometric properties, maps describing road network connectivity properties, maps describing roadway physical properties (such as traffic speed, traffic volume, the number of vehicular and cyclist traffic lanes, lane width, lane traffic directions, or lane marker types and locations, or combinations thereof), and maps describing the spatial locations of road features such as crosswalks, traffic signs or other travel signals of various types. In some embodiments, the map is generated in real-time based on the data received by the perception system.

[0067] In another example, localization system 406 receives Global Navigation Satellite System (GNSS) data generated by a global positioning system (GPS) receiver. In some examples, localization system 406 receives GNSS data associated with the location ofthe vehicle in the area and localization system 406 determines a latitude and longitude of the vehicle in the area. In such an example, localization system 406 determines the position of the vehicle in the area based on the latitude and longitude of the vehicle. In some embodiments, localization system 406 generates data associated with the position of the vehicle. In some examples, localization system 406 generates data associated with the position of the vehicle based on localization system 406 determining the position of the vehicle. In such an example, the data associated with the position of the vehicle includes data associated with one or more semantic properties corresponding to the position of the vehicle.

[0068] In some embodiments, control system 408 receives data associated with at least one trajectory from planning system 404 and control system 408 controls operation of the vehicle. In some examples, control system 408 receives data associated with at least one trajectory from planning system 404 and control system 408 controls operation of the vehicle by generating and transmitting control signals to cause a powertrain control system (e.g., DBW system 202h, powertrain control system 204, and / or the like), a steering control system (e.g., steering control system 206), and / or a brake system (e.g., brake system 208) to operate. For example, control system 408 is configured to perform operational functions such as a lateral vehicle motion control or a longitudinal vehicle motion control. The lateral vehicle motion control causes activities necessary for the regulation of the y-axis component of vehicle motion. The longitudinal vehicle motion control causes activities necessary for the regulation of the x-axis component of vehicle motion. In an example, where a trajectory includes a left turn, control system 408 transmits a control signal to cause steering control system 206 to adjust a steering angle of vehicle 200, thereby causing vehicle 200 to turn left. Additionally, or alternatively, control system 408 generates and transmits control signals to cause other devices (e.g., headlights, turn signal, door locks, windshield wipers, and / or the like) of vehicle 200 to change states.

[0069] In some embodiments, perception system 402, planning system 404, localization system 406, and / or control system 408 implement at least one machine learning model (e.g., at least one multilayer perceptron (MLP), at least one convolutional neural network (CNN), at least one recurrent neural network (RNN), at least one autoencoder, at leastone transformer, and / or the like). In some examples, perception system 402, planning system 404, localization 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-noted systems. In some examples, perception system 402, planning system 404, localization 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 and / or the like). An example of an implementation of a machine learning model is included below with respect to FIGS. 6A-6C.

[0070] Database 410 stores data that is transmitted to, received from, and / or updated by perception system 402, planning system 404, localization system 406 and / or control system 408. In some examples, database 410 includes a storage component (e.g., a storage component that is the same as or similar to storage component 308 of FIG. 3) that stores data and / or software related to the operation and uses at least one system of autonomous vehicle compute 400. In some embodiments, database 410 stores data associated with 2D and / or 3D maps of at least one area. In some examples, database 410 stores data associated with 2D and / or 3D maps of a portion of a city, multiple portions of multiple cities, multiple cities, a county, a state, a State (e.g., a country), and / or the like). In such an example, a vehicle (e.g., a vehicle that is the same as or similar to vehicles 102 and / or vehicle 200) can drive along one or more drivable regions (e.g., single-lane roads, multi-lane roads, highways, back roads, off road trails, and / or the like) and cause at least one LiDAR sensor (e.g., a LiDAR sensor that is the same as or similar to LiDAR sensors 202b) to generate data associated with an image representing the objects included in a field of view of the at least one LiDAR sensor.

[0071] In some embodiments, database 410 can be implemented across a plurality of devices. In some examples, database 410 is included in a vehicle (e.g., a vehicle that is the same as or similar to vehicles 102 and / or vehicle 200), an autonomous vehicle system (e.g., an autonomous vehicle system that is the same as or similar to remote AV system 114, a fleet management system (e.g., a fleet management system that is the same as or similar to fleet management system 116 of FIG. 1 , a V2I system (e.g., a V2I system that is the same as or similar to V2I system 118 of FIG. 1 ) and / or the like.Example Systems and Techniques for Autonomous Navigation of Road Intersections

[0072] In general, the systems and techniques described herein enable vehicles to autonomously navigate a road intersection. In some implementations, the systems and techniques described herein are embodied in and / or performed using the systems described herein (e.g., the vehicles 102a-102n and / or 200 described with reference to FIGS. 1 and 2). In some implementations, the systems and techniques described herein are embodied in and / or performed, at least in part, using the autonomous vehicle compute 400 (e.g., as described with reference to FIG. 4).

