Managing efficiency of image processing

Abstract

There is provided a method for managing image processing efficiency, the method may include: obtaining raw data associated with an original image from at least one camera using at least one SoC; generating an updated image based on the original image from the at least one camera, where the updated image represents photons received by the at least one camera; providing the updated image to an image and signal processing pipeline; processing the updated image using an image and signal processing pipeline; and operating the vehicle based at least in part on information obtained from the analysis of the processed image.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 363,621, filed on April 26, 2022, and is incorporated herein in its entirety by reference to the disclosures of U.S. Non-Provisional Patent Application No. 18139526, filed on April 26, 2023, entitled “SCALABLE CONFIGURABLE CHIP ARCHITECTURE,” and U.S. Non-Provisional Patent Application No. 18139857, filed on April 26, 2023, entitled “DISTRIBUTED COMPUTING ARCHITECTURE WITH SHARED MEMORY FOR AUTONOMOUS ROBOTIC SYSTEMS.” Background Art

[0003] Autonomous vehicles contain computer processors and sensors to navigate roads and other drivable areas. Information from the sensors is pre-processed before being used for navigation. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

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

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

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

[0010] Figure 5 is a diagram of an implementation of a process for managing efficiency of image processing; and

[0011] Figure 6 is a flow chart of a process for managing efficiency of image processing. DETAILED DESCRIPTION

[0012] 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.

[0013] 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 the 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 a schematic element in the accompanying drawings is not intended to mean that such an element is required in all embodiments, nor is it intended to mean that the features represented by such an element cannot be included in some embodiments or cannot be combined with other elements in some embodiments.

[0014] 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.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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."

[0019] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various 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.

[0020] General Overview

[0021] Vehicles, such as autonomous vehicles, can process images from a camera of the vehicle using a streamlined image processing pipeline that omits steps that are not necessary for images used, for example, in navigation of the vehicle. For example, images captured by a camera are typically processed using an image and signal processing pipeline (ISP). Some processing steps produce images that are more suitable for the human eye, but are not necessary for images used in navigation of the vehicle. These steps can be omitted in the pipeline.

[0022] Some advantages of these techniques include faster image processing of camera data. By omitting unnecessary image processing steps, images captured by vehicle cameras can be prepared for navigation more quickly.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] The remote AV system 114 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the network 112, the fleet management system 116, and / or the V2I system 118 via the network 112. In an example, the remote AV system 114 includes a server, a server group, and / or other similar devices. In some embodiments, the remote AV system 114 is co-located with the fleet management system 116. In some embodiments, the remote AV system 114 participates in the installation of some or all of the components of the vehicle (including autonomous systems, autonomous vehicle computing, and / or software implemented by autonomous vehicle computing, etc.). In some embodiments, the remote AV system 114 maintains (e.g., updates and / or replaces) these components and / or software during the life of the vehicle.

[0031] The queue management system 116 includes at least one device configured to communicate with the vehicles 102, the V2I devices 110, the remote AV systems 114, and / or the V2I infrastructure systems 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), etc.).

[0032] 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.).

[0033] 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.

[0034] 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 the same or 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 maneuver-based functions, features and / or devices, etc., which enables the vehicle 200 to operate partially or completely without human intervention, including but not limited to fully autonomous vehicles (e.g., vehicles that abandon reliance on human intervention, such as Level 5 ADS-operated vehicles, etc.), highly autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in certain situations, such as Level 4 ADS-operated vehicles, etc.), and / or conditionally autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in limited situations, such as Level 3 ADS-operated vehicles, etc.)). In one embodiment, the autonomous system 202 includes the operational or tactical functions required to enable the vehicle 200 to operate in road traffic and continuously perform part or all of a dynamic driving task (DDT). In another embodiment, the autonomous system 202 includes an advanced driver assistance system (ADAS) that includes driver support features. The autonomous system 202 supports various levels of driving automation ranging from no driving automation (e.g., level 0) to full driving automation (e.g., level 5). For a detailed description of fully autonomous vehicles and highly autonomous vehicles, reference can be made to SAE International's standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, 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 ridesharing company.

[0035] 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, and safety controller 202g.

[0036] 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.

[0037] 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 including cameras described herein in that the camera 202a may include one or more cameras with a wide field of view (e.g., a wide-angle lens, a fisheye lens, and / or a lens with a viewing angle of about 120 degrees or more, etc.) to generate images related to as many physical objects as possible.

[0038] 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 configured to communicate with the communication device 202e 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 it encounters 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.

[0039] The radio detection and ranging (radar) sensor 202c includes a communication device 202e, an autonomous vehicle computing device 202f, and / or a safety controller 202g via a bus (e.g., Figure 3 The radar sensor 202c includes at least one device that communicates with a bus (same or similar to the bus 302 of the embodiment of the present invention). The radar sensor 202c includes a system configured to transmit (pulsed or continuous) radio waves. The radio waves transmitted by the radar sensor 202c include radio waves within a predetermined spectrum. In some embodiments, during operation, the radio waves transmitted by the radar sensor 202c encounter physical objects and are reflected back to the radar sensor 202c. In some embodiments, the radio waves transmitted by the radar sensor 202c are not reflected by some objects. In some embodiments, at least one data processing system associated with the radar sensor 202c generates a signal representing an object included in the field of view of the radar sensor 202c. For example, at least one data processing system associated with the radar sensor 202c generates an image representing the boundaries of the physical object and / or the surface of the physical object (e.g., the topology of the surface), etc. In some examples, the image is used to determine the boundaries of the physical objects in the field of view of the radar sensor 202c.

