Managing vehicle resources based on context

Abstract

A method for managing vehicle resources based on a context is provided. The method may include obtaining information representative of an environment of a vehicle from at least one sensor; determining a current context of the environment of the vehicle based on the information representative of the environment of the vehicle; determining a level of computing resources suitable for the determined current scenario based on the determined current scenario; and adjusting at least one parameter associated with the at least one sensor based on the determined level of the computing resource. A system and a computer program product are also provided.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 363,599, filed on April 26, 2022, and incorporates by reference the disclosures of the following applications in their entirety: U.S. Non-Provisional Patent Application No. 18139526, filed on April 26, 2023, entitled “SCALABLECONFIGURABLE CHIP ARCHITECTURE” and U.S. Non-Provisional Patent Application No. 18139857, filed on April 26, 2023, entitled “DISTRIBUTED COMPUTING ARCHITECTURE WITH SHARED MEMORY FOR AUTONOMOUS ROBOTIC SYSTEMS.” Background Art

[0003] Autonomous vehicles contain computer processors and sensors to navigate on roads and other drivable areas. The conditions encountered by autonomous vehicles may be different. Different types of conditions require different types of actions by the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0004] Figure 1 is an example environment in which a vehicle including one or more components of an autonomous system may be implemented;

[0005] Figure 2 is a diagram of one or more systems of a vehicle including an autonomous system;

[0006] Figure 3 yes Figure 1 and Figure 2 a diagram of one or more devices and / or components of one or more systems;

[0007] Figure 4A is a diagram of some components of an autonomous system;

[0008] Figure 4B is a graph of the implementation of a neural network;

[0009] Figure 4C and Figure 4D is a diagram illustrating an example operation of a CNN;

[0010] Figure 4E is a graph of the implementation of a machine learning model;

[0011] Figure 5 is a diagram of an implementation of a process for managing vehicle resources based on scenarios; and

[0012] Figure 6is a flow chart of a process for managing vehicle resources based on scenarios. DETAILED DESCRIPTION

[0013] In the following description, for the purpose of explanation, many specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be apparent that the embodiments described in the present disclosure can be implemented without these specific details. In some instances, well-known configurations and devices are illustrated in block diagram form to avoid unnecessarily obscuring aspects of the present disclosure.

[0014] In the accompanying drawings, for ease of description, the specific arrangement or order of schematic elements (such as those representing systems, devices, modules, instruction blocks and / or data elements, etc.) is illustrated. However, those skilled in the art will understand that, unless explicitly described, the specific order or arrangement of schematic elements in the accompanying drawings is not intended to mean that a specific processing order or sequence, or separation of processing is required. In addition, unless explicitly described, the inclusion of schematic elements in the accompanying drawings is not intended to mean that such elements are required in all embodiments, nor is it intended to mean that the features represented by such elements cannot be included in some embodiments or cannot be combined with other elements in some embodiments.

[0015] In addition, in the accompanying drawings, connecting elements (such as solid or dotted lines or arrows, etc.) are used to illustrate the connection, relationship or association between or among two or more other schematic elements, and the absence of any such connecting elements is not intended to mean that there can be no connection, relationship or association. In other words, some connections, relationships or associations between elements are not illustrated in the accompanying drawings so as not to obscure the present disclosure. In addition, for ease of illustration, a single connecting element can be used to represent multiple connections, relationships or associations between elements. For example, if the connecting element represents the communication of a signal, data or instruction (e.g., "software instruction"), it will be understood by those skilled in the art that such an element can represent one or more than one signal path (e.g., bus) that may be needed to affect the communication.

[0016] Although the terms "first", "second" and / or "third", etc. are used to describe various elements, these elements should not be limited by these terms. The terms "first", "second" and / or "third" are only used to distinguish one element from another. For example, a first contact may be referred to as a second contact, and similarly, a second contact may be referred to as a first contact without departing from the scope of the described embodiments. Both the first contact and the second contact are contacts, but they are not the same contacts.

[0017] The terms used in the specification of the various embodiments described herein are included only for the purpose of describing specific embodiments and are not intended to be limiting. As used in the specification of the various embodiments described and the appended claims, the singular forms "a", "an" and "the" are also intended to include plural forms and can be used interchangeably with "one or more than one" or "at least one", unless the context clearly indicates otherwise. It will also be understood that the term "and / or" as used herein refers to and includes any and all possible combinations of one or more than one of the associated listed items. It will also be understood that when the terms "include", "comprise", "have" and / or "have" are used in this specification, the stated features, integers, steps, operations, elements and / or components are specifically stated, but the presence or addition of one or more than one other features, integers, steps, operations, elements, components and / or groups thereof are not excluded.