[0073] In an example implementation, an autonomous vehicle approaches a road intersection, and detects other vehicles in and / or around the intersection. Based on the detection, the autonomous vehicle determines “tracks” representing predicted trajectories of the other vehicles through the intersection. Further, the autonomous vehicle identifies a subset of the tracks that could result in a collision between the autonomous vehicle and another vehicle. Further still, the autonomous vehicle determines a precedence of travel by the autonomous vehicle and the other vehicles based on the identified tracks, and proceeds through the intersection according to the determined precedence. As an example, the autonomous vehicle can yield to one or more vehicles that have precedence over the autonomous vehicle. As another example, the autonomous vehicle can proceed through the intersection prior to one or more other vehicles over which the autonomous vehicle has precedence.

[0074] In some implementations, the autonomous vehicle also determines a stop station for temporarily stopping the autonomous vehicle. As an example, a stop station can refer to a location and / or area that is between the autonomous vehicle and the road intersection, at which the autonomous vehicle can safely stop for a period of time, prior to proceeding through the intersection.

[0075] Further, in some implementations, the autonomous vehicle performs at least some of these operations without the aid of manually identified stop stations and / or precedence areas (e.g., stop stations and / or precedence areas that have been manually annotated on a map by a user).

[0076] The embodiments described herein can provide various technical benefits.

[0077] At least in some embodiments, these systems and techniques enable an autonomous vehicle to navigate an intersection in a safe manner. For example, thesesystems and techniques minimize or otherwise reduce the likelihood that the autonomous vehicle collides with other vehicles in the intersection.

[0078] Further, these systems and techniques reduce an autonomous vehicle’s reliance on manually provided information in performing autonomous operations (also referred to as “manual annotations”). For example, these systems and techniques enable autonomous vehicle to automatically determine precedence of travel through an intersection, without requiring that users manually identify precedence areas with respect to the intersection. As an example, precedence areas can refer to areas in the environment that, when occupied by another vehicle, signify that the other vehicle takes precedence over the autonomous vehicle.

[0079] As another example, these systems and techniques enable autonomous vehicle to automatically identify safe locations at which to stop, without requiring that users manually identify stop stations with respect to the intersection.

[0080] Accordingly, the autonomous vehicle can operate more safely in a variety of different environments, while reducing its reliance on pre-determined information regarding those environments (e.g., compared to systems and techniques that rely on manually provided information regarding the environment).

[0081] FIG. 5A shows an example process 500 for autonomously navigating a road intersection. In some implementations, the process 500 is performed, at least in part, by the systems described herein (e.g., the vehicles 102a-102n and / or 200 described with reference to FIGS. 1 and 2, the autonomous vehicle compute 400 described with reference to FIG. 4, etc.).

[0082] In the process 500, a system (e.g., the autonomous vehicle compute 400) performs collision tube checking with respect to the vehicle (block 502).

[0083] In general, a collision tube refers to the region of space that the vehicle currently occupies and / or is predicted to occupy (e.g., within a particular interval of time in the future). In some implementations, a collision tube additionally includes a buffer region around the region of space that the vehicle current occupies and / or is predicted to occupy. In some implementations, a collision tube may be referred to as an “ego collision tube.”

[0084] For example, referring to FIG. 5B a vehicle 200 has a particular dimension 522 in the lateral direction (e.g., extending from the left side of the vehicle to the right side of thevehicle 200). Further, the vehicle 200 is predicted to travel along a trajectory 524 (also referred to as an “ego track”). The collision tube 526 includes (i) the region of space that is occupied by the vehicle 200 and / or is expected to be occupied by the vehicle 200 along the trajectory 524, and (ii) a buffer region that extends the region of space by a particular length 528 (e.g., 1 foot, 2 feet, 3 feet, or any other length) in the lateral direction from each of the right and left sides of the vehicle 200.

[0085] As another example, the vehicle 200 has a particular dimension 530 in the longitudinal direction (e.g., extending from the front to the rear of the vehicle 200). The collision tube 526 includes (i) the region of space that is occupied by the vehicle 200 and / or is expected to be occupied by the vehicle 200 along the trajectory 524, and (ii) a buffer region that extends the region of space by a particular length 532 (e.g., 1 foot, 2 feet, 3 feet, or any other length) in the longitudinal direction from each of the front and rear of the vehicle 200.

[0086] In some implementations, the trajectory 524 of the vehicle is predicted, at least in part, by the autonomous vehicle compute 400 (e.g., the planning system 404). Further, the collision tube 526 is determined based on the predicted trajectory.

[0087] Further, the system detects one or more other vehicles in the environment, and predicts a trajectory of each of those vehicles (also referred to as an “agent track”). As an example, the system can detect one or more other vehicle in or in proximity to a road intersection (e.g., a crossing point between one or more roads, streets, etc.). As another example, the system can predict a trajectory that each of the detected vehicles will traverse through the road intersection.

[0088] In some implementations, the other vehicles are detected, at least in part, by the by the autonomous vehicle compute 400 (e.g., using the perception system 402). In some implementations, the other vehicles are detected based on machine learning, such as using a neural network (e.g., as described with reference to FIGS. 6A-6C).

[0089] In some implementations, the trajectories of the other vehicles are predicted, at least in part, by the by the autonomous vehicle compute 400 (e.g., using the planning system 404). In some implementations, the trajectories of the other vehicles are predicted based on machine learning, such as using a neural network (e.g., as described with reference to FIGS. 6A-6C).