[0040] 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 3At 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.

[0041] 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 DBW (drive-by-wire) 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 DBW (drive-by-wire) 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).

[0042] 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 1 V2I 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.

[0043] 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.

[0044] 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 signals, headlights, door locks, and / or windshield wipers, etc.).

[0045] The powertrain control system 204 includes at least one device configured to communicate with the DBW system 202h. In some examples, the powertrain control system 204 includes at least one controller and / or actuator, etc. In some embodiments, the powertrain control system 204 receives control signals from the DBW system 202h, and the powertrain control system 204 causes the vehicle 200 to make longitudinal vehicle movements 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 make lateral vehicle movements 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.

[0046] The steering control system 206 includes at least one device configured to rotate one or more wheels of the vehicle 200. In some examples, the steering control system 206 includes at least one controller and / or actuator, etc. In some embodiments, the steering control system 206 rotates the two front wheels and / or the two rear wheels of the vehicle 200 to the left or right to turn the vehicle 200 left or right. In other words, the steering control system 206 causes the activities necessary to adjust the y-axis component of the vehicle's motion.

[0047] 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.

[0048] In some embodiments, vehicle 200 includes at least one platform sensor (not explicitly illustrated) for measuring or inferring a property of a state or condition of vehicle 200. In some examples, vehicle 200 includes platform sensors such as a global positioning system (GPS) receiver, an inertial measurement unit (IMU), wheel rate sensors, wheel brake pressure sensors, wheel torque sensors, engine torque sensors, and / or steering angle sensors. Although braking system 208 is Figure 2 Although illustrated as being located on the proximal side of the vehicle 200 , the braking system 208 may be located anywhere in the vehicle 200 .

[0049] Reference now Figure 3 , a schematic diagram of an exemplary device 300. As illustrated, device 300 includes a processor 304, a memory 306, a storage component 308, an input interface 310, an output interface 312, a communication interface 314, and a bus 302. In some embodiments, device 300 corresponds to: at least one device of vehicle 102 (e.g., at least one device of a system of vehicle 102); and / or one or more devices of network 112 (e.g., one or more devices of a system of network 112). In some embodiments, one or more devices of vehicle 102 (e.g., one or more devices of a system of vehicle 102), and / or one or more devices of network 112 (e.g., one or more devices of a system of network 112) include at least one device 300 and / or at least one component of device 300. As Figure 3As shown, apparatus 300 includes a bus 302 , a processor 304 , a memory 306 , a storage component 308 , an input interface 310 , an output interface 312 , and a communication interface 314 .

[0050] The bus 302 includes components that permit communication between components of the device 300. In some cases, the processor 304 includes a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), and / or an accelerated processing unit (APU), etc.), a digital signal processor (DSP), and / or any processing component that can be programmed to perform at least one function (e.g., a field programmable gate array (FPGA) and / or an application specific integrated circuit (ASIC), etc.). The memory 306 includes a random access memory (RAM), a read-only memory (ROM), and / or another type of dynamic and / or static storage device (e.g., flash memory, magnetic memory, and / or optical memory, etc.) that stores data and / or instructions for use by the processor 304.

[0051] 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.

[0052] 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.).

[0053] 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.

[0054] 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 305 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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 in software (e.g., software instructions stored in a memory), computer hardware (e.g., by a microprocessor, microcontroller, application specific integrated circuit (ASIC) and / or field programmable gate array (FPGA), etc.), or a combination of computer software and computer hardware. It will also be understood that in some embodiments, the autonomous vehicle software 400 is configured to communicate with a remote system (e.g., an autonomous vehicle system that is the same or similar to the remote AV system 114, a fleet management system 116 that is the same or similar to the fleet management system 116, and / or a V2I system that is the same or similar to the V2I system 118, etc.).

[0060] 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.

[0061] 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 tactical function-related tasks required to operate the vehicle 102 in traffic on the road. Tactical efforts include maneuvering the vehicle in traffic during the trip, including but not limited to deciding whether and when to overtake other vehicles, change lanes, or select appropriate speeds, accelerations, decelerations, 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.

[0062] 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.

[0063] 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.

[0064] 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 activities necessary to adjust the y-axis component of the vehicle motion. Longitudinal vehicle motion control causes the activities necessary to adjust the x-axis component of the vehicle motion. In the example, in the case where the trajectory includes a left turn, the control system 408 transmits a control signal to cause the steering control system 206 to adjust the steering angle of the vehicle 200, thereby turning the vehicle 200 left. Additionally or alternatively, the control system 408 generates and transmits control signals to cause other devices of the vehicle 200 (eg, headlights, turn signals, door locks, and / or windshield wipers, etc.) to change states.

