Systems, methods, and storage media for vehicle sensor management

Abstract

The present disclosure provides systems, methods, and storage media for vehicle sensor management. A method for managing vehicle sensors is provided, which can include determining a stopping distance for a vehicle to travel on a route, identifying one or more sensors of the vehicle having respective detection ranges that are less than the stopping distance, and deactivating at least one operation of at least one of the one or more sensors upon identifying the one or more sensors. Systems and computer program products are also provided.

Description

Technical Field

[0001] This application relates to systems, methods, and storage media for vehicle sensor management. Background Technology

[0002] A typical autonomous vehicle (AV) system has multiple different types of vehicle sensors to facilitate the operation of the vehicle. These sensors can consume a significant amount of power. Battery-powered electric AVs may experience reduced range due to the large amount of energy consumed by the sensors, some of which may be wasted on sensor operations not used for the operation of the vehicle itself. Summary of the Invention

[0003] According to one aspect of this disclosure, a method for vehicle sensor management is provided, comprising: using at least one processor to determine a stopping distance of the vehicle traveling on a route; using the at least one processor to identify one or more sensors of the vehicle having a corresponding detection range less than the stopping distance; and, upon identifying the one or more sensors, using the at least one processor to deactivate at least one operation of at least one of the one or more sensors.

[0004] According to another aspect of this disclosure, a system for vehicle sensor management is provided, comprising: at least one processor; and at least one non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform the aforementioned method.

[0005] According to another aspect of this disclosure, at least one non-transitory storage medium is provided, which stores instructions that, when executed by at least one processor, cause the at least one processor to perform the aforementioned method. Attached Figure Description

[0006] Figure 1 It is an example environment that can realize a vehicle that includes one or more components of an autonomous system;

[0007] Figure 2 It is a diagram of one or more systems that include autonomous vehicles;

[0008] Figure 3 yes Figure 1 and Figure 2 A diagram of one or more devices and / or one or more system components;

[0009] Figure 4 It is a diagram of some components of an autonomous system;

[0010] Figure 5An example of a LiDAR system is shown;

[0011] Figure 6 This demonstrates the operation of the LiDAR system;

[0012] Figure 7 A block diagram of the control system is shown;

[0013] Figure 8A A block diagram of a controller including a power management module is shown;

[0014] Figure 8B A diagram showing a power management module coupled to multiple sensors;

[0015] Figure 9 This illustrates an example of vehicle sensor management using sensors to detect range and stopping distance; and

[0016] Figure 10 Example of a process used to manage vehicle sensors for power management. Detailed Implementation

[0017] In the following description, numerous specific details are set forth for purposes of explanation in order to provide a thorough understanding of this disclosure. However, it will be apparent that the embodiments described herein can be practiced without these specific details. In some instances, well-known constructions and apparatuses are illustrated in block diagram form to avoid unnecessarily obscuring aspects of this disclosure.

[0018] In the accompanying drawings, for ease of description, specific arrangements or orders of schematic elements (such as those representing systems, devices, modules, instruction blocks, and / or data elements) are illustrated. However, those skilled in the art will understand that, unless explicitly described, the specific order or arrangement of schematic elements in the drawings is not intended to imply a requirement for a particular processing order or sequence, or separation of processes. Furthermore, unless explicitly described, the inclusion of schematic elements in the drawings is not intended to imply that such elements are required in all embodiments, nor is it intended to imply that features represented by such elements cannot be included in some embodiments or cannot be combined with other elements in some embodiments.

[0019] Furthermore, in the accompanying drawings, connecting elements (such as solid or dashed lines or arrows) are used to illustrate connections, relationships, or associations between or among two or more other schematic elements. The absence of any such connecting element does not imply that connections, relationships, or associations cannot exist. In other words, some connections, relationships, or associations between elements are not illustrated in the drawings so as not to obscure the content of this disclosure. Additionally, for ease of illustration, a single connecting element may be used to represent multiple connections, relationships, or associations between elements. For example, if a connecting element represents communication of signals, data, or instructions (e.g., "software instructions"), those skilled in the art will understand that such an element may represent one or more signal paths (e.g., a bus) that may be necessary to influence the communication.

[0020] 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 used only to distinguish one element from another. For example, without departing from the scope of the described embodiments, a first contact may be referred to as a second contact, and similarly, a second contact may be referred to as a first contact. Both the first contact and the second contact are contacts, but they are not the same contact. The first contact and the second contact are different contacts.

[0021] The terminology used in the description of the various embodiments described herein is included for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various embodiments described and the appended claims, the singular forms “a,” “an,” and “the” are also intended to include the plural forms and may be used interchangeably with “one or more” or “at least one” unless the context clearly indicates otherwise. It will also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items. It will also be understood that when the terms “comprising,” “including,” “possessing,” and / or “having” are used in this specification, they specifically indicate the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0022] As used herein, the terms "communication" and "to communicate" refer to at least one of the following: receiving, receiving, transmitting, conveying, and / or providing information (or information represented by, for example, data, signals, messages, instructions, and / or commands). For a unit (e.g., an apparatus, system, component of an apparatus or system, and / or combinations thereof) that wants to communicate with another unit, this means that the unit is able to receive information directly or indirectly 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 essentially wired and / or wireless. Furthermore, two units can communicate with each other even if the transmitted information can be modified, processed, relayed, and / or routed between the first and second units. For example, the first unit can communicate with the second unit even if it passively receives information and does not actively transmit information to the second unit. As another example, the first unit can communicate with the second unit if at least one intermediary unit (e.g., a third unit located between the first and second units) processes information received from the first unit and transmits the processed information to the second unit. In some embodiments, a message may refer to a network packet that includes data (e.g., a data packet, etc.).

[0023] As used herein, depending on the context, the term "if" may optionally be interpreted as "when," "in," "in response to being determined," and / or "in response to being detected," etc. Similarly, depending on the context, the phrases "if determined" or "if [the stated condition or event] is detected" may optionally be interpreted as "in response to being determined," "in response to being determined," "or" "in response to being detected," and / or "in response to being detected," etc. Furthermore, as used herein, the terms "have," "possess," or "own," etc., are intended to be open-ended terms. Additionally, unless explicitly stated otherwise, the phrase "based on" is intended to mean "at least partially based on."

[0024] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. Numerous specific details are set forth in the following detailed description in order to provide a thorough understanding of the various embodiments described. However, it will be apparent to those skilled in the art that the various embodiments described can be practiced without these specific details. In other instances, well-known methods, processes, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0025] General Overview

[0026] In some aspects and / or embodiments, the systems, methods, and computer program products described herein include and / or implement vehicle sensor management. The vehicle (e.g., an autonomous vehicle) is configured to manage multiple different types of sensors (e.g., short-range and long-range sensors) for intelligent power management. Specifically, the vehicle's control system may deactivate some operations of one or more short-range sensors (e.g., laser ignition of a short-range LiDAR sensor) or completely disable the short-range sensors (e.g., turn off their power) during high-speed operation (e.g., at 40 mph or higher on a highway) when the detection range of a short-range sensor (e.g., 0 to 20 feet) is determined to be shorter than the vehicle's stopping distance corresponding to its current high-speed movement (e.g., 50 or 60 feet). The control system may partially or completely deactivate the short-range sensors based on one or more characteristics of the short-range sensors (e.g., priority, start-up time, rear-facing / forward-facing, active / passive). The control system can keep some high-priority short-range sensors fully operational even at high speeds, while deactivating / disabling other short-range sensors. The control system can reactivate or enable short-range sensors during low-speed operation (e.g., below 40 mph) and / or by determining that the vehicle's stopping distance is less than a distance threshold (e.g., the vehicle's detection range). The control system keeps long-range sensors with detection ranges greater than the stopping distance (e.g., 20 to 1000 feet) operational. The control system can prioritize energy and / or computing resources for higher-priority tasks among long-range sensors (e.g., processing long-range sensor data or improving response time).

[0027] The technologies for managing autonomous driving behavior, implemented using the systems, methods, and computer program products described herein, offer several advantages. First, these technologies can reduce the power usage (e.g., energy power and / or computing power) of a vehicle (e.g., a battery-powered electric autonomous vehicle) by disabling specific sensors (e.g., short-range sensors) that are not used during certain operations (e.g., high-speed operations), thereby reducing the vehicle's energy consumption. Second, these technologies can increase the vehicle's driving range and / or battery life by disabling unnecessary sensors to save energy consumption. Third, these technologies can improve performance (e.g., reduce response time and process large amounts of long-range sensor data) by reprioritizing computing resources (e.g., central processing unit (CPU) power) for high-priority long-range sensors. Fourth, these technologies can efficiently improve sensor classification and management by determining whether a sensor is unusable for high-speed operations based on whether the vehicle's stopping distance exceeds the sensor's detection range.