[0090] In some implementations, the system predicts a single trajectory for at least some of the other vehicles (e.g., representing the trajectory that each of those vehicle is most likely to travel). In some implementations, the system predicts multiple trajectories for at least some of the other vehicles (e.g., representing multiple trajectories that each of those vehicles may travel).

[0091] Further, the system determines whether the collision tube 526 of the vehicle 200 collides with any of the predicted trajectories of the other vehicles. As an example, the system determines whether the collision tube 526 overlaps, intersects, or coincides in space with any of the predicted trajectories of the other vehicles.

[0092] Referring back to FIG. 5A, the system identifies each of the trajectories of the other vehicles that collides with the collision tube 526 of the vehicle 200, and performs lane association with respect to each of those trajectories (block 504).

[0093] In general, the system performs lane association for each of the identified trajectories by identifying lanes of travel along the road(s) that correspond to the identified trajectories. For example, the system can identify several lanes of travel along the roads (e.g., leading up to the road intersection, in the road intersection, and / or heading away from the road intersection). Further, for each of the identified trajectories, the system can identify the lane of travel that coincides with the identified trajectories. This is useful, for example, in determining the lanes in which the other vehicles are expected to travel along the road (e.g., representing a “track” of that vehicle). In some implementations, the system identifies lanes of travel along a road based on sensor data (e.g., sensor data obtained by the perception system 402), such as sensor data representing lane markings, reflectors, signs, or other features that are indicative of a lane of travel. In some implementations, the system identifies lanes of travel along a road based on localization data (e.g., localization data obtained by the localization system 406), such as LiDAR data, GNSS data, and / or map data that are indicative of a lane of travel. In some implementations, the system identifies lanes of travel along a road based on machine learning, such as using a neural network (e.g., as described with reference to FIGS. 6A- 6C).

[0094] In some implementations, the system associates a trajectory with a lane of travel by determining that the direction of trajectory is consistent with the traffic direction of thelane of travel. As an example, the system determines that the trajectory represents a vehicle traveling along the road in a particular direction, and associates the trajectory with a lane of travel that conveys traffic in the same direction.

[0095] In some implementations, the system determines a traffic direction of a lane of travel based on sensor data (e.g., sensor data obtained by the perception system 402), such as sensor data representing lane markings, reflectors, signs, vehicles (e.g., a direction of travel of vehicles in that lane) or other features that are indicative of a traffic direction of travel of a lane. In some implementations, the system determines a traffic direction of a lane of travel based on localization data (e.g., localization data obtained by the localization system 406), such as LiDAR data, GNSS data, and / or map data that are indicative of a traffic direction of a lane. In some implementations, the system determines a traffic direction of lane of travel based on machine learning, such as using a neural network (e.g., as described with reference to FIGS. 6A-6C).

[0096] In some implementations, the system determines a traffic direction of a lane of travel based on one or more traffic rules. For example, a traffic rule can specify that for a road with two way traffic, the lane(s) located on the right side of the road convey traffic in one direction, and the lane(s) located on the left side of the road convey traffic in the opposite direction.

[0097] In some implementations, the system associates a trajectory with a lane of travel by determining that the trajectory and the lane of travel coincide in space along at least a portion of the trajectory. As an example, the system determines that the trajectory along a particular portion of the road, and associates the trajectory with a lane of travel that occupies the same portion of the road.

[0098] In some implementations, the system performs lane association by additionally identifying connectors between two or more lanes (also referred to as “lane connectors”). For example, referring to FIG. 5C, the system of the vehicle 200 identifies another vehicle 540 at a road intersection 542. Further, the system identifies several lanes 544a-544d extending towards and / or away from the road intersection 542. Further, the system identifies several lane connectors 546a-546c that connect respective pairs of the lanes 544a-544d along which the vehicle 540 may travel through the road intersection 542.

[0099] In some implementations, the system identifies connectors between two or more lanes by determining a traffic direction of each the lanes, and identifying a connector having a traffic direction that is consistent with the traffic directions of each of the lanes (e.g., such that traffic is conveyed from one lane, along the lane connector, and along the other lane without violating the traffic direction of any of the lanes or connectors).

[0100] In some implementations, the system identifies connectors between two or more lanes by determining a position of each of the lanes relative to the other lanes of the road, and determining that one lane spatially aligns with the other.

[0101] For example, referring to FIG. 5C, the lane 544a has a traffic direction towards the road intersection 542. Further, the lane 544b has a traffic direction away from the road intersection 542. The system determines that, of the lanes on the same road and having the same traffic direction, the lane 544b is the lane nearest to the lane 544a. Based on this lane configuration, the system identifies a lane connector 546a that connects the lane 544a with the lane 544b.

[0102] In some implementations, the system detects other vehicles in the environment of the vehicle 200, and performs lane association for the predicted trajectory (or trajectories) of each of the detected vehicles.

[0103] In some implementations, the system also performs lane association for the predicted trajectory of the vehicle 200 itself.

[0104] In some implementations, the system performs lane association by identifying the lanes and / or connectors that are within a particular distance (e.g. , radius) from the vehicle 200, and determining whether one or more of the identified lanes and / or connectors are associated with predicted trajectory of the vehicle 200. In some implementations, the distance or radius is a tunable value (e.g., selected by an administrator or operator of the vehicle 200).