[0018] As used herein, the terms "communication" and "communicating" refer to at least one of receiving, receiving, transmitting, transmitting and / or providing information (or information represented by, for example, data, signals, messages, instructions and / or commands, etc.). For a unit (e.g., a device, a system, a component of a device or system, and / or a combination thereof) to communicate with another unit, this means that the unit is able to directly or indirectly receive information from the other unit and / or send (e.g., transmit) information to the other unit. This can refer to a direct or indirect connection that is wired and / or wireless in nature. In addition, even if the transmitted information can be modified, processed, relayed and / or routed between the first unit and the second unit, the two units can communicate with each other. For example, even if the first unit passively receives information and does not actively transmit information to the second unit, the first unit can communicate with the second unit. As another example, if at least one intermediary unit (e.g., a third unit located between the first unit and the second unit) processes the information received from the first unit and transmits the processed information to the second unit, the first unit can communicate with the second unit. In some embodiments, a message may refer to a network packet (eg, a data packet, etc.) that includes data.

[0019] As used herein, the term "if" is optionally interpreted to mean "when," "at," "in response to being determined to be," and / or "in response to being detected," etc., depending on the context. Similarly, the phrases "if it is determined" or "if [the stated condition or event] is detected" are optionally interpreted to mean "when determining," "in response to being determined to be" or "when [the stated condition or event] is detected," and / or "in response to being detected," etc., depending on the context. In addition, as used herein, the terms "have," "have," or "possess," etc. are intended to be open-ended terms. Furthermore, unless expressly stated otherwise, the phrase "based on" is intended to mean "based at least in part on."

[0020] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various embodiments described. However, it will be apparent to one of ordinary skill in the art that the various embodiments described may be implemented without these specific details. In other cases, well-known methods, processes, components, circuits, and networks have not yet been described in detail in order not to unnecessarily obscure aspects of the embodiments.

[0021] General Overview

[0022] Vehicles, such as autonomous vehicles, can adapt their use of computing resources to the scenarios encountered by the vehicle. Scenarios are categories of conditions in the vehicle's environment, for example, activities occurring in the vehicle's environment. Different scenarios require different amounts of vehicle resources when the vehicle is operating in the corresponding scenario. For example, a scenario that includes a lot of pedestrian traffic may require more computing resources (including battery power and communication network bandwidth) because navigation near pedestrians requires more complex calculations and more information to be collected and processed from the vehicle's sensors. Therefore, the vehicle can increase available resources in more demanding scenarios and reduce available resources in less demanding scenarios.

[0023] Some of the advantages of these technologies include better power management without sacrificing vehicle functionality. Since fewer computing resources are used in less demanding scenarios, the vehicle can save on power consumption and communication network bandwidth.

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

[0025] Vehicles 102a-102n (individually referred to as vehicles 102 and collectively referred to as vehicles 102) include at least one device configured to transport goods and / or people. In some embodiments, vehicles 102 are configured to communicate with V2I devices 110, remote AV systems 114, fleet management systems 116, and / or V2I systems 118 via network 112. In some embodiments, vehicles 102 include cars, buses, trucks, and / or trains, etc. In some embodiments, vehicles 102 are similar to vehicles 200 described herein (see Figure 2 ). In some embodiments, vehicles 200 in the set of vehicles 200 are associated with an autonomous queue manager. In some embodiments, vehicles 102 travel along respective routes 106a-106n (individually referred to as routes 106 and collectively referred to as routes 106) as described herein. In some embodiments, one or more vehicles 102 include an autonomous system (e.g., an autonomous system that is the same as or similar to autonomous system 202).

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

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

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

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

[0030] The network 112 includes one or more wired and / or wireless networks. In an example, the network 112 includes a cellular network (e.g., a long-term evolution (LTE) network, a third generation (3G) network, a fourth generation (4G) network, a fifth generation (5G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., a public switched telephone network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber-based network, a cloud computing network, etc., and / or a combination of some or all of these networks, etc.

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

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

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

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

[0035] Reference now Figure 2 , vehicle 200 (which can be Figure 1 102) includes, or is associated with, autonomous system 202, powertrain control system 204, steering control system 206, and braking system 208. In some embodiments, vehicle 200 is similar to vehicle 102 (see Figure 1 ) is the same or similar. In some embodiments, the autonomous system 202 is configured to give the vehicle 200 autonomous driving capabilities (e.g., implementing at least one driving automation or maneuver-based function, feature and / or device that enables the vehicle 200 to operate partially or completely without human intervention, including but not limited to fully autonomous vehicles (e.g., vehicles that abandon reliance on human intervention such as Level 5 ADS-operated vehicles, etc.), highly autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in certain situations such as Level 4 ADS-operated vehicles, etc.), and / or conditionally autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in limited situations such as Level 3 ADS-operated vehicles, etc.)). In one embodiment, the autonomous system 202 includes the operational or tactical functionality required to enable the vehicle 200 to operate in traffic on the road and continuously perform part or all of the dynamic driving task (DDT). In another embodiment, the autonomous system 202 includes an advanced driver assistance system (ADAS) that includes driver support features. The autonomous system 202 supports various levels of driving automation ranging from no driving automation (e.g., level 0) to full driving automation (e.g., level 5). For a detailed description of fully autonomous vehicles and highly autonomous vehicles, reference can 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, the vehicle 200 is associated with an autonomous queue manager and / or a ridesharing company.