[0028] System Overview

[0029] Now for reference Figure 1 Example environment 100 is illustrated, in which vehicles including autonomous systems and vehicles not including autonomous systems operate. As illustrated, environment 100 includes vehicles 102a-102n, objects 104a-104n, routes 106a-106n, area 108, vehicle-to-infrastructure (V2I) device 110, network 112, remote autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118. Vehicles 102a-102n, vehicle-to-infrastructure (V2I) device 110, network 112, autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118 are interconnected via wired connections, wireless connections, or a combination of wired and wireless connections (e.g., establishing connections for communication, etc.). In some embodiments, objects 104a-104n are interconnected with at least one of vehicles 102a-102n, vehicle-to-infrastructure (V2I) devices 110, network 112, autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118 via wired connection, wireless connection, or a combination of wired and wireless connection.

[0030] Vehicles 102a-102n (specifically referred to as vehicle 102 and collectively as vehicle 102) include at least one device configured to transport goods and / or people. In some embodiments, vehicle 102 is configured to communicate with V2I device 110, remote AV system 114, queue management system 116 and / or V2I system 118 via network 112. In some embodiments, vehicle 102 includes cars, buses, trucks and / or trains, etc. In some embodiments, vehicle 102 is associated with vehicle 200 described herein (see Figure 2 The vehicles 102 are the same as or similar to autonomous vehicles 202. In some embodiments, vehicles 200 in a group of vehicles 200 are associated with an autonomous queue manager. In some embodiments, as described herein, vehicles 102 travel along corresponding routes 106a-106n (each individually referred to as route 106 and collectively as route 106). 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).

[0031] Objects 104a-104n (each individually referred to as object 104 and collectively as object 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.). Each object 104 (e.g., located at a fixed location and for a period of time) is either stationary or (e.g., having a speed and associated with at least one trajectory) moving. In some embodiments, object 104 is associated with a corresponding location in area 108.

[0032] Routes 106a-106n (each individually referred to as Route 106 and collectively as Route 106) are each associated with (e.g., defining) a series of actions (also referred to as trajectories) along which the connecting AV can navigate. Each Route 106 begins with an initial state (e.g., a state corresponding to a first spatiotemporal location and / or speed, etc.) and ends with a final target state (e.g., a state corresponding to a second spatiotemporal location different from the first spatiotemporal location) or a target area (e.g., a subspace of an acceptable state (e.g., a termination state)). In some embodiments, a first state includes a location where one or more individuals will board the AV, and a second state or area includes a location where one or more individuals boarding the AV will disembark. In some embodiments, Route 106 includes multiple acceptable state sequences (e.g., multiple spatiotemporal location sequences) associated with multiple trajectories (e.g., defining multiple trajectories). In the example, Route 106 includes only high-level actions or imprecise state locations, such as a series of connecting roads indicating a change of direction at a roadway intersection. Additionally or alternatively, route 106 may include more precise actions or states, such as, for example, specific target lanes or precise locations within a lane area and target rates at those locations. In the example, route 106 includes multiple precise state sequences along at least one high-level action with a finite look-ahead horizon leading to an intermediate target, wherein the cumulative combination of successive iterations of the finite horizon state sequences corresponds to multiple trajectories that collectively form a high-level route terminating at a final target state or region.

[0033] Region 108 includes a physical area (e.g., a geographic region) that the vehicle 102 can navigate. In the example, region 108 includes at least one state (e.g., a country, a province, a single state among multiple 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, region 108 includes at least one named arterial road (referred to herein as a "road"), such as a highway, interstate highway, park road, city street, etc. Additionally or alternatively, in some examples, region 108 includes at least one unnamed road, such as a driving lane, a section of a parking lot, a section of vacant land and / or undeveloped area, dirt road, etc. In some embodiments, a road includes at least one lane (e.g., a portion of the road that the vehicle 102 can traverse). In the example, a road includes at least one lane associated with at least one lane marking (e.g., identified based on at least one lane marking).

[0034] The Vehicle-to-Infrastructure (V2I) device 110 (sometimes referred to as a Vehicle-to-Everything (V2X) device) includes at least one device configured to communicate with vehicle 102 and / or V2I system 118. In some embodiments, V2I device 110 is configured to communicate with vehicle 102, remote AV system 114, queue management system 116, and / or V2I system 118 via network 112. In some embodiments, V2I device 110 includes radio frequency identification (RFID) devices, signs, cameras (e.g., two-dimensional (2D) and / or three-dimensional (3D) cameras), lane markings, streetlights, parking meters, etc. In some embodiments, V2I device 110 is configured to communicate directly with vehicle 102. Additionally or alternatively, in some embodiments, V2I device 110 is configured to communicate with vehicle 102, remote AV system 114, and / or queue management system 116 via V2I system 118. In some embodiments, V2I device 110 is configured to communicate with V2I system 118 via network 112.

[0035] Network 112 includes one or more wired and / or wireless networks. In the example, network 112 includes cellular networks (e.g., Long Term Evolution (LTE) networks, third-generation (3G) networks, fourth-generation (4G) networks, fifth-generation (5G) networks, Code Division Multiple Access (CDMA) networks, etc.), Public Land Mobile Networks (PLMNs), Local Area Networks (LANs), Wide Area Networks (WANs), Metropolitan Area Networks (MANs), telephone networks (e.g., Public Switched Telephone Networks (PSTN)), private networks, self-organizing networks, intranets, the Internet, fiber-based networks, cloud computing networks, etc., and / or combinations of some or all of these networks.

[0036] The remote AV system 114 includes at least one device configured to communicate with the vehicle 102, V2I device 110, network 112, queue management system 116, and / or V2I system 118 via network 112. In examples, the remote AV system 114 includes a server, server group, and / or other similar devices. In some embodiments, the remote AV system 114 is located in the same location as the queue 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. In some embodiments, the remote AV system 114 maintains (e.g., updates and / or replaces) these components and / or software during the lifespan of the vehicle.

[0037] The queue management system 116 includes at least one device configured to communicate with vehicle 102, V2I device 110, remote AV system 114, and / or V2I system 118. In examples, the queue management system 116 includes servers, server groups, and / or other similar devices. In some embodiments, the queue management system 116 is associated with a ride-sharing 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)).

[0038] 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 queue management system 116 via a network 112. In some examples, the V2I system 118 is configured to communicate with the V2I device 110 via a connection different from 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 private entity (e.g., a private entity maintaining the V2I device 110).

[0039] supply Figure 1 The number and arrangement of the elements are shown as examples. (and) Figure 1 Compared to the illustrated elements, there may be additional elements, fewer elements, different elements, and / or elements arranged differently. Additionally or alternatively, at least one element of environment 100 may be described as being composed of… Figure 1 One or more functions performed by at least one different element of environment 100. Additionally or alternatively, at least one group of elements of environment 100 may perform one or more functions described as performed by at least one different group of elements of environment 100.

[0040] Now for reference Figure 2 The vehicle 200 includes an autonomous system 202, a powertrain control system 204, a steering control system 206, and a braking system 208. In some embodiments, the vehicle 200 and the vehicle 102 (see...) Figure 1 The vehicle 200 is similar to or the same as the vehicle in question. In some embodiments, the vehicle 200 has autonomous capabilities (e.g., implementing at least one function, feature, and / or device that enables the vehicle 200 to operate partially or fully without human intervention, including but not limited to fully autonomous vehicles (e.g., vehicles that abandon human intervention) and / or highly autonomous vehicles (e.g., vehicles that abandon human intervention in certain situations)). For a detailed description of fully autonomous and highly autonomous vehicles, refer to SAE International's standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, the entire contents of which are incorporated herein by reference. In some embodiments, the vehicle 200 is associated with an autonomous queue manager and / or a ride-sharing company.

[0041] Autonomous system 202 includes a sensor suite comprising 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 traveled by vehicle 200). In some embodiments, autonomous system 202 uses one or more devices included in autonomous system 202 to generate data associated with environment 100 as described herein. The data generated by one or more devices of autonomous system 202 may be used by one or more systems as described herein to observe the environment in which vehicle 200 is located (e.g., environment 100). In some embodiments, autonomous system 202 includes communication device 202e, autonomous vehicle computing 202f, and safety controller 202g.

