Distributed computing system for environmental sensor data of vehicle

Abstract

Systems, methods, and computer program products of a distributed computing system for a vehicle are provided. A vehicle, such as an autonomous vehicle, may have a hardware architecture that includes a master embedded system and a plurality of slave embedded systems. The master embedded system and the slave embedded system may each include, for example, a system-on-chip (SoC). Each slave embedded system may be assigned a plurality of sensors of the vehicle, may process data generated by the assigned sensors, and may communicate their processed outputs to the master embedded system. The master embedded system may control the timing of the slave embedded systems, such as by sequentially rotating through the respective slave embedded systems, so that the timing of data generation and processing of the sensors may be controlled. Each slave embedded system may communicate with the master embedded system via a high-speed interface.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 334,890, filed on April 26, 2022, and incorporates by reference the disclosures of the following applications in their entirety: U.S. non-provisional patent application with attorney case number 46154-0438001 / I2022068, filed on April 26, 2023, and entitled “SCALABLE CONFIGURABLE CHIP ARCHITECTURE”; and U.S. non-provisional patent application with attorney case number 46154-0445001 / I2022075, filed on April 26, 2023, and entitled “DISTRIBUTED COMPUTING ARCHITECTURE WITH SHARED MEMORY FOR AUTONOMOUSROBOTIC SYSTEMS.” Background Art

[0003] An autonomous vehicle is capable of sensing its surroundings and navigating without human input. Upon receiving data representing the environment and / or any other parameter, the vehicle processes the data to determine its movement decisions (e.g., stop, move forward / backward, turn, etc.). These decisions are intended to safely navigate the vehicle along a selected path to avoid obstacles and react to various scenarios (such as the presence, movement, etc. of other vehicles, pedestrians, and / or any other objects). Timely detection of objects and decisive decision making are important for the safe operation of the vehicle and / or its components. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

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

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

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

[0010] Figure 5A is a diagram of an implementation of a distributed computing system used by a vehicle;

[0011] Figure 5B is a diagram of another implementation of a distributed computing system for use with a vehicle;

[0012] Figure 5C It is for Figure 5B A diagram of a sector implementation of a distributed computing system;

[0013] Figure 5D yes Figure 5C Another diagram of the partitioning implementation of ;

[0014] Figure 6 yes Figure 5B A diagram of the processing used by the distributed computing system; and

[0015] Figure 7 An example of a process for predicting agent importance for autonomous driving is illustrated in accordance with some embodiments of the techniques discussed in this disclosure. DETAILED DESCRIPTION

[0016] In the following description, for the purpose of explanation, many specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be apparent that the embodiments described in the present disclosure can be implemented without these specific details. In some instances, well-known configurations and devices are illustrated in block diagram form to avoid unnecessarily obscuring aspects of the present disclosure.

[0017] In the accompanying drawings, for ease of description, the specific arrangement or order of schematic elements (such as those representing systems, devices, modules, instruction blocks and / or data elements, etc.) is illustrated. However, those skilled in the art will understand that, unless explicitly described, the specific order or arrangement of schematic elements in the accompanying drawings is not intended to mean that a specific processing order or sequence, or separation of processing is required. In addition, unless explicitly described, the inclusion of schematic elements in the accompanying drawings is not intended to mean that such elements are required in all embodiments, nor is it intended to mean that the features represented by such elements cannot be included in some embodiments or cannot be combined with other elements in some embodiments.

[0018] In addition, in the accompanying drawings, connecting elements (such as solid or dotted lines or arrows, etc.) are used to illustrate the connection, relationship or association between or among two or more other schematic elements, and the absence of any such connecting elements is not intended to mean that there can be no connection, relationship or association. In other words, some connections, relationships or associations between elements are not illustrated in the accompanying drawings so as not to obscure the present disclosure. In addition, for ease of illustration, a single connecting element can be used to represent multiple connections, relationships or associations between elements. For example, if the connecting element represents the communication of a signal, data or instruction (e.g., "software instruction"), it will be understood by those skilled in the art that such an element can represent one or more than one signal path (e.g., bus) that may be needed to affect the communication.

[0019] Although the terms "first", "second" and / or "third", etc. are used to describe various elements, these elements should not be limited by these terms. The terms "first", "second" and / or "third" are only used to distinguish one element from another. For example, a first contact may be referred to as a second contact, and similarly, a second contact may be referred to as a first contact without departing from the scope of the described embodiments. Both the first contact and the second contact are contacts, but they are not the same contacts.

[0020] The terms used in the specification of the various embodiments described herein are included only for the purpose of describing specific embodiments and are not intended to be limiting. As used in the specification of the various embodiments described and the appended claims, the singular forms "a", "an" and "the" are also intended to include plural forms and can be used interchangeably with "one or more than one" or "at least one", unless the context clearly indicates otherwise. It will also be understood that the term "and / or" as used herein refers to and includes any and all possible combinations of one or more than one of the associated listed items. It will also be understood that when the terms "include", "comprise", "have" and / or "have" are used in this specification, the stated features, integers, steps, operations, elements and / or components are specifically stated, but the presence or addition of one or more than one other features, integers, steps, operations, elements, components and / or groups thereof are not excluded.

[0021] As used herein, the terms "communication" and "communicating" refer to at least one of receiving, receiving, transmitting, transmitting and / or providing information (or information represented by, for example, data, signals, messages, instructions and / or commands, etc.). For a unit (e.g., a device, a system, a component of a device or system, and / or a combination thereof) to communicate with another unit, this means that the unit is able to directly or indirectly receive information from the other unit and / or send (e.g., transmit) information to the other unit. This can refer to a direct or indirect connection that is wired and / or wireless in nature. In addition, even if the transmitted information can be modified, processed, relayed and / or routed between the first unit and the second unit, the two units can communicate with each other. For example, even if the first unit passively receives information and does not actively transmit information to the second unit, the first unit can communicate with the second unit. As another example, if at least one intermediary unit (e.g., a third unit located between the first unit and the second unit) processes the information received from the first unit and transmits the processed information to the second unit, the first unit can communicate with the second unit. In some embodiments, a message may refer to a network packet (eg, a data packet, etc.) that includes data.

[0022] As used herein, the term "if" is optionally interpreted to mean "when," "at," "in response to being determined to be," and / or "in response to being detected," etc., depending on the context. Similarly, the phrases "if it is determined" or "if [the stated condition or event] is detected" are optionally interpreted to mean "when determining," "in response to being determined to be" or "when [the stated condition or event] is detected," and / or "in response to being detected," etc., depending on the context. In addition, as used herein, the terms "have," "have," or "possess," etc. are intended to be open-ended terms. Furthermore, unless expressly stated otherwise, the phrase "based on" is intended to mean "based at least in part on."

[0023] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various embodiments described. However, it will be apparent to one of ordinary skill in the art that the various embodiments described may be implemented without these specific details. In other cases, well-known methods, processes, components, circuits, and networks have not yet been described in detail in order not to unnecessarily obscure aspects of the embodiments.

[0024] General Overview

[0025] Autonomous robotic systems such as autonomous vehicle (AV) computing can have a hardware architecture including a master embedded system and multiple slave embedded systems. The master embedded system and the slave embedded system can each include a system on chip (SoC), for example. Each slave embedded system can be assigned multiple sensors (e.g., cameras, LiDAR sensors, Radar sensors, etc.) of the vehicle, can process the data generated by the assigned sensors, and can communicate the processed output to the master embedded system. The master embedded system can control the timing of the slave embedded system, such as by rotating through each slave embedded system in sequence, so that the data generation and processing timing of the sensor can be controlled. Each slave embedded system can communicate with the master embedded system via a high-speed interface.

[0026] Some of the advantages of these technologies include allowing distributed computing on vehicles, which can reduce power consumption and / or reduce costs. The master embedded system controls the synchronization of multiple slave embedded systems, which can provide low latency in data generation and processing. Each sensor of the vehicle can be assigned to one of the slave embedded systems, which can improve the control of data generation of the sensor by controlling the timing of each slave embedded system by the master embedded system, and / or because each slave embedded system is only responsible for processing data from the assigned sensor and can start processing such data without waiting for any other slave embedded system to process the data generated by the assigned sensor, thus reducing the delay in processing the data generated by the sensor. Each sensor assigned to a specific slave embedded system can be in the same physical partition of the vehicle, which can allow efficient identification of which sensor is or should be assigned to a specific slave embedded system, and / or efficient control of a specific sensor by the master embedded system to the synchronization control of the slave embedded system. Each slave embedded system can communicate with the master embedded system via a high-speed interface, which can, for example, allow communication data in the absence of delays caused by using traditional IEEE 1588 / gPTP (generalized precision time protocol) by allowing board-to-board communication.