[0065] 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 multilayer perceptron (MLP), at least one convolutional neural network (CNN), at least one recurrent neural network (RNN), at least one autoencoder, and / or at least one transformer, etc.). In some examples, perception system 402, planning system 404, positioning system 406, and / or control system 408 implement at least one machine learning model alone or in combination with one or more of the above systems. In some examples, perception system 402, planning system 404, positioning system 406, and / or control system 408 implement at least one machine learning model as part of a pipeline (e.g., a pipeline for identifying one or more objects located in an environment, etc.). The following is about FIG. 4B to FIG. 4D Includes examples of implementations of machine learning models.

[0066] 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.

[0067] 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) and the like.

[0068] Reference now Figure 4B , a diagram illustrating an implementation of a machine learning model. More specifically, a diagram illustrating an implementation of a convolutional neural network (CNN) 420. For purposes of illustration, the following description of CNN 420 will be with respect to implementing CNN 420 by perception system 402. However, it will be understood that in some examples, CNN 420 (e.g., one or more components of CNN 420) is implemented by other systems (such as planning system 404, positioning system 406, and / or control system 408, etc.) other than or in addition to perception system 402. Although CNN 420 includes certain features as described herein, these features are provided for purposes of illustration and are not intended to limit the present disclosure.

[0069] CNN 420 includes a plurality of convolutional layers including a first convolutional layer 422, a second convolutional layer 424, and a convolutional layer 426. In some embodiments, CNN 420 includes a subsampling layer 428 (sometimes referred to as a pooling layer). In some embodiments, subsampling layer 428 and / or other subsampling layers have a dimension that is smaller than the dimension of the upstream system (i.e., the number of nodes). With the subsampling layer 428 having a dimension that is smaller than the dimension of the upstream layer, CNN 420 merges the amount of data associated with the initial input and / or output of the upstream layer, thereby reducing the amount of computation required for CNN 420 to perform downstream convolution operations. Additionally or alternatively, with the subsampling layer 428 being associated with (e.g., configured to perform) at least one subsampling function (as described below with respect to Figure 4C and Figure 4D As described above, CNN 420 incorporates the amount of data associated with the initial input.

[0070] The perception system 402 performs the convolution operation based on the perception system 402 providing the respective inputs and / or outputs associated with each of the first convolution layer 422, the second convolution layer 424, and the convolution layer 426 to generate the respective outputs. In some examples, the perception system 402 implements the CNN 420 based on the perception system 402 providing data as input to the first convolution layer 422, the second convolution layer 424, and the convolution layer 426. In such examples, the perception system 402 provides the data as input to the first convolution layer 422, the second convolution layer 424, and the convolution layer 426 based on the perception system 402 receiving data from one or more different systems (e.g., one or more systems of a vehicle that is the same or similar to the vehicle 102, a remote AV system that is the same or similar to the remote AV system 114, a queue management system that is the same or similar to the queue management system 116, and / or a V2I system that is the same or similar to the V2I system 118, etc.). The following is about Figure 4C Includes a detailed description of the convolution operation.

[0071] In some embodiments, the perception system 402 provides data associated with the input (referred to as the initial input) to the first convolutional layer 422, and the perception system 402 generates data associated with the output using the first convolutional layer 422. In some embodiments, the perception system 402 provides the output generated by the convolutional layer as input to a different convolutional layer. For example, the perception system 402 provides the output of the first convolutional layer 422 as input to the subsampling layer 428, the second convolutional layer 424, and / or the convolutional layer 426. In such an example, the first convolutional layer 422 is referred to as an upstream layer, and the subsampling layer 428, the second convolutional layer 424, and / or the convolutional layer 426 are referred to as downstream layers. Similarly, in some embodiments, the perception system 402 provides the output of the subsampling layer 428 to the second convolutional layer 424 and / or the convolutional layer 426, and in this example, the subsampling layer 428 will be referred to as the upstream layer, and the second convolutional layer 424 and / or the convolutional layer 426 will be referred to as the downstream layer.

[0072] In some embodiments, before the perception system 402 provides the input to the CNN 420, the perception system 402 processes the data associated with the input provided to the CNN 420. For example, the perception system 402 processes the data associated with the input provided to the CNN 420 based on the perception system 402 normalizing the sensor data (e.g., image data, LiDAR data, and / or radar data, etc.).

[0073] In some embodiments, CNN 420 generates an output based on perception system 402 performing convolution operations associated with each convolution layer. In some examples, CNN 420 generates an output based on perception system 402 performing convolution operations associated with each convolution layer and the initial input. In some embodiments, perception system 402 generates an output and provides the output to fully connected layer 430. In some examples, perception system 402 provides the output of convolution layer 426 to fully connected layer 430, wherein fully connected layer 430 includes data associated with multiple feature values ​​referred to as F1, F2, ..., FN. In this example, the output of convolution layer 426 includes data associated with multiple output feature values ​​representing predictions.

[0074] In some embodiments, perception system 402 identifies a prediction from the plurality of predictions based on perception system 402 identifying a feature value associated with a highest likelihood of being a correct prediction from the plurality of predictions. For example, where fully connected layer 430 includes feature values ​​F1, F2, ..., FN and F1 is the largest feature value, perception system 402 identifies the prediction associated with F1 as the correct prediction from the plurality of predictions. In some embodiments, perception system 402 trains CNN 420 to generate the predictions. In some examples, perception system 402 trains CNN 420 to generate the predictions based on perception system 402 providing training data associated with the predictions to CNN 420.