[0042] Camera 202a includes components configured to communicate with communication device 202e, autonomous vehicle computing 202f, and / or safety controller 202g via a bus (e.g., with...). Figure 3At least one means of communicating with the same or similar bus as bus 302. Camera 202a includes at least one camera (e.g., a digital camera using a light sensor such as a charge-coupled device (CCD), a thermal camera, an infrared (IR) camera, and / or an event camera, etc.) for capturing images of physical objects (e.g., cars, buses, curbs, and / or people, etc.). In some embodiments, camera 202a generates camera data as output. In some examples, camera 202a generates camera data including image data associated with an image. In this example, the image data may specify at least one parameter corresponding to the image (e.g., image characteristics such as exposure, brightness, etc., and / or image timestamp, etc.). In such examples, the image may be in a format (e.g., RAW, JPEG, and / or PNG, etc.). In some embodiments, camera 202a includes multiple independent cameras configured (e.g., positioned on) a vehicle to capture images for stereoscopic imaging (stereoscopic vision). In some examples, camera 202a includes generating image data and transmitting the image data to an autonomous vehicle computing 202f and / or a queue management system (e.g., with...). Figure 1 The queue management system 116 (same as or similar to a queue management system) has multiple cameras. In such an example, the autonomous vehicle calculation 202f determines the depth of one or more objects in the fields of view of at least two of the multiple cameras based on image data from at least two cameras. In some embodiments, camera 202a is configured to capture images of objects within a distance relative to camera 202a (e.g., up to 100 meters and / or up to 1 kilometer, etc.). Therefore, camera 202a includes features such as sensors and lenses optimized for sensing objects at one or more distances relative to camera 202a.

[0043] In embodiments, 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 providing visual navigation information. In some embodiments, camera 202a generates traffic light detection (TLD) data (or traffic light data) associated with one or more images. In some examples, camera 202a generates TLD data associated with one or more images including formats such as RAW, JPEG, and / or PNG. In some embodiments, camera 202a, which generates TLD data, differs from other systems containing cameras described herein in that 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 an angle of view of about 120 degrees or greater) to generate images associated with as many physical objects as possible.

[0044] The laser detection and ranging (LiDAR) sensor 202b includes components configured to communicate with a communication device 202e, an autonomous vehicle computing unit 202f, and / or a safety controller 202g via a bus (e.g., with...). Figure 3 At least one means for communicating with a bus (same as or similar to bus 302). The LiDAR sensor 202b includes a system configured to emit light from a 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 it encounters. The LiDAR sensor 202b also includes at least one photosensor that detects the light after it has encountered a physical object. In some embodiments, at least one data processing system associated with the LiDAR sensor 202b generates an image (e.g., point clouds and / or combined point clouds, 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 and / or surfaces of a physical object (e.g., surface topology). In such examples, this image is used to determine the boundaries of the physical object within the field of view of the LiDAR sensor 202b. This image can be a 2D or 3D image. The LiDAR sensor 202b can provide the 2D or 3D location of the object. This is discussed in further detail below and... Figure 8B As illustrated, the LiDAR sensor 202b may include a short-range LiDAR sensor 202b-S and / or a long-range LiDAR sensor 202b-L.

[0045] The radio detection and ranging (radar) sensor 202c includes components configured to communicate with the communication device 202e, the autonomous vehicle computing 202f, and / or the safety controller 202g via a bus (e.g., with...). Figure 3At least one device communicating with a bus (same as or similar to bus 302). The radar sensor 202c includes a system configured to emit (pulsed or continuous) radio waves. The radio waves emitted by the radar sensor 202c include radio waves within a predetermined spectrum. In some embodiments, during operation, the radio waves emitted by the radar sensor 202c encounter a physical object and are reflected back to the radar sensor 202c. In some embodiments, the radio waves emitted by the radar sensor 202c are not reflected by some objects. In some embodiments, at least one data processing system associated with the radar sensor 202c generates a signal representing objects included in the field of view of the radar sensor 202c. For example, at least one data processing system associated with the radar sensor 202c generates an image representing the boundaries and / or surfaces of the physical objects (e.g., surface topology). In some examples, this image is used to determine the boundaries of physical objects in the field of view of the radar sensor 202c. This image can be a 2D image or a 3D image. The radar sensor 202c can provide 2D or 3D position and velocity of the object.

[0046] Microphone 202d includes components configured to communicate with communication device 202e, autonomous vehicle computing 202f, and / or safety controller 202g via a bus (e.g., with...). Figure 3 At least one means of communicating with the same or similar bus as bus 302. Microphone 202d includes one or more microphones (e.g., array microphones and / or external microphones, etc.) that capture audio signals and generate data associated with (e.g., representing the audio signals). In some examples, microphone 202d includes transducer means and / or similar means. In some embodiments, one or more systems described herein may receive data generated by microphone 202d and determine the position (e.g., distance, etc.) and / or type of an object relative to vehicle 200 based on the audio signal associated with the data.

[0047] The communication device 202e includes at least one device configured to communicate with a camera 202a, a LiDAR sensor 202b, a radar sensor 202c, a microphone 202d, an autonomous vehicle computing system 202f, a safety controller 202g, and / or a drive-by-wire (DBW) system 202h. For example, the communication device 202e may include communication with… Figure 3 The communication device 202e is the same as or similar to the communication interface 314. In some embodiments, the communication device 202e includes a vehicle-to-vehicle (V2V) communication device (e.g., a device for enabling wireless communication of data between vehicles).

[0048] The autonomous vehicle computing 202f includes at least one device configured to communicate with a camera 202a, a LiDAR sensor 202b, a radar sensor 202c, a microphone 202d, a communication device 202e, a security controller 202g, and / or a 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 tablets) and / or servers (e.g., computing devices including one or more central processing units and / or graphics processing units). In some embodiments, the autonomous vehicle computing 202f is the same as or similar to the autonomous vehicle computing 400 described herein. 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 systems 114 are the same as or similar to autonomous vehicle systems), queue management systems (e.g., with...). Figure 1 The queue management system 116 is the same as or similar to the queue management system 116), and V2I devices (e.g., with Figure 1 V2I devices (same as or similar to V2I devices 110) and / or V2I systems (e.g., with V2I devices 110) Figure 1 The V2I system 118 communicates with the same or similar V2I system.

[0049] The safety controller 202g includes at least one device configured to communicate with a camera 202a, a LiDAR sensor 202b, a radar sensor 202c, a microphone 202d, a communication device 202e, an autonomous vehicle computing system 202f, and / or a DBW system 202h. In some examples, the 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 the vehicle 200 (e.g., powertrain control system 204, steering control system 206, and / or braking system 208, etc.). In some embodiments, the safety controller 202g is configured to generate control signals that take precedence over (e.g., override) the control signals generated and / or transmitted by the autonomous vehicle computing system 202f.

[0050] 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 the vehicle 200, including one or more devices (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 (e.g., turn signals, headlights, door locks, and / or windshield wipers, etc.) of the vehicle 200.

[0051] 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 start moving forward, stop moving forward, start moving backward, stop moving backward, accelerate in a certain direction, decelerate in a certain direction, make a left turn and / or make a right turn, etc. In examples, the powertrain control system 204 increases, keeps the same, or decreases the energy (e.g., fuel and / or electricity, etc.) supplied to the motor of the vehicle, thereby causing at least one wheel of the vehicle 200 to rotate or not rotate.

[0052] 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 causes the two front wheels and / or the two rear wheels of the vehicle 200 to turn left or right, thereby causing the vehicle 200 to turn left or right.

[0053] The braking system 208 includes at least one device configured to actuate one or more brakes to decelerate and / or keep the vehicle 200 stationary. In some examples, the braking system 208 includes at least one controller and / or actuator configured to close one or more calipers associated with one or more wheels of the vehicle 200 on the respective rotor of the vehicle 200. Additionally or alternatively, in some examples, the braking system 208 includes an automatic emergency braking (AEB) system and / or a regenerative braking system, etc.

[0054] In some embodiments, the vehicle 200 includes at least one platform sensor (not explicitly illustrated) for measuring or inferring the nature of the state or conditions of the vehicle 200. In some examples, the vehicle 200 includes platform sensors such as a Global Positioning System (GPS) receiver, an Inertial Measurement Unit (IMU), a wheel rate sensor, a wheel brake pressure sensor, a wheel torque sensor, an engine torque sensor, and / or a steering angle sensor.

[0055] Now for reference Figure 3 A schematic diagram of device 300 is illustrated. 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 system of vehicle 102); and / or one or more devices of network 112 (e.g., one or more devices of system of network 112). In some embodiments, one or more devices of vehicle 102 (e.g., one or more devices of system of vehicle 102), and / or one or more devices of network 112 (e.g., one or more devices of system of network 112) include at least one device 300 and / or at least one component of device 300. Figure 3 As shown, the device 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.

