Opportunistic lane change commitment and abortion

Abstract

Provided are methods for opportunistic lane changes and abortions, which can include determining a location and speed of a plurality of moving vehicles (502) in a lane adjacent to the autonomous vehicle (200), identifying a plurality of gaps (506) corresponding to the plurality of moving vehicles, determining a size of the plurality of gaps and a position of the plurality of gaps, determining a cost to navigate to each of the plurality of gaps, selecting a gap from the plurality of gaps based on the determined cost, and causing the autonomous vehicle (200) to navigate to the selected gap. System and computer program products are also provided.

Description

Attorney Docket No. MOTN.160WO / PI2023209OPPORTUNISTIC LANE CHANGE COMMITMENT AND ABORTIONINCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application No.63 / 775,631, filed on March 21, 2025 and titled “OPPORTUNISTIC LANE CHANGE COMMITMENT AND ABORTION,” which is hereby incorporated herein by reference in its entirety.BACKGROUNDBRIEF DESCRIPTION OF THE FIGURES

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

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

[0004] FIG. 3 is a diagram of components of one or more devices and / or one or more systems of FIGS. 1 and 2;

[0005] FIG. 4A is a diagram of certain components of an autonomous system;

[0006] FIG. 4B is a diagram of an implementation of a neural network;

[0007] FIG. 4C and 4D are a diagram illustrating example operation of a CNN;

[0008] FIGS. 5A-5B are diagrams of an example opportunistic lane change;

[0009] FIG. 6 is a diagram of an example opportunistic lane change; and

[0010] FIG. 7 is a flowchart of a process for opportunistic lane change and abortion.DETAILED DESCRIPTION

[0011] 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.Attorney Docket No. MOTN.160WO / PI2023209

[0012] 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 required 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.

[0013] 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.

[0014] 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.

[0015] 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 andAttorney Docket No. MOTN.160WO / PI2023209encompasses 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.

[0016] 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 first unit 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.

[0017] 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.

[0018] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specificAttorney Docket No. MOTN.160WO / PI2023209details 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.General Overview

[0019] In some aspects and / or embodiments, systems, methods, and computer program products described herein include and / or implement the ability to identify multiple gaps in the adjacent lane (such as, gaps in front of, between, and behind multiple cars). The autonomous vehicle (AV) can determine a cost (e.g., an expected value) of navigating to each of the identified gaps. The AV can then select and navigate to a gap based on its determined cost.

[0020] By virtue of the implementation of systems, methods, and computer program products described herein, techniques for opportunistic lane change commitment and abortion are included. Advantages of these techniques include the AV’s ability to perceive available gaps in the adjacent lane leading to a reduction in lane change abortions.

[0021] 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. In some 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.

[0022] 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 someAttorney Docket No. MOTN.160WO / PI2023209embodiments, vehicles 102 include cars, buses, trucks, trains, and / or the like. Tn some embodiments, vehicles 102 are the same as, or similar to, vehicles 200, described herein (see FIG.2). hi 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).

[0023] 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.

[0024] 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 locations at 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 stateAttorney Docket No. MOTN.160WO / PI2023209sequences cumulatively correspond to a plurality of trajectories that collectively form the high level route to terminate at the final goal state or region.

[0025] 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.

[0026] Vehicle-to-Infrastructure (V2I) device 110 (sometimes referred to as a Vehicle-to-Infrastructure or Vehicle-to-Everything (V2X) device) includes at least one device configured to 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 frequency identification (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.

[0027] 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 adAttorney Docket No. MOTN.160WO / PI2023209hoc network, an intranet, the Internet, a fiber optic-based network, a cloud computing network, etc., a combination of some or all of these networks, and / or the like.

[0028] 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 colocated 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.

[0029] Fleet management system 116 includes at least one device configured to be in communication with vehicles 102, V2I device 110, remote AV system 114, and / or V2I infrastructure system 118. 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 organization that controls operation of multiple vehicles (e.g., vehicles that include autonomous systems and / or vehicles that do not include autonomous systems) and / or the like).

[0030] 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).

[0031] 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 oneAttorney Docket No. MOTN.160WO / PI2023209set 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.

[0032] Referring now to FIG. 2, vehicle 200 (which may be the same as, or similar to vehicle 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.

[0033] 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, describedAttorney Docket No. MOTN.160WO / PI2023209herein. 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.

[0034] 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 camera data 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.

[0035] 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 202aAttorney Docket No. MOTN.160WO / PI2023209generates 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.

[0036] Light Detection and Ranging (LiDAR) sensors 202b include at least one device configured to be in communication with communication device 202e, autonomous vehicle compute 202f, and / or safety controller 202g via a bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3). LiDAR sensors 202b include a system configured to transmit light from a light emitter (e.g., a laser transmitter). Light emitted by LiDAR sensors 202b include light (e.g., infrared light and / or the like) that is outside of the visible spectrum. 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.

[0037] 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 notAttorney Docket No. MOTN.160WO / PI2023209reflected by some objects. Tn 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.

[0038] 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.

[0039] 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).

[0040] 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 someAttorney Docket No. MOTN.160WO / PI2023209embodiments autonomous vehicle compute 202f is configured to be in communication with an autonomous vehicle system (e.g., an autonomous vehicle system that is the same as or similar to remote AV system 114 of FIG. 1). a fleet management system (e.g., a fleet management system that is the same as or similar to fleet management system 116 of FIG. 1), a V2I device (e.g., a V2I device that is the same as or similar to V2I device 110 of FIG. 1), and / or a V2I system (e.g., a V2I system that is the same as or similar to V2I system 118 of FIG. 1).

[0041] 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.

[0042] 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.

[0043] 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 moving backward, accelerate in a direction, decelerate in a direction or to make lateral vehicle motion such as performing a left turn, performing a right turn,Attorney Docket No. MOTN.160WO / PI2023209and / or the like. Tn 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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), at least one device of remote AV system 114, fleet management system 116, V2I system 118, and / or one or more devices of network 112 (e.g., one or more devices of a system of network 112). In some embodiments, one or more devices of vehicles 102 (e.g., one or more devices of a system of vehicles 102 such as at least one device of remote AV system 114, fleet management system 116, and V2I system 118, and / or one or moreAttorney Docket No. MOTN.160WO / PI2023209devices of network 112 (e.g., one or more devices of a system of network 112) include at least one device 300 and / or at least one component of device 300. As 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.

[0048] 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), read-only memory (ROM), and / or another type of dynamic and / or static storage device (e.g., flash memory, magnetic memory, optical memory, and / or the like) that stores data and / or instructions for use by processor 304.

[0049] 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, FEASH-EPROM, NV-RAM, and / or another type of computer readable medium, along with a corresponding drive.

[0050] Input interface 310 includes a component that permits device 300 to receive information, such as via user input (e.g., a touchscreen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, a camera, and / or the like). Additionally or alternatively, in some embodiments input interface 310 includes a sensor that senses information (e.g., a global positioning system (GPS) receiver, an accelerometer, a gyroscope, an actuator, and / or the like). Output interface 312 includes a component that provides output information from device 300 (e.g., a display, a speaker, one or more light-emitting diodes (LEDs), and / or the like).

[0051] 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 toAttorney Docket No. MOTN.160WO / PI2023209another device. Tn 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.

[0052] 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.

[0053] 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.

[0054] Memory 306 and / or storage component 308 includes data storage or at least one data structure (e.g., a database and / or the like). Device 300 is capable of receiving information from, storing information in, communicating information to, or searching information stored in the data storage or the at least one data structure in memory 306 or storage component 308. In some examples, the information includes network data, input data, output data, or any combination thereof.