[0105] In some implementations, the system can identify lanes and / or connectors for the vehicle 200 based on a pose angle or orientation of the vehicle 200. For example, the system can determine that the front of the vehicle is oriented in a particular direction, and identify lanes and / or connectors in that direction (e.g., within a particular distance or radius from the vehicle 200).

[0106] Further, the system can identify lanes and / or connectors for the vehicle 200 based on a velocity of the vehicle 200. For example, the system can determine that, given the current velocity of a vehicle 200, the vehicle can safely traverse only a subset of lanes (e.g., based on the performance limitations of the vehicle, such as braking ability, acceleration ability, turn ability, etc.). For each of the lanes or connectors in this subset, the system can determine whether the lanes and / or connectors are associated with predicted trajectory of the vehicle 200.

[0107] In some implementations, if the system is unable to identify any lanes and / or connectors to associate with the trajectory of the vehicle 200 within a particular distance of radius, the system can extend the distance or radius of the search (e.g., by identifying the lanes and / or connectors that are within a greater distance or radius from the vehicle 200, and determining whether one or more of the identified lanes and / or connectors are associated with predicted trajectory of the vehicle 200).

[0108] Referring back to FIG. 5A, the system determines precedence between each of the associated lanes and / or connectors (block 506).

[0109] As an example, during the lane association process, the system determines that the vehicle 200 is associated with a first set of lane(s) and / or connector(s) in or around a road intersection. Further, during the lane association process, the system determines that another vehicle at the road intersection associated with a second set of lane(s) and / or connector(s) in or around the road intersection. The system determines whether any of the lanes and / or connectors of the first set coincides with any of the lanes and / or connectors of the second set (e.g., intersect, overlap, etc.). If so, the system determines whether traffic traveling on one of the sets of lane(s) and / or connector(s) takes precedence over traffic traveling the other one of the sets of lanes and / or connector(s). Further, the system causes the vehicle 200 to traverse the road intersection based on the determination (e.g., using the control system 408).

[0110] In some implementations, system determines that the lane(s) and / or connector(s) associated with the vehicle 200 take precedence over the lane(s) and / or connector(s) associated with the other vehicle at the road intersection. Based on this determination, the system causes the vehicle 200 to traverse the road intersection prior to the other vehicle.

[0111] In some implementations, system determines that the lane(s) and / or connector(s) associated with the other vehicle at the road intersection take precedence over the lane(s) and / or connector(s) associated with the vehicle 200. Based on this determination, the system causes the vehicle 200 to yield to the other vehicle before traversing the road intersection. For example, the system can cause the vehicle 200 to temporarily stop before the road intersection or in the road intersection, prior to crossing into the lane(s) or connector(s) associated with the other vehicle. After the other vehicle has traversed the intersection, the system causes the vehicle 200 to traverse the road intersection.

[0112] In some implementations, the system determines precedence among lanes and / or connectors based on the traffic directions of the lanes and / or connectors, the positons of the lanes and / or connectors relative to one another, and based on one or more sets of traffic rules.

[0113] For example, a traffic rule may pertain to a situation in which the vehicle 200 and another vehicle are on opposing sides of a road intersection. The traffic rule can specify that, if the vehicle 200 is traversing straight forward through the intersection and the other vehicle is turning left through the intersection (e.g., such that the other vehicle would cut off the vehicle 200), the vehicle 200 takes precedence over the other vehicle. Based on this traffic rule, the system causes the vehicle 200 to traverse the road intersection prior to the other vehicle.

[0114] As another example, a traffic rule may pertain to a situation in which the vehicle 200 and another vehicle are on opposing sides of a road intersection. The traffic rule can specify that, if the vehicle 200 is turning left through the intersection and the other vehicle is traversing straight through the intersection (e.g., such that the vehicle 200 would cut off the other vehicle), the other vehicle takes precedence over the vehicle 200. Based on this determination, the system causes the vehicle 200 to yield to the other vehicle. After the other vehicle has traversed the intersection, the system causes the vehicle 200 to traverse the road intersection.

[0115] Although example traffic rules are described above, these are merely illustrative examples. In practice, other traffic rules can be implemented in connection with this process, either instead of or in addition to those described herein.

[0116] In some implementations, the system identifies one or more stop stations for temporarily stopping the vehicle 200. As an example, a stop station can refer to a location and / or area that is between the autonomous vehicle and the road intersection, at which the vehicle 200 can safely stop for a period of time, prior to proceeding through the road intersection (e.g., to allow other vehicles to traverse the road intersection first). As another example, a stop station can refer to a location and / or area in the road intersection, at which the vehicle 200 can safely stop for a period of time, prior to proceeding through the rest of the road intersection (e.g., to allow other vehicles to finish traversing the road intersection first). In some implementations, a stop station may also be referred to as a “turn stop” (e.g., a location and / or area at which the vehicle 200 can safely stop for a period of time, prior to performing a turn through the road intersection).