[0056] Bus 302 includes components for communication between the components of the licensed device 300. In some embodiments, processor 304 is implemented in hardware, software, or a combination of hardware and software. In some examples, processor 304 includes a processor (e.g., a central processing unit (CPU), graphics processing unit (GPU), and / or accelerated processing unit (APU), a microphone, a digital signal processor (DSP), and / or any processing component that can be programmed to perform at least one function (e.g., a field-programmable gate array (FPGA) and / or application-specific integrated circuit (ASIC), etc.). Memory 306 includes random access memory (RAM), read-only memory (ROM), and / or another type of dynamic and / or static storage device (e.g., flash memory, magnetic memory, and / or optical memory, etc.) that stores data and / or instructions for use by processor 304.

[0057] Storage component 308 stores data and / or software related to the operation and use of device 300. In some examples, storage component 308 includes hard disks (e.g., magnetic disks, optical disks, magneto-optical disks, and / or solid-state disks), compact discs (CDs), digital versatile discs (DVDs), floppy disks, cassette tapes, magnetic tapes, CD-ROMs, RAM, PROMs, EPROMs, FLASH-EPROMs, NV-RAMs, and / or other types of computer-readable media, and corresponding drives.

[0058] Input interface 310 includes components that enable the device 300 to receive information, such as via user input (e.g., a touchscreen display, keyboard, keypad, mouse, buttons, switches, microphone, and / or camera). Additionally or alternatively, in some embodiments, input interface 310 includes sensors for sensing information (e.g., a Global Positioning System (GPS) receiver, accelerometer, gyroscope, and / or actuator). Output interface 312 includes components for providing output information from device 300 (e.g., a display, speaker, and / or one or more light-emitting diodes (LEDs)).

[0059] In some embodiments, the communication interface 314 includes transceiver-like components (e.g., a transceiver and / or separate receivers and transmitters) that enable the licensing device 300 to communicate with other devices via a wired connection, a wireless connection, or a combination of wired and wireless connections. In some examples, the communication interface 314 enables the licensing device 300 to receive information from and / or provide information to another device. In some examples, the 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, etc. Interfaces and / or cellular network interfaces, etc.

[0060] In some embodiments, device 300 performs one or more of the processes described herein. Device 300 performs these processes based on software instructions stored in a computer-readable medium, such as memory 306 and / or storage component 308, executed by processor 304. Computer-readable medium (e.g., non-transitory computer-readable medium) is defined herein as a non-transitory memory device. A non-transitory memory device includes storage space located within a single physical storage device or storage space distributed across multiple physical storage devices.

[0061] In some embodiments, software instructions are read from another computer-readable medium or from another device via communication interface 314 into memory 306 and / or storage component 308. When executed, the software instructions stored in memory 306 and / or storage component 308 cause processor 304 to perform one or more processes described herein. Additionally or alternatively, hard-wired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Therefore, unless explicitly stated otherwise, the embodiments described herein are not limited to any particular combination of hardware circuitry and software.

[0062] The memory 306 and / or storage component 308 include a data storage unit or at least one data structure (e.g., a database). The device 300 is capable of receiving information from the data storage unit or at least one data structure in the memory 306 or storage component 308, storing the information in the data storage unit or at least one data structure, communicating information to the data storage unit or at least one data structure, or searching 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.

[0063] In some embodiments, device 300 is configured to execute software instructions stored in the memory of memory 306 and / or another device (e.g., another device identical or similar to device 300). As used herein, the term "module" refers to at least one instruction stored in the memory of memory 306 and / or the other device, which, when executed by the processor of processor 304 and / or the processor of another device (e.g., another device identical or similar to device 300), causes device 300 (e.g., at least one component of device 300) to perform one or more processes as described herein. In some embodiments, modules are implemented in software, firmware, and / or hardware, etc.

[0064] supply Figure 3 The number and arrangement of components are illustrated as examples. In some embodiments, with Figure 3 Compared to the illustrated components, device 300 may include additional components, fewer components, different components, or components arranged differently. Additionally or alternatively, a group of components of device 300 (e.g., one or more components) may perform one or more functions described as being performed by another component or another group of components of device 300.

[0065] Autonomous Vehicle Architecture

[0066] Now for reference Figure 4The diagram illustrates an example block diagram of an autonomous vehicle computing 400 (sometimes referred to as an "AV stack"). As illustrated, the autonomous vehicle computing 400 includes a perception system 402 (sometimes referred to as a perception module), a planning system 404 (sometimes referred to as a planning module), a positioning system 406 (sometimes referred to as a positioning module), a control system 408 (sometimes referred to as a control module), and a database 410. In some embodiments, the perception system 402, planning system 404, positioning system 406, control system 408, and database 410 are included in and / or implemented in the vehicle's automatic navigation system (e.g., the autonomous vehicle computing 202f of vehicle 200). Additionally or alternatively, in some embodiments, the perception system 402, planning system 404, positioning system 406, control system 408, and database 410 are included in one or more separate systems (e.g., one or more systems that are the same as or similar to the autonomous vehicle computing 400, etc.). In some examples, the perception system 402, planning system 404, positioning system 406, control system 408, and database 41 are included in one or more independent systems located within the vehicle and / or at least one remote system as described herein. In some embodiments, any and / or all of the systems included in the autonomous vehicle computing 400 are implemented in software (e.g., software instructions stored in memory), computer hardware (e.g., via microprocessors, microcontrollers, application-specific integrated circuits (ASICs), and / or field-programmable gate arrays (FPGAs), etc.), or a combination of computer software and computer hardware. It will also be understood that in some embodiments, the autonomous vehicle computing 400 is configured to communicate with remote systems (e.g., autonomous vehicle systems identical or similar to remote AV system 114, queue management systems identical or similar to queue management systems 116, and / or V2I systems identical or similar to V2I system 118, etc.).

[0067] 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) that is associated with one or more physical objects within the field of view of the at least one camera (e.g., representing the one or more physical objects). In such examples, the perception system 402 classifies 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 physical objects by the perception system 402, the perception system 402 transmits data associated with the classification of the physical objects to the planning system 404.

[0068] 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 from the sensing system 402 (e.g., the data associated with the classification of physical objects described above), and the planning system 404 updates at least one trajectory or generates at least one different trajectory based on the data generated by the sensing system 402. In some embodiments, the planning system 404 receives data associated with the updated location 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.

[0069] In some embodiments, 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, 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, positioning system 406 receives data associated with at least one point cloud from multiple LiDAR sensors, and positioning system 406 generates a composite point cloud based on the individual point clouds. In these examples, positioning system 406 compares the at least one point cloud or composite point cloud with a two-dimensional (2D) and / or three-dimensional (3D) map of the area stored in database 410. Then, based on the comparison of the at least one point cloud or composite point cloud with the map, positioning system 406 determines the location of the vehicle in the area. In some embodiments, the map includes a composite point cloud of the area generated prior to navigation of the vehicle. In some embodiments, the map includes, but is not limited to, a high-precision map of the geometry of the roadway, a map describing the connectivity of the road network, a map describing the physical properties of the roadway (such as traffic speed, traffic flow, the number of vehicle and bicycle lanes, lane width, lane traffic direction, or the type and location of lane markings, or combinations thereof), and a map describing the spatial locations of road features (such as pedestrian crossings, traffic signs, or various types of other traffic lights). In some embodiments, the map is generated in real time based on data received by the sensing system.

[0070] 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 the location of a vehicle in an area, and positioning system 406 determines the latitude and longitude of the vehicle in the area. In such examples, positioning system 406 determines the location 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 location 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 location of the vehicle. In such examples, the data associated with the location of the vehicle includes data associated with one or more semantic properties corresponding to the location of the vehicle.

[0071] 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). In an example, where the trajectory includes a left turn, the control system 408 transmits control signals to cause the steering control system 206 to adjust the steering angle of the vehicle 200, thereby causing the vehicle 200 to turn left. Additionally or alternatively, the control system 408 generates and transmits control signals to change the state of other devices of the vehicle 200 (e.g., headlights, turn signals, door locks, and / or windshield wipers, etc.).

[0072] In some embodiments, the 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 multilayer 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, the perception system 402, planning system 404, positioning system 406, and / or control system 408 implement at least one machine learning model individually or in combination with one or more of the aforementioned systems. In some examples, the 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 the environment, etc.).