[0027] Reference now Figure 1, illustrates an example environment 100 in which vehicles including autonomous systems and vehicles not including autonomous systems operate. As illustrated, the environment 100 includes vehicles 102a-102n, objects 104a-104n, routes 106a-106n, areas 108, vehicle-to-infrastructure (V2I) devices 110, a network 112, a remote autonomous vehicle (AV) system 114, a fleet management system 116, and a V2I system 118. The vehicles 102a-102n, the vehicle-to-infrastructure (V2I) devices 110, the network 112, the autonomous vehicle (AV) system 114, the fleet management system 116, and the V2I system 118 are interconnected (e.g., establish connections for communication, etc.) via wired connections, wireless connections, or a combination of wired or wireless connections. In some embodiments, objects 104a-104n are interconnected with at least one of vehicles 102a-102n, vehicle-to-infrastructure (V2I) devices 110, networks 112, autonomous vehicle (AV) systems 114, fleet management systems 116, and V2I systems 118 via wired connections, wireless connections, or a combination of wired or wireless connections.

[0028] Vehicles 102a-102n (individually referred to as vehicles 102 and collectively referred to as vehicles 102) include at least one device configured to transport goods and / or people. In some embodiments, vehicles 102 are configured to communicate with V2I devices 110, remote AV systems 114, fleet management systems 116, and / or V2I systems 118 via network 112. In some embodiments, vehicles 102 include cars, buses, trucks, and / or trains, etc. In some embodiments, vehicles 102 are similar to vehicles 200 described herein (see Figure 2 ). In some embodiments, vehicles 200 in the set of vehicles 200 are associated with an autonomous queue manager. In some embodiments, vehicles 102 travel along respective routes 106a-106n (individually referred to as routes 106 and collectively referred to as routes 106) as described herein. In some embodiments, one or more vehicles 102 include an autonomous system (e.g., an autonomous system that is the same as or similar to autonomous system 202).

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

[0030] Routes 106a-106n (individually referred to as routes 106 and collectively referred to as routes 106) are each associated with (e.g., specify a series of actions (also referred to as trajectories) connecting states along which the AV can navigate. Each route 106 begins at an initial state (e.g., a state corresponding to a first spatiotemporal location and / or speed, etc.) and ends at a final target state (e.g., a state corresponding to a second spatiotemporal location different from the first spatiotemporal location) or a target zone (e.g., a subspace of acceptable states (e.g., terminal states)). In some embodiments, the first state includes a location where one or more individuals will board the AV, and the second state or zone includes one or more locations where one or more individuals boarding the AV will disembark. In some embodiments, routes 106 include multiple acceptable state sequences (e.g., multiple spatiotemporal location sequences) that are associated with (e.g., define multiple trajectories). In an example, routes 106 include only high-level actions or imprecise state locations, such as a series of connecting roads indicating a change of direction at a roadway intersection, etc. Additionally or alternatively, the route 106 may include more precise actions or states, such as, for example, specific target lanes or precise locations within lane regions and target speeds at those locations, etc. In an example, the route 106 includes a plurality of precise state sequences along at least one high-level action with a limited look-ahead horizon to an intermediate target, wherein a combination of consecutive iterations of the limited horizon state sequences cumulatively correspond to a plurality of trajectories that collectively form a high-level route terminating at a final target state or region.

[0031] The area 108 includes a physical area (e.g., a geographic region) in which the vehicle 102 can navigate. In an example, the area 108 includes at least one state (e.g., a country, a province, a separate state of a plurality of states included in a country, etc.), at least a portion of a state, at least one city, at least a portion of a city, etc. In some embodiments, the area 108 includes at least one named thoroughfare (referred to herein as a "road"), such as a highway, an interstate highway, a parkway, a city street, etc. Additionally or alternatively, in some examples, the area 108 includes at least one unnamed road, such as a driveway, a section of a parking lot, a section of an open space and / or undeveloped area, a dirt road, etc. In some embodiments, the road includes at least one lane (e.g., a portion of the road that the vehicle 102 can traverse). In an example, the road includes at least one lane associated with (e.g., identified based on) at least one lane marking line.

[0032] The vehicle-to-infrastructure (V2I) device 110 (sometimes referred to as a vehicle-to-infrastructure or vehicle-to-everything (V2X) device) includes at least one device configured to communicate with the vehicle 102 and / or the V2I system 118. In some embodiments, the V2I device 110 is configured to communicate with the vehicle 102, the remote AV system 114, the fleet management system 116, and / or the V2I system 118 via the network 112. In some embodiments, the V2I device 110 includes a radio frequency identification (RFID) device, a sign, a camera (e.g., a two-dimensional (2D) and / or three-dimensional (3D) camera), lane markings, street lights, parking meters, etc. In some embodiments, the V2I device 110 is configured to communicate directly with the vehicle 102. Additionally or alternatively, in some embodiments, the V2I device 110 is configured to communicate with the vehicle 102, the remote AV system 114, and / or the fleet management system 116 via the V2I system 118. In some embodiments, the V2I device 110 is configured to communicate with the V2I system 118 via the network 112.

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

[0034] The remote AV system 114 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the network 112, the fleet management system 116, and / or the V2I system 118 via the network 112. In an example, the remote AV system 114 includes a server, a server group, and / or other similar devices. In some embodiments, the remote AV system 114 is co-located with the fleet management system 116. In some embodiments, the remote AV system 114 participates in the installation of some or all of the components of the vehicle (including autonomous systems and / or autonomous vehicle software implemented by autonomous vehicle computing, etc.). In some embodiments, the remote AV system 114 maintains (e.g., updates and / or replaces) these components and / or software during the life of the vehicle.

[0035] The queue management system 116 includes at least one device configured to communicate with the vehicles 102, the V2I devices 110, the remote AV system 114, and / or the V2I system 118. In an example, the queue management system 116 includes a server, a server group, and / or other similar devices. In some embodiments, the queue management system 116 is associated with a ridesharing company (e.g., an organization for controlling the operation of multiple vehicles (e.g., vehicles including autonomous systems and / or vehicles not including autonomous systems)).

[0036] In some embodiments, the V2I system 118 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the remote AV system 114, and / or the fleet management system 116 via the network 112. In some examples, the V2I system 118 is configured to communicate with the V2I device 110 via a connection other than the network 112. In some embodiments, the V2I system 118 includes a server, a server group, and / or other similar devices. In some embodiments, the V2I system 118 is associated with a municipality or a private agency (e.g., a private agency for maintaining the V2I device 110, etc.).

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

[0038] Reference now Figure 2 , vehicle 200 (which can be Figure 1 102) includes, or is associated with, autonomous system 202, powertrain control system 204, steering control system 206, and braking system 208. In some embodiments, vehicle 200 is similar to vehicle 102 (see Figure 1 ). In some embodiments, the autonomous system 202 is configured to give the vehicle 200 autonomous driving capabilities (e.g., implementing at least one of the following driving automatic or maneuvering-based functions, features, and / or devices, etc., which enables the vehicle 200 to partially or completely operate without human intervention, including but not limited to fully autonomous vehicles (e.g., vehicles that abandon reliance on human intervention, such as Level 5 ADS-operated vehicles, etc.), highly autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in certain situations, such as Level 4 ADS-operated vehicles, etc.), and / or conditionally autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in limited situations). In one embodiment, the autonomous system 202 includes the operational or tactical functionality required to enable the vehicle 200 to operate in on-road traffic and continuously perform part or all of the dynamic driving tasks (DDT). In another embodiment, the autonomous system 202 includes an advanced driver assistance system (ADAS) including driver support features. The autonomous system 202 supports various levels of driving automation ranging from no driving automation (e.g., level 0) to full driving automation (e.g., level 5). For a detailed description of fully autonomous vehicles and highly autonomous vehicles, reference may be made to SAE International's standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, the entire contents of which are incorporated by reference. In some embodiments, the vehicle 200 is associated with an autonomous queue manager and / or a ride-sharing company.

[0039] Autonomous system 202 includes a sensor suite that includes one or more devices such as camera 202a, LiDAR sensor 202b, Radar sensor 202c, and microphone 202d. In some embodiments, autonomous system 202 may include more or fewer devices and / or different devices (e.g., ultrasonic sensors, inertial sensors, GPS receivers (discussed below), and / or odometer sensors for generating data associated with an indication of the distance that vehicle 200 has traveled, etc.). In some embodiments, autonomous system 202 uses one or more devices included in autonomous system 202 to generate data associated with environment 100 described herein. Data generated by one or more devices of autonomous system 202 can be used by one or more systems described herein to observe the environment (e.g., environment 100) in which vehicle 200 is located. In some embodiments, autonomous system 202 includes communication device 202e, autonomous vehicle computing 202f, drive-by-wire (DBW) system 202h, and safety controller 202g.

[0040] The camera 202a includes a communication device 202e, an autonomous vehicle computer 202f, and / or a safety controller 202g configured to communicate with the communication device 202e via a bus (e.g., Figure 3 The camera 202a includes at least one device for communicating with the autonomous vehicle computing 202f (e.g., an image processing unit 202a, a bus ... Figure 1In some embodiments, the autonomous vehicle computing 202f determines a depth to one or more objects in a field of view of at least two of the plurality of cameras based on image data from the at least two cameras. In some embodiments, the camera 202a is configured to capture images of objects within a distance relative to the camera 202a (e.g., up to 100 meters and / or up to 1 kilometer, etc.). Thus, the camera 202a includes features such as sensors and lenses that are optimized for sensing objects at one or more distances relative to the camera 202a.