[0117] In some implementations, the system identifies one or more stop stations by identifying a first set of lane(s) and / or connector(s) associated with the vehicle 200. Further, the system identifies a second set of lane(s) and / or connector(s) that coincide (e.g., intersect, overlap, etc.) with the first set and take precedence over the first set. The system identifies one or more locations or regions along the first set that are positioned before the intersection between the first set and the second set. The one or more identified locations or regions can be used as stop stations for the vehicle 200 when traversing the road intersection.

[0118] As an example, FIG. 5D shows a first lane connector 562 for the vehicle 200 at a road intersection, and a second lane connector 564 for another vehicle 566 in the road intersection. The system determines that the first lane connector 562 and the second lane connector 564 coincides in a region 506, and that the second lane connector 654 takes precedence over the first lane connector 652. Based on this determination, the system identifies a region 566 prior to the region 506 as a stop station for the vehicle 200.

[0119] In some implementations, the system determines stop stations for a vehicle 200 without requiring manual feedback from a user (e.g., manual feedback from a user specifying stop stations and / or precedence areas with respect to a road intersection). Accordingly, the vehicle 200 can operate more safely in a variety of different environments, while reducing its reliance on pre-determined information regarding those environments. Nevertheless, in some implementations, the system can also operatebased on information that is manually provided by the user (e g., in addition to or instead of the techniques described herein).

[0120] Referring now to FIG. 6A, illustrated is a diagram of an implementation of a machine learning model. More specifically, illustrated is a diagram of an implementation of a convolutional neural network (CNN) 620. For purposes of illustration, the following description of CNN 620 will be with respect to an implementation of CNN 620 by the autonomous vehicle compute 400 (e.g., to perform one or more of the operations described herein). However, it will be understood that in some examples CNN 620 (e.g., one or more components of CNN 620) is implemented by other systems different from, or in addition to, the autonomous vehicle compute 400. While CNN 620 includes certain features as described herein, these features are provided for the purpose of illustration and are not intended to limit the present disclosure.

[0121] CNN 620 includes a plurality of convolution layers including first convolution layer 622, second convolution layer 624, and convolution layer 626. In some embodiments, CNN 620 includes sub-sampling layer 628 (sometimes referred to as a pooling layer). In some embodiments, sub-sampling layer 628 and / or other subsampling layers have a dimension (i.e., an amount of nodes) that is less than a dimension of an upstream system. By virtue of sub-sampling layer 628 having a dimension that is less than a dimension of an upstream layer, CNN 620 consolidates the amount of data associated with the initial input and / or the output of an upstream layer to thereby decrease the amount of computations necessary for CNN 620 to perform downstream convolution operations. Additionally, or alternatively, by virtue of sub-sampling layer 628 being associated with (e.g., configured to perform) at least one subsampling function (as described below with respect to FIGS. 6B and 6C), CNN 620 consolidates the amount of data associated with the initial input.

[0122] The autonomous vehicle compute 400 performs convolution operations based on the autonomous vehicle compute 400 providing respective inputs and / or outputs associated with each of first convolution layer 622, second convolution layer 624, and convolution layer 626 to generate respective outputs. In some examples, the autonomous vehicle compute 400 implements CNN 620 based on the autonomous vehicle compute 400 providing data as input to first convolution layer 622, second convolution layer 624,and convolution layer 626. In such an example, the autonomous vehicle compute 400 provides the data as input to first convolution layer 622, second convolution layer 624, and convolution layer 626 based on the autonomous vehicle compute 400 receiving data from one or more sensors. A detailed description of convolution operations is included below with respect to FIG. 6B.

[0123] In some embodiments, the autonomous vehicle compute 400 provides data associated with an input (referred to as an initial input) to first convolution layer 622 and the autonomous vehicle compute 400 generates data associated with an output using first convolution layer 622. In some embodiments, provides an output generated by a convolution layer as input to a different convolution layer. For example, the autonomous vehicle compute 400 provides the output of first convolution layer 622 as input to subsampling layer 428, second convolution layer 624, and / or convolution layer 626. In such an example, first convolution layer 622 is referred to as an upstream layer and subsampling layer 628, second convolution layer 624, and / or convolution layer 626 are referred to as downstream layers. Similarly, in some embodiments, the autonomous vehicle compute 400 provides the output of sub-sampling layer 628 to second convolution layer 624 and / or convolution layer 626 and, in this example, sub-sampling layer 628 would be referred to as an upstream layer and second convolution layer 624 and / or convolution layer 626 would be referred to as downstream layers.

[0124] In some embodiments, the autonomous vehicle compute 400 processes the data associated with the input provided to CNN 620 before the autonomous vehicle compute 400 provides the input to CNN 620. For example, the autonomous vehicle compute 400 processes the data associated with the input provided to CNN 620 based on the autonomous vehicle compute 400 normalizing sensor data (e.g., image data, LiDAR data, radar data, sensor signals, and / or the like).

[0125] In some embodiments, CNN 620 generates an output based on the autonomous vehicle compute 400 performing convolution operations associated with each convolution layer. In some examples, CNN 620 generates an output based on the autonomous vehicle compute 400 performing convolution operations associated with each convolution layer and an initial input. In some embodiments, the autonomous vehicle compute 400 generates the output and provides the output as fully connected layer 630. In someexamples, the autonomous vehicle compute 400 provides the output of convolution layer 626 as fully connected layer 630, where fully connected layer 630 includes data associated with a plurality of feature values referred to as F1 , F2 . . . FN. In this example, the output of convolution layer 626 includes data associated with a plurality of output feature values that represent a prediction.