[0073] Database 410 stores data transmitted to, received from, and / or updated by the sensing system 402, planning system 404, positioning system 406, and / or control system 408. In some examples, database 410 includes storage components for storing operation-related data and / or software, and for computing 400 using autonomous vehicles (e.g., with...). Figure 3(The storage component 308 is the same as or similar to the storage component 308). In some embodiments, database 410 stores data associated with 2D and / or 3D maps of at least one area. In some examples, database 410 stores data associated with 2D and / or 3D maps of a part of a city, multiple parts of multiple cities, multiple cities, counties, states, and / or countries (e.g., countries). In such examples, a vehicle (e.g., the same as or similar to vehicle 102 and / or vehicle 200) can drive along one or more drivable areas (e.g., single-lane roads, multi-lane roads, highways, remote roads, and / or off-road roads, etc.) and causes at least one LiDAR sensor (e.g., the same as or similar to LiDAR sensor 202b) to generate data associated with images representing objects included in the field of view of the at least one LiDAR sensor.

[0074] In some embodiments, database 410 may be implemented across multiple devices. In some examples, database 410 includes a vehicle (e.g., a vehicle identical or similar to vehicle 102 and / or vehicle 200), an autonomous vehicle system (e.g., an autonomous vehicle system identical or similar to remote AV system 114), and a queue management system (e.g., with...). Figure 1 Queue management system 116 (same as or similar to queue management system) and / or V2I system (e.g., with Figure 1 Among the V2I systems (118 similar to or similar V2I systems), etc.

[0075] LiDAR system

[0076] Figure 5 An example of a LiDAR system 502 is shown (e.g., Figure 2 The LiDAR sensor 202b is shown. The LiDAR system 502 emits light 504a-504c from a emitter 506 (e.g., a laser emitter). The light emitted by the LiDAR system is typically not in the visible spectrum; for example, infrared light is often used. Some of the emitted light 504b encounters a physical object 508 (e.g., a vehicle) and is reflected back to the LiDAR system 502. (The light emitted from the LiDAR system typically does not penetrate the physical object, e.g., a solid physical object.) The LiDAR system 502 also has one or more photodetectors 510 for detecting the reflected light. In an embodiment, one or more data processing systems associated with the LiDAR system generate an image 512 representing the field of view 514 of the LiDAR system. Image 512 includes information representing the boundary 516 of the physical object 508. Thus, image 512 is used to determine the boundary 516 of one or more physical objects near the AV.

[0077] Figure 6 Additional details of the operation of the LiDAR system 502 are shown. As described above, the vehicle 200 detects the boundaries of physical objects based on the characteristics of the data points detected by the LiDAR system 502. Figure 6 As shown, flat objects such as ground 602 will reflect light 604a-604d emitted from LiDAR system 502 in a consistent manner. In other words, because LiDAR system 502 emits light at a consistent interval, ground 602 will reflect light back to LiDAR system 502 at the same consistent interval. When vehicle 200 travels on ground 602, LiDAR system 502 will continue to detect light reflected by the next effective surface point 606 if nothing obstructs its path. However, if object 608 obstructs its path, the light 604e-604f emitted by LiDAR system 502 will be reflected from points 610a-610b in a manner inconsistent with the expected consistency. Based on this information, vehicle 200 can determine the presence of object 608.

[0078] Autonomous Vehicle Control

[0079] Figure 7 Show (for example, as) Figure 4 The diagram shows a block diagram 700 of the inputs and outputs of the control system 408. The control system operates according to a controller 702, which includes, for example,: one or more processors (e.g., one or more computer processors such as a microprocessor or microcontroller or both); short-term and / or long-term data storage devices (e.g., memory area random access memory or flash memory or both); ROM; and storage devices; and instructions stored in the memory that, when executed (e.g., by one or more processors), perform the operation of the controller 702.

[0080] In one embodiment, controller 702 receives data representing a desired output 704. The desired output 704 typically includes speed, such as rate and heading. The desired output 704 may be based, for example, from (e.g., as...) Figure 4The data received by the planning system 404 (as shown) is used as follows. Based on the desired output 704, the controller 702 generates data that can be used as throttle input 706 and steering input 708. Throttle input 706 indicates, for example, engaging the throttle of the vehicle 200 (e.g., acceleration control) to achieve the magnitude of the desired output 704 by engaging the steering pedal or another throttle control. In some examples, throttle input 706 also includes data that can be used to engage the brakes of the vehicle 200 (e.g., deceleration control). Steering input 708 indicates the steering angle, such as the steering control of the AV (e.g., steering wheel, steering angle actuator, or other function for controlling the steering angle), which should be positioned to achieve the desired output 704.

[0081] In one embodiment, controller 702 receives feedback used when adjusting inputs provided to throttle and steering. For example, if vehicle 200 encounters an obstacle 710, such as a hill, the measured rate 712 of vehicle 200 drops below the desired output rate. In another embodiment, any measured output 714 is provided to controller 702 so that necessary adjustments can be made, for example, based on the difference 713 between the measured rate and the desired output. The measured output 714 includes measured position 716, measured speed 718 (including rate and heading), measured acceleration 720, and other outputs measurable by the sensors of vehicle 200.

[0082] In one embodiment, information related to interference 710 is pre-detected by sensors such as cameras, LiDAR sensors, or radar sensors, and this information is provided to the predictive feedback module 722. The predictive feedback module 722 then provides information that the controller 702 can use to make appropriate adjustments. For example, if the vehicle 200's sensors detect ("see") a hill, the controller 702 can use this information to prepare to engage the throttle at an appropriate time to avoid significant deceleration.

[0083] Figure 8A A block diagram 800 shows the inputs, outputs, and components of controller 702. Controller 702 has a rate analyzer 802 that affects the operation of throttle / brake controller 804. For example, the rate analyzer 802 instructs throttle / brake controller 804 to accelerate or decelerate using throttle / brake 806 based on feedback received by, for example, controller 702 and processed by the rate analyzer 802.

[0084] The controller 702 also has a lateral tracking controller 808 that affects the operation of the steering wheel controller 810. For example, the lateral tracking controller 808 instructs the steering wheel controller 810 to adjust the position of the steering angle actuator 812 based on feedback received by the controller 702 and processed by the lateral tracking controller 808.

[0085] Controller 702 receives several inputs for determining how to control the throttle / brake 806 and steering angle actuator 812. Planning system 404 provides controller 702 with information, for example, to select the heading of vehicle 200 at the start of operation and to determine which road segment vehicle 200 will cross when it reaches an intersection. Positioning system 406 provides controller 702 with information describing the current location of vehicle 200, for example, so that controller 702 can determine whether vehicle 200 is at the expected location based on the positive control of throttle / brake 806 and steering angle actuator 812. In embodiments, controller 702 receives information from other inputs 814, such as information received from a database, computer network, etc.

[0086] The vehicle 200 includes a power management module 820, which manages the energy resources and / or computing resources of the vehicle 200's sensors for operation of the vehicle 200. In one embodiment, such as Figure 8A As illustrated, the power management module 820 is included within the controller 702. In one embodiment, the power management module 820 is external to the controller 702, for example, included within... Figure 2 Autonomous system 202 or Figure 4 The calculation of autonomous vehicles is 400.

[0087] Vehicle 200 includes multiple sensors, such as... Figure 1 The sensor 121 shown. The sensor may include sensors for sensing or measuring properties of the environment of the vehicle 200, such as camera 202a, LiDAR 202b, radar 202c, microphone 202d, traffic light detection (TLD) system, ultrasonic sensor, time-of-flight (TOF) depth sensor and rate sensor.

[0088] Sensors can be classified into different groups / types based on one or more attributes or characteristics. These attributes or characteristics include: the priority of the sensor's output data, the sensor's priority, the sensor's startup time, whether the sensor is facing backwards or forwards, whether it is an active or passive sensor, the sensor's detection range, and the sensor's applicable operation (e.g., high-speed or low-speed operation). Figure 8B As illustrated, the LiDAR sensor 202b can be a short-range LiDAR sensor 202b-S or a long-range LiDAR sensor 202b-L.

[0089] Figure 8BA power management module 820 is shown, coupled to multiple sensors (e.g., camera 202a, short-range LiDAR sensors 202b-S, long-range LiDAR sensors 202b-L, radar 202c, and microphone 202d). The power management module 820 is configured to manage energy and / or computing resources used by the sensors using their properties or characteristics. Figure 8B As illustrated, the power management module 820 includes multiple power relay components (e.g., 822a, 822b-S, 822b-L, 822c, 822d), each power relay component being coupled to a corresponding sensor (e.g., 202a, 202b-S, 202b-L, 202c, 202d) and configured to energize or de-energize the corresponding sensor.