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

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

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

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

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

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

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

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

[0044] Safety controller 202g includes at least one device configured to communicate with camera 202a, LiDAR sensor 202b, Radar sensor 202c, microphone 202d, communication device 202e, autonomous vehicle computing 202f, and / or DBW system 202h. In some examples, safety controller 202g includes one or more controllers (electrical controllers and / or electromechanical controllers, etc.) configured to generate and / or transmit control signals to operate one or more devices of vehicle 200 (e.g., powertrain control system 204, steering control system 206, and / or braking system 208, etc.). In some embodiments, safety controller 202g is configured to generate control signals that take precedence over (e.g., override) control signals generated and / or transmitted by autonomous vehicle computing 202f.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0066] In some embodiments, perception system 402, planning system 404, positioning system 406, and / or control system 408 implement at least one machine learning model (e.g., at least one multi-layer perceptron (MLP), at least one convolutional neural network (CNN), at least one recurrent neural network (RNN), at least one autoencoder, and / or at least one transformer, etc.). In some examples, perception system 402, planning system 404, positioning system 406, and / or control system 408 implement at least one machine learning model alone or in combination with one or more of the above systems. In some examples, perception system 402, planning system 404, positioning system 406, and / or control system 408 implement at least one machine learning model as part of a pipeline (e.g., a pipeline for identifying one or more objects located in an environment, etc.). The following is about FIG. 4B to FIG. 4D Includes examples of implementations of machine learning models.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0087] Reference now Figure 4E , a diagram illustrating an implementation of a machine learning model. More specifically, a diagram illustrating an implementation of a transformer model 482 is illustrated. In some example embodiments, the transformer model 482 may implement the perception system 402, the planning system 404, the positioning system 406, and / or the control system 408. As will be described in more detail, the transformer model 482 may include a self-attention mechanism to capture the relative importance and relationships between different parts of the input 483. For example, where the input 483 is an image (e.g., of an environment near a vehicle), when generating an output 495 including one or more labels that classify one or more objects present in the image, for example, the self-attention mechanism of the transformer model 482 may capture the relative importance and relationships between different parts (or patches) of the image. Although the transformer model 482 includes certain features as described herein, these features are provided for illustrative purposes and are not intended to limit the present disclosure.

[0088] like Figure 4E As shown, the transformer model 482 may include an encoder stack having a plurality of encoders 484 (or encoding layers) coupled to a decoder stack having a plurality of decoders 486 (or decoding layers). Figure 4EIn the example shown, an input 483 (e.g., embeddings of individual portions of the input 483) flows through successive encoders 484, where the output of the last encoder 484 is passed to each decoder 486 in the decoder stack. For example, in some cases, each encoder 484 in the encoder stack can generate an encoding that contains information about which portions of the input 483 are related to each other. In addition, the output of one encoder 484 can be passed as input to the next encoder 484 in the encoder stack. Thus, in some cases, a first encoder 484 in the encoder stack can generate a first encoding of the input 483 (e.g., embeddings of individual portions of the input 483), and a next encoder 484 in the encoder stack can generate a second encoding of the first encoding.

[0089] like Figure 4E As shown, in some cases, each encoder 484 can include a self-attention layer 485 and a feed-forward network 487. Various portions of the input 483 (e.g., various embedded portions of the input 483) can flow through their own paths in the encoder 484, wherein the self-attention layer 485 determines the relationship (or association) between the various individual portions of the input 483. For example, where the input 483 is an image, the self-attention layer 485 can determine the relationship between different portions (or patches) of the image. In doing so, the self-attention layer 485 enables the encoder 484 to generate a context-aware encoding of the input 483, wherein the encoding of the various individual portions of the input 483 incorporates weighted values ​​corresponding to other portions of the input 483. For example, in some cases, an encoding of a first portion of input 483 (e.g., a first embedding of the first portion of input 483) can be generated to incorporate a first value corresponding to a second portion of input 483 and a second value corresponding to a third portion of input 483, wherein the first value and the second value are weighted to reflect the extent to which the second portion and the third portion of input 483 should affect the encoding of the first portion of input 483. In some cases, self-attention layer 485 may include a multi-headed attention mechanism, wherein each head applies a different set of weights (e.g., query, key, and value weight matrices) to incorporate other portions of input 483. It should be understood that the weights (e.g., query, key, and value weight matrices) applied by self-attention layer 485 can be learned during training of transformer model 482.

[0090] Reference again Figure 4E, the decoder stack can decode the input 483 based on the attention vector output by the last encoder 484 in the encoder stack to generate an output 495, where each decoder 486 in the decoder stack successively decodes the output of the previous decoder 486. For example, the first decoder 486 in the decoder stack can generate a first decoding of the input 483 (e.g., embeddings of individual portions of the input 483), and the next decoder 486 in the decoder stack can generate a second decoding of the first decoding. Figure 4E As shown, each decoder 486 may include a self-attention layer 489, an encoder-decoder attention layer 491, and a feed-forward network 493. The self-attention layer 489 of the decoder 486 may enable the decoder 486 to generate a context-aware decoding of the input 483, wherein the decoding of each individual portion of the input 483 incorporates weighted values ​​corresponding to one or more previous portions of the input 483. On the other hand, the encoder-decoder attention layer 491 may determine weighted values ​​indicating the relative importance of each corresponding portion of the input 483 based at least on the attention vector output by the last encoder 484 of the encoder stack.