[0055] 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 ofAttorney Docket No. MOTN.160WO / PI2023209device 300) to perform one or more processes described herein. Tn some embodiments, a module is implemented in software, firmware, hardware, and / or the like.

[0056] 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.

[0057] 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 system 408, and database 410 are included in one or more standalone systems that are located in a vehicle and / or at least one remote system as described herein. 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 sameAttorney Docket No. MOTN.160WO / PI2023209as or similar to fleet management system 116, a V2T system that is the same as or similar to V2I system 118, and / or the like).

[0058] 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. In some cases, the perception system 402 may determine the position and orientation of objects in an environment relative to the vehicle 200. In certain cases, the perception system 402 may determine the velocity of the objects in the environment relative to the vehicle 200. For example, using the data associated with the physical objects, which may include image data, LiDAR data, radar data, etc., the perception system 402 may classify the object, identify its location relative to the vehicle 200, and determine the velocity / acceleration of the object.

[0059] 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-related tasks 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 406Attorney Docket No. MOTN.160WO / PI2023209and 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.

[0060] 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.

[0061] 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 of the vehicle in the area and localization system 406 determines a latitude and longitude of the vehicle in the area. In such an example, localization system 406 determines the position of the vehicle in the area based on the latitude and longitude of the vehicle. 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 positionAttorney Docket No. MOTN.160WO / PI2023209of the vehicle includes data associated with one or more semantic properties corresponding to the position of the vehicle.

[0062] 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.

[0063] 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 least one 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. 4B-4D.

[0064] 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 systemAttorney Docket No. MOTN.160WO / PI2023209408. Tn 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.

[0065] 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.

[0066] Referring now to FIG. 4B, 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) 420. For purposes of illustration, the following description of CNN 420 will be with respect to an implementation of CNN 420 by perception system 402. However, it will be understood that in some examples CNN 420 (e.g., one or more components of CNN 420) is implemented by other systems different from, or in addition to. perception system 402 such as planning system 404, localization system 406, and / or control system 408. While CNN 420 includes certain features as described herein, these features are provided for the purpose of illustration and are not intended to limit the present disclosure.

[0067] CNN 420 includes a plurality of convolution layers including first convolution layer 422, second convolution layer 424, and convolution layer 426. In some embodiments. CNN 420 includes sub-sampling layer 428 (sometimes referred to as a pooling layer). In someAttorney Docket No. MOTN.160WO / PI2023209embodiments, sub-sampling layer 428 and / or other subsampling layers have a dimension (i.e., an amount of nodes) that is less than a dimension of an upstream system. By virtue of sub-sampling layer 428 having a dimension that is less than a dimension of an upstream layer, CNN 420 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 420 to perform downstream convolution operations. Additionally, or alternatively, by virtue of sub-sampling layer 428 being associated with (e.g., configured to perform) at least one subsampling function (as described below with respect to FIGS. 4C and 4D), CNN 420 consolidates the amount of data associated with the initial input.

[0068] Perception system 402 performs convolution operations based on perception system 402 providing respective inputs and / or outputs associated with each of first convolution layer 422, second convolution layer 424, and convolution layer 426 to generate respective outputs. In some examples, perception system 402 implements CNN 420 based on perception system 402 providing data as input to first convolution layer 422, second convolution layer 424, and convolution layer 426. In such an example, perception system 402 provides the data as input to first convolution layer 422, second convolution layer 424, and convolution layer 426 based on perception system 402 receiving data from one or more different systems (e.g., one or more systems of a vehicle that is the same as or similar to vehicle 102), a remote AV system that is the same as or similar to remote AV system 114, a fleet management system that is the same as or similar to fleet management system 116, a V2I system that is the same as or similar to V2I system 118, and / or the like). A detailed description of convolution operations is included below with respect to FIG. 4C.

[0069] In some embodiments, perception system 402 provides data associated with an input (referred to as an initial input) to first convolution layer 422 and perception system 402 generates data associated with an output using first convolution layer 422. In some embodiments, perception system 402 provides an output generated by a convolution layer as input to a different convolution layer. For example, perception system 402 provides the output of first convolution layer 422 as input to sub-sampling layer 428, second convolution layer 424, and / or convolution layer 426. In such an example, first convolution layer 422 is referred to as an upstream layer and sub- sampling layer 428. second convolution layer 424, and / or convolution layer 426 are referred to as downstream layers. Similarly, in some embodiments perception system 402 provides theAttorney Docket No. MOTN.160WO / PI2023209output of sub-sampling layer 428 to second convolution layer 424 and / or convolution layer 426 and, in this example, sub-sampling layer 428 would be referred to as an upstream layer and second convolution layer 424 and / or convolution layer 426 would be referred to as downstream layers.

[0070] In some embodiments, perception system 402 processes the data associated with the input provided to CNN 420 before perception system 402 provides the input to CNN 420. For example, perception system 402 processes the data associated with the input provided to CNN 420 based on perception system 402 normalizing sensor data (e.g., image data, LiDAR data, radar data, and / or the like).

[0071] In some embodiments, CNN 420 generates an output based on perception system 402 performing convolution operations associated with each convolution layer. In some examples, CNN 420 generates an output based on perception system 402 performing convolution operations associated with each convolution layer and an initial input. In some embodiments, perception system 402 generates the output and provides the output as fully connected layer 430. In some examples, perception system 402 provides the output of convolution layer 426 as fully connected layer 430, where fully connected layer 430 includes data associated with a plurality of feature values referred to as Fl, F2 . . . FN. In this example, the output of convolution layer 426 includes data associated with a plurality of output feature values that represent a prediction.

[0072] In some embodiments, perception system 402 identifies a prediction from among a plurality of predictions based on perception system 402 identifying a feature value that is associated with the highest likelihood of being the correct prediction from among the plurality of predictions. For example, where fully connected layer 430 includes feature values Fl, F2, . . . FN, and Fl is the greatest feature value, perception system 402 identifies the prediction associated with Fl as being the correct prediction from among the plurality of predictions. In some embodiments, perception system 402 trains CNN 420 to generate the prediction. In some examples, perception system 402 trains CNN 420 to generate the prediction based on perception system 402 providing training data associated with the prediction to CNN 420.

[0073] Referring now to FIGS. 4C and 4D, illustrated is a diagram of example operation of CNN 440 by perception system 402. In some embodiments, CNN 440 (e.g., one or more components of CNN 440) is the same as, or similar to, CNN 420 (e.g., one or more components of CNN 420) (see FIG. 4B).Attorney Docket No. MOTN.160WO / PI2023209

[0074] At step 450, perception system 402 provides data associated with an image as input to CNN 440 (step 450). For example, as illustrated, perception system 402 provides the data associated with the image to CNN 440. 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.

[0075] At step 455, CNN 440 performs a first convolution function. For example. CNN 440 performs the first convolution function based on CNN 440 providing the values representing the image as input to one or more neurons (not explicitly illustrated) included in first convolution layer 442. 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).

[0076] In some embodiments, CNN 440 performs the first convolution function based on CNN 440 multiplying the values provided as input to each of the one or more neurons included in first convolution layer 442 with the values of the filter that corresponds to each of the one or more neurons. For example. CNN 440 can multiply the values provided as input to each of the one or more neurons included in first convolution layer 442 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 442 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.