[0041] In an embodiment, the camera 202a includes at least one camera configured to capture one or more images associated with one or more traffic lights, street signs, and / or other physical objects that provide visual navigation information. In some embodiments, the camera 202a generates traffic light data associated with the one or more images. In some examples, the camera 202a generates TLD (traffic light detection) data associated with one or more images including a format (e.g., RAW, JPEG, and / or PNG, etc.). In some embodiments, the camera 202a that generates TLD data differs from other systems incorporating cameras described herein in that the camera 202a may include one or more cameras with a wide field of view (e.g., a wide-angle lens, a fisheye lens, and / or a lens with a viewing angle of approximately 120 degrees or greater, etc.) to generate images related to as many physical objects as possible.

[0042] The light detection and ranging (LiDAR) sensor 202b includes a communication device 202e, an autonomous vehicle computing device 202f, and / or a safety controller 202g via a bus (e.g., Figure 3The LiDAR sensor 202b includes at least one device that communicates with a bus (the same or similar bus as the bus 302 of the embodiment of the present invention). The LiDAR sensor 202b includes a system configured to emit light from a light emitter (e.g., a laser emitter). The light emitted by the LiDAR sensor 202b includes light outside the visible spectrum (e.g., infrared light, etc.). In some embodiments, during operation, the light emitted by the LiDAR sensor 202b encounters a physical object (e.g., a vehicle) and is reflected back to the LiDAR sensor 202b. In some embodiments, the light emitted by the LiDAR sensor 202b does not penetrate the physical object encountered by the light. The LiDAR sensor 202b also includes at least one light detector that detects the light emitted from the light emitter after encountering the physical object. In some embodiments, at least one data processing system associated with the LiDAR sensor 202b generates an image (e.g., a point cloud and / or a combined point cloud, etc.) representing objects included in the field of view of the LiDAR sensor 202b. In some examples, at least one data processing system associated with the LiDAR sensor 202b generates an image representing the boundaries of the physical object and / or the surface of the physical object (e.g., the topology of the surface), etc. In such examples, the image is used to determine the boundaries of the physical object in the field of view of the LiDAR sensor 202b.

[0043] The radio detection and ranging (Radar) sensor 202c includes a sensor configured to communicate with the communication device 202e, the autonomous vehicle computing 202f, and / or the safety controller 202g via a bus (e.g., Figure 3 At least one device for communicating with a bus (same or similar bus as bus 302 of the embodiment of the present invention). Radar sensor 202c includes a system configured to transmit (pulsed or continuous) radio waves. The radio waves transmitted by Radar sensor 202c include radio waves within a predetermined spectrum. In some embodiments, during operation, the radio waves transmitted by Radar sensor 202c encounter physical objects and are reflected back to Radar sensor 202c. In some embodiments, the radio waves transmitted by Radar sensor 202c are not reflected by some objects. In some embodiments, at least one data processing system associated with Radar sensor 202c generates a signal representing an object included in the field of view of Radar sensor 202c. For example, at least one data processing system associated with Radar sensor 202c generates an image representing the boundary of a physical object and / or the surface of a physical object (e.g., the topology of the surface), etc. In some examples, the image is used to determine the boundary of a physical object in the field of view of Radar sensor 202c.

[0044] The microphone 202d includes a microphone configured to communicate with the communication device 202e, the autonomous vehicle computing device 202f, and / or the safety controller 202g via a bus (e.g., Figure 3 At least one device for communicating with the vehicle 200 (the same or similar bus as bus 302 of FIG. 1 ). Microphone 202d includes one or more microphones (e.g., an array microphone and / or an external microphone, etc.) that capture an audio signal and generate data associated with (e.g., representing) the audio signal. In some examples, microphone 202d includes a transducer device and / or the like. In some embodiments, one or more systems described herein can receive the data generated by microphone 202d and determine the position (e.g., distance, etc.) of an object relative to vehicle 200 based on an audio signal associated with the data.

[0045] The communication device 202e includes at least one device configured to communicate with the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, the microphone 202d, the autonomous vehicle computing 202f, the safety controller 202g, and / or the drive-by-wire (DBW) system 202h. For example, the communication device 202e may include at least one device configured to communicate with the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, the microphone 202d, the autonomous vehicle computing 202f, the safety controller 202g, and / or the drive-by-wire (DBW) system 202h. Figure 3 The communication device 202e may be a device that is the same as or similar to the communication interface 314 of the vehicle. In some embodiments, the communication device 202e includes a vehicle-to-vehicle (V2V) communication device (eg, a device for enabling wireless communication of data between vehicles).

[0046] The autonomous vehicle computing 202f includes at least one device configured to communicate with the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, the microphone 202d, the communication device 202e, the safety controller 202g and / or the DBW system 202h. In some examples, the autonomous vehicle computing 202f includes devices such as client devices, mobile devices (e.g., cellular phones and / or tablet computers, etc.), and / or servers (e.g., computing devices including one or more central processing units and / or graphics processing units, etc.). In some embodiments, the autonomous vehicle computing 202f is configured to implement the autonomous vehicle software 400 described herein. In an embodiment, the autonomous vehicle computing 202f is the same as or similar to a distributed computing architecture. Additionally or alternatively, in some embodiments, the autonomous vehicle computing 202f is configured to communicate with an autonomous vehicle system (e.g., with Figure 1 remote AV system 114 of the same or similar autonomous vehicle system), a fleet management system (e.g., Figure 1 of the same or similar queue management system as the queue management system 116), V2I devices (e.g., Figure 1V2I device 110 that is the same as or similar to V2I device 110) and / or V2I system (e.g., Figure 1 The V2I system 118 may communicate with the same or similar V2I system.

[0047] Safety controller 202g includes at least one device configured to communicate with camera 202a, LiDAR sensor 202b, Radar sensor 202c, microphone 202d, communication device 202e, autonomous vehicle computing 202f, and / or DBW system 202h. In some examples, safety controller 202g includes one or more controllers (electrical controllers and / or electromechanical controllers, etc.) configured to generate and / or transmit control signals to operate one or more devices of vehicle 200 (e.g., powertrain control system 204, steering control system 206, and / or braking system 208, etc.). In some embodiments, safety controller 202g is configured to generate control signals that take precedence over (e.g., override) control signals generated and / or transmitted by autonomous vehicle computing 202f.

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

[0049] The powertrain control system 204 includes at least one device configured to communicate with the DBW system 202h. In some examples, the powertrain control system 204 includes at least one controller and / or actuator, etc. In some embodiments, the powertrain control system 204 receives a control signal from the DBW system 202h, and the powertrain control system 204 causes the vehicle 200 to perform longitudinal vehicle movement (such as starting to move forward, stopping to move forward, starting to move backward, stopping to move backward, accelerating in a certain direction, decelerating in a certain direction, etc.), or to perform lateral vehicle movement (such as turning left and / or turning right, etc.). In an example, the powertrain control system 204 increases, maintains the same, or decreases the energy (e.g., fuel and / or electricity, etc.) provided to the motor of the vehicle, thereby rotating or not rotating at least one wheel of the vehicle 200.

[0050] The steering control system 206 includes at least one device configured to rotate one or more wheels of the vehicle 200. In some examples, the steering control system 206 includes at least one controller and / or actuator, etc. In some embodiments, the steering control system 206 rotates the two front wheels and / or the two rear wheels of the vehicle 200 to the left or right to turn the vehicle 200 left or right. In other words, the steering control system 206 causes the activity required to regulate the y-axis component of the vehicle's motion.

[0051] Braking system 208 includes at least one device configured to actuate one or more brakes to decelerate and / or hold vehicle 200 stationary. In some examples, braking system 208 includes at least one controller and / or actuator configured to cause one or more calipers associated with one or more wheels of vehicle 200 to close on respective rotors of vehicle 200. Additionally or alternatively, in some examples, braking system 208 includes an automatic emergency braking (AEB) system and / or a regenerative braking system, etc.

[0052] In some embodiments, vehicle 200 includes at least one platform sensor (not explicitly illustrated) for measuring or inferring a property of a state or condition of vehicle 200. In some examples, vehicle 200 includes platform sensors such as a global positioning system (GPS) receiver, an inertial measurement unit (IMU), wheel rate sensors, wheel brake pressure sensors, wheel torque sensors, engine torque sensors, and / or steering angle sensors. Although brake system 208 is illustrated as being located at Figure 2 The braking system 208 is located on the proximal side of the vehicle 200 , but the braking system 208 can be located anywhere in the vehicle 200 .