[0091] Figure 5 A diagram illustrating an example system 500 for managing vehicle resources based on scenarios, by which the techniques of the present disclosure may be implemented. Figure 5 As shown, the example system 500 includes a vehicle 502, a machine learning (ML) detection model 504, a situational awareness system 506, a sensor and computing unit controller 508, a sensor 510, a processor 512, a power supply 514, a prediction and planning component 516, and a vehicle status component 518. The example system 500 may also be incorporated (as shown in reference Figure 1 4) and other components associated with the operation of the vehicle 502. The vehicle 502 includes components such as reference Figure 2 The vehicle 502 may be configured to include or be coupled to any one of the machine learning (ML) detection model 504, the situational recognition system 506, the sensor and computing unit controller 508, the sensor 510, the processor 512, the power supply 514, the prediction and planning component 516, and the vehicle status component 518. The vehicle 502 may be configured to perform one or more operations for managing vehicle resources based on the situation predicted by the ML detection model 504.

[0092] like Figure 5 As illustrated, the ML detection model 504 may include one or more devices communicatively coupled to the vehicle 502. The ML detection model 504 may include Figure 4D 504). In some embodiments, one or more functions associated with the ML detection model 504 described herein may be implemented by one or more other devices, or implemented in coordination with one or more other devices. The functions of the ML detection model 504 may include identifying potential scenarios for the vehicle 502 based on data received from the sensor 510, the processor 512, and / or the power supply 514. The ML detection model 504 may include, for example, a logical neural network (LNN) 520, an invertible neural network (INN) 522, and / or a recursive neural network (RNN) 524 (as described in reference to FIG. FIG. 4B to FIG. 4E The neural networks 520, 522, 524 of the ML detection model 504 may be trained based on different types of sensor data to predict vehicle operation scenarios. The ML detection model 504 may provide data received from the vehicle's sensors 510 as input to one or more machine learning models 522, 524, 526 trained based on scenario data. The output of the machine learning model identifies a specific scenario. In some embodiments, the output of the neural networks 520, 522, 524 of the ML detection model 504 and the output of the prediction and planning component 516 may be used in scenario recognition performed by the scenario recognition system 506. The prediction and planning component 516 may include one or more processors configured to predict future operations of the vehicle 502 given various past operations associated with vehicle maneuvers. In some embodiments, the prediction and planning component 516 may include an ML prediction model that may model the energy consumption of different vehicle components when making predictions.

[0093] In some embodiments, the context recognition system 506 may include one or more devices communicatively coupled to the vehicle 502, the ML detection model 504, the prediction and planning component 516, and the sensor and computing unit controller 508. For example, the context recognition system 506 may be configured to process inputs received from the ML detection model 504 and the prediction and planning component 516 to identify contexts encountered by the vehicle 502. Contexts may include categories of conditions in the vehicle's environment, such as activities occurring in the vehicle's environment. The context recognition system 506 may use information about the vehicle's environment to identify a context corresponding to a current context among several discrete types of contexts. The context recognition system 506 may generate a computing resource allocation plan that includes an allocation of an amount of computing resources appropriate for the identified context. Examples of computing resources include resources associated with the sensor 510, the processor 512, the power supply 514 (e.g., battery resources), and communication resources (e.g., network resources). The context recognition system 506 may transmit the computing resource allocation plan to the sensor and computing unit controller 508.

[0094] The sensor and computing unit controller 508 may include one or more devices communicatively coupled to the vehicle 502, the situational awareness system 506, the sensor 510, the processor 512, and the power supply 514. For example, the sensor and computing unit controller 508 may receive input from the prediction and planning component 516 and the vehicle condition component 518 to generate adjustments to one or more sensor parameters. In some embodiments, the sensor and computing unit controller 508 may adjust one or more entries of the computing resource allocation plan according to the setting criteria and / or setting ranges included in the lookup table.

[0095] Sensor(s) 510 (e.g., reference Figure 2The cameras 202a, LiDAR sensors 202b, Radar sensors 202c, microphones 202d, communication devices 202e, safety controllers 202g, and / or DBW systems 202h) depicted may be configured to monitor various parameters associated with the vehicle 502. For example, certain sensor(s) 510 may monitor and / or detect changes occurring in the environment of the vehicle, while other sensor(s) 510 may monitor and / or detect various aspects associated with operational aspects of the vehicle 502. Any information and / or data transmitted by the sensor(s) 510 to the ML detection model 504 (or any other processing component) may be used to predict vehicle operational scenarios including travel paths, directions, speeds, and / or other movement and / or resource consumption parameters. Such information and / or data includes information and / or data related to one or more other agents in the vehicle's environment (e.g., vehicles, pedestrians, objects, etc.) and / or information and / or data related to one or more components of vehicle 502 including processor 512 and / or power supply 514. Operation of vehicle 502 includes adjusting vehicle resources associated with movement of vehicle 502 and / or parameters of maneuvers performed by vehicle 502.