[0075] Reference now Figure 4C and Figure 4D , a diagram illustrating an example operation of CNN 440 utilizing perception system 402. In some embodiments, CNN 440 (e.g., one or more components of CNN 440) is coupled to CNN 420 (e.g., one or more components of CNN 420) (see Figure 4B ) are the same or similar.

[0076] At step 450, perception system 402 provides data associated with the image as input to CNN 440 (step 450). For example, as illustrated, perception system 402 provides data associated with the image to CNN 440, where the image is a grayscale image represented as values ​​stored in a two-dimensional (2D) array. In some embodiments, the data associated with the image may include data associated with a color image represented as values ​​stored in a three-dimensional (3D) array. Additionally or alternatively, the data associated with the image may include data associated with an infrared image and / or a radar image, etc.

[0077] At step 455, CNN 440 performs a first convolution function. For example, based on CNN 440 providing a value representing an image as an input to one or more neurons (not explicitly illustrated) included in first convolution layer 442, CNN 440 performs a first convolution function. In this example, the value representing the image may correspond to a value of a region (sometimes referred to as a receptive field) representing the image. In some embodiments, each neuron is associated with a filter (not explicitly illustrated). The filter (sometimes referred to as a kernel) may be represented as an array of values ​​corresponding in size to the value provided as input to the neuron. In one example, the filter may be configured to identify edges (e.g., horizontal lines, vertical lines, and / or straight lines, etc.). In successive convolution layers, the filters associated with the neurons may be configured to continuously identify more complex patterns (e.g., arcs and / or objects, etc.).

[0078] In some embodiments, CNN 440 performs a first convolution function based on CNN 440 multiplying the values ​​of each neuron provided as input to one or more neurons included in the first convolution layer 442 by the values ​​of the filters corresponding to each neuron in the same or more neurons. For example, CNN 440 may multiply the values ​​of each neuron provided as input to one or more neurons included in the first convolution layer 442 by the values ​​of the filters corresponding to each neuron in the one or more neurons to generate a single value or an array of values ​​as output. In some embodiments, the collective output of the neurons of the first convolution layer 442 is referred to as a convolution output. In some embodiments, when each neuron has the same filter, the convolution output is referred to as a feature map.

[0079] In some embodiments, CNN 440 provides the output of each neuron of the first convolutional layer 442 to the neurons of the downstream layer. For clarity, the upstream layer may be a layer that transmits data to a different layer (referred to as the downstream layer). For example, CNN 440 may provide the output of each neuron of the first convolutional layer 442 to the corresponding neurons of the subsampling layer. In the example, CNN 440 provides the output of each neuron of the first convolutional layer 442 to the corresponding neurons of the first subsampling layer 444. In some embodiments, CNN 440 adds a bias value to the set of all values ​​provided to each neuron of the downstream layer. For example, CNN 440 adds a bias value to the set of all values ​​provided to each neuron of the first subsampling layer 444. In such an example, CNN 440 determines the final value to be provided to each neuron of the first subsampling layer 444 based on the set of all values ​​provided to each neuron and the activation function associated with each neuron of the first subsampling layer 444.

[0080] At step 460, CNN 440 performs a first subsampling function. For example, based on CNN 440 providing the values ​​output by first convolutional layer 442 to the corresponding neurons of first subsampling layer 444, CNN 440 may perform the first subsampling function. In some embodiments, CNN 440 performs the first subsampling function based on an aggregation function. In an example, CNN 440 performs the first subsampling function based on CNN 440 determining the maximum input (referred to as a maximum pooling function) among the values ​​provided to a given neuron. In another example, CNN 440 performs the first subsampling function based on CNN 440 determining the average input (referred to as an average pooling function) among the values ​​provided to a given neuron. In some embodiments, based on CNN 440 providing values ​​to the respective neurons of first subsampling layer 444, CNN 440 generates an output, which is sometimes referred to as a subsampled convolution output.

[0081] At step 465, CNN 440 performs a second convolution function. In some embodiments, CNN 440 performs the second convolution function in a manner similar to how CNN 440 performs the first convolution function described above. In some embodiments, CNN 440 performs the second convolution function based on CNN 440 providing the value output by first subsampling layer 444 as input to one or more neurons (not explicitly illustrated) included in second convolution layer 446. In some embodiments, as described above, each neuron of second convolution layer 446 is associated with a filter. As described above, the filter (one or more) associated with second convolution layer 446 can be configured to recognize more complex patterns than the filter associated with first convolution layer 442.

[0082] In some embodiments, the CNN 440 performs a second convolution function based on the CNN 440 multiplying the value of each neuron provided as input to the one or more neurons included in the second convolution layer 446 by the value of the filter corresponding to each neuron of the one or more neurons. For example, the CNN 440 may multiply the value of each neuron provided as input to the one or more neurons included in the second convolution layer 446 by the value of the filter corresponding to each neuron of the one or more neurons to generate a single value or a value array as an output.

[0083] In some embodiments, the CNN 440 provides the output of each neuron of the second convolutional layer 446 to the neurons of the downstream layer. For example, the CNN 440 may provide the output of each neuron of the first convolutional layer 442 to the corresponding neurons of the subsampling layer. In an example, the CNN 440 provides the output of each neuron of the first convolutional layer 442 to the corresponding neurons of the second subsampling layer 448. In some embodiments, the CNN 440 adds a bias value to the set of all values ​​provided to each neuron of the downstream layer. For example, the CNN 440 adds a bias value to the set of all values ​​provided to each neuron of the second subsampling layer 448. In such an example, the CNN 440 determines the final value provided to each neuron of the second subsampling layer 448 based on the set of all values ​​provided to each neuron and the activation function associated with each neuron of the second subsampling layer 448.