[0053] Reference now Figure 3, which illustrates a schematic diagram of an apparatus 300. As illustrated, the apparatus 300 includes a processor 304, a memory 306, a storage component 308, an input interface 310, an output interface 312, a communication interface 314, and a bus 302. In some embodiments, device 300 corresponds to: at least one device of vehicle 102 (e.g., at least one device of a system of vehicle 102); at least one device of V2I device 110 (e.g., at least one device of a system of V2I device 110); at least one device of AV system 114 (e.g., at least one device of a system of AV system 114); at least one device of queue management system 116 (e.g., at least one device of a system of queue management system 116); at least one device of V2I system 118 (e.g., at least one device of a system of V2I system 118); at least one device of camera 202a (e.g., at least one device of a system of camera 202a); at least one device of LiDAR sensor 202b (e.g., at least one device of a system of LiDAR sensor 202b); at least one device of Radar sensor 202c (e.g., at least one device of a system of Radar sensor 202c); at least one device of microphone 202d (e.g., at least one device of a system of microphone 202d); devices); at least one device of communication device 202e (e.g., at least one device of a system of communication device 202e); at least one device of autonomous vehicle computing 202f (e.g., at least one device of a system of autonomous vehicle computing 202f); at least one device of safety controller 202g (e.g., at least one device of a system of safety controller 202g); at least one device of DBW system 202h (e.g., at least one device of a system of DBW system 202h); at least one device of powertrain control system 204 (e.g., at least one device of a system of powertrain control system 204); at least one device of steering control system 206 (e.g., at least one device of a system of steering control system 206); at least one device of braking system 208 (e.g., at least one device of a system of braking system 208); at least one device of platform sensor (e.g., at least one device of a system of platform sensor); and / or one or more devices of network 112 (e.g., one or more devices of a system of network 112).In some embodiments, one or more devices of the vehicle 102 (e.g., one or more devices of the system of the vehicle 102), one or more devices of the V2I device 110 (e.g., one or more devices of the system of the V2I device 110), one or more devices of the AV system 114 (e.g., one or more devices of the system of the AV system 114), one or more devices of the queue management system 116 (e.g., one or more devices of the system of the queue management system 116), one or more devices of the V2I system 118 (e.g., one or more devices of the system of the V2I system 118), ), one or more devices of the camera 202a (e.g., one or more devices of the system of the camera 202a), one or more devices of the LiDAR sensor 202b (e.g., one or more devices of the system of the LiDAR sensor 202b), one or more devices of the Radar sensor 202c (e.g., one or more devices of the system of the Radar sensor 202c), one or more devices of the microphone 202d (e.g., one or more devices of the system of the microphone 202d), one or more devices of the communication device 202e one or more devices (e.g., one or more devices of a system of communication devices 202e), one or more devices of autonomous vehicle computing 202f (e.g., one or more devices of a system of autonomous vehicle computing 202f), one or more devices of safety controller 202g (e.g., one or more devices of a system of safety controller 202g), one or more devices of DBW system 202h (e.g., one or more devices of a system of DBW system 202h), one or more devices of powertrain control system 204 (e.g., powertrain control system One or more devices of the system of control system 204 ), one or more devices of the steering control system 206 (e.g., one or more devices of the system of steering control system 206 ), one or more devices of the braking system 208 (e.g., one or more devices of the system of braking system 208 ), one or more devices of the platform sensor (e.g., one or more devices of the system of platform sensor), and / or one or more devices of the network 112 (e.g., one or more devices of the system of network 112 ) include at least one device 300 and / or at least one component of device 300 . For example. Figure 3 As shown, apparatus 300 includes a bus 302 , a processor 304 , a memory 306 , a storage component 308 , an input interface 310 , an output interface 312 , and a communication interface 314 .

[0054] The bus 302 includes components for communication between components of the licensing device 300. In some embodiments, the processor 304 is implemented in hardware, software, or a combination of hardware and software. In some examples, the processor 304 includes a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), and / or an accelerated processing unit (APU), etc.), a microphone, a digital signal processor (DSP), and / or any processing component that can be programmed to perform at least one function (e.g., a field programmable gate array (FPGA) and / or an application-specific integrated circuit (ASIC), etc.). The memory 306 includes a random access memory (RAM), a read-only memory (ROM), and / or another type of dynamic and / or static storage device (e.g., flash memory, magnetic memory, and / or optical memory, etc.) that stores data and / or instructions for use by the processor 304.

[0055] Storage component 308 stores data and / or software related to the operation and use of device 300. In some examples, storage component 308 includes a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optical disk, and / or a solid-state disk, etc.), a compact disk (CD), a digital versatile disk (DVD), a floppy disk, a cassette, a tape, a CD-ROM, a RAM, a PROM, an EPROM, a FLASH-EPROM, an NV-RAM, and / or another type of computer-readable medium, and a corresponding drive.

[0056] The input interface 310 includes components that permit the device 300 to receive information, such as via user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, and / or a camera, etc.). Additionally or alternatively, in some embodiments, the input interface 310 includes a sensor for sensing information (e.g., a global positioning system (GPS) receiver, an accelerometer, a gyroscope, and / or an actuator, etc.). The output interface 312 includes components for providing output information from the device 300 (e.g., a display, a speaker, and / or one or more light emitting diodes (LEDs), etc.).

[0057] In some embodiments, communication interface 314 includes a transceiver-like component (e.g., a transceiver and / or a separate receiver and transmitter, etc.) that permits device 300 to communicate with other devices via a wired connection, a wireless connection, or a combination of a wired connection and a wireless connection. In some examples, communication interface 314 permits device 300 to receive information from another device and / or provide information to another device. In some examples, communication interface 314 includes an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, interface and / or cellular network interface, etc.

[0058] In some embodiments, the device 300 performs one or more processes described herein. The device 300 performs these processes based on the processor 304 executing software instructions stored by a computer-readable medium such as a memory 306 and / or a storage component 308. Computer-readable media (e.g., non-transitory computer-readable media) are defined herein as non-transitory memory devices. Non-transitory memory devices include storage space located within a single physical storage device or storage space distributed across multiple physical storage devices.

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

[0060] The memory 306 and / or the storage component 308 include a data storage unit or at least one data structure (e.g., a database, etc.). The device 300 can receive information from the data storage unit or at least one data structure in the memory 306 or the storage component 308, store information in the data storage unit or at least one data structure, communicate information to the data storage unit or at least one data structure, or search for information stored in the data storage unit or at least one data structure. In some examples, the information includes network data, input data, output data, or any combination thereof.

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

[0062] supply Figure 3 The number and arrangement of components illustrated are examples. Figure 3The device 300 may include additional components, fewer components, different components, or differently arranged components than those illustrated. Additionally or alternatively, a set of components (e.g., one or more components) of the device 300 may perform one or more functions described as being performed by another component or set of components of the device 300.

[0063] Reference now Figure 4A , illustrates an example block diagram of autonomous vehicle software 400 (sometimes referred to as an "AV stack"). As illustrated, autonomous vehicle software 400 includes a perception system 402 (sometimes referred to as a perception module), a planning system 404 (sometimes referred to as a planning module), a positioning system 406 (sometimes referred to as a positioning module), a control system 408 (sometimes referred to as a control module), and a database 410. In some embodiments, perception system 402, planning system 404, positioning system 406, control system 408, and database 410 are included in and / or implemented in an automatic navigation system of a vehicle (e.g., autonomous vehicle computing 202f of vehicle 200). Additionally or alternatively, in some embodiments, perception system 402, planning system 404, positioning system 406, control system 408, and database 410 are included in one or more independent systems (e.g., one or more systems that are the same or similar to autonomous vehicle software 400, etc.). In some examples, the perception system 402, planning system 404, positioning system 406, control system 408, and database 410 are included in one or more independent systems located in the vehicle and / or at least one remote system as described herein. In some embodiments, any and / or all of the systems included in the autonomous vehicle software 400 are implemented by computer hardware (e.g., by a microprocessor, microcontroller, application specific integrated circuit (ASIC) and / or field programmable gate array (FPGA), etc.), chiplets, or distributed computing architecture. It will also be understood that in some embodiments, the autonomous vehicle software 400 is configured to communicate with a remote system (e.g., an autonomous vehicle system that is the same or similar to the remote AV system 114, a fleet management system 116 that is the same or similar to the fleet management system 116, and / or a V2I system that is the same or similar to the V2I system 118, etc.).

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

[0065] In some embodiments, the planning system 404 receives data associated with a destination and generates data associated with at least one route (e.g., route 106) along which a vehicle (e.g., vehicle 102) can travel toward the destination. In some embodiments, the planning system 404 periodically or continuously receives data (e.g., data associated with the classification of physical objects described above) from the perception system 402, and the planning system 404 updates at least one trajectory or generates at least one different trajectory based on the data generated by the perception system 402. In other words, the planning system 404 can perform tasks related to tactical functions required to operate the vehicle 102 in traffic on the road. Tactical efforts involve maneuvering the vehicle in traffic during the journey, which includes but is not limited to deciding whether and when to overtake another vehicle, change lanes, or select an appropriate rate, acceleration, deceleration, etc. In some embodiments, the planning system 404 receives data associated with an updated position of the vehicle (e.g., vehicle 102) from the positioning system 406, and the planning system 404 updates at least one trajectory or generates at least one different trajectory based on the data generated by the positioning system 406.