[0096] Processor 512 may include, for example, a reference Figure 3 The processor 304 and the like may be one or more processors (eg, a central processing unit (CPU), a graphics processing unit (GPU), and / or an accelerated processing unit (APU), etc.). Figure 6 As described above, processor 512 may perform processing based on the identified scenario. One or more components of vehicle 502 (e.g., sensor(s) 510, processor 512, motor, steering control system, or reference Figure 1 4) can be processed based on the processor 512 executing software instructions according to the identified (ongoing or future predicted) scenario.

[0097] The power supply 514 may include one or more power supply sources (e.g., rechargeable batteries) to provide power to one or more components of the vehicle 502 (e.g., sensor(s) 510, processor 512, motor, steering control system, or reference Figure 1 4) to provide energy (e.g., fuel and / or electricity, etc.). Power supply 514 can be configured to provide at least enough power for minimal energy consumption. Power supply 514 can adjust the supplied power level relative to the identified (ongoing or future predicted) scenario.

[0098] In some embodiments, one or more components of the example system 500 (e.g., any of the ML detection model 504, the situational recognition system 506, the sensor and computing unit controller 508, the sensor 510, the processor 512, the power supply 514, the prediction and planning component 516, and the vehicle status component 518) may be embodied in an integrated circuit (e.g., a "system on a chip" [SoC]) having functionality specific to the integrated component. A system on a chip (SoC) refers to an integrated circuit (or "chip") that integrates all or most of the components of a computing system and / or other electronic system. These components include, for example, a central processing unit (CPU), input / output (I / O) devices, memory, storage, etc. Other components may include various communication components, a graphics processing unit (GPU), etc. These components may be integrated on a single substrate or microchip. Various digital, analog, mixed signal and / or radio frequency (RF) signal processing functions, etc. may also be incorporated. The SoC may integrate a microcontroller, a microprocessor, and / or one or more processor cores with a GPU, Wi-Fi and / or cellular network radio components, etc. Similar to how a microcontroller integrates a microprocessor with peripheral circuits and memory, a SoC can be viewed as integrating a microcontroller with even more advanced peripherals. In some embodiments, the SoC can be a single integrated circuit package that includes hardware components corresponding to the context recognition system 506 (e.g., an application specific integrated circuit), hardware components corresponding to the sensor and computing unit controller 508 (e.g., an integrated flash memory component), and one or more hardware components corresponding to the machine learning models 520, 522, 524. In some embodiments, the SoC includes multiple SoCs that communicate with each other. For example, the context recognition system 506 can be integrated in one SoC and the processor 512 can be integrated in another SoC. In some embodiments, the SoC includes: multiple hardware components, such that at least one hardware component is configured to obtain information representing the environment of the vehicle; at least one hardware component that includes the context recognition system 506; at least one hardware component that includes the sensor and computing unit controller 508; and at least one hardware component that is configured to adjust one or more sensor parameters of the sensor 510.

[0099] Continue to refer Figure 5 , one or more functions will be described as being performed by the example system 500 . Figure 5 The number and arrangement of components and / or devices of the example system 500 shown are provided as examples. Figure 5 There may be additional systems and / or devices, fewer systems and / or devices, different systems and / or devices, or differently arranged systems and / or devices than shown. Figure 5Two or more of the systems and / or devices shown may be implemented in a single system or a single device, or Figure 5 The single system or single device shown may be implemented as multiple distributed systems or devices. Additionally or alternatively, a system set or device set (e.g., one or more systems, one or more devices) of the example system 500 may perform one or more functions corresponding to different types of scenarios, which are described as being performed by other system sets or other device sets of the example system 500. Examples of scenario types include "high traffic scenario", "congested pedestrian scenario", "highway scenario", "local road scenario", "parking lot scenario", or other scenarios that the vehicle 502 may encounter. In some embodiments, different scenarios require different amounts of vehicle resources when the vehicle is operating in the corresponding scenario. For example, a scenario that includes a large amount of pedestrian traffic may require more computing resources (including battery power and communication network bandwidth) because navigation near pedestrians requires more complex calculations and more information collected and processed from the vehicle's sensors.

[0100] Scenario recognition system 506 may be used to allocate an amount of computing resources appropriate to the scenario. Examples of computing resources include processor resources, sensor resources, network resources, and battery resources. For example, a "crowded pedestrian scenario" may require the use of a greater amount of computing resources to accurately sense a large number of pedestrians and safely navigate around these pedestrians. In such a scenario, vehicle 502 may operate its sensors 510 in a manner that collects the maximum amount of data per unit time. Examples of such sensor operations include operating a camera at a high frame rate (e.g., Figure 2 ) and operating the LiDAR sensor 510 (e.g., camera 202a shown in FIG. 1 ) at a high rotation frequency (e.g., the number of 360 degree rotations about an axis within a set time interval (such as during 1 second, etc.) Figure 2 2b). Additionally, in this scenario, vehicle 502 may operate one or more of its processors 512 at a high clock rate (e.g., Figure 2 The autonomous vehicle shown calculates 202f).