[0084] At step 470, CNN 440 performs a second subsampling function. For example, based on CNN 440 providing the values ​​output by second convolutional layer 446 to corresponding neurons of second subsampling layer 448, CNN 440 may perform a second subsampling function. In some embodiments, based on CNN 440 using an aggregation function, CNN 440 performs a second subsampling function. In an example, as described above, based on CNN 440 determining the maximum input or average input among the values ​​provided to a given neuron, CNN 440 performs a first subsampling function. In some embodiments, based on CNN 440 providing values ​​to respective neurons of second subsampling layer 448, CNN 440 generates an output.

[0085] At step 475, CNN 440 provides the output of each neuron of the second subsampling layer 448 to the fully connected layer 449. For example, CNN 440 provides the output of each neuron of the second subsampling layer 448 to the fully connected layer 449 so that the fully connected layer 449 generates an output. In some embodiments, the fully connected layer 449 is configured to generate an output associated with a prediction (sometimes referred to as a classification). The prediction may include an indication that the objects included in the image provided as input to CNN 440 include objects and / or sets of objects, etc. In some embodiments, the perception system 402 performs one or more operations and / or provides data associated with the prediction to the various systems described herein.

[0086] Reference now Figure 5 , illustrates a diagram of an example system 500 for managing the efficiency of image processing, by which the techniques of the present disclosure may be implemented. Figure 5 As shown, the example system 500 includes a vehicle 502, a camera 504, an image and signal processing pipeline (ISP) 506, and a vehicle navigation system 508. The example system 500 may also include (as shown in FIG. Figure 1-4D ) and other components associated with the operation of vehicle 502. Vehicle 502 includes components such as reference Figure 2 The vehicle 502 may be configured to include or be coupled to any of the camera 504, the ISP 506, and the vehicle navigation system 508. The vehicle 502 may be configured to perform one or more operations based on images captured by the camera 504 and processed by the ISP 506.

[0087] Camera 504 may include one or more sensing devices (e.g., Figure 2507A). For example, camera 504 may include a sensor configured to generate raw image 507A and an electronics module configured to generate image 507B. The image sensor contained within the sensor module may include any of a variety of video sensing devices, including, for example, a charge coupled device (CCD), a complementary metal oxide semiconductor (CMOS), a vertically stacked CMOS device, or a multi-sensor array that uses a prism to divide light between sensors. In some embodiments, the image sensor may include a CMOS device having multiple (e.g., millions) of photoelectric cells.

[0088] In some embodiments, the camera 504 can generate raw image data 507A by controlling the acquisition time of the sensor module and by approximating the amount of photons rather than waiting for the photons to release energy from the capture element (e.g., CCD). In some configurations, the camera 504 can be configured to output the image 507B as a video (e.g., a collection of frames acquired at multiple time points during a time interval) at a set resolution and a set frame rate (e.g., which can vary based on the state of the vehicle 502, such as being lower for a stationary state and higher for a moving state). Additionally, the image sensor of the camera 504 can be configured to provide variable resolution by selectively outputting only a predetermined portion of the sensor (e.g., the portion can vary based on the state of the vehicle 502, such as being narrower for a stationary state and wider for a moving state). The camera 504 can also be configured to downsample the output of the sensor and then process the output to produce a video output at a set resolution. For example, the raw image data 507A from the sensor can be "windowed" (reduced to a set size), thereby reducing the size of the output image 507B and allowing a higher readout speed. As another example, sensor modules of the camera 504 with different sensor sizes can be swapped according to settings associated with the state of the vehicle 502. The camera 504 can be configured to apply a filter (e.g., a Bayer pattern filter) to upsample the raw image data 507A output by the sensor to produce an image 507B output at a modified (e.g., higher or lower) resolution. In some embodiments, the sensor outputs raw image data 507A representing the amplitude of red light, green light, or blue light detected by each photoelectric unit of the image sensor through its chipset (not shown). In the modular camera 504 of the example system 500, any of a variety of sensor sizes or other sensor characteristics can be utilized. The electronics contained in the sensor and electronics module can be digital signal processing electronics for processing the raw image data 507A captured by the sensor.

[0089] The sensor module can be configured to deliver any of a variety of desired performance characteristics. The camera 504 can include a separate compression module, or the compression electronics can be included in the sensor module. The compression electronics can be in the form of a separate chip, or the compression electronics can be implemented with software and other processors. For example, the compression electronics can be in the form of a commercially available compression chip for performing compression techniques to generate image 507B according to the settings of ISP 506. The camera 504 can include a sensor module that is configured to perform any type of compression processing on the raw image data 507A from the sensor. In some embodiments, the sensor module performs a compression technique configured to reduce the size of the image 507B. The image 507B can be derived from the raw image data 507A. For example, the image 507B can represent the photons received by the camera 504. The camera 504 can transmit the image 507B and optionally the raw image data 507A to the ISP 506 for additional processing.