[0126] In some embodiments, the autonomous vehicle compute 400 identifies a prediction from among a plurality of predictions based on the autonomous vehicle compute 400 identifying a feature value that is associated with the highest likelihood of being the correct prediction from among the plurality of predictions. For example, where fully connected layer 630 includes feature values F1 , F2, . . . FN, and F1 is the greatest feature value, the autonomous vehicle compute 400 identifies the prediction associated with F1 as being the correct prediction from among the plurality of predictions. In some embodiments, the autonomous vehicle compute 400 trains CNN 620 to generate the prediction. In some examples, the autonomous vehicle compute 400 trains CNN 620 to generate the prediction based on the autonomous vehicle compute 400 providing training data associated with the prediction to CNN 620.

[0127] Referring now to FIGS. 6B and 6C, illustrated is a diagram of example operation of CNN 640 by the autonomous vehicle compute 400. In some embodiments, CNN 640 (e.g., one or more components of CNN 640) is the same as, or similar to, CNN 620 (e.g., one or more components of CNN 620) (see FIG. 6A).

[0128] At step 650, the autonomous vehicle compute 400 provides data associated with sensor data (e.g., images, point clouds, sensor signals, etc.) as input to CNN 640 (step 650). For example, as illustrated, the autonomous vehicle compute 400 provides the data associated with the image to CNN 640, where the image is a greyscale 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, the 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, a radar image, and / or the like.

[0129] At step 655, CNN 640 performs a first convolution function. For example, CNN 640 performs the first convolution function based on CNN 640 providing the valuesrepresenting the image as input to one or more neurons (not explicitly illustrated) included in first convolution layer 642. In this example, the values representing the image can correspond to values representing a region of the image (sometimes referred to as a receptive field). In some embodiments, each neuron is associated with a filter (not explicitly illustrated). A filter (sometimes referred to as a kernel) is representable as an array of values that corresponds in size to the values provided as input to the neuron. In one example, a filter may be configured to identify edges (e.g., horizontal lines, vertical lines, straight lines, and / or the like). In successive convolution layers, the filters associated with neurons may be configured to identify successively more complex patterns (e.g., arcs, objects, and / or the like).

[0130] In some embodiments, CNN 640 performs the first convolution function based on CNN 640 multiplying the values provided as input to each of the one or more neurons included in first convolution layer 642 with the values of the filter that corresponds to each of the one or more neurons. For example, CNN 640 can multiply the values provided as input to each of the one or more neurons included in first convolution layer 642 with the values of the filter that corresponds to each of the one or more neurons to generate a single value or an array of values as an output. In some embodiments, the collective output of the neurons of first convolution layer 642 is referred to as a convolved output. In some embodiments, where each neuron has the same filter, the convolved output is referred to as a feature map.

[0131] In some embodiments, CNN 640 provides the outputs of each neuron of first convolutional layer 642 to neurons of a downstream layer. For purposes of clarity, an upstream layer can be a layer that transmits data to a different layer (referred to as a downstream layer). For example, CNN 640 can provide the outputs of each neuron of first convolutional layer 642 to corresponding neurons of a subsampling layer. In an example, CNN 640 provides the outputs of each neuron of first convolutional layer 642 to corresponding neurons of first subsampling layer 644. In some embodiments, CNN 640 adds a bias value to the aggregates of all the values provided to each neuron of the downstream layer. For example, CNN 640 adds a bias value to the aggregates of all the values provided to each neuron of first subsampling layer 644. In such an example, CNN 640 determines a final value to provide to each neuron of first subsampling layer 644based on the aggregates of all the values provided to each neuron and an activation function associated with each neuron of first subsampling layer 644.

[0132] At step 660, CNN 440 performs a first subsampling function. For example, CNN 640 can perform a first subsampling function based on CNN 640 providing the values output by first convolution layer 642 to corresponding neurons of first subsampling layer 644. In some embodiments, CNN 640 performs the first subsampling function based on an aggregation function. In an example, CNN 640 performs the first subsampling function based on CNN 640 determining the maximum input among the values provided to a given neuron (referred to as a max pooling function). In another example, CNN 640 performs the first subsampling function based on CNN 640 determining the average input among the values provided to a given neuron (referred to as an average pooling function). In some embodiments, CNN 640 generates an output based on CNN 640 providing the values to each neuron of first subsampling layer 644, the output sometimes referred to as a subsampled convolved output.

[0133] At step 665, CNN 640 performs a second convolution function. In some embodiments, CNN 640 performs the second convolution function in a manner similar to how CNN 640 performed the first convolution function, described above. In some embodiments, CNN 640 performs the second convolution function based on CNN 640 providing the values output by first subsampling layer 644 as input to one or more neurons (not explicitly illustrated) included in second convolution layer 646. In some embodiments, each neuron of second convolution layer 646 is associated with a filter, as described above. The filter(s) associated with second convolution layer 646 may be configured to identify more complex patterns than the filter associated with first convolution layer 642, as described above.