[0066] In some embodiments, the positioning system 406 receives data associated with (e.g., representing) a location of a vehicle (e.g., vehicle 102) in an area. In some examples, the positioning system 406 receives LiDAR data associated with at least one point cloud generated by at least one LiDAR sensor (e.g., LiDAR sensor 202b). In some examples, the positioning system 406 receives data associated with at least one point cloud from multiple LiDAR sensors, and the positioning system 406 generates a combined point cloud based on each point cloud. In these examples, the positioning system 406 compares the at least one point cloud or the combined point cloud with a two-dimensional (2D) and / or three-dimensional (3D) map of the area stored in the database 410. Then, based on the positioning system 406 comparing the at least one point cloud or the combined point cloud with the map, the positioning system 406 determines the position of the vehicle in the area. In some embodiments, the map includes a combined point cloud of the area generated before the navigation of the vehicle. In some embodiments, the map includes, but is not limited to, a high-precision map of roadway geometry, a map describing the road network connectivity, a map describing the physical properties of the roadway (such as traffic speed, traffic volume, the number of vehicle and bicycle traffic lanes, lane width, lane traffic direction or the type and location of lane markings, or a combination thereof, etc.), and a map describing the spatial location of road features (such as crosswalks, traffic signs, or various types of other driving lights, etc.). In some embodiments, the map is generated in real time based on data received by the perception system.

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

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

[0069] In some embodiments, perception system 402, planning system 404, positioning system 406, and / or control system 408 implement at least one machine learning model (e.g., at least one multi-layer perceptron (MLP), at least one convolutional neural network (CNN), at least one recurrent neural network (RNN), at least one autoencoder, and / or at least one transformer, etc.). In some examples, perception system 402, planning system 404, positioning system 406, and / or control system 408 implement at least one machine learning model alone or in combination with one or more of the above systems. In some examples, perception system 402, planning system 404, positioning system 406, and / or control system 408 implement at least one machine learning model as part of a pipeline (e.g., a pipeline for identifying one or more objects located in an environment, etc.).

[0070] Database 410 stores data transmitted to, received from, and / or updated by perception system 402, planning system 404, positioning system 406, and / or control system 408. In some examples, database 410 includes a storage component (e.g., a storage component) for storing data and / or software related to operations and using at least one system of autonomous vehicle software 400. Figure 3In some embodiments, database 410 stores data associated with a 2D and / or 3D map of at least one area. In some examples, database 410 stores data associated with a 2D and / or 3D map of a portion of a city, portions of multiple cities, multiple cities, counties, states, and / or countries (State) (e.g., a country), etc. In such an example, a vehicle (e.g., a vehicle that is the same or similar to vehicle 102 and / or vehicle 200) can be driven along one or more drivable areas (e.g., single-lane roads, multi-lane roads, highways, remote roads, and / or off-road roads, etc.) and cause at least one LiDAR sensor (e.g., a LiDAR sensor that is the same or similar to LiDAR sensor 202b) to generate data associated with an image representing an object included in the field of view of the at least one LiDAR sensor.

[0071] In some embodiments, database 410 can be implemented across multiple devices. In some examples, database 410 includes a vehicle (e.g., a vehicle that is the same or similar to vehicle 102 and / or vehicle 200), an autonomous vehicle system (e.g., an autonomous vehicle system that is the same or similar to remote AV system 114), a fleet management system (e.g., a vehicle that is the same or similar to remote AV system 114), and a fleet management system (e.g., a vehicle that is the same or similar to remote AV system 114). Figure 1 The same or similar queue management system as the queue management system 116 of FIG. 1 and / or the V2I system (e.g., Figure 1 The V2I system 118 is the same as or similar to the V2I system 118).

[0072] FIG. 5A to FIG. 5D Example vehicles (such as reference Figure 1 The vehicle 102 and / or reference Figure 4A An example of a distributed computing system used by the autonomous vehicle software 400, etc. Figure 5A , illustrating an example of a system 500 for distributed computing on an autonomous robotic system, such as an autonomous vehicle (e.g., a vehicle the same as or similar to vehicle 102 and / or vehicle 200), etc. The discussion of system 500 refers to a vehicle (e.g., an AV), but is similarly applicable to other autonomous robotic systems.

[0073] The example system 500 includes a main embedded system 502 (in Figure 5A ), multiple slave embedded systems 504a to 504N (also labeled as "embedded system #0" in FIG. Figure 5A ), and a plurality of sensor sets 506a to 506N (also labeled as "embedded system #1" to "embedded system #N" in Figure 5A504a to 504N). Each of the slave embedded systems 504a to 504N can be configured to communicate with the master embedded system 502. Each of the plurality of sensor sets 506a to 506N can be configured to communicate with one of the plurality of slave embedded systems 504a to 504N to be assigned to one of the slave embedded systems 504a to 504N. In the example system 500, "N" represents an integer equal to or greater than 2, for example, 2, 3, 4, 5, 6, 7, 8, etc.

[0074] The master embedded system 502 may be configured to control the timing of the slave embedded systems 504a to 504N or synchronize the timing of the slave embedded systems 504a to 504N, such as by sequentially rotating through each of the slave embedded systems 504a to 504N to time the operations performed by the slave embedded systems 504a to 504N. In some embodiments, the control or synchronization of the timing by the master embedded system may be based on the rotation of at least one LiDAR sensor in the sensor set 506a to 506N.

[0075] Each of the slave embedded systems 504a to 504N may be configured to communicate with an associated one of the sensor sets 506a to 506N, so that the timing of data generation and data processing of the sensors may be controlled by the master embedded system 502. For example, the master embedded system 502 may be configured to sequentially transmit to each of the slave embedded systems 504a to 504N a request for sensor data from the slave embedded systems 504a to 504N, thereby prompting each of the slave embedded systems 504a to 504N to receive data from the associated sensor set 506a to 506N and process the data. The processed data may be transmitted as an output by each of the slave embedded systems 504a to 504N to the master embedded system 502. The data processing performed by the slave embedded systems 504a to 504N may be consistent with the reference Figure 2 The data processing discussed is similar.

[0076] In some embodiments, any of the master embedded system 502, the slave embedded systems 504a to 504N, and the sensor sets 506a to 506N may be included in an autonomous system (eg, Figure 2 The master embedded system 502 and the slave embedded systems 504a to 504N may be coupled to the autonomous vehicle computing (e.g., Figure 2The main embedded system 502 may be configured to communicate with the autonomous system's DBW system (such as Figure 2 The DBW system 202h, etc.) communicates with the vehicle so that the DBW system controls the vehicle based at least in part on data received from the master embedded system 502. Figure 2 In an embodiment of the present invention, the camera 202a, LiDAR sensor 202b, Radar sensor 202c, and microphone 202d are each configured to communicate with the autonomous vehicle computer 202f.

[0077] In some embodiments, a vehicle may be divided into multiple partitions. Figure 5A The embodiment of the present invention shows the partitions as partitions #1 to #N. In general, the partitions represent physical areas of the vehicle where the sensors of sensor sets 506a to 506N can be located (e.g., sensors of sensor set #1 are located in partition #1 and sensors of sensor set #N are located in partition #N). Each of the sensor sets 506a to 506N can be configured to generate data in associated partitions #1 to #N of the 360° environment around the vehicle, and the sensor sets 506a to 506N are configured to collectively sense the 360° environment around the vehicle. As shown, for example, in Figure 5A As shown in FIG. 5 , each of the slave embedded systems 504 a to 504 N may be assigned to one of the partitions, wherein embedded system #1 is assigned to partition #1 and embedded system #N is assigned to partition #N.

[0078] Each sensor set in the sensor sets 506a to 506N includes a plurality of sensors. Examples of sensors include Figure 2 The same or similar camera as the camera 202a, Figure 2 The same or similar LiDAR sensor as the LiDAR sensor 202b, Figure 2 Radar sensor 202c is the same as or similar to Radar sensor 202c. Figure 2202d, or any other type of sensing device. In some embodiments, each sensor in the sensor set 506a to 506N is a different type of sensor. Since a particular type of sensor will not be assigned to a slave embedded system that already includes the particular type of sensor, including different sensor types can allow each slave embedded system in the slave embedded systems 504a to 504N to be associated with only one sensor of a particular type (e.g., a camera, a LiDAR sensor, etc.), which can facilitate the assignment of sensors to partitions (e.g., to respective slave embedded systems each associated with a partition). In some embodiments, each sensor set in the sensor set 506a to 506N may include an equal number of sensors. Since each slave embedded system 504a to 504N is assigned the same number of sensors, the uniform distribution of sensors between the slave embedded systems 504a to 504N can help balance the processing on all the slave embedded systems 504a to 504N. In some embodiments, the sensor set 506a to 506N may include different numbers of sensors that are unevenly distributed between the slave embedded systems 504a to 504N.

[0079] In some embodiments, each sensor set in the sensor sets 506a to 506N includes at least one Radar sensor and at least one other type of sensor (e.g., at least one camera and / or at least one LiDAR sensor). Each slave embedded system in the slave embedded systems 504a to 504N can be configured to fuse the processed sensor data (including the Radar sensor data and at least one other type of sensor data) to provide the output of the fused data to the master embedded system 502.