[0077] In some embodiments, CNN 440 provides the outputs of each neuron of first convolutional layer 442 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 440 can provide the outputs of each neuron of first convolutional layer 442 toAttorney Docket No. MOTN.160WO / PI2023209corresponding neurons of a subsampling layer. In an example, CNN 440 provides the outputs of each neuron of first convolutional layer 442 to corresponding neurons of first subsampling layer 444. In some embodiments, CNN 440 adds a bias value to the aggregates of all the values provided to each neuron of the downstream layer. For example, CNN 440 adds a bias value to the aggregates of all the values provided to each neuron of first subsampling layer 444. In such an example, CNN 440 determines a final value to provide to each neuron of first subsampling layer 444 based on the aggregates of all the values provided to each neuron and an activation function associated with each neuron of first subsampling layer 444.

[0078] At step 460, CNN 440 performs a first subsampling function. For example, CNN 440 can perform a first subsampling function based on CNN 440 providing the values output by first convolution layer 442 to corresponding neurons of first subsampling layer 444. In some embodiments, CNN 440 performs the first subsampling function based on an aggregation function. In an example, CNN 440 performs the first subsampling function based on CNN 440 determining the maximum input among the values provided to a given neuron (referred to as a max pooling function). In another example, CNN 440 performs the first subsampling function based on CNN 440 determining the average input among the values provided to a given neuron (referred to as an average pooling function). In some embodiments, CNN 440 generates an output based on CNN 440 providing the values to each neuron of first subsampling layer 444, the output sometimes referred to as a subsampled convolved output.

[0079] At step 465, CNN 440 performs a second convolution function. In some embodiments, CNN 440 performs the second convolution function in a manner similar to how CNN 440 performed the first convolution function, described above. In some embodiments. CNN 440 performs the second convolution function based on CNN 440 providing the values output by first subsampling layer 444 as input to one or more neurons (not explicitly illustrated) included in second convolution layer 446. In some embodiments, each neuron of second convolution layer 446 is associated with a filter, as described above. The filter(s) associated with second convolution layer 446 may be configured to identify more complex patterns than the filter associated with first convolution layer 442, as described above.

[0080] In some embodiments, CNN 440 performs the second convolution function based on CNN 440 multiplying the values provided as input to each of the one or more neurons included in second convolution layer 446 with the values of the filter that corresponds to each ofAttorney Docket No. MOTN.160WO / PI2023209the one or more neurons. For example, CNN 440 can multiply the values provided as input to each of the one or more neurons included in second convolution layer 446 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.

[0081] In some embodiments, CNN 440 provides the outputs of each neuron of second convolutional layer 446 to neurons of a downstream layer. For example. CNN 440 can provide the outputs of each neuron of first convolutional layer 442 to corresponding neurons of a subsampling layer. In an example, CNN 440 provides the outputs of each neuron of first convolutional layer 442 to corresponding neurons of second subsampling layer 448. In some embodiments, CNN 440 adds a bias value to the aggregates of all the values provided to each neuron of the downstream layer. For example, CNN 440 adds a bias value to the aggregates of all the values provided to each neuron of second subsampling layer 448. In such an example, CNN 440 determines a final value to provide to each neuron of second subsampling layer 448 based on the aggregates of all the values provided to each neuron and an activation function associated with each neuron of second subsampling layer 448.

[0082] At step 470, CNN 440 performs a second subsampling function. For example, CNN 440 can perform a second subsampling function based on CNN 440 providing the values output by second convolution layer 446 to corresponding neurons of second subsampling layer 448. In some embodiments. CNN 440 performs the second subsampling function based on CNN 440 using an aggregation function. In an example, CNN 440 performs the first subsampling function based on CNN 440 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 440 providing the values to each neuron of second subsampling layer 448.

[0083] At step 475, CNN 440 provides the output of each neuron of second subsampling layer 448 to fully connected layers 449. For example, CNN 440 provides the output of each neuron of second subsampling layer 448 to fully connected layers 449 to cause fully connected layers 449 to generate an output. In some embodiments, fully connected layers 449 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 440 includes an object, a set of objects, and / or the like. In someAttorney Docket No. MOTN.160WO / PI2023209embodiments, perception system 402 performs one or more operations and / or provides the data associated with the prediction to a different system, described herein.Opportunistic Lane Change

[0084] As described herein, autonomous vehicles (AV) may occasionally attempt to switch lanes while operating on a multi-lane road around other vehicles. Lane changes in the proximity of multiple other vehicles can be difficult for AV’s to perform. For example, current techniques may identify a single gap created by a single other vehicle. As another example, an AV may recognize a gap created by another vehicle in the adjacent lane, however, the AV may not be pressured into making the lane change by a vehicle in the same lane (as the AV). In some such cases, the AV may wait too long to change lanes and / or fail to change lanes, which may result in a missed turn or exit.

[0085] To address these issues, an AV may employ various techniques to identify gaps corresponding to moving vehicles in an adjacent lane to the AV. The gaps may be identified based on the location and / or speed of the moving vehicles.

[0086] Upon identifying the available gaps, the AV may determine a “cost” (e.g., an expected value of navigating to the gap also referred to herein as a “gap cost”) of selecting each available gap. In some cases, the cost of navigating to each respective gap may be based on the determined size of the gap (e.g., the space between the lagging and leading vehicle), the determined position of the gap (e.g., relative to the AV), the determined speed of the lagging vehicle and / or leading vehicle creating the gap, the speed of the AV, predicted affect on other vehicles or other agents in the environment, time, the distance from the route, etc. The AV may select a gap based on the determined (gap) cost. For example, the AV may select the gap with a lower or the lowest cost. Once a gap is selected, the AV may navigate to the selected gap.

[0087] FIG. 5A is a diagram illustrating an example of a vehicle 200 attempting an opportunistic lane change into an adjacent lane occupied by an agent 502. In the illustrated example, the environment 500 includes the vehicle 200, a lagging agent 502, and a plurality of available gaps 504 and 506, however, it will be understood, that the environment may include fewer or more lanes, vehicles, and / or other objects, etc.

[0088] One or more components illustrated in the environment 500 may utilize part of the autonomous vehicle compute 400. For example, the vehicle 200 may utilize the perception system 402 to determine the position and speed of the lagging agent 502. In such cases, theAttorney Docket No. MOTN.160WO / PI2023209planning system 404 may use the output from the perception system 402 to select a gap and a trajectory (e.g., accelerate, decelerate, or maintain velocity enter the gap) based on the vehicle’s position and / or speed. Additionally, the planning system 404 may also be in communication with the control system 408 to cause the AV to navigate to the selected gap.

[0089] The lagging agent 502 may be a vehicle operating in an adjacent lane to the vehicle 200. The lagging agent 502 may be operating at a determinable (via the perception system 402) position relative to the vehicle 200. For example, the lagging agent 502 may be operating behind the vehicle 200. The lagging agent 502 may also be operating at a determinable (via the perception system 402) velocity. The velocity of the lagging agent 502 may assist the planning system 404 in selecting a trajectory for the vehicle 200. For example, if the perception system 402 determines that the lagging agent 502 is operating at a lower velocity than the vehicle 200, the planning system 404 may determine that changing lanes immediately is more efficient than waiting.

[0090] In some cases, the gap 504 may be located behind the lagging agent 502. The size of the gap 504 may be determined based on the position and speed of the lagging agent 502. In some cases, if the velocity of the lagging agent 502 exceeds the velocity of the vehicle 200, the size of the gap 504 will increase as the lagging agent 502 moves closer to the vehicle 200. In some such cases, the planning system 404 may predict that the size of the gap 504 will grow and the position of the gap 504 will become closer to the vehicle 200 as the lagging agent 502 moves closer to the vehicle 200. As the gap 504 grows and / or moves closer to the vehicle 200, the planning system 404 may determine that the cost of selecting the gap 504 is decreasing.