[0134] In some embodiments, CNN 640 performs the second convolution function based on CNN 640 multiplying the values provided as input to each of the one or more neurons included in second convolution layer 646 with the values of the filter that corresponds to each of the one or more neurons. For example, CNN 640 can multiply the values provided as input to each of the one or more neurons included in second convolution layer 646 with the values of the filter that corresponds to each of the one or more neurons to generate a single value or an array of values as an output.

[0135] In some embodiments, CNN 640 provides the outputs of each neuron of second convolutional layer 646 to neurons of a downstream layer. For example, CNN 640 can provide the outputs of each neuron of first convolutional layer 642 to corresponding neurons of a subsampling layer. In an example, CNN 640 provides the outputs of each neuron of first convolutional layer 642 to corresponding neurons of second subsampling layer 648. In some embodiments, CNN 640 adds a bias value to the aggregates of all the values provided to each neuron of the downstream layer. For example, CNN 640 adds a bias value to the aggregates of all the values provided to each neuron of second subsampling layer 648. In such an example, CNN 640 determines a final value to provide to each neuron of second subsampling layer 648 based on the aggregates of all the values provided to each neuron and an activation function associated with each neuron of second subsampling layer 648.

[0136] At step 670, CNN 640 performs a second subsampling function. For example, CNN 640 can perform a second subsampling function based on CNN 640 providing the values output by second convolution layer 646 to corresponding neurons of second subsampling layer 648. In some embodiments, CNN 640 performs the second subsampling function based on CNN 640 using an aggregation function. In an example, CNN 640 performs the first subsampling function based on CNN 640 determining the maximum input or an average input among the values provided to a given neuron, as described above. In some embodiments, CNN 440 generates an output based on CNN 640 providing the values to each neuron of second subsampling layer 648.

[0137] At step 675, CNN 640 provides the output of each neuron of second subsampling layer 648 to fully connected layers 649. For example, CNN 640 provides the output of each neuron of second subsampling layer 648 to fully connected layers 649 to cause fully connected layers 649 to generate an output. In some embodiments, fully connected layers 649 are configured to generate an output associated with a prediction (sometimes referred to as a classification). The prediction may include an indication that an object included in the image provided as input to CNN 640 includes an object, a set of objects, and / or the like. In some embodiments, the autonomous vehicle compute 400 performs one or more operations and / or provides the data associated with the prediction to a different system, described herein.

[0138] Referring now to FIG. 7, illustrated is a flowchart of a process 700 for autonomously navigating a road intersection by a vehicle. In some embodiments, one or more of the steps described with respect to process 700 are performed (e.g., completely, partially, and / or the like) by one or more components of an autonomous vehicle, such as one or more processors that implement the autonomous vehicle compute 400 shown in FIG. 4. Additionally, or alternatively, in some embodiments one or more steps described with respect to process 700 are performed (e.g., completely, partially, and / or the like) by another device or group of devices separate from an autonomous vehicle, such as by one or more processors of a remote computer system (e.g., a remote AV system 114, fleet management system 116, V2I system 118, etc.).

[0139] According to the process 700, one or more processors determine a candidate trajectory for an autonomous vehicle to traverse a road intersection (block 702).

[0140] The one or more processors identify one or more additional vehicles in an environment of the autonomous vehicle (block 704).

[0141] The one or more processors determine one or more predicted trajectories of the one or more additional vehicles through the road intersection (block 706).

[0142] The one or more processors select at least some of the predicted trajectories based on a proximity between the candidate trajectory and each of the one or more prediction trajectories (block 708).

[0143] In some implementations, selecting at least some of the predicted trajectories can include determining a region corresponding to the candidate trajectory, identifying each of the one or more predicted trajectories that are within a threshold distance from the region, and selecting each of the one or more predicted trajectories that are within the threshold distance from the region. Further, determining the region can include determining a lateral dimensional of the region, where the lateral dimension of the region is greater than a lateral dimension of the autonomous vehicle.

[0144] In some implementations, selecting at least some of the predicted trajectories can include determining an orientation of the autonomous vehicle, and selecting at least some of the predicted trajectories based on the orientation of the autonomous vehicle.

[0145] The one or more processors determine a precedence order based on the candidate trajectory and the selected trajectories (block 710).

[0146] In some implementations, determining the precedence order can include, for each of the selected trajectories, determining a road lane for that selected trajectory and determining the precedence order based on the road lane for that selected trajectory.

[0147] In some implementations, for each of the selected trajectories, the road lane for that selected trajectory can be determined based on at least one of: an orientation of the additional vehicle corresponding to that selected trajectory, or a direction of travel of that additional vehicle.

[0148] In some implementations, determining the precedence order can include, for each of the selected trajectories, determining a traffic direction of the road lane for that selected trajectory and determining the precedence order based on the traffic direction of the road lane for that selected trajectory.

[0149] In some implementations, determining the precedence order can include determining the precedence order based on one or more traffic rules.

[0150] The one or more processors cause the autonomous vehicle to traverse through the road intersection based on the candidate trajectory and the precedence order (block 712).