[0101] In contrast, some scenarios do not require the use of as many computing resources. For example, a "parking lot scenario" may use fewer computing resources than the "crowded pedestrian scenario" described above. In the "parking lot scenario," the vehicle 502 may safely navigate in a parking lot (e.g., an empty parking lot) using less information collected and processed from the surrounding environment. In the "parking lot scenario," the camera may be set to a lower frame rate (e.g., 30 FPS instead of 60 FPS), the LiDAR sensor may be set to a lower rotation frequency (e.g., 20 Hz instead of 60 Hz), and one or more of the vehicle's processors may be set to a lower clock rate (e.g., 1 GHz instead of 2 GHz). The settings for the "parking lot scenario" use less power and may extend the battery life of the vehicle's systems. Because less data is collected and processed, the vehicle's communication network (e.g., Figure 3 The bus 302 shown uses less bandwidth.

[0102] In use, information from one or more sensors in the vehicle's sensors 510 may be provided to one or more machine learning models 520, 522, 524 trained based on the context data. The output of the ML model identifies a particular context. In some embodiments, the output of the machine learning models 520, 522, 524 may be used by the vehicle prediction and planning component 516 (e.g., the perception system 402 and the planning system 404 shown in FIG. 4).

[0103] The context recognition system 506 can process the identified contexts to determine appropriate levels of computing resources that can be adjusted by the sensor and computing unit controller 508 using a lookup table. In some implementations, techniques other than lookup tables are used (e.g., a machine learning model can be used in place of a lookup table). In some embodiments, the appropriate level of computing resources corresponds to a threshold level of the resource. For example, the lookup table can include multiple thresholds such that each threshold corresponds to one or more contexts. In such embodiments, the contexts are arranged in order from the least demanding to the most demanding computing resources, and each threshold corresponds to a set of configuration parameters.

[0104] In some embodiments, the lookup table also has multiple options for the level of computing resources to be used for a particular scenario. For example, the level of computing resources can vary according to the vehicle condition 518 (e.g., fully operating in motion, partially operating in a stationary state, or operating in an energy-saving mode when a reduction in battery life is detected). As an example, if the vehicle condition indicates that the vehicle 502 is traveling at a high speed, then a particular scenario may require more computing resources than the vehicle 502 is traveling at a lower rate. The lookup table includes levels of resources suitable for (e.g., above a rate threshold) high speeds and levels of resources suitable for low speeds. Other types of vehicle conditions (e.g., vehicle temperature) may require different levels of resources for similar scenarios.

[0105] The output of the sensor and computing unit controller 508 using the lookup table is used to adjust one or more parameters of the vehicle's system. In some embodiments, one or more parameters of the vehicle's sensor 510 can be adjusted to operate at different levels (e.g., camera frame rate or LiDAR frequency). In some embodiments, one or more parameters of the vehicle's processor 512 (e.g., processor clock rate) are adjusted. In some embodiments, one or more parameters of the vehicle's power supply 514 (e.g., voltage or current limit) are adjusted.

[0106] Reference now Figure 6 , illustrates a flow chart of a process 600 for managing vehicle resources based on scenarios. In some embodiments, one or more of the steps described with respect to process 600 are performed by Figure 5 In some embodiments, one or more of the steps described with respect to process 600 are performed by other devices or groups of devices (e.g., completely and / or partially, etc.) separate from or including vehicle 502 and situational awareness system 506. For example, one or more of the operations described with respect to process 600 are performed by a vehicle (e.g., referenced to FIG. 1 ). Figure 1 The vehicles 102a, 102b, 102n described or referenced Figure 2 The vehicle 200 described or referenced Figure 5In some embodiments, the process 600 is performed by the perception system 402, planning system 404, and / or control system 408 (e.g., completely, partially, sequentially, and / or non-sequentially, etc.) of the autonomous vehicle software 400 of the example vehicle 502). Additionally or alternatively, in some embodiments, one or more steps described with respect to the process 600 are performed by other devices or groups of devices (e.g., completely, partially, sequentially, and / or non-sequentially, etc.) separate from or including the autonomous vehicle software 400 and / or the example system 500.

[0107] At 602, information representing an environment of a vehicle is obtained. This may be obtained from one or more sensing devices (e.g., reference Figure 1 The V2I device 110 described in reference Figure 2 The camera 202a, LiDAR sensor 202b, Radar sensor 202c and microphone 202d described in reference to FIG. 4, the perception system 402 described in reference to FIG. Figure 5 The sensor 510 of the vehicle described as being attached to the vehicle obtains information. The sensing device can be attached to the vehicle or integrated in the vehicle to identify one or more operating parameters of the intelligent agent and / or the internal components of the vehicle present in the environment around the vehicle. For example, the sensing device can monitor parameters associated with the intelligent agent related to the movement of the corresponding vehicle and parameters associated with the vehicle to which the sensing device is attached. Parameters associated with the environment around the vehicle may include, but are not limited to, parameters associated with the movement of other vehicles (e.g., speed, direction, etc.) and / or parameters associated with other objects (e.g., pedestrians, light poles, etc.). Parameters associated with the vehicle may include, but are not limited to, parameters associated with the state of the vehicle, such as heading, driving speed, etc. In addition, parameters associated with the vehicle may include, but are not limited to, parameters associated with the operating condition and indicators or potential failures of the vehicle, such as tire inflation pressure, oil level, transmission fluid temperature, etc.