[0091] In certain cases, the gap 506 may be located in front of the lagging agent 502. The cost to move into the gap 506 may depend on the position and velocity of the agent 502 relative to the vehicle 200. In some cases, and as illustrated in FIG. 5A, the lagging agent 502 is located behind the vehicle 200 on the multi-lane road. As a result, in the illustrated example, the size of the gap 506 is larger than the size of the gap 504. The gap 506 is also positioned directly adjacent to the vehicle 200, allowing the vehicle 200 to merge into the gap 506 without waiting and while maintaining velocity (e.g., without accelerating / decelerating). In contrast, to move into gap 504, the vehicle 200 would have to wait and / or decelerate.

[0092] In some cases, the velocity of the lagging agent 502 may be less than the velocity of the vehicle 200 and / or the lagging agent 502 may decelerate, which may indicate to theAttorney Docket No. MOTN.160WO / PI2023209vehicle 200 that the agent 502 is yielding to the lane change of the vehicle 200. Tn some such cases, the planning system 404 may determine that the cost to select gap 506 instead of gap 504 is lower due to the larger size of the gap 506, the position of the gap 506 being closer to the vehicle 200 than the position of the gap 504, and the relative velocity and / or deceleration of the lagging agent 502. The planning system 404 may select the gap 506 based on its cost relative to the cost of gap 504. The planning system 404 may generate a lane change trajectory based on the selected gap 506 and communicate the generated trajectory to the control system 408 to cause the vehicle 200 to change lanes.

[0093] FIG. 5B illustrates an example diagram of a vehicle 200 attempting an opportunistic lane change into an adjacent lane occupied by multiple agents. In the illustrated example, a lagging agent 552, similar to the lagging agent 502, and a plurality of gaps 554, 556, and 560, similar to the plurality of gaps 504 and 506, are shown. The environment 550 may further include a leading agent 558. It will be understood that the environment 550 can include the same or different (fewer or more) areas as those of environment 500.

[0094] The environment 550 includes a plurality of gaps created by vehicles operating in the adjacent lane similar to at least a portion of the adjacent lane shown in environment 500. In the environment 550, the number of gaps is different. Specifically, in addition to the gap behind the lagging agent 552, a leading agent 558 present in the adjacent lane creates a gap between the lagging agent 552 and leading agent 558, and a gap in front of the leading agent 558.

[0095] The leading agent 558 may be a vehicle operating in an adjacent lane to the vehicle 200. The leading agent 558 may be operating at a determinable (via the perception system 402) position relative to the vehicle 200. For example, the leading agent 558 may be operating slightly ahead of the vehicle 200. The position of the leading agent 558 may assist the planning system 404 in determining the size of the plurality of gaps 556 and 560.

[0096] The leading agent 558 may also be operating at a determinable velocity. The velocity of the leading agent 558 may assist the planning system 404 in predicting future sizes of the plurality of the gaps 556 and 560. In some cases, the velocity of the leading agent 558 may be greater than the velocity of the vehicle 200, which may increase the size of gap 556 and decrease the size of gap 560. The planning system 404 may use the predicted future sizes of the plurality of gaps 554, 556, and 560 in determining a cost associated with navigating to each of the plurality of gaps 554, 556, and 560.Attorney Docket No. MOTN.160WO / PI2023209

[0097] Based on the determined position and size of the plurality of gaps 554, 556, and 560 (via the perception system 402), the planning system 404 can determine a (gap) cost associated with navigating to each of the plurality of gaps 554, 556, and 560. In some cases, as part of determining the gap cost, the planning system can determine a velocity profile, acceleration profile, deceleration profile, and / or time to reach a particular gap. For example, the planning system 404 may determine the velocity (e.g., 65 mph), the amount of acceleration (e.g., accelerate by 5 mph to 65 mph), and / or time (e.g., 6 seconds) to navigate to the gap 560 (before initiating the lane change). As another example, the planning system 404 may determine the velocity (e.g., 57 mph), amount of deceleration (e.g., decelerate by 3mph to 57 mph), and / or time (e.g., 4 seconds) to navigate to the gap 554 (before initiating the lane change). As another example, the planning system 404 may determine the velocity (e.g., 60 mph), amount of acceleration (0 mph), and / or time (e.g., 0 seconds)to navigate to the gap 556 (before initiating the lane change).

[0098] In certain cases, the planning system 404 may determine a separate cost for each of velocity (e.g., velocity cost), acceleration (e.g., acceleration cost), and / or time (e.g., timing cost) to navigate to the gap. For example, the planning system 404 may determine a velocity cost based on the determined velocity to navigate to a respective gap, an acceleration cost based on the determined acceleration profile / deceleration profile to navigate to a respective gap, and / or a timing cost based on the determined time to navigate to a respective gap.

[0099] Other costs may be used to determine the gap cost. In some cases, the planning system may use a distance from route cost to determine the gap cost. The distance to route cost may correspond to the distance from the vehicle 200 from a predetermined route. For example, the vehicle 200 may generate a lane-based route that indicates particular lanes in which the vehicle is to be located throughout the route. The lane-based route may indicate particular lanes at particular locations where the vehicle is to be located, where the vehicle is to change lanes, turn left / right, etc.

[0100] During navigation, the distance to route cost may reflect whether the vehicle 200 is in the lane indicated by the lane-based route or not. The planning system 404 may assign a higher cost to a lane that is not the lane indicated as part of the lane-based route at a particular location. Accordingly, if the lane-based route indicates that the vehicle should be in a left-most lane at point A and the vehicle 200 is in a different lane (or predicted to be in a different lane at point A based on the different gaps), the planning system 404 may assign a higher cost to remainAttorney Docket No. MOTN.160WO / PI2023209in the same lane (also referred to herein as the current lane or anchor lane), which may result in a relatively lower gap cost or lane change cost to change lanes.

[0101] In some cases, the planning system 404 may assign a higher cost the longer the vehicle is in a lane other than the lane indicated in the lane-based route. In this way, the planning system 404 may incentivize the vehicle 200 to change lanes.

[0102] In some cases, the timing cost may be based on or associated with the distance from route cost. For example, if a vehicle waits too long to change lanes, it may miss an exit or a turn. In some cases, the planning system 404 may determine a predicted (future) location of the vehicle based on the time associated with arriving at the different gaps (to begin the lane change). If the predicted future locations would result in the vehicle 200 being in a lane that is different from the lane-based route at that particular location, the planning system 404 may assign a higher (relative) weight to that timing cost and / or to the corresponding gap cost.

[0103] In some cases, the planning system 404 may determine the gap cost for some or all of the plurality of gaps 554, 556. and 560 based on the predicted affect the lane change may have on other agents (also referred to herein as agent effect cost). In some cases, the planning system 404 may determine that moving into gap 560 according to the determined velocity profile and acceleration profile may not affect agent 552 or agent 558 (e.g„ the speed of vehicle 200 when it reaches gap 560 and change lanes will be greater than the speed of agent 558 such that agent 558 does not have to decelerate or change lanes, etc.), moving into gap 556 is likely to result in agent 552 decelerating, and moving into gap 554 may not affect agent 552 or agent 558 (e.g., because agents 552, 558 would be in front of the vehicle 200 at gap 554, moving into gap 554 will not result in agents 552, 558 decelerating). For example, based on the calculated velocity and / or acceleration of the agent 552, the planning system 404 may determine that the gap 556 is shrinking and that moving into the gap 556 may result in the agent 552 having to decelerate. Accordingly, the planning system 404 may assign a higher agent effect cost (cost based on the effect on other agents) to moving into gap 556 than to moving into gaps 554 or 560.