[0151] In some implementations, causing the autonomous vehicle to traverse through the road intersection can include actuating one or more physical controls of the autonomous vehicle.

[0152] In some implementations, causing the autonomous vehicle to traverse through the road intersection can include determining a stop station for the autonomous vehicle based on the precedence order, and causing the autonomous vehicle to operate based on determining the stop station. The stop stations represents a location for the autonomous vehicle to stop prior to entering the road intersection.

[0153] In some implementations, the stop station can be determined, at least in part, by determining an intersection between the candidate trajectory and a subset of the selected trajectories, where the subset of the selected trajectories take precedence over the candidate trajectory. Further, a determination is made that the stop station is or otherwise corresponds to a region along the candidate trajectory prior to the intersection between the candidate trajectory and the subset of the selected trajectories.

[0154] In some implementations, the process 700 can also include updating at least some of the one or more predicted trajectories of the one or more additional vehicles. Further, the process 700 can include removing at least some of the one or more predicted trajectories of the one or more additional vehicles based on updating the at least some of the one or more predicted trajectories of the one or more additional vehicles.

[0155] In the foregoing description, aspects and embodiments of the present disclosure have been described with reference to numerous specific details that can vary from implementation to implementation. Accordingly, the description and drawings are to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the invention, and what is intended by the applicants to be the scope of the invention, is the literal and equivalent scope of the set of claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction. Any definitions expressly set forth herein for terms contained in such claims shall govern the meaning of such terms as used in the claims. In addition, when we use the term “further comprising,” in the foregoing description or following claims, what follows this phrase can be an additional step or entity, or a sub-step / sub-entity of a previously- recited step or entity.

Claims

WHAT IS CLAIMED IS:

1. A computer-implemented method, comprising: determining, using at least one processor, a candidate trajectory for an autonomous vehicle to traverse a road intersection; identifying, using the at least one processor, one or more additional vehicles in an environment of the autonomous vehicle; determining, using the at least one processor, one or more predicted trajectories of the one or more additional vehicles through the road intersection; selecting, using the at least one processor, at least some of the predicted trajectories based on a proximity between the candidate trajectory and each of the one or more prediction trajectories; determining, using the at least one processor, a precedence order based on the candidate trajectory and the selected trajectories; and causing, using the at least one processor, the autonomous vehicle to traverse through the road intersection based on the candidate trajectory and the precedence order.

2. The method of claim 1 , wherein causing the autonomous vehicle to traverse through the road intersection comprises actuating one or more physical controls of the autonomous vehicle.

3. The method of claim 1 , wherein causing the autonomous vehicle to traverse through the road intersection comprises: determining a stop station for the autonomous vehicle based on the precedence order, and causing the autonomous vehicle to operate based on determining the stop station, wherein the stop station represents a location for the autonomous vehicle to stop prior to entering the road intersection.

4. The method of claim 3, wherein determining the stop station comprises:determining an intersection between the candidate trajectory and a subset of the selected trajectories, wherein the subset of the selected trajectories take precedence over the candidate trajectory, and determining, as the stop station, a region along the candidate trajectory prior to the intersection between the candidate trajectory and the subset of the selected trajectories.

5. The method of claim 1 , wherein selecting at least some of the predicted trajectories comprises: determining a region corresponding to the candidate trajectory, identifying each of the one or more predicted trajectories that are within a threshold distance from the region, and selecting each of the one or more predicted trajectories that are within the threshold distance from the region.

6. The method of claim 5, wherein determining the region comprises determining a lateral dimensional of the region, wherein the lateral dimension of the region is greater than a lateral dimension of the autonomous vehicle.

7. The method of claim 1 , wherein selecting at least some of the predicted trajectories comprises: determining an orientation of the autonomous vehicle, and selecting at least some of the predicted trajectories based on the orientation of the autonomous vehicle.

8. The method of claim 1 , wherein determining the precedence order comprises: for each of the selected trajectories, determining a road lane for that selected trajectory and determining the precedence order based on the road lane for that selected trajectory.

9. The method of claim 8, wherein, for each of the selected trajectories, the road lane for that selected trajectory is determined based on at least one of:an orientation of the additional vehicle corresponding to that selected trajectory, or a direction of travel of that additional vehicle.

10. The method of claim 8, wherein determining the precedence order comprises: for each of the selected trajectories, determining a traffic direction of the road lane for that selected trajectory and determining the precedence order based on the traffic direction of the road lane for that selected trajectory.

11. The method of claim 1 , wherein determining the precedence order comprises determining the precedence order based on one or more traffic rules.

12. The method of claim 1 , further comprising: updating at least some of the one or more predicted trajectories of the one or more additional vehicles, and removing at least some of the one or more predicted trajectories of the one or more additional vehicles based on updating the at least some of the one or more predicted trajectories of the one or more additional vehicles.

13. A system comprising: at least one processor; and at least one non-transitory storage media storing instructions that, when executed by the at least one processor, cause the at least one processor perform the method of any of claims 1 to 12.

14. At least one non-transitory storage media storing instructions that, when executed by at least one processor, cause the at least one processor perform the method of any of claims 1 to 12.

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