[0108] At 604, a current context of the vehicle's environment is determined. In some embodiments, the current context is determined based on information representing the vehicle's environment. In some embodiments, determining the current context includes providing at least one machine learning model (e.g., Figure 5 The machine learning models 522, 524, 526 shown provide information representing the environment of the vehicle. In such an embodiment, at least one machine learning model generates at least one output associated with the current scenario. The scenarios can be classified based on scenario types. Scenario types can include "high traffic scenario", "congested pedestrian scenario", "highway scenario", "local road scenario", "parking lot scenario" or other scenarios that the vehicle may encounter.

[0109] At 606, a level of computing resources appropriate for the determined current scenario is determined. In some embodiments, the level of computing resources is determined based on the determined current scenario. In some embodiments, determining the level of computing resources includes accessing a lookup table (e.g., referring to Figure 5 In some embodiments, the vehicle status information is determined. For example, the current vehicle status indicates whether the vehicle is stationary or moving. In such embodiments, the level of computing resources appropriate for the determined current scenario is determined based at least in part on the current vehicle status.

[0110] At 608, at least one parameter associated with at least one sensor is adjusted. In some embodiments, the adjustment is made based on the determined level of computing resources. In an example using a lookup table, at least one parameter is adjusted based on information associated with an entry of the lookup table. In some embodiments, at least one sensor includes a LiDAR, and at least one parameter includes a rotation frequency of the LiDAR. In some embodiments, at least one sensor includes a camera, and at least one parameter includes a frame rate of the camera. In some embodiments, the voltage or current limit of the power supply of the vehicle is adjusted based on the determined level of computing resources. In this way, the amount of power consumed by the vehicle is consistent with the scenario encountered by the vehicle. In some embodiments, the bandwidth of the communication network of the vehicle is adjusted based on the determined level of computing resources. For example, elements of a communication network that implements a higher bandwidth are powered off to save battery power. In some embodiments, the clock rate of one or more processors of the vehicle is adjusted based on the determined level of computing resources. For example, reducing the clock rate of the processor saves battery power.

[0111] According to some non-limiting embodiments or examples, a vehicle is provided, comprising: at least one computer-readable medium storing computer-executable instructions; at least one processor communicatively coupled to at least one sensor and configured to execute the computer-executable instructions, the execution performing operations including: obtaining information representing an environment of the vehicle from at least one sensor; determining a current scenario of the environment of the vehicle based on the information representing the environment of the vehicle; determining a level of computing resources suitable for the determined current scenario based on the determined current scenario; and adjusting at least one parameter associated with at least one sensor based on the determined level of computing resources.

[0112] According to some non-limiting embodiments or examples, a method is provided, comprising: obtaining information representing an environment of a vehicle from at least one sensor; determining a current scenario of the environment of the vehicle based on the information representing the environment of the vehicle; determining a level of computing resources suitable for the determined current scenario based on the determined current scenario; and adjusting at least one parameter associated with at least one sensor based on the determined level of computing resources.

[0113] According to some non-limiting embodiments or examples, at least one non-transitory computer-readable medium is provided, which includes one or more instructions that, when executed by at least one processor, cause the at least one processor to perform operations including: obtaining information representing an environment of a vehicle from at least one sensor; determining a current scenario of the environment of the vehicle based on the information representing the environment of the vehicle; determining a level of computing resources suitable for the determined current scenario based on the determined current scenario; and adjusting at least one parameter associated with at least one sensor based on the determined level of computing resources.

[0114] According to some non-limiting embodiments or examples, an integrated circuit is provided, comprising: at least one hardware component configured to obtain information representing an environment of a vehicle; at least one hardware component comprising a context recognition system configured to determine a current context of the environment of the vehicle based on the information representing the environment of the vehicle; at least one hardware component comprising a lookup table capable of being used to determine a level of computing resources suitable for the determined current context; and at least one hardware component configured to adjust at least one parameter associated with at least one sensor based on the determined level of computing resources.

[0115] Other non-limiting aspects or embodiments are set forth in the following numbered clauses:

[0116] Item 1: A vehicle comprising: at least one computer-readable medium storing computer-executable instructions; at least one processor communicatively coupled to at least one sensor and configured to execute the computer-executable instructions, wherein the execution performs operations including: obtaining information representing an environment of the vehicle from the at least one sensor; determining a current scenario of the environment of the vehicle based on the information representing the environment of the vehicle; determining a level of computing resources suitable for the determined current scenario based on the determined current scenario; and adjusting at least one parameter associated with the at least one sensor based on the determined level of computing resources.

[0117] Clause 2: A vehicle as described in clause 1, wherein determining a current context of the vehicle's environment includes providing information representing the vehicle's environment to at least one machine learning model, wherein providing the information causes the at least one machine learning model to generate at least one output associated with the current context.

[0118] Clause 3: A vehicle according to any of the preceding clauses, wherein determining the level of computing resources appropriate for a determined current scenario comprises accessing an entry of a lookup table, the entry corresponding to the determined current scenario, and wherein the at least one parameter is adjusted based on information associated with the entry.

[0119] Clause 4: A vehicle as described in any of the preceding clauses, wherein the operation includes determining information related to a current vehicle condition, wherein a level of computing resources appropriate for the determined current scenario is determined based at least in part on the current vehicle condition.