[0080] In some embodiments, the master embedded system 502 and the slave embedded systems 504a to 504N may be, or may be part of, autonomous vehicle computing, such as autonomous vehicle computing 202f. Sensor sets 506a to 506N may each be configured to communicate with the slave embedded systems 504a to 504N associated therewith, respectively (e.g., sensor set #1 506a is configured to communicate with embedded system #1 504a and sensor set #N 506N is configured to communicate with embedded system #N 504N). In an example communication configuration, the sensors of the sensor sets 506a to 506N do not communicate directly with the master embedded system 502, but rather communicate directly with the slave embedded systems 504a to 504N associated therewith. The data generated by the various sensors may be processed by the various slave embedded systems 504a to 504N, rather than by a single device (e.g., by the master embedded system 502), which may allow for low latency in data generation and processing. Generally, low latency can allow for better control of a vehicle because data can be generated and processed more quickly for use in controlling the vehicle (or, as described above, another autonomous robotic system).

[0081] In some embodiments, the main embedded system 502 and the slave embedded systems 504a to 504N may each include a system on chip (SoC). SoC refers to an integrated circuit (or "chip") that integrates all or most of the components of a computing system and / or other electronic system. Such components include, for example, a central processing unit (CPU), input / output (I / O) devices, memory, storage, etc. Other components may include various communication components, a graphics processing unit (GPU), etc. These components may be integrated on a single substrate or microchip. Various digital, analog, mixed signal and / or radio frequency (RF) signal processing functions may also be incorporated. SoC can integrate a microcontroller, a microprocessor and / or one or more processor cores with a GPU, Wi-Fi and / or cellular network radio components, etc. Similar to how a microcontroller integrates a microprocessor with peripheral circuits and memory, a SoC can be viewed as integrating a microcontroller with even more advanced peripherals.

[0082] In some embodiments, each of the slave embedded systems 504a to 504N can be configured to communicate with the master embedded system 502 via a high-speed interface. Each of the slave embedded systems 504a to 504N can communicate with the master embedded system 502 via a high-speed interface, which can allow data to be communicated without the delay caused by using a traditional generalized precision time protocol (e.g., IEEE 1588 / gPTP). For example, in the case where the master embedded system 502 and the slave embedded systems 504a to 504N each include a SoC, a high-speed interface can be used to implement chip-to-chip or board-to-board communication, thereby reducing the delay caused by using traditional IEEE 1588 / gPTP.

[0083] In some embodiments, the system 500 is scalable. The system 500 is scalable, which allows at least one slave embedded system to be added to the system 500 and / or at least one of the slave embedded systems 504a to 504N to be removed from the system 500. The system 500 can be scalable, for example, to allow an outdated or failed slave embedded system to be replaced with another slave embedded system, and / or (for example, due to the increase in the number of slave embedded systems 504a to 504N in the system 500) to allow each slave embedded system to be responsible for less data processing, and thus latency can be further reduced. In the case where the total number of slave embedded systems 504a to 504N of the system 500 changes (for example, due to the addition and / or removal of at least one slave embedded system, "N" changes from one integer value to another different integer value), the partitions of the vehicle can be reallocated (for example, by the master embedded system 502) to reflect the new number of slave embedded systems 504a to 504N (for example, so that the number of partitions is equal to "N"). As a result of the change in the number of slave embedded systems 504a to 504N, the sensors in each of the sensor sets 506a to 506N, and thus the sensors assigned to each of the slave embedded systems 504a to 504N, may change accordingly.

[0084] Reference now Figure 5B , illustrating an example of a system 510 for distributed computing on an autonomous robotic system, such as an autonomous vehicle (e.g., a vehicle the same as or similar to vehicle 102 and / or vehicle 200), etc. The discussion of system 510 refers to a vehicle (e.g., an AV), but is similarly applicable to other autonomous robotic systems.

[0085] Figure 5B Example system 510 illustrates Figure 5A The example system 510 includes: a main embedded system 502 (in Figure 5B ), which corresponds to the main embedded system 502; four partitions (in Figure 5B 1, "Partition #2", "Partition #3", and "Partition #4"); four slave embedded systems 504a, 504b, 504c, 504d (in Figure 5B 504N), each of which is configured to communicate with the master embedded system 502 and corresponds to the slave embedded systems 504a through 504N; and four sensor sets 506a, 506b, 506c, 506d (also labeled as "embedded system #1," "embedded system #2," "embedded system #3," and "embedded system #4" in FIG. 504 , each of which is configured to communicate with the master embedded system 502 and corresponds to the slave embedded systems 504a through 504N; and four sensor sets 506a, 506b, 506c, 506d (in Figure 5B 4), each of which is configured to communicate with one of a plurality of slave embedded systems 504a, 504b, 504c, 504d and corresponds to sensor sets 506a to 506N.

[0086] like Figure 5B As illustrated, each of the sensor sets 506a, 506b, 506c, 506d includes the same number of sensors (three in the embodiment) and the same type of sensors (in the illustrated embodiment: cameras 512a, 512b, 512c, 512d (e.g., Figure 2 202a); LiDAR sensors 514a, 514b, 514c, 514d (e.g., Figure 2 202b of the same or similar LiDAR sensor); and Radar sensors 516a, 516b, 516c, 516d (e.g., Figure 2 Each of the sensor sets 506a, 506b, 506c, 506d may include a Radar sensor 516a, 516b, 516c, 516d and two other types of sensors (a camera 512a, 512b, 512c, 512d and a LiDAR sensor 514a, 514b, 514c, 514d), which may provide earlier fusion and reduced latency compared to conventional systems.

[0087] Figure 5C The example includes four partitions #1, #2, #3, #4 (such as reference Figure 5B1, #2, #3, #4, etc.) of the vehicle 520. The zones #1, #2, #3, #4 in the illustrated embodiment represent substantially equal physical areas of the vehicle. In other embodiments, at least one of the zones #1, #2, #3, #4 may represent a different sized physical area of ​​the vehicle than the other zones of the zones #1, #2, #3, #4.

[0088] The example vehicle 520 has a dedicated slave embedded system 504a, 504b, 504c, 504d for each partition #1, #2, #3, #4, thereby allowing for an uneven distribution of algorithms required to support processing sensor data (e.g., data received at the slave embedded systems 504a, 504b, 504c, 504d from their respective sensor sets 506a, 506b, 506c, 506d). One partition #1, #2, #3, #4 of the vehicle may have different processing requirements than one or more of the other partitions #1, #2, #3, #4. For example, the sensor set 506a, 506b, 506c, 506d at the front of the vehicle 520 (e.g., sensor set #4 506d used by zone #4 and sensor set #1 506a used by zone #1) may require very different processing than the sensor set 506a, 506b, 506c, 506d at the rear of the vehicle 520 (e.g., sensor set #2 506b used by zone #2 and sensor set #3 506c used by zone #3) to achieve safe and effective vehicle control. The slave embedded systems 504a, 504b, 504c, 504d associated with the front of the vehicle 520 (e.g., embedded system #4 504d used by partition #4 and embedded system #1 504a used by partition #1) may include (e.g., have stored on the SoC memory) the algorithms required to process sensor data collected from the front of the vehicle 520, while the slave embedded systems 504a, 504b, 504c, 504d associated with the rear of the vehicle 520 (e.g., embedded system #2 504b used by partition #2 and embedded system #3 504c used by partition #3) may include the algorithms required to process sensor data collected from the rear of the vehicle. For another example, the sensor types may vary greatly between the various physical areas of the vehicle 520. Thus, the slave embedded systems 504a, 504b, 504c, 504d may include (e.g., already stored on the SoC memory) the algorithms required to process specific sensors in their associated sensor sets 506a, 506b, 506c, 506d, and because fewer algorithms need to be stored in each individual slave embedded system 504a, 504b, 504c, 504d, memory may be saved and / or cheaper SoC components may be required.

[0089] The distribution of sensors across partitions can also enable redundancy in both the operation of the slave embedded systems 504a, 504b, 504c, 504d and the operation of the sensors. For example, in the event of a failure of the slave embedded system 504a, the partition sensors supported by the slave embedded system 504a are recoupled to one or more of the other slave embedded systems 504b, 504c, 504d without degrading the performance of a vehicle such as the vehicle 102 or the autonomous vehicle 200. Similarly, in the event of a failure of one or more sensors in partition #1, sensors in other partitions #2, #3, #4 can support the operation of the vehicle. The slave embedded system 504a associated with the (one or more) failed sensor can be reassigned to a diagnostic task to recover the (one or more) failed sensor, or, in the event that the sensor is not recoverable, reassigned to support the sensors of the backup partitions #2, #3, #4.

[0090] Figure 5D The example includes four partitions (e.g., Figure 5C Partitions #1, #2, #3, #4) and Figure 5B In an example of a system 530 for distributed computing on a vehicle similar to system 510, each of the four partitions has an associated slave embedded system (e.g., Figure 5B From one of the embedded systems 504a, 504b, 504c, 504d or Figure 5A In the illustrated embodiment, the sensor set (e.g., Figure 5B Sensor set 506a, 506b, 506c, 506d or Figure 5A Each sensor set in the sensor set 506a to 506N) is connected to Figure 5B The example system 530 is different than the example system 510. The discussion of the example system 530 refers to a vehicle (eg, an AV), but may be similarly applicable to other autonomous robotic systems.