[0104] It will be understood that other agents may exist in the environment 550, such as one or more agents in the same lane as the vehicle 200 (or anchor lane), such as one or more agents behind the vehicle 200. In some case, the planning system 404 may calculate an agent effect cost for the vehicles in the anchor lane, which may affect the gap cost associated with the different gaps 554, 556, 560. For example, decelerating to enter gap 554 may adversely affect vehicles inAttorney Docket No. MOTN.160WO / PI2023209the anchor lane, whereas accelerating to gap 560 and / or entering gap 556 may not affect vehicles in the anchor lane.

[0105] In some cases, the planning system 404 may combine the individual costs (e.g., velocity cost, acceleration cost, timing cost, agent affect cost) and / or characteristics of the gap (e.g., size, position relative to the vehicle 200, growth state, e.g., growing / shrinking) into the gap cost that reflects the cost to navigate to the particular gap. In certain cases, the planning system may weight the costs based on a predetermined weighting. For example, the agent effect cost may be weighted more heavily than the velocity cost when determining the gap cost.

[0106] In some cases, the planning system 404 may select one of the gaps based on the determined gap cost (e.g., the gap with a lower or lowest gap cost compared to other gaps). In some cases, the gap cost may include a cost relative to changing lanes (e.g., a lane change cost). In certain cases, the gap cost may form part of a lane change cost. The lane change cost may include costs associated with changing from one lane to another. Such costs may include the cost (in terms of safety) to move from one lane to another, the cost associated with increasing / decreasing the distance to a calculated route and / or destination, etc.

[0107] In some cases, the planning system 404 may generate a trajectory to cause the vehicle 200 to navigate to the selected gap (e.g., a lane change trajectory). In certain cases, the planning system 404 may generate multiple trajectories associated with different actions. For example, the planning system 404 may generate one or more lane change trajectories corresponding to moving into the lane or a different lane and / or a lane keep trajectory corresponding to staying in the current lane. The planning system 404 may select one of the trajectories for execution by the vehicle 200 (e.g., select a trajectory that the vehicle 200 is to follow). In certain cases, the planning system 404 may select a trajectory based on an associated trajectory cost (e.g., select a trajectory with a lower or the lowest cost). The planning system 404 may then communicate the selected trajectory to the control system 408 to operate the vehicle 200.

[0108] In some cases, the planning system 404 may, due to changes in the determined costs of the different gaps over time, change the selected gap rapidly (e.g., multiple times a second). This frequent change (or chatter) may result in erratic behavior by the vehicle (e.g., the vehicle may change between controls and actions associated with initiating a lane change and actions associated with a lane keep multiple times over a relatively short period of time, such as one or more seconds). To address this, the planning system 404 may incorporate a decision lockAttorney Docket No. MOTN.160WO / PI2023209and / or hysteric threshold. For example, once the planning system 404 has selected a gap and / or trajectory, the planning system 404 may retain the selected gap and / or trajectory for a predetermined amount of time (e.g., may not revisit the gap decision and / or trajectory for a predetermined amount of time). As another example, once the planning system 404 selects a gap and / or a trajectory, the planning system 404 may artificially decrease the cost associated with the selected gap or selected trajectory for a predetermined period of time (and / or artificially increase the cost associated with the non-selected gap(s) or non-selected trajectories for a predetermined period of time) and / or modify a threshold for selecting another gap or trajectory for the predetermined period of time to make it less likely that another gap or trajectory would be selected.. Accordingly, if the costs of the gaps or trajectories change by less than a threshold amount, the planning system 404 may continue to select the previously selected gap and / or lane change trajectory. Accordingly, the control system 404 may reduce the amount of chatter

[0109] In some cases, the system may lack urgency to change lanes (e.g., once a gap has been selected). For example, if there are no vehicles in front of or behind the vehicle 200 in the environment 550 (which may have otherwise induced urgent behavior from the planning system 404) after the planning system 404 selects a gap, the planning system 404 may delay calculating or selecting a lane change trajectory and / or communicating the lane change trajectory to the control system 408. To address this, the planning system 404 may use a cost associated with staying in the current lane (e.g., a lane keep cost) to induce the lane change. For example, the planning system 404 may increase the lane keep cost over time (e.g., incrementally, by steps, using a function, etc.). As the lane keep cost increases relative to the gap cost or lane change cost, the planning system 404 can increase the likelihood that a trajectory associated with changing lanes is selected for execution by the vehicle 200. In this way, the planning system 404 may increase urgency of selecting a lane change trajectory and communicating the lane change trajectory to the control system 408. By decreasing the time associated with selecting a lane change trajectory, the planning system 404 may reduce lane aborts caused by late and / or slow lane changes.

[0110] FIG. 6 is a diagram illustrating a vehicle 200 attempting an opportunistic lane change into an adjacent lane where another vehicle is operating behind the vehicle 200. In the illustrated example, a lagging agent 602, similar to the lagging agent 502, is shown. The environment 600 may further include a gap 604. It will be understood that the environment 600Attorney Docket No. MOTN.160WO / PI2023209can include the same or different (fewer or more) areas, vehicles, etc., as those of environment 500.

[0111] In the illustrated example, the environment 600 includes a vehicle operating behind the vehicle 200 similar to at least a portion of the lagging agent 502 in environment 500. In the environment 600, the position of the lagging agent 602 is different. Specifically, in the environment 600, the lagging agent 602 is operating in the same lane as the vehicle 200.

[0112] In some cases, the gap 604 may be the (only) gap available in the adjacent lane (e.g., there may be no moving vehicles in the adjacent lane that are visible by the vehicle 200).

[0113] In some cases, the planning system 404 may determine the cost of selecting the gap 604 and changing lanes based on a predicted (future) position and / or action of the lagging agent 602. For example, the lagging agent 602 may be accelerating and navigating closer to the vehicle 200. In some such cases, based on the acceleration profile, velocity, location relative to the vehicle 200, size of the gap 604, and location of the agent 602 relative to the gap 604, the planning system 404 may predict that the lagging agent 602 is preparing to change lanes into the adjacent lane (and occupy the gap 604) and predict a (future) location of the agent 602 in one or more seconds.

[0114] The planning system 404 may determine the cost of making the lane change into the gap 604 based on the prediction associated with the lagging agent 602 (e.g., based on the predicted location (over time) and / or action of the agent 602). For example, the planning system 404 may calculate an agent effect cost based on the predicted (future) position and / or (future) action of the agent 062 in one or more seconds. In some cases, the planning system may determine the agent effect cost by using the predicted (future) location and / or (future) action of the agent 602 to determine that a lane change into the adjacent lane by the vehicle 200 may to result in causing the agent 602 to decelerate, change its trajectory, and / or remain in the current lane. Based on the agent effect cost, the planning system 404 may determine the lane change cost or gap cost. Based on the lane change cost or gap cost (and a lane keep cost), the planning system 404 may determine to stay in the same lane or change lanes.

[0115] In certain cases, the lane keep cost may be based on the agent effect cost, which may correspond to a cost on the agent 602 if the vehicle 200 remains in the current lane. For example, the planning system 404 may determine that if it stays in the same lane, it will cause the agent 602 to decelerate. Accordingly, the planning system 404 may increase the agent effect costAttorney Docket No. MOTN.160WO / PI2023209for staying in the same lane, which may increase the lane keep cost (associated with a lane keep trajectory). As described herein, an increased lane keep cost may increase the likelihood of the planning system 404 selecting a lane change trajectory over a lane keep trajectory, and the likelihood of the planning system communicating the lane change trajectory to the control system 408 (and the control system 408 executing the lane change based on the lane change trajectory).