[0090] The power management module 820 distributes computing resources (e.g., CPU, graphics processing unit (GPU), or field-programmable gate array (FPGA) processing power) among the sensors. For example, it provides sensor output data to other systems within the vehicle 200 (e.g., sensing system 402, planning system 404, control system 408, and / or database 410). Sensor output data can be processed individually or collectively. In this embodiment, processing output data from the first sensor has a higher priority than processing output data from a second or other sensor within the vehicle 200. The power management module 820 can allocate more computing resources to the first sensor compared to the second or other sensors using different priority levels of the sensors.

[0091] The power management module 820 manages the energy resources (e.g., power output) used by the sensor. The power management module 820 can activate / deactivate some operations of the sensor (e.g., laser ignition of a LiDAR sensor), or fully enable / deactivate the sensor (e.g., turn the sensor's power output on / off). The power output can be the electrical power provided by one or more batteries.

[0092] Sensors consume significant amounts of energy and / or computational power, some of which is wasted on unnecessary sensor operation. As discussed in further detail below, the power management module 820 is configured to manage sensors for intelligent power management. Specifically, when the detection range of a short-range sensor is determined to be shorter than the stopping distance of the vehicle 200, the power management module 820 deactivates some operations of one or more short-range sensors or completely disables the short-range sensors during high-speed operation. On the other hand, the power management module 820 keeps long-range sensors with detection ranges greater than the stopping distance operational. Since one or more short-range sensors are partially or completely disabled, more resources become available. The power management module 820 can prioritize energy and / or computational resources for higher-priority tasks among the sensors. The power management module 820 reactivates or enables short-range sensors during low-speed operation and / or by determining that the stopping distance of the vehicle 200 is shorter than a distance threshold (e.g., the detection range of the vehicle 200). In this way, the power management module 820 reduces the energy consumption of short-range sensors, increases the driving range and / or battery life of the vehicle 200, and improves the performance of the vehicle 200 (e.g., shortens the response time of higher-priority sensors and processes large amounts of long-range sensor data).

[0093] Sensors can be classified as rear-facing sensors and forward-facing sensors. Rear-facing sensors are configured to monitor the environment behind or around the vehicle 200, while forward-facing sensors are configured to monitor the environment in front of or around the vehicle 200. Rear-facing sensors can be short-range sensors, while forward-facing sensors can be either short-range or long-range sensors. A power management module 820 is configured to power on the rear-facing sensors and de-power the forward-facing sensors during rearward (or backward-facing) driving. The power management module 820 is also configured to de-power the rear-facing sensors and power on the forward-facing sensors during forward driving (e.g., driving on a highway). In this way, the power management module 820 reduces the energy consumption of the forward-facing sensors during rearward driving and the rear-facing sensors during forward driving.

[0094] Figure 9 Example 900 of vehicle sensor management is shown, which uses sensors to detect the range and stopping distance of vehicle 200 traveling on a route. Vehicle 200 has one or more short-range sensors and one or more long-range sensors. In some embodiments, the short-range sensor includes a LiDAR sensor (e.g., Figure 2 202b or Figure 5 Or 6 of 502), radar sensors (e.g., Figure 2202c), camera sensor (e.g., Figure 2 202a), microphone sensor (e.g., Figure 2 The long-range sensor includes one or more of the following: a 202d ultrasonic sensor and a TOF depth sensor. In some embodiments, the long-range sensor includes a LiDAR sensor (e.g., 202d). Figure 2 202b or Figure 5 Or 6 of 502), radar sensors (e.g., Figure 2 202c), camera sensor (e.g., Figure 2 202a), microphone sensor (e.g., Figure 2 This includes one or more of the following: a 202d-type short-range sensor, an ultrasonic sensor, and a TOF depth sensor. Short-range and long-range sensors can be of the same type but with different detection ranges (e.g., LiDAR sensors). For illustrative purposes only, these techniques are described below with respect to short-range and long-range LiDAR sensors. However, these techniques are equally applicable to other types of short-range and long-range sensors and / or other combinations of short-range and long-range sensors.

[0095] like Figure 9 As illustrated, when the vehicle 200 is traveling on the route, the vehicle 200 (e.g., Figure 7 and 8A The controller 702 in the middle (e.g., using the driving speed of the vehicle 200, one or more deceleration parameters, or one or more timing parameters) determines the stopping distance D for the vehicle 200 to stop safely. Stop For example, the stopping distance (e.g., 100 feet) for high driving speeds (e.g., greater than 40 mph) is greater than the stopping distance (e.g., 30 feet) for low driving speeds (e.g., less than 40 mph).

[0096] The vehicle 200 can also use a braking mechanism (e.g., an emergency braking mechanism or a comfort braking mechanism) to determine the stopping distance D. Stop For example, at the same driving speed, the stopping distance for comfort braking of vehicle 200 is greater than the stopping distance for emergency braking of vehicle 200. For example, the sensors of vehicle 200 detect disturbances within a short distance in front of the vehicle (e.g., such as...). Figure 7 The interference shown is 710), and the vehicle 200 can use an emergency braking mechanism to stop the vehicle 200 with a deceleration higher than that of the comfort braking mechanism.

[0097] like Figure 9 The illustrated short-range sensor has a detection range D SRFurthermore, long-range sensors have a detection range greater than D. SR Detection range D LR In the example, the detection range D SR It can detect objects within a range of 0 to 20 feet, and the detection range is D. LR Within a range of 20 feet to 1000 feet. Short-range sensors can be mounted on the front of vehicle 200, while long-range sensors can be mounted on the top of vehicle 200.

[0098] In one embodiment, the vehicle 200 determines whether a sensor is a short-range or long-range sensor by determining whether the sensor's detection range is less than or greater than the vehicle 200's current stopping distance. If the sensor's detection range is less than the current stopping distance, the vehicle 200 determines the sensor to be a short-range sensor. If the sensor's detection range is equal to or greater than the current stopping distance, the vehicle 200 determines the sensor to be a long-range sensor. In one embodiment, a distance threshold is defined as the current stopping distance minus a buffer distance. If the sensor's detection range is less than the distance threshold, the vehicle 200 determines the sensor to be a short-range sensor. If the sensor's detection range is equal to or greater than the distance threshold, the vehicle 200 determines the sensor to be a long-range sensor.

[0099] The vehicle 200 can dynamically determine its stopping distance D. Stop The system identifies short-range and long-range sensors in vehicle sensors and then manages vehicle sensors based on the identification of these sensors.

[0100] In some cases, during high-speed operation of the vehicle 200 (e.g., while driving on a highway), the vehicle 200 is configured not to detect the environment of the vehicle within a distance less than its current stopping distance. In this case, the distance is less than a distance threshold (e.g., stopping distance D). Stop Detection range D SR Short-range sensors are not used for the operation of vehicle 200. Vehicle 200 (e.g., such as...) Figure 8A and 8B The power management module 820 shown can manage the power used for the short-range sensor in one or more ways. This is useful when the short-range sensor is needed for the operation of the vehicle 200 (e.g., when the detection range D of the short-range sensor is within a certain range). SR Equal to or greater than the current stopping distance D of the vehicle Stop When the vehicle is traveling at a speed less than a speed threshold (e.g., 40 mph), the vehicle can reactivate the short-range sensors to operate.

[0101] In one embodiment, the vehicle 200 partially disables the short-range sensor, for example, by disabling one or more operations of the short-range sensor or by ceasing to provide computing resources to one or more components of the short-range sensor. In an example, the vehicle 200 disables the laser ignition of the short-range LiDAR sensor, for example, by turning off the power for laser emission and detection. Some computing resources (e.g., CPU and / or FPGA) can still operate. In this way, the short-range sensor can be quickly disabled to save energy and computing resources, and it can also be quickly reactivated for operation.

[0102] In one embodiment, the vehicle 200 completely disables the short-range sensor, for example, by turning off or stopping the power supply to the short-range sensor. In this way, all energy power and computing resources used for the short-range sensor are saved. The saved energy power and computing resources can then be used for higher-priority tasks, such as processing long-range sensor data.

[0103] The control system can partially or completely disable the short-range sensor using one or more characteristics of the short-range sensor (e.g., start-up time, priority, back-facing / forward-facing, active / passive).

[0104] In one embodiment, the vehicle sets a time threshold (e.g., 3 seconds) for the short-range sensor to start or restart. The vehicle determines whether the start time of the short-range sensor is greater than or less than the time threshold. If the start time is less than the time threshold, i.e., the short-range sensor can be quickly restarted, the vehicle completely deactivates the short-range sensor in response to determining, for example, that the short-range sensor's detection range is less than the vehicle's stopping distance, that the short-range sensor is not needed. If the start time is greater than or equal to the time threshold, i.e., the short-range sensor cannot be quickly restarted, the vehicle partially deactivates the short-range sensor, enabling a quick restart for operation.