[0091] Figure 5D Examples of each of the four partitions #1, #2, #3, and #4 and the following data are illustrated: camera data 532a, 532b, 532c, and 532d, respectively generated by a camera and configured to be communicated to an associated slave embedded system in the embedded system; LiDAR data 534a, 534b, 534c, and 534d, respectively generated by a LiDAR sensor and configured to be communicated to an associated slave embedded system in the embedded system; and Radar data 536a, 536b, 536c, and 536d, respectively generated by a Radar sensor. Figure 5DAlso illustrated is a main embedded system (eg, Figure 5B The main embedded system 502 or Figure 5A 528 of synchronization performed by the main embedded system 502). In the illustrated embodiment, partition #1 is associated with two cameras, two LiDAR sensors, and one Radar sensor; partition #2 is associated with three cameras, three LiDAR sensors, and three Radar sensors; partition #3 is associated with three cameras, three LiDAR sensors, and three Radar sensors; and partition #4 is associated with two cameras, two LiDAR sensors, and two Radar sensors. In some embodiments, the sensor sets may overlap two partitions. As in, for example, Figure 5D As shown in FIG. 1 , the first sensor set overlaps with partition #2 and partition #3, and the second sensor set overlaps with partition #1 and partition #4. Thus, the first sensor set is configured to transmit the collected data to each embedded system in embedded system #2 and embedded system #3, and the second sensor set is configured to transmit the generated data to each embedded system in embedded system #1 and embedded system #4. Although Figure 5D Four partitions are illustrated, but the main embedded system 502 can control any number of partitions, such as six, eight, ten, twelve, or any other integer number.

[0092] Reference now Figure 6 , which illustrates the display Figure 5D An example of a flow of synchronization over time for an example system 530. As shown, data generation and processing begins at partition #1 and continues sequentially through partition #2, partition #3, and partition #4, after which the series begins to repeat starting again from partition #1. Figure 6 Also illustrated is an implementation of data generation and processing that may be performed by each of the four slave embedded systems, each associated with one of the partitions #1, #2, #3, and #4. As illustrated, "data buff" represents data buffering (e.g., data received at the slave embedded system from its associated set of sensors). Data buffering may be followed by "pre-proc," which represents pre-processing of the received sensor data by the slave embedded system. Pre-processing may be followed by "CNN," which represents, for example, a network representation of a sensor data set received from the slave embedded system. Figure 4DThe convolutional neural network processing discussed above and the like. CNN may be followed by "post-proc", which represents the final processing of the data. The slave embedded system is configured to transmit the output of the final processing to the master embedded system. Data generation and processing of one partition does not need to be ended before the next partition begins its data generation and processing, for example, the slave embedded system may not have yet transmitted the output to the master embedded system. Such timing can provide low latency in data generation and processing.

[0093] This processing Figure 6 , but the process may be repeated any number of times. In addition, the process may not end with a complete cycle through all partitions, e.g., Figure 6 As shown, it is possible not to end at partition #4.

[0094] Figure 7 An example monitoring process 700 is illustrated according to some embodiments of the current subject matter. Process 700 may be performed by Figures 1 to 5D The example systems 100, 200, 300, 400, 500, 520, 530 shown in FIG. 7 are performed. For example, one or more of the operations described with respect to process 700 are performed by a vehicle (e.g., reference Figure 1 The vehicles 102a, 102b, 102n or reference Figure 2 The vehicle 200, or reference Figure 5A In some embodiments, the processing 700 is performed by the perception system 402, planning system 404, and / or control system 408 (e.g., completely, partially, sequentially, and / or non-sequentially, etc.) of the autonomous vehicle software 400 of the example system 500). Additionally or alternatively, in some embodiments, one or more steps described with respect to the process 700 are performed by another device or group of devices (e.g., completely, partially, sequentially, non-sequentially, etc.) separate from the autonomous vehicle software 400 and / or the example system 500 or including the autonomous vehicle software 400 and / or the example system 500.

[0095] At 702, a system is generated (eg, reference Figure 5AThe synchronization planning to be performed by the master embedded system of the example system 500 described above. The synchronization planning may include the following data, which is used to control the timing of multiple slave embedded systems corresponding to multiple partitions of the vehicle or synchronize the timing, such as by sequentially rotating through each slave embedded system to time the operations performed by the slave embedded system. In some embodiments, the synchronization planning may include a sequence defining a loop order, wherein the slave embedded systems corresponding to the multiple partitions are activated by the master embedded system. For example, the synchronization may include information for controlling the timing of sensors of each slave embedded system for generating the following data, which is used for the operation of the vehicle, including the operation of one of the turn signal lights, headlights, door locks, windshield wipers, powertrain control systems, steering control systems, and braking systems. In some embodiments, if the additional slave embedded system is configured to be added to the autonomous system, the synchronization planning may be updated so that the additional slave embedded system is configured to be included in the loop execution schedule. The additional slave embedded system may be communicatively coupled to the master embedded system so that the master embedded system may be configured to synchronize the timing of the slave embedded system and the additional slave embedded system. The sensor set may be configured to be reallocated after the addition of an additional slave embedded system, such that the plurality of slave embedded systems and the additional slave embedded system are each allocated a sensor set.

[0096] At 704, the partitions of the vehicle are determined based on the synchronization plan and the timing for the deactivation of the completed partition and the activation of another (subsequent) partition. The slave embedded system of the determined partition can receive an activation trigger to perform processing that can be associated with the operation of at least one sensing device of the vehicle. As shown in reference Figure 2 The sensing device may include at least one of the following: a camera, a motion sensor, an image capture device, a scanner, a keypad sensing device, a LiDAR, a Radar, a microphone, an ultrasonic sensor, an inertial sensor, a GPS receiver, an odometer sensor, and any combination thereof. For example, the sensing device may include: at least one camera configured to detect optical light and generate image data associated with the environment outside the vehicle; at least one LiDAR sensor configured to detect light reflected from at least one object in the environment outside the vehicle and generate LiDAR data associated with the environment outside the vehicle; and at least one Radar sensor configured to detect radio waves from at least one object in the environment outside the vehicle and generate Radar data associated with the environment outside the vehicle. Each sensor set in the sensor set may be configured to generate data related to the environment in a partition of the 360° environment around the vehicle; and the sensor set is configured to collectively sense the 360° environment around the vehicle.

[0097] At 706, data detected by the sensing devices of the determined partitioned slave embedded system is buffered. The slave embedded system is configured to buffer and process data received from its assigned set of sensors.

[0098] At 708, the buffered data is processed. Data processing may include pre-processing, predictive execution, and / or post-processing. Pre-processing may include application of filters (e.g., for data denoising) to optimize data processing and result accuracy. Predictive execution may include providing the pre-processed data as input to a machine learning model (e.g., as referenced). Figure 4D For example, predictive execution may include identifying one or more objects located in an environment, etc. Post-processing may include data aggregation based on events and / or agent types to optimize data transmission between the slave embedded system and the master embedded system.

[0099] At 710, the results of the data processing are transmitted from the determined partitioned slave embedded system to the master embedded system. The master embedded system can be configured to control the operation of the vehicle based on the received results of the data processing. Examples of vehicle operations can include maneuvers of the vehicle to ensure safe movement of the vehicle along the set path.

[0100] According to some non-limiting embodiments or examples, a vehicle is provided, comprising: at least one computer-readable medium storing computer-executable instructions; a master embedded system configured to execute the computer-executable instructions; a plurality of slave embedded systems, each configured to execute the computer-executable instructions and each communicatively coupled to the master embedded system, the master embedded system configured to synchronize the timing of the plurality of slave embedded systems; and a plurality of sensor sets, each of the sensor sets being assigned to one of the plurality of slave embedded systems and communicatively coupled to one of the plurality of slave embedded systems, each sensor being configured to generate data relating to an environment external to the vehicle, and each sensor being configured to provide the data as output to the slave embedded system of the plurality of slave embedded systems to which the sensor is communicatively coupled; wherein each of the plurality of slave embedded systems is configured to process data received from the assigned sensor set and transmit the output of the data processing to the master embedded system.

[0101] According to some non-limiting embodiments or examples, a method is provided, which includes: utilizing each sensor set among a plurality of sensor sets of a vehicle to generate data related to an environment outside the vehicle; utilizing each sensor set among the plurality of sensor sets to output the generated data to an assigned slave embedded system among a plurality of slave embedded systems of the vehicle; utilizing a master embedded system of the vehicle to synchronize the timing of the plurality of slave embedded systems; and utilizing each slave embedded system among the plurality of slave embedded systems to process data received from the assigned sensor set and transmit the processed output to the master embedded system.