[0116] In some cases, based on the lane change cost (or gap cost) and the lane keep cost, the planning system 404 may choose to change lanes or remain in the current lane. For example, the planning system 404 may determine that the lane keep cost is lower than the lane change cost and select a lane keep trajectory that maintains the vehicle 200 in the same lane. As another example, the planning system may determine that, despite the predicted action of the agent 602, the lane keep cost is higher than the lane change cost and select a lane change trajectory that causes the vehicle 200 to change lanes.

[0117] In certain cases, if the planning system 404 chooses to change lanes, it may initiate the lane change without delay (e.g., to avoid changing lanes at the same time as the lagging agent 602 and / or to move out of the way of the lagging agent 602 in the anchor lane). Alternatively, as described herein, the planning system 404 may choose to abort the lane change and wait for the lagging agent 602 to change lanes. In some cases, the abort (and agent 602 moving to the adjacent lane) may result in an environment similar to environment 500, described herein with reference to FIG. 5A.

[0118] FIG. 7 is a flowchart of a process 700 for opportunistic lane changes. The process 700 may be executed, for example, using one or more processors or computing devices associated with the vehicle 200. such as a processor or computing device associated with the perception system 402, the planning system 404 and / or the control system 408. For simplicity, the process 700 will be described as being performed by a system.

[0119] At block 702, the system determines a location and a speed of a plurality of moving vehicles in a lane adjacent to an autonomous vehicle. In some cases, the plurality of moving vehicles may include a leading agent and / or a lagging agent. As described herein, in some cases, the system may determine the location and speed of other vehicles using the perception system 402. Moreover, as described herein, in some cases, the vehicle 200 may operate on a multilane road that allows vehicles to change lanes.Attorney Docket No. MOTN.160WO / PI2023209

[0120] At block 704, the system identifies a plurality of gaps in an adjacent lane relative to the plurality of moving vehicles. For example, as described herein, leading and lagging agents in the adjacent can create gaps in the adjacent lane (e.g., behind the lagging agent, between the lagging agent and leading agent, and in front of the leading agent). In some cases, the system may identify the gaps using data from the perception system 402.

[0121] At block 706, the system can determine (physical) characteristics of the plurality of gaps, such as but not limited to, a size of the gaps, a position of the gaps relative to the vehicle 200, whether the gaps are expanding or contracting, etc. As described herein, based on the determined location of the agents relative to each other and relative to the vehicle 200, the system can determine the size of the gaps. In some cases, the size of the gaps can be determined using data from perception system 402. In certain cases, the system may determine whether the different gaps are increasing or decreasing in size based on the relative velocity of the vehicles to each other.

[0122] At block 708, the system can determine a gap cost to navigate to each of the plurality of gaps. In some cases, the cost to navigate to each of the plurality of gaps can be determined based on the determined physical characteristic of the gaps, such as but not limited to, the size of the plurality of gaps (e.g., lower cost for larger gaps), the determined position of the plurality of gaps relative to the autonomous vehicle (e.g., lower cost for gaps closer to a current position of the AV), state of growth of the gaps (e.g., lower cost for gaps that are expanding), determined speed of the plurality of moving vehicles (e.g., lower cost for gaps that are moving closer to the AV based on the relative speed of the vehicle and other agents).

[0123] In some cases, the cost to navigate to each of the plurality of gaps can be determined based on any one or any combination of the physical characteristics of the gaps, a velocity cost, acceleration cost, timing cost, agent affect cost, and / or distance from route cost.

[0124] As described herein, the velocity cost may correspond to the difference between a current velocity and a target velocity that enables the vehicle to reach a particular gap (e.g., by increasing or decreasing the current velocity). For example, the larger the difference between the current velocity and target velocity may result in a correspondingly higher velocity costs. In certain cases, a negative difference between the current velocity and the target velocity (e.g., a reduction in speed) may be associated with a higher velocity cost (or vice versa), which may indicate a preference for speeding up to slowing down (or vice versa). In some cases, the velocity cost mayAttorney Docket No. MOTN.160WO / PI2023209increase linearly with the difference in speeds. In certain cases, the velocity cost may experience a step up (e.g., step function increase), for example, when a target cost exceeds a speed limit.

[0125] The acceleration cost may correspond to the amount of acceleration / deceleration to reach the target velocity within a particular time. For example, a larger acceleration / deceleration may correspond to a higher acceleration cost. In certain cases, the same amount of deceleration may result in a higher cost relative to the same amount of acceleration (or vice versa) because deceleration (e.g., braking) may be disfavored (or vice versa). For example, slowing down to reach a gap may be disfavored compared to speeding up to reach a gap (or vice versa). In some cases, the acceleration cost may increase linearly with the magnitude of the acceleration. In certain cases, the acceleration cost may experience a step up (e.g., step function increase), for example, when the magnitude of the acceleration corresponds to an unsafe amount of acceleration / deceleration.

[0126] The agent affect cost may correspond to a determined effect on agents in the environment (e.g., whether and how much an agent will have to change velocity, accelerate / decelerate, or change positions, to accommodate the action or lane change of the vehicle). For example, a higher expected change in velocity, acceleration / deceleration, or change in position of an agent may result in a higher agent effect cost. In certain cases, a negative change in velocity or deceleration may result in a higher agent affect cost relative to the same amount of positive change in velocity or acceleration because deceleration by an agent (e.g.. braking) may be disfavored. In some cases, the agent effect cost may increase linearly with the magnitude of the effect on the agent. In certain cases, the agent effect cost may experience a step up (e.g., step function increase), for example, when the magnitude of the effect on the agent corresponds to an unsafe maneuver by the agent (e.g., unsafe deceleration profile associated with collision avoidance).

[0127] The distance from route cost may correspond to whether the current lane or future lane are part of a lane-level route plan. For example, if the current lane is not part of the lane-level route plan at that location, and a particular gap keeps the vehicle in the current lane for longer, the distance from route cost may be higher than for another gap that moves the vehicle into the lane that is part of the lane-level route plan more quickly (e.g., the cost to not follow the lanelevel route plan goes up the more distance that is traveled in a lane other than the lane in the lanelevel route plan).Attorney Docket No. MOTN.160WO / PI2023209

[0128] The timing cost may correspond to an (estimated) amount of time for the vehicle to reach the gap (e.g., to begin the lane change maneuver). For example, the timing cost may increase with the (estimated) amount of time to reach the gap. In certain cases, the timing cost may be associated with the distance from route cost. For example, the timing cost may increase with the estimated amount of time that the vehicle is not in part of a lane-level route (e.g., as the vehicle navigates to a respective gap). In some cases, the timing cost may increase linearly with the amount of time to reach a gap. In certain cases, the timing cost may experience a step up (e.g., step function increase), for example, when the amount would result in the vehicle missing a turn, exit, etc.

[0129] As described herein, any one or any combination of the aforementioned costs or data may be used to determine a gap cost for the respective gaps. In some cases, the different costs may be weighted relative to each other to determine the gap cost such that one of the costs is relied on more heavily in determining the gap cost.