[0105] In one embodiment, the vehicle determines whether the short-range sensor is a passive or active sensor. For example, a camera sensor is a passive sensor that only detects information, while a LiDAR sensor is an active sensor that generates laser light and detects reflected / diffracted light used for information detection. In response to being determined to be an active sensor, the vehicle may partially deactivate the short-range sensor, allowing it to be quickly restarted for operation or kept operational for certain tasks (e.g., for specific driving conditions such as nighttime driving). Conversely, for example, during specific driving conditions such as nighttime driving, the vehicle may completely deactivate the short-range sensor in response to being determined to be a passive sensor.

[0106] In one embodiment, the vehicle determines whether a short-range sensor is a low-priority or high-priority sensor. High-priority sensors (e.g., LiDAR sensors) are more important to the operation of the vehicle than low-priority sensors (e.g., ultrasonic sensors). Depending on the priority level, the vehicle can partially deactivate a high-priority sensor, allowing it to be quickly reactivated for operation or kept operational for certain tasks. Conversely, in response to determining that a short-range sensor is a low-priority sensor, the vehicle can completely deactivate that short-range sensor.

[0107] The vehicle can add a buffer distance or time for deactivating short-range sensors. For example... Figure 9 As illustrated, the vehicle can determine a range D greater than that of the short-range sensor. SR The deactivation distance threshold D for short-range sensors Deactivate In response to the current stopping distance D determined to be a vehicle. Stop Greater than the disabling distance threshold D Deactivate The vehicle can disable at least one operation of the short-range sensor.

[0108] The vehicle can also add some buffer distance or time for reactivating short-range sensors. For example... Figure 9 As illustrated, the vehicle can determine a range D greater than that of the short-range sensor. SR Activation distance threshold D for short-range sensors Activate In response to the current stopping distance D determined to be a vehicle. Stop Less than the activation distance threshold D Activate The vehicle can reactivate the short-range sensors for operation. The vehicle can use one or more attributes of the short-range sensors (e.g., response time and / or startup time) to determine the deactivation distance threshold D. Deactivate and activation distance threshold D Activate Disconnection distance threshold D Deactivate It can be greater than the activation distance threshold D Activate .

[0109] As described above, during high-speed operation of the vehicle 200, the vehicle 200 can identify one or more short-range sensors, each with a detection range smaller than the vehicle's stopping distance, and (partially or completely) disable at least one of these short-range sensors. Even during high-speed operation, the vehicle 200 can keep high-priority short-range sensors fully operational while disabling other short-range sensors. For long-range sensors with a detection range greater than the stopping distance (e.g., 20 feet to 1000 feet), the vehicle 200 maintains the operation of the long-range sensors. When one or more short-range sensors are disabled, the vehicle 200 has more energy or computational resources available for the operating sensors (including one or more long-range sensors and / or one or more high-priority short-range sensors). For example, the vehicle 200 can prioritize the operating sensors and allocate more computational power to long-range sensors with higher priority levels compared to one or more other sensors.

[0110] In one embodiment, during low-speed operation of the vehicle 200 (e.g., while driving on local roads), the vehicle 200 identifies one or more long-range sensors, each with a detection range significantly greater than the current stopping distance (e.g., 2, 5, 10 times, or more), which may not be used for low-speed operation. The vehicle 200 may partially or completely deactivate at least one of the long-range sensors to conserve energy or computing resources. The vehicle 200 may then reallocate energy and / or computing resources among the operating sensors, allocating more power to high-priority tasks (e.g., processing large amounts of short-range sensor data). For example, the vehicle 200 may also reactivate the deactivated long-range sensors when it is determined that the vehicle's driving speed is greater than a rate threshold and / or the detection range of the long-range sensors is not significantly greater than the current stopping distance.

[0111] Processing for implementing sensor management in vehicles

[0112] Figure 10 This illustration illustrates a process 1000 for implementing vehicle sensor management during operation of a vehicle having an autonomous driving system, according to one or more embodiments. In some embodiments, the process 1000 (e.g., wholly and / or partially, etc.) is performed by, for example, Figure 2 The autonomous system 202 of the illustrated vehicle 200 performs the tasks. Additionally or alternatively, in some embodiments, processing 1000 (e.g., wholly and / or partially, etc.) is performed by other means or groups of means separate from or including the autonomous system (e.g., Figure 1The remote AV system 114 shown is used for this purpose. The autonomous system includes a control system (e.g., Figure 4 The control system 408 shown. The control system includes a controller (e.g., Figure 7 and 8A The controller 702 shown. The controller may include a power management module (e.g., Figure 8A and 8B (Module 820 in the example). Similarly, embodiments may include different and / or additional operations or processing operations performed in a different order.

[0113] like Figure 10 As shown, process 1000 begins with the autonomous system determining the stopping distance of the vehicle traveling on the route (1002). For example, the autonomous system uses at least one of the vehicle's driving speed, one or more deceleration parameters, and one or more timing parameters to determine the stopping distance. The autonomous system can obtain the vehicle's driving speed from the output of a speed sensor. In one embodiment, the autonomous system uses at least a braking mechanism (e.g., an emergency braking mechanism or a comfort braking mechanism) to determine the stopping distance. The autonomous system can analyze the vehicle's environment on the route, for example, through the output of vehicle sensors, and select which braking mechanism to use to determine the stopping distance.

[0114] Process 1000 continues as follows: The autonomous system identifies one or more sensors (1004) of the vehicle with a corresponding detection range smaller than the vehicle's stopping distance. For example, the autonomous system identifies one or more short-range sensors of the vehicle with a detection range (one or more) smaller than the determined stopping distance. In some cases, the autonomous system also identifies one or more long-range sensors of the vehicle with a detection range (one or more) equal to or greater than the determined stopping distance. Figure 9 As illustrated, each sensor has a corresponding detection range; for example, a short-range sensor has a D... SR Or, a long-range sensor with D LR One or more short-range sensors may include at least one of the following: a light detection and ranging (LiDAR) sensor, a radio detection and ranging (RADAR) sensor, a microphone sensor, and a camera sensor.

[0115] In some embodiments, the autonomous system determines whether the vehicle's driving speed on the route exceeds a speed threshold (e.g., 40 mph), and identifies one or more sensors of the vehicle in response to determining that the vehicle's driving speed exceeds the speed threshold. For example, the autonomous system identifies one or more sensors for intelligent power management in response to determining that the vehicle is operating at high speed (e.g., driving on a highway).

[0116] Continuing with reference to process 1000, when one or more sensors are identified, the autonomous system deactivates at least one operation of at least one of the at least one sensors (1006). In one embodiment, the autonomous system determines a deactivation distance threshold for at least one sensor that is greater than the corresponding detection range of at least one sensor (e.g., to add some buffer time or distance for deactivation). Figure 9 D shown Deactivate The autonomous system then determines whether the stopping distance of the vehicle is greater than a deactivation distance threshold for at least one sensor. In response to determining that the stopping distance of the vehicle is greater than the deactivation distance threshold for at least one sensor, the autonomous system deactivates at least one operation of at least one sensor.

[0117] The control system can partially or completely disable the short-range sensor based on one or more characteristics of the short-range sensor (e.g., priority, back-facing / forward-facing, startup time, active / passive).

[0118] In one embodiment, the autonomous system disables at least one operation of at least one sensor by ceasing to provide computing power to at least one or more components of at least one sensor.

[0119] In one embodiment, the autonomous system determines whether at least one sensor is a passive or active device, and in response to determining that the at least one sensor is a passive device, selects to completely disable the at least one sensor, or in response to determining that the at least one sensor is an active device, selects to partially disable the at least one sensor, for example, turning off the power supply for laser ignition of the LiDAR sensor, but still running the CPU and FPGA.

[0120] In one embodiment, the autonomous system determines whether at least one sensor is a low-priority sensor or a high-priority sensor, and selects to completely disable the at least one sensor in response to determining that the at least one sensor is a low-priority sensor, or selects to partially disable the at least one sensor in response to determining that the at least one sensor is a high-priority sensor.

[0121] In one embodiment, the autonomous system determines whether the startup time of at least one sensor is less than a time threshold, and in response to determining that the startup time of the at least one sensor is less than the time threshold, selects to completely disable the at least one sensor, or in response to determining that the startup time of the at least one sensor is equal to or greater than the time threshold, selects to partially disable the at least one sensor.