[0090] ISP 506 may include one or more devices communicatively coupled to vehicle 502, camera 504, and vehicle navigation system 508. For example, ISP 506 may include a combination of software and hardware configured as front-end processing logic 510A, pipeline processing logic 510B, and back-end processing logic 510C. Front-end processing logic 510A, pipeline processing logic 510B, and back-end processing logic 510C may each be configured to process received images. In some embodiments, front-end processing logic 510A, pipeline processing logic 510B, and back-end processing logic 510C may each apply different filters to images 507A, 507B, such as for adjusting color balance, applying HDR (high definition range) filters, applying denoising filters, applying gamma correction filters, and / or applying brightness filters. The image 507B and optional raw image data 507A received from the camera 504 may first be processed by the ISP front-end processing logic 510A and analyzed to capture image statistics, which may be used to determine one or more control parameters of the ISP pipeline logic 510B and / or the camera 504 .

[0091] The ISP front-end processing logic 510A may be configured to process the image 507B and optionally the raw image data 507A. The ISP front-end processing logic 510A may operate in its own clock domain and may provide an asynchronous interface to the sensor interface to support images 507A, 507B of different sizes and timing requirements that may vary based on different states of the vehicle 502. In some embodiments, the front-end processing logic 510A may process the raw image data 507A received from the camera 504 and may use the processing results of the raw image data to update the settings of the camera 504. The ISP front-end processing logic 510A may process the images 507A, 507B pixel by pixel in multiple formats. For example, each image pixel may have a bit depth of 8, 10, 12, or 14 bits. The ISP front-end processing logic 510A may perform one or more image processing operations on the raw image data 507A and collect statistics about the image data 507B. The image processing operations and the collection of statistics may be performed with the same or different bit depth precision. For example, in one embodiment, the processing of images 507A, 507B can be performed with 14-bit precision. In a given example, for the purpose of image processing, the raw pixel data with a bit depth less than 14 bits (e.g., 8 bits, 10 bits, 12 bits) received by the ISP front-end processing logic 510A can be upsampled to 14 bits. In some embodiments, the front-end processing logic 510A can perform statistical processing at 8 bits, and for statistical purposes, images 507A, 507B with a higher bit depth can be downsampled to an 8-bit format. Downsampling to 8 bits can reduce the hardware size (e.g., area) and also reduce the processing / computational complexity of statistical data. In some embodiments, the front-end processing logic 510A can use images 507A, 507B for spatial averaging to allow data denoising. In some embodiments, the front-end processing logic 510A can perform temporal filtering and / or binning compensation filtering on images 507A, 507B. The front end processing logic 510A may send the processed image data to the ISP pipeline logic 510B for additional processing before being sent to the vehicle navigation system 508 .

[0092] For example, the ISP pipeline logic 510B may receive the "front end" processed data directly from the ISP front end processing logic 510A and may provide additional processing of the image data, for example, in the raw domain and in RGB and YCbCr color spaces. The image data processed by the ISP pipeline logic 510B may then be sent to the back end processing logic 510C to generate the processed image 512. The back end processing logic 510C may include a transmission formatting engine, such as a compression engine for encoding the processed image 512, to optimize the data transmission to the vehicle navigation system 508.

[0093] Vehicle navigation system 508 may include one or more devices communicatively coupled to vehicle 502, camera 504, and ISP 506. For example, vehicle navigation system 508 may receive processed images 512 as input from ISP 506 to provide one or more instructions for operating vehicle 502. For example, vehicle navigation system 508 may include a perception system (e.g., referring to Figure 4A Perception system 402 as described), planning system (e.g., reference Figure 4A Planning system 404 described above), positioning system (e.g., reference Figure 4A Positioning system 406 described above), control system (e.g., reference Figure 4A The vehicle navigation system 508 may receive a processed image 512 including data associated with at least one physical object in the environment (e.g., data used by the vehicle navigation system 508 to detect the at least one physical object) and classify the at least one physical object. In some examples, the vehicle navigation system 508 receives a processed image 512 associated with (e.g., used to represent) one or more physical objects within the field of view of the camera 504. In such examples, the vehicle navigation system 508 classifies the at least one physical object based on one or more groupings of physical objects (e.g., bicycles, vehicles, traffic signs, and / or pedestrians, etc.), and may determine a maneuver for the vehicle 502. In some embodiments, the vehicle navigation system 508 transmits data associated with one or more instructions for operating the vehicle 502 to other components of the vehicle 502 to perform operations of the vehicle 502 including vehicle navigation.

[0094] In some embodiments, one or more components of the example system 500, such as the ISP 506 and (a portion of) the optional vehicle navigation system 508, are included in an integrated circuit such as a "system on a chip" (SoC) 512 having functions specific to implementing the ISP 506. SoC 510 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, etc. may also be included. The SoC may integrate a microcontroller, a microprocessor, and / or one or more processor cores with a GPU, Wi-Fi and / or cellular network radio components, etc. Similar to how a microcontroller integrates a microprocessor with peripheral circuits and memory, the SoC 510 may be viewed as integrating a microcontroller with even more advanced peripherals. In some embodiments, ISP 506 includes one or more SoCs 510. In some examples, one or more steps 510A-C correspond to one or more SoCs 510. For example, front-end processing logic 510A, pipeline processing logic 510B, and back-end processing logic 510C can each be included in an SoC 510 that provides input to other SoCs 510 representing other components of ISP 506.