[0102] According to some non-limiting embodiments or examples, at least one non-transitory computer-readable medium is provided, which includes one or more instructions, which, when executed by at least one processor, cause the at least one processor to perform operations, the operations including: using each sensor set among a plurality of sensor sets of a vehicle to generate data related to the environment outside the vehicle; using each sensor set among the plurality of sensor sets to output the generated data to an assigned slave embedded system among a plurality of slave embedded systems of the vehicle; using a master embedded system of the vehicle to synchronize the timing of the plurality of slave embedded systems; and using each slave embedded system among the plurality of slave embedded systems to process data received from the assigned sensor set and transmit the processed output to the master embedded system.

[0103] Further non-limiting aspects or embodiments are set forth in the following numbered clauses:

[0104] Item 1: A vehicle comprising: at least one computer-readable medium storing computer-executable instructions; a master embedded system configured to execute the computer-executable instructions; a plurality of slave embedded systems, each configured to execute the computer-executable instructions and each communicatively coupled to the master embedded system, the master embedded system configured to synchronize the timing of the plurality of slave embedded systems; and a plurality of sensor sets, each of the sensor sets being assigned to and communicatively coupled to one of the plurality of slave embedded systems, each sensor being configured to generate data relating to an environment external to the vehicle, and each sensor being configured to provide the data as an output to a slave embedded system of the plurality of slave embedded systems to which the sensor is communicatively coupled, wherein each of the plurality of slave embedded systems is configured to process data received from the assigned sensor set and transmit the output of the data processing to the master embedded system.

[0105] Clause 2: A vehicle according to clause 1, wherein each of the sensor sets comprises: at least one camera configured to detect optical light and generate image data associated with an environment external to the vehicle, at least one light detection and ranging sensor, i.e., at least one LiDAR sensor, configured to detect light reflected from at least one object in the environment external to the vehicle and generate LiDAR data associated with the environment external to the vehicle, and at least one radio detection and ranging sensor, i.e., at least one Radar sensor, configured to detect radio waves from at least one object in the environment external to the vehicle and generate Radar data associated with the environment external to the vehicle.

[0106] Clause 3: A vehicle according to any of the preceding clauses, wherein each sensor set in the sensor set is configured to generate data related to the environment in a partition of the 360° environment around the vehicle, and the sensor sets are configured to collectively sense the 360° environment around the vehicle.

[0107] Clause 4: A vehicle as described in any of the preceding clauses, wherein said synchronizing comprises controlling the timing for generating said data.

[0108] Clause 5: The vehicle of any preceding clause, wherein the plurality of slave embedded systems are communicatively coupled to the master embedded system via a high-speed interface.

[0109] Clause 6: The vehicle of any preceding clause, further comprising an autonomous system comprising the master embedded system, the plurality of slave embedded systems, and the plurality of sensor sets.

[0110] Clause 7: A vehicle according to any of the preceding clauses, wherein the main embedded system is configured to transmit the output to a wire control system of the vehicle, and the wire control system is configured to generate a control signal based at least in part on the output received from the main embedded system, and the control signal is configured to control at least one device of the vehicle.

[0111] Clause 8: A vehicle as described in any of the preceding clauses, wherein the at least one device includes at least one of a turn signal lamp, a headlight, a door lock, a windshield wiper, a powertrain control system, a steering control system, and a braking system.

[0112] Clause 9: A vehicle according to any of the preceding clauses, wherein each of the plurality of slave embedded systems is configured to process data received from the assigned set of sensors, and the processing includes pre-processing of the data and post-processing of the data.

[0113] Clause 10: A vehicle according to any of the preceding clauses, wherein at least one additional slave embedded system is configured to be added to the autonomous system, such that the at least one additional slave embedded system is configured to execute the computer-executable instructions and is communicatively coupled to the master embedded system, and such that the master embedded system is configured to synchronize the timing of the multiple slave embedded systems and the at least one additional slave embedded system.

[0114] Clause 11: A vehicle according to any of the preceding clauses, wherein the plurality of sensor sets are configured to be reallocated after adding the at least one additional slave embedded system, wherein each embedded system of the plurality of slave embedded systems and the at least one additional slave embedded system is assigned a sensor set.

[0115] Clause 12: A method comprising: utilizing each sensor set of a plurality of sensor sets of a vehicle to generate data related to an environment external to the vehicle; utilizing each sensor set of the plurality of sensor sets to output the generated data to an assigned slave embedded system of a plurality of slave embedded systems of the vehicle; utilizing a master embedded system of the vehicle to synchronize the timing of the plurality of slave embedded systems; and utilizing each slave embedded system of the plurality of slave embedded systems to process data received from the assigned sensor set and transmit the processed output to the master embedded system.

[0116] Clause 13: A non-transitory computer-readable storage medium comprising at least one program for execution by at least one processor of a first device, the at least one program comprising instructions that, when executed by the at least one processor, cause the first device to perform the method according to clause 12.

[0117] In the previous description, aspects and embodiments of the present disclosure have been described with reference to many specific details, which may vary depending on the implementation. Therefore, the description and the accompanying drawings should be regarded as illustrative, not restrictive. The only and exclusive indication of the scope of the invention, and the applicant's expectation that the content of the scope of the invention is the literal and equivalent scope of the claims issued from this application in the specific form of the claims, including any subsequent amendments. Any definition of the terms used to be included in such claims that are clearly set forth herein should be based on the meaning of such terms as used in the claims. In addition, when the term "also includes" is used in the previous description or the attached claims, the following of the phrase may be an additional step or entity, or a sub-step / sub-entity of the previously described step or entity.

Claims

1. A vehicle, comprising: at least one computer-readable medium storing computer-executable instructions; a host embedded system configured to execute the computer executable instructions; a plurality of slave embedded systems, each configured to execute the computer-executable instructions and each communicatively coupled to the master embedded system, the master embedded system configured to synchronize the timing of the plurality of slave embedded systems; as well as a plurality of sensor sets, each of the sensor sets being assigned to and communicatively coupled to one of the plurality of slave embedded systems, each sensor being configured to generate data relating to an environment external to the vehicle, and each sensor being configured to provide the data as an output to the slave embedded system of the plurality of slave embedded systems to which the sensor is communicatively coupled, Each of the plurality of slave embedded systems is configured to process data received from the assigned sensor set and transmit the output of the data processing to the master embedded system.

2. The vehicle according to claim 1, wherein: Each sensor set in the sensor set includes: at least one camera configured to detect optical light and generate image data associated with an environment external to the vehicle, at least one light detection and ranging sensor, i.e., at least one LiDAR sensor, configured to detect light reflected from at least one object in an environment external to the vehicle and to generate LiDAR data associated with the environment external to the vehicle, and At least one radio detection and ranging sensor, ie, at least one Radar sensor, is configured to detect radio waves from at least one object in an environment external to the vehicle and to generate Radar data associated with the environment external to the vehicle.

3. The vehicle according to claim 1 or 2, wherein: Each of the sensor sets is configured to generate data relating to the environment in a sector of the 360° environment around the vehicle, and The sensor set is configured to collectively sense a 360° environment around the vehicle.

4. The vehicle according to any one of claims 1 to 3, wherein: The synchronizing includes controlling the timing for generating the data.

5. A vehicle according to any one of the preceding claims, wherein: The plurality of slave embedded systems are communicatively coupled to the master embedded system via a high-speed interface.

6. The vehicle of any of the preceding claims, further comprising an autonomous system comprising the master embedded system, the plurality of slave embedded systems, and the plurality of sensor sets.

7. The vehicle according to claim 6, wherein: The master embedded system is configured to transmit the output to a drive-by-wire system of the vehicle, the drive-by-wire system being configured to generate a control signal based at least in part on the output received from the master embedded system, the control signal being configured to control at least one device of the vehicle.

8. The vehicle according to claim 7, wherein: The at least one device includes at least one of a turn signal lamp, a headlight, a door lock, a windshield wiper, a powertrain control system, a steering control system, and a braking system.

9. A vehicle according to any one of the preceding claims, wherein: Each of the plurality of slave embedded systems is configured to process data received from the assigned set of sensors, and The processing includes pre-processing of the data and post-processing of the data.

10. A vehicle according to any one of claims 6 to 9, wherein: At least one additional slave embedded system is configured to be added to the autonomous system, such that the at least one additional slave embedded system is configured to execute the computer-executable instructions and is communicatively coupled to the master embedded system, and such that the master embedded system is configured to synchronize the timing of the plurality of slave embedded systems and the at least one additional slave embedded system.

11. The vehicle according to claim 10, wherein: The plurality of sensor sets are configured to be reallocated after the at least one additional slave embedded system is added, wherein each of the plurality of slave embedded systems and the at least one additional slave embedded system is allocated a sensor set.

12. A method comprising: generating data related to an environment external to the vehicle using each of a plurality of sensor sets of the vehicle; outputting the data to an assigned slave embedded system of a plurality of slave embedded systems of the vehicle using each sensor set of the plurality of sensor sets; synchronizing the timing of the plurality of slave embedded systems using the master embedded system of the vehicle; as well as The data received from the assigned set of sensors is processed using each of the plurality of slave embedded systems, and the output of the processing is transmitted to the master embedded system.

13. A non-transitory computer-readable storage medium comprising at least one program for execution by at least one processor of a first device, the at least one program comprising instructions which, when executed by the at least one processor, cause the first device to perform the method according to claim 12.

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