[0122] The autonomous system dynamically updates the stopping distance as the vehicle travels along the route. In one embodiment, at a time following at least one operation that disables at least one sensor, the autonomous system determines the current stopping distance of the vehicle traveling along the route. The autonomous system determines whether the current stopping distance of the vehicle is less than the activation distance threshold of at least one sensor (e.g., as...). Figure 9 D shown Activate The activation distance threshold can be greater than the sensor's detection range (e.g., to add some buffer time or distance for reactivation). In response to the current stopping distance of the determined vehicle being less than the activation distance threshold of at least one sensor, the autonomous system can reactivate at least one sensor for operation. In one embodiment, the activation distance threshold is less than the deactivation distance threshold. In this way, the autonomous system can control the sensor to deactivate later and reactivate earlier for operation, which can effectively add buffer time or distance for operation.

[0123] In one embodiment, the autonomous system determines whether the vehicle's current driving rate is less than a rate threshold (e.g., 40 mph). The controller may reactivate at least one sensor to operate in response to the determination that the vehicle's driving rate is less than the rate threshold (e.g., the vehicle has switched to low-speed operation or is driving on a local road).

[0124] In one embodiment, when a sensor is reactivated, the autonomous system tracks when the sensor was last reactivated. If the elapsed time since the last reactivation exceeds a specific time threshold, the autonomous system can decide to deactivate the sensor. In this way, the autonomous system can add a time lag feature to the sensor to avoid ping-ponging on / off states; this time lag feature can be used separately or as an alternative to buffer time or distance.

[0125] The autonomous system can also identify one or more sensors (e.g., long-range sensors) with a detection range greater than the vehicle's stopping distance. These sensors include at least one of a LiDAR sensor, a radar sensor, a microphone sensor, and a camera sensor. In one example, the autonomous system determines that the detection range of a second sensor is greater than the stopping distance and maintains the operation of the second sensor. The autonomous system can also maintain the operation of the second sensor in response to determining that the vehicle's driving speed exceeds a speed threshold.

[0126] In one embodiment, at a time following the deactivation of at least one operation of at least one sensor, the autonomous system allocates a specific amount of computational power to the second sensor. When the at least one sensor is activated and in operation, this specific amount of computational power is greater than the initial amount of computational power allocated to the second sensor.

[0127] In one embodiment, the autonomous system prioritizes multiple operational vehicle sensors (e.g., long-range and / or short-range sensors) based on energy / computing resources. The autonomous system can then reallocate energy / computing resources using the priority levels of the operational sensors. For example, if a second sensor has a higher priority than one or more other sensors, more computing power can be allocated to the second sensor.

[0128] In the preceding description, aspects and embodiments of this disclosure have been described with reference to numerous specific details, which may vary from implementation to implementation. Therefore, the specification and drawings should be considered illustrative rather than restrictive. The sole and exclusive indication of the scope of this invention, and what the applicant expects to be the scope of this invention, is the literal and equivalent scope of the claims published from this application in the specific form of the published claims, including any subsequent amendments. Any definitions of terms expressly set forth herein for inclusion in such claims should be taken as meaning as such terms are used in the claims. Furthermore, when the term “comprising” is used in the preceding specification or appended claims, what follows that phrase may be an additional step or entity, or a sub-step / sub-entity of a previously stated step or entity.

Claims

1. A method for managing sensors in a vehicle, comprising: Use at least one processor to determine the stopping distance of the vehicle on the route; The at least one processor is used to identify one or more sensors of the vehicle having a corresponding fixed detection range smaller than the stopping distance; When identifying one or more sensors, the at least one processor is used to deactivate at least one operation of at least one of the identified sensors having a corresponding detection range smaller than the stop distance; At a time after the at least one operation of the at least one sensor is deactivated, the at least one processor is used to determine whether the current stopping distance of the vehicle traveling on the route is less than the activation distance threshold of the at least one sensor, wherein the activation distance threshold is greater than the detection range of the at least one sensor; as well as In response to determining that the current stopping distance of the vehicle is less than the activation distance threshold of the at least one sensor, the at least one sensor is reactivated to operate.

2. The method according to claim 1, wherein, Identifying one or more sensors includes: The at least one processor is used to determine whether the driving speed of the vehicle on the route exceeds a speed threshold; and In response to determining that the driving speed of the vehicle is greater than the speed threshold, the one or more sensors of the vehicle are identified.

3. The method according to claim 2, wherein, Determining the stopping distance of the vehicle includes: The stopping distance of the vehicle is determined using at least the vehicle's driving speed.

4. The method according to claim 2 or 3, wherein, Determining the stopping distance of the vehicle includes: At least a braking mechanism should be used to determine the stopping distance.

5. The method according to any one of claims 1 to 3, wherein, At least one operation of deactivating at least one of the one or more sensors includes: Determine a deactivation distance threshold for the at least one sensor, wherein the deactivation distance threshold is greater than the corresponding detection range of the at least one sensor; The at least one processor is used to determine whether the stopping distance of the vehicle is greater than the stopping distance threshold of the at least one sensor; and In response to determining that the stopping distance of the vehicle is greater than the deactivation distance threshold of the at least one sensor, the at least one operation of the at least one sensor is deactivated.

6. The method according to claim 1, wherein, The activation distance threshold is less than the deactivation distance threshold for deactivating the at least one operation of the at least one sensor.

7. The method according to claim 1, comprising: The at least one processor is used to determine whether the driving speed of the vehicle on the route is less than a speed threshold; as well as In response to determining that the driving speed of the vehicle is less than the speed threshold, the at least one sensor is reactivated to operate.

8. The method according to any one of claims 1 to 3, wherein, The operation of deactivating at least one of the one or more sensors includes: Stop providing computing power to one or more components of the at least one sensor.

9. The method according to any one of claims 1 to 3, further comprising: Determine whether the at least one sensor is a passive or active device. The operation of deactivating at least one of the at least one sensors in one or more sensors includes one of the following: In response to determining that the at least one sensor is a passive device, the at least one sensor is completely deactivated; and In response to determining that the at least one sensor is an active device, the at least one sensor is partially deactivated.

10. The method according to any one of claims 1 to 3, further comprising: Determine whether the at least one sensor is facing the rear sensor or the front sensor. The operation of deactivating at least one of the one or more sensors includes: In response to determining that the at least one sensor is a forward-facing sensor and the vehicle is driving forward, the at least one sensor is deactivated.

11. The method according to any one of claims 1 to 3, further comprising: Determine whether the at least one sensor is a low-priority sensor or a high-priority sensor. The operation of deactivating at least one of the at least one sensors in one or more sensors includes one of the following: In response to determining that the at least one sensor is a low-priority sensor, the at least one sensor is completely deactivated; and In response to determining that the at least one sensor is a high-priority sensor, the at least one sensor is partially deactivated.

12. The method according to any one of claims 1 to 3, further comprising: Determine whether the start-up time of the at least one sensor is less than a time threshold. The operation of deactivating at least one of the at least one sensors in one or more sensors includes one of the following: In response to the determination that the start-up time of the at least one sensor is less than the time threshold, the at least one sensor is completely deactivated; and The at least one sensor is partially deactivated in response to the determination that the activation time of the at least one sensor is equal to or greater than the time threshold.

13. The method according to any one of claims 1 to 3, wherein, The one or more sensors include at least one of the following: a light detection and ranging sensor, i.e., a LiDAR sensor; a radio detection and ranging sensor, i.e., a RADAR sensor; a microphone sensor; and a camera sensor.

14. The method according to any one of claims 1 to 3, further comprising: The at least one processor is used to determine that the detection range of the second sensor is greater than the stop distance; as well as The at least one processor is used to maintain the operation of the second sensor.

15. The method according to claim 14, wherein, The operation of the second sensor is maintained in response to the determination that the driving speed of the vehicle exceeds a speed threshold.

16. The method according to any one of claims 1 to 3, further comprising: At a time following the deactivation of at least one operation of the at least one sensor, the at least one processor allocates a specific amount of computational power to a second sensor having a detection range greater than the stopping distance. Wherein, when the at least one sensor is activated, a certain amount of computational power is greater than the initial amount of computational power provided to the second sensor.

17. The method of claim 16, further comprising: Prioritize multiple sensors based on computing resources. The second sensor has a higher priority than one or more other sensors among the plurality of sensors.

18. The method according to claim 14, wherein, The second sensor includes at least one of the following: a light detection and ranging sensor, namely a LiDAR sensor; a radio detection and ranging sensor, namely a RADAR sensor; a microphone sensor; and a camera sensor.

19. A system for managing sensors in a vehicle, comprising: At least one processor; as well as At least one non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform the method according to any one of claims 1 to 18.

20. At least one non-transitory storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform the method according to any one of claims 1 to 18.

21. A computer program product comprising instructions that, when executed by at least one processor, cause the at least one processor to perform the method according to any one of claims 1 to 18.

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