[0095] Continue to refer Figure 5 , one or more functions will be described as being performed by the example system 500 . Figure 5 The number and arrangement of components and / or devices of the example system 500 shown are provided as examples. Figure 5 There may be additional systems and / or devices, fewer systems and / or devices, different systems and / or devices, or differently arranged systems and / or devices than those shown. Figure 5 Two or more of the systems and / or devices shown may be implemented in a single system or a single device, or Figure 5The single system or single device shown may be implemented as multiple distributed systems or devices. Additionally or alternatively, a collection of systems or a collection of devices (e.g., one or more systems, one or more devices) of the example system 500 may perform one or more functions corresponding to different types of scenarios described as being performed by another collection of systems or another collection of devices of the example system 500. Examples of scenario types include "high traffic flow scenario", "congested pedestrian scenario", "highway scenario", "local road scenario", "parking lot scenario", or other scenarios that the vehicle 502 may encounter.

[0096] In some embodiments, the camera 504 generates raw image data 507A including an approximation of the amount of photons. The ISP 506 can be used to enhance an image 507B generated from the raw image data 507A in which the amount of captured photons is approximated. For example, the amount of captured photons can be approximated (using a parabolic approximation of a photon transfer curve based on an initial set of detected photon amounts) to save time and conserve resources of the example system 500. The ISP can process the raw image data 507A captured using the photon amount approximation technique to generate a processed image 512 in a format usable by the vehicle navigation system 508 to control the operation of the vehicle 502.

[0097] In some embodiments, the image processing performed by ISP 506 can be adapted based on the planned use of image 507B. For example, if processed image 512 is planned to be displayed on a user interface for human visualization (e.g., to make the components or colors of the image more obvious to the human eye), ISP 506 can perform additional image processing steps. As another example, when processed image 512 is not intended to be viewed by the human eye (for use by vehicle automated navigation processing (e.g., perception and planning) that does not involve human vision), some image processing steps can be excluded. Therefore, when ISP 506 is applied to image 507B in certain circumstances, one or more steps are omitted. For example, if processed image 512 is planned for use for a purpose that does not require a particular processing step to be performed, the corresponding processing step can be omitted, and the image undergoing processing can be provided by the previous processing step to the subsequent processing step. By omitting one or more image processing steps, ISP 506 can generate processed image 512 in a shorter amount of time than if all processing steps were performed. Reference Figure 6 Further details regarding processing performed by the example system 500 are described.

[0098] Reference now Figure 6 , which illustrates a flow chart of an example process 600 for managing the efficiency of image processing. In some embodiments, one or more steps described with respect to process 600 are performed by Figure 5 In some embodiments, the steps of process 600 are performed by one or more hardware components (e.g., SoC). Figure 5 The SoC is described in more detail. For example, one or more operations described with respect to the example process 600 may be performed by a vehicle (e.g., reference Figure 1 The vehicles 102a, 102b, 102n described or referenced Figure 2 The vehicle 200 described, or referenced Figure 5 In some embodiments, the example process 600 is performed by the perception system 402, planning system 404, and / or control system 408 (e.g., completely, partially, sequentially, and / or non-sequentially, etc.) of the autonomous vehicle software 400 of the described vehicle 502). Additionally or alternatively, in some embodiments, one or more steps described for the example process 600 are performed by other devices or groups of devices (e.g., completely, partially, sequentially, and / or 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.

[0099] At 602, from a camera that may be attached or coupled to a vehicle (e.g., reference Figure 2 The camera 202a and / or Figure 5The camera 504 shown in the figure obtains raw image data. For example, the camera can generate an image of the vehicle's surroundings, which includes agents that may interfere with the passage and / or maneuvering of the vehicle to which the camera is attached. The photon quantity approximation technique can be used to generate raw image data. In some embodiments, the raw image data is obtained by the camera according to the (stationary or moving) condition of the vehicle. For example, a "crowded pedestrian scenario" may require the use of a large number of image frames per second (with a higher resolution) in order to accurately perceive a large number of pedestrians and safely navigate around these pedestrians. In such a scenario, the vehicle can operate the camera in a manner that collects the most amount of data per unit time. Examples of such camera operations include operating the camera at a high rotation frequency. In contrast, some scenarios do not require the use of so many computing resources. As another example, a "parking lot scenario" may use fewer computing resources than the above-mentioned "crowded pedestrian scenario". In a "parking lot scenario," the camera can be set to a lower frame rate (e.g., 30 FPS instead of 60 FPS) and a lower rotation frequency (e.g., 20 Hz instead of 60 Hz), in which case the vehicle can navigate safely while using less power and the battery life of the vehicle systems can be extended. Because less data is collected and processed, the vehicle's communication network (e.g., Figure 3 The bus 302 shown uses less bandwidth.

[0100] At 604, an updated image is generated based on processing of the raw image from the camera (e.g., by an electronic module of the camera). The processing of the raw image may include noise removal techniques to minimize possible spatial variations, and optionally include compression techniques configured to reduce the size of the image. The noise removal techniques may be based on the camera configuration (e.g., sensor noise performance) and image acquisition settings. The updated image may include a representation of photons received by the camera corresponding to a signal level that is related to a distance relative to the imaged agent within the environment.