[0130] At block 710, the system can select a gap of the plurality of gaps based on the determined gap cost of each of the plurality of gaps. As described herein, the selected gap from the plurality of gaps may be the gap that the system determines to be a gap with a lower cost than one or more other gaps and / or the gap with the lowest gap cost. In some cases, the system may select a gap based on a different criteria other than lowest overall cost, such as the yield behavior of the moving vehicles and / or another metric that can influence the cost of selecting the gap.

[0131] At block 712, the system can cause the vehicle 200 to navigate to the selected gap. In some cases, as part of navigating to the selected gap, the system may generate a lane change trajectory based on the selected gap. In certain cases, the gap cost associated with the selected gap may be used as the trajectory (or lane change) cost associated with the generated lane change trajectory and / or the gap cost may be used to generate a trajectory cost associated with the generated lane change.

[0132] In some cases, the system may generate multiple lane change trajectories to navigate to the selected gap, a different gap, and / or to a different lane. In certain cases, the system may generate a lane change trajectory cost for some or all of the lane change trajectories. In certain cases, the system may generate one or more lane keep trajectories to keep the vehicle in the anchor lane and may generate a lane keep trajectory cost for some or all of the lane keep trajectories.Attorney Docket No. MOTN.160WO / PI2023209

[0133] In certain cases, the system may compare the trajectory costs associated with the generated trajectories. For example, the system may compare one or more lane change trajectory costs, and / or one or more lane keep trajectory costs.

[0134] In some cases, the system may select one of the generated trajectories based on the trajectory cost associated with the respective trajectory. As described herein, the selected trajectory may be the trajectory associated with a trajectory cost that is lower than one or more other trajectories and / or the trajectory with the lowest trajectory cost. As described herein, the selected trajectory may correspond with the gap selected at block 710.

[0135] The system may use the selected trajectory to navigate the vehicle. For example, the system may generate and transmit control signals to cause a powertrain control system (e.g., a DBW system, powertrain control system, and / or the like), a steering control system, and / or a brake system to operate in a manner that causes the vehicle to follow the selected trajectory. For example, in some cases, the system may cause the vehicle 200 to accelerate to navigate to the selected gap. In further cases, the system may cause the vehicle 200 to decelerate to navigate to the selected gap. In further cases, the system may cause the vehicle 200 to maintain its velocity to navigate to the selected gap. The system may cause the vehicle 200 to perform fewer or more functions to navigate to the selected gap.

[0136] The example process 700 can be implemented, for example, by a computing device that includes a processor and a non-transitory computer-readable medium. The non-transitory computer readable medium can be storing computer-executable instructions that, when executed by the processor, can cause the processor to perform the operations of the illustrated process 700. Additionally or alternatively, the process 700 can be implemented using a non-transitory computer-readable medium storing computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform the operations of the process 700 of FIG. 7.

[0137] Fewer, more, or different blocks may be included in the process 700. MorFor example, although the process 700 references determining a size and position of a plurality of gaps relative to the vehicle 200, it should be understood that in certain cases the system may predict a future size of at least one of the plurality of gaps. In some such cases, the cost to navigate to one or more of the plurality of gaps may be determined based on a determination of a cost to navigate to the at least one gap based on the predicted future size of the at least one gap.Attorney Docket No. MOTN.160WO / PI2023209

[0138] Moreover, the blocks of the process 700 may be performed in a different order and / or concurrently. For example, the system may concurrently perform blocks 702-708 for the same or different blocks. In some such cases, the system may, for a particular gap, concurrently identify the, determine its physical characteristics, and determine a cost for navigating to the gap. Moreover, the system may concurrently perform these functions for multiple blocks in parallel thereby reducing the amount of time to determine costs and select a gap.

[0139] 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.

[0140] Various additional example embodiments of the disclosure can be described by the following clauses:Clause 1: A method, comprising: determining a location and speed of a plurality of moving vehicles in a lane adjacent to an autonomous vehicle; identifying a plurality of gaps corresponding to the plurality of moving vehicles; determining a size of the plurality of gaps and a position of the plurality of gaps relative to the autonomous vehicle; determining a cost to navigate to each of the plurality of gaps based on the determined size of the plurality of gaps, the determined position of the plurality of gaps relative to the autonomous vehicle, and the determined speed of the plurality of moving vehicles; selecting a gap of the plurality of gaps based on the determined cost of each of the plurality of gaps; and causing the autonomous vehicle to navigate to the selected gap.Clause 2: The method of Clause 1, wherein determining the size of the plurality of gaps and the position of the plurality of gaps relative to the autonomous vehicle comprises predicting a future size of at least one gap of the plurality of gaps.Attorney Docket No. MOTN.160WO / PI2023209Clause 3: The method of Clause 2, wherein determining the cost to navigate to one or more of the plurality of gaps comprises determining a cost to navigate to the at least one gap based on the predicted future size of the at least one gap.Clause 4: The method of any one of Clauses 1 through 3, wherein determining the cost to navigate to each of the plurality of gaps comprises: determining at least one of an amount of acceleration, an amount of deceleration, an amount of time, or a speed required to navigate to the gap- Clause 5: The method of any one of Clauses 1 through 4, wherein selecting the gap of the plurality of gaps comprises selecting the gap with a lowest cost.Clause 6: The method of any one of Clauses 1 through 5, wherein navigating to the selected gap comprises causing the autonomous vehicle to accelerate.Clause 7: The method of any one of Clauses 1 through 6, wherein navigating to the selected gap comprises causing the autonomous vehicle to decelerate.Clause 8: The method of any one of Clauses 1 through 7, wherein navigating to the selected gap comprises causing the autonomous vehicle to maintain velocity.Clause 9: A system, comprising: at least one processor; and non-transitory computer-readable medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising: determine a location and speed of a plurality of moving vehicles in a lane adjacent to an autonomous vehicle; identify a plurality of gaps corresponding to the plurality of moving vehicles; determine a size of the plurality of gaps and a position of the plurality of gaps relative to the autonomous vehicle; determine a cost to navigate to each of the plurality of gaps based on the determined size of the plurality of gaps, the determined position of the plurality of gaps relative to the autonomous vehicle, and the determined speed of the plurality of moving vehicles; select a gap of the plurality of gaps based on the determined cost of each of the plurality of gaps: and cause the autonomous vehicle to navigate to the selected gap.Clause 10: The system of Clause 9, wherein determining the size of the plurality of gaps and the position of the plurality of gaps relative to the autonomous vehicle comprises predicting a future size of at least one gap of the plurality of gaps.Attorney Docket No. MOTN.160WO / PI2023209Clause 11: The system of Clause 10, wherein determining the cost to navigate to one or more of the plurality of gaps comprises determining a cost to navigate to the at least one gap based on the predicted future size of the at least one gap.Clause 12: The system of any one of Clauses 9 through 11, wherein determining the cost to navigate to each of the plurality of gaps comprises: determining at least one of an amount of acceleration, an amount of deceleration, an amount of time, or a speed required to navigate to the gap- Clause 13: The system of any one of Clauses 9 through 12, wherein selecting the gap of the plurality of gaps comprises selecting the gap with a lowest cost.Clause 14: The system of any one of Clauses 9 through 13, wherein navigating to the selected gap comprises causing the autonomous vehicle to accelerate.Clause 15: The system of any one of Clauses 9 through 14, wherein navigating to the selected gap comprises causing the autonomous vehicle to decelerate.Clause 16: The system of any one of Clauses 9 through 15, wherein navigating to the selected gap comprises causing the autonomous vehicle to maintain velocity.Clause 17: A non-transitory computer readable medium comprising instructions stored thereon that, when executed by at least one processor, cause the at least one processor to carry out operations comprising: determining, using the at least one processor, a location and speed of a plurality of moving vehicles in a lane adjacent to an autonomous vehicle; identifying, using the at least one processor, a plurality of gaps corresponding to the plurality of moving vehicles; determining, using the at least one processor, a size of the plurality of gaps and a position of the plurality of gaps relative to the autonomous vehicle; determining, using the at least one processor, a cost to navigate to each of the plurality of gaps based on the determined size of the plurality of gaps, the determined position of the plurality of gaps relative to the autonomous vehicle, and the determined speed of the plurality of moving vehicles; selecting, using the at least one processor, a gap of the plurality of gaps based on the determined cost of each of the plurality of gaps; and causing, using the at least one processor, the autonomous vehicle to navigate to the selected gap.Clause 18: The non-transitory computer readable medium of Clause 17, wherein determining the size of the plurality of gaps and the position of the plurality of gaps relative to the autonomous vehicle comprises predicting a future size of at least one gap of the plurality of gaps.Attorney Docket No. MOTN.160WO / PI2023209Clause 19: The non-transitory computer readable medium of Clause 18, wherein determining the cost to navigate to one or more of the plurality of gaps comprises determining a cost to navigate to the at least one gap based on the predicted future size of the at least one gap.Clause 20: The non-transitory computer readable medium of any one of Clauses 17 through 19, wherein determining the cost to navigate to each of the plurality of gaps comprises: determining at least one of an amount of acceleration, an amount of deceleration, an amount of time, or a speed required to navigate to the gap.Clause 21: The non-transitory computer readable medium of any one of Clauses 17 through 20, wherein selecting the gap of the plurality of gaps comprises selecting the gap with a lowest cost.Clause 22: The non-transitory computer readable medium of any one of Clauses 17 through 21, wherein navigating to the selected gap comprises causing the autonomous vehicle to accelerate.Clause 23: The non-transitory computer readable medium of any one of Clauses 17 through 22, wherein navigating to the selected gap comprises causing the autonomous vehicle to decelerate.Clause 24: The non-transitory computer readable medium of any one of Clauses 17 through 23, wherein navigating to the selected gap comprises causing the autonomous vehicle to maintain velocity.