[0120] Clause 5: A vehicle as described in any of the preceding clauses, wherein the at least one sensor comprises a LiDAR sensor, and the at least one parameter comprises a rotational frequency of the LiDAR sensor.

[0121] Clause 6: A vehicle as described in any of the preceding clauses, wherein the at least one sensor comprises a camera, and the at least one parameter comprises a frame rate of the camera.

[0122] Clause 7: A vehicle as recited in any preceding clause, wherein the vehicle further comprises a power supply, the operation comprising adjusting a voltage of the power supply based on a level of computing resources appropriate for the determined current context.

[0123] Clause 8: The vehicle of any preceding clause, wherein the vehicle further comprises a communication network, the operation comprising adjusting a bandwidth of the communication network based on the level of computing resources determined to be appropriate for the current scenario.

[0124] Clause 9: A vehicle as recited in any preceding clause, wherein the operation comprises adjusting a clock rate of the processor based on a level of computing resources appropriate for the determined current scenario.

[0125] Clause 10: A method comprising performing the operations according to any of the preceding clauses.

[0126] Clause 11: A non-transitory computer-readable storage medium comprising at least one program for execution by one or more processors of a first device, the at least one program comprising instructions that, when executed by the one or more processors, cause the first device to perform the method according to clause 10.

[0127] Item 12: An integrated circuit comprising: at least one hardware component configured to obtain information representing an environment of a vehicle; at least one hardware component comprising a context recognition system configured to determine a current context of the environment of the vehicle based on the information representing the environment of the vehicle; at least one hardware component comprising a lookup table capable of being used to determine a level of computing resources appropriate for the determined current context; and at least one hardware component configured to adjust at least one parameter associated with at least one sensor based on the determined level of computing resources.

[0128] In the previous description, aspects and embodiments of the present disclosure have been described with reference to many specific details, which may vary depending on the implementation. Therefore, the description and the accompanying drawings should be regarded as illustrative, not restrictive. The only and exclusive indication of the scope of the invention, and the applicant's expectation that the content of the scope of the invention is the literal and equivalent scope of the claims issued from this application in the specific form of the claims, including any subsequent amendments. Any definition of the terms used to be included in such claims that are clearly set forth herein should be based on the meaning of such terms as used in the claims. In addition, when the term "also includes" is used in the previous description or the attached claims, the following of the phrase may be an additional step or entity, or a sub-step / sub-entity of the previously described step or entity.

Claims

1. A vehicle, comprising: at least one computer-readable medium storing computer-executable instructions; At least one processor communicatively coupled to at least one sensor and configured to execute the computer executable instructions, the execution performing operations including: obtaining information representative of the environment of the vehicle from the at least one sensor; determining a current context of the vehicle's environment based on the information representative of the vehicle's environment; determining a level of computing resources appropriate for the current scenario based on the current scenario; as well as At least one parameter associated with the at least one sensor is adjusted based on the level of the computing resources.

2. The vehicle according to claim 1, wherein: Determining a current context of the vehicle's environment includes providing information representing the vehicle's environment to at least one machine learning model, wherein providing the information causes the at least one machine learning model to generate at least one output associated with the current context.

3. A vehicle according to any one of the preceding claims, wherein: Determining a level of computing resources appropriate for the current scenario includes accessing an entry of a lookup table, the entry corresponding to the current scenario, and the at least one parameter is adjusted based on information associated with the entry.

4. A vehicle according to any one of the preceding claims, wherein: The operations include determining information related to current vehicle conditions, a level of computing resources appropriate for the current scenario being determined based at least in part on the current vehicle conditions.

5. A vehicle according to any one of the preceding claims, wherein: The at least one sensor comprises a LiDAR sensor, and the at least one parameter comprises a rotation frequency of the LiDAR sensor.

6. A vehicle according to any one of the preceding claims, wherein: The at least one sensor comprises a camera, and the at least one parameter comprises a frame rate of the camera.

7. A vehicle according to any one of the preceding claims, wherein: The vehicle also includes a power supply, and the operations include adjusting a voltage of the power supply based on a level of computing resources appropriate for the current scenario.

8. A vehicle according to any one of the preceding claims, wherein: The vehicle also includes a communication network, and the operations include adjusting a bandwidth of the communication network based on a level of computing resources appropriate for the current scenario.

9. A vehicle according to any one of the preceding claims, wherein: The operations include adjusting a clock rate of the processor based on a level of computing resources applicable to the current context.

10. A method comprising performing the operations according to any one of claims 1 to 9.

11. A non-transitory computer-readable storage medium comprising at least one program for execution by one or more processors of a first device, the at least one program comprising instructions which, when executed by the one or more processors, cause the first device to perform the method according to claim 10.

12. An integrated circuit comprising: at least one hardware component configured to obtain information representative of an environment of a vehicle; comprising at least one hardware component of a context recognition system configured to determine a current context of the vehicle's environment based on information representative of the vehicle's environment; at least one hardware component comprising a lookup table operable to determine a level of computing resources appropriate for the current scenario; as well as At least one hardware component is configured to adjust at least one parameter associated with at least one sensor based on the level of the computing resource.

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