Claims

Attorney Docket No. MOTN.160WO / PI2023209WHAT TS CLAIMED IS:

1. A method, comprising:determining a location and speed of a plurality of moving vehicles in a lane adjacent to an autonomous vehicle;identifying a plurality of gaps corresponding to the plurality of moving vehicles; determining a size of the plurality of gaps and a position of the plurality of gaps relative to the autonomous vehicle;determining a cost to navigate to each of the plurality of gaps based on the determined size of the plurality of gaps, the determined position of the plurality of gaps relative to the autonomous vehicle, and the determined speed of the plurality of moving vehicles;selecting a gap of the plurality of gaps based on the determined cost of each of the plurality of gaps; andcausing the autonomous vehicle to navigate to the selected gap.

2. The method of claim 1, wherein determining the size of the plurality of gaps and the position of the plurality of gaps relative to the autonomous vehicle comprises predicting a future size of at least one gap of the plurality of gaps.

3. The method of claim 2, wherein determining the cost to navigate to one or more of the plurality of gaps comprises determining a cost to navigate to the at least one gap based on the predicted future size of the at least one gap.

4. The method of any one of claims 1 through 3, wherein determining the cost to navigate to each of the plurality of gaps comprises:determining at least one of an amount of acceleration, an amount of deceleration, an amount of time, or a speed required to navigate to the gap.

5. The method of any one of claims 1 through 4, wherein selecting the gap of the plurality of gaps comprises selecting the gap with a lowest cost.

6. The method of any one of claims 1 through 5, wherein navigating to the selected gap comprises causing the autonomous vehicle to accelerate.Attorney Docket No. MOTN.160WO / PI20232097. The method of any one of claims 1 through 6, wherein navigating to the selected gap comprises causing the autonomous vehicle to decelerate.

8. The method of any one of claims 1 through 7, wherein navigating to the selected gap comprises causing the autonomous vehicle to maintain velocity.

9. A system, comprising:at least one processor; andnon-transitory computer-readable medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:determine a location and speed of a plurality of moving vehicles in a lane adjacent to an autonomous vehicle;identify a plurality of gaps corresponding to the plurality of moving vehicles; determine a size of the plurality of gaps and a position of the plurality of gaps relative to the autonomous vehicle;determine a cost to navigate to each of the plurality of gaps based on the determined size of the plurality of gaps, the determined position of the plurality of gaps relative to the autonomous vehicle, and the determined speed of the plurality of moving vehicles;select a gap of the plurality of gaps based on the determined cost of each of the plurality of gaps; andcause the autonomous vehicle to navigate to the selected gap.

10. The system of claim 9, wherein determining the size of the plurality of gaps and the position of the plurality of gaps relative to the autonomous vehicle comprises predicting a future a size of at least one gap of the plurality of gaps.

11. The system of claim 10, wherein determining the cost to navigate to one or more of the plurality of gaps comprises determining a cost to navigate to the at least one gap based on the predicted future size of the at least one gap.

12. The system of any one of claims 9 through 11, wherein determining the cost to navigate to each of the plurality of gaps comprises:Attorney Docket No. MOTN.160WO / PI2023209determining at least one of an amount of acceleration, an amount of deceleration, an amount of time, or a speed required to navigate to the gap.

13. The system of any one of claims 9 through 12, wherein selecting the gap of the plurality of gaps comprises selecting the gap with a lowest cost.

14. The system of any one of claims 9 through 13, wherein navigating to the selected gap comprises causing the autonomous vehicle to accelerate.

15. The system of any one of claims 9 through 14, wherein navigating to the selected gap comprises causing the autonomous vehicle to decelerate.

16. The system of any one of claims 9 through 15, wherein navigating to the selected gap comprises causing the autonomous vehicle to maintain velocity.

17. A non-transitory computer readable medium comprising instructions stored thereon that, when executed by at least one processor, cause the at least one processor to carry out operations comprising:determining, using the at least one processor, a location and speed of a plurality of moving vehicles in a lane adjacent to an autonomous vehicle;identifying, using the at least one processor, a plurality of gaps corresponding to the plurality of moving vehicles;determining, using the at least one processor, a size of the plurality of gaps and a position of the plurality of gaps relative to the autonomous vehicle;determining, using the at least one processor, a cost to navigate to each of the plurality of gaps based on the determined size of the plurality of gaps, the determined position of the plurality of gaps relative to the autonomous vehicle, and the determined speed of the plurality of moving vehicles;selecting, using the at least one processor, a gap of the plurality of gaps based on the determined cost of each of the plurality of gaps: andcausing, using the at least one processor, the autonomous vehicle to navigate to the selected gap.Attorney Docket No. MOTN.160WO / PI202320918. The non-transitory computer readable medium of claim 17, wherein determining the size of the plurality of gaps and the position of the plurality of gaps relative to the autonomous vehicle comprises predicting, using the at least one processor, a future size of at least one gap of the plurality of gaps.

19. The non-transitory computer readable medium of claim 18, wherein determining the cost to navigate to one or more of the plurality of gaps comprises determining, using the at least one processor, a cost to navigate to the at least one gap based on the predicted future size of the at least one gap.

20. The non-transitory computer readable medium of any one of claims 17 through 19, wherein determining the cost to navigate to each of the plurality of gaps comprises:Determining, using the at least one processor, at least one of an amount of acceleration, an amount of deceleration, an amount of time, or a speed required to navigate to the gap.

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