[0101] At 606, the image with the processed information is provided to an image and signal processing (ISP) pipeline (e.g., Figure 5 ISP 506 shown). Processing information may be provided as metadata generated by different components of the vehicle. The processing information may indicate a planned use of the image associated with one or more processing operations of the ISP pipeline. For example, the planned use may indicate whether the image is planned to be used by a user interface (e.g., reference Figure 2The communication device 202e) described above is displayed for viewing by the human eye. In the event that the planned use of the image indicates that the image is planned to be excluded from display by the user interface, at least a portion of the processing to be performed by the ISP can be omitted from the image processing. For example, if the image is not planned to be viewed by the human eye, the color balancing step can be omitted.

[0102] At 608, the ISP pipeline may process the image according to the determined processing plan. In some embodiments, different portions of the image processing are performed by different components of the ISP pipeline even if the ISP pipeline omits steps identified as omitted. The image processing may include applying multiple filters to obtain information about the vehicle's surroundings.

[0103] At 610, the vehicle is operated based at least in part on information obtained from the analysis of the processed images. For example, the processed images are provided to a vehicle navigation system (e.g., reference Figure 4A The perception system 402 described above is used to identify objects in the vicinity of the vehicle to enable safe maneuvers of the vehicle to safely navigate around the identified agents to avoid collision.

[0104] According to some non-limiting embodiments or examples, a vehicle is provided, comprising: at least one computer-readable medium storing computer-executable instructions; at least one processor communicatively coupled to at least one camera and configured to execute the computer-executable instructions, wherein the execution includes the following operations: using at least one SoC, obtaining raw data associated with an original image from at least one camera; using the at least one SoC, generating an updated image based on the original image from the at least one camera, wherein the updated image represents photons received by the at least one camera; using the at least one SoC, providing the updated image to an image and signal processing pipeline; using the at least one SoC, processing the updated image using the image and signal processing pipeline; and transmitting instructions for causing the vehicle to operate based at least in part on information obtained from analysis of the processed image.

[0105] 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 causes the at least one processor to perform operations, the operations including: using at least one SoC, obtaining raw data associated with an original image from at least one camera; using the at least one SoC, generating an updated image based on the original image from the at least one camera, wherein the updated image represents photons received by the at least one camera; using the at least one SoC, providing the updated image to an image and signal processing pipeline; using the at least one SoC, processing the updated image using the image and signal processing pipeline; and transmitting instructions that cause the vehicle to operate based at least in part on information obtained from analysis of the processed image.

[0106] According to some non-limiting embodiments or examples, a method is provided, comprising: using at least one SoC, obtaining raw data associated with an original image from at least one camera; using the at least one SoC, generating an updated image based on the original image from the at least one camera, wherein the updated image represents photons received by the at least one camera; using the at least one SoC, providing the updated image to an image and signal processing pipeline; using the at least one SoC, processing the updated image using the image and signal processing pipeline; and transmitting instructions for operating the vehicle based at least in part on information obtained from analysis of the processed image.

[0107] According to some non-limiting embodiments or examples, an integrated circuit is provided, comprising: a first hardware component configured to obtain an image from the at least one camera and provide the image to an image and signal processing pipeline; a second hardware component comprising the image and signal processing pipeline, wherein the second hardware component is configured to identify steps in the image and signal processing pipeline to be omitted in the processing of the image and to process the image while omitting the steps identified to be omitted; and a third hardware component configured to provide the processed image to a system of a vehicle to operate the vehicle based at least in part on information obtained from analysis of the processed image.

[0108] Other non-limiting aspects or embodiments are set forth in the following numbered clauses:

[0109] Item 1: A vehicle comprising: at least one computer-readable medium storing computer-executable instructions; at least one processor communicatively coupled to at least one camera and configured to execute the computer-executable instructions, wherein the execution includes the following operations: using at least one SoC, obtaining raw data associated with an original image from at least one camera; using the at least one SoC, generating an updated image based on the original image from the at least one camera, wherein the updated image represents photons received by the at least one camera; using the at least one SoC, providing the updated image to an image and signal processing pipeline; using the at least one SoC, processing the updated image using the image and signal processing pipeline; and transmitting instructions for causing the vehicle to operate based at least in part on information obtained from analysis of the processed image.

[0110] Clause 2: The vehicle of clause 1, said operations comprising: identifying steps in said image and signal processing pipeline to be omitted in processing of said updated image.

[0111] Clause 3: A vehicle as described in any of the preceding clauses, wherein the processing of the updated image omits steps identified as omitted.

[0112] Clause 4: A vehicle as described in any of the preceding clauses, wherein identifying steps in the image and signal processing pipeline to be omitted in the processing of the updated image includes omitting one or more hardware components.

[0113] Clause 5: A vehicle as described in any of the preceding clauses, wherein identifying steps of the image and signal processing pipeline to be omitted in processing of the updated image is determined based on a state of the vehicle.

[0114] Clause 6: A vehicle as described in any of the preceding clauses, wherein the image and signal processing pipeline includes one or more hardware components.

[0115] Clause 7: A vehicle as described in any of the preceding clauses, wherein the image and signal processing pipeline includes one or more SOCs.

[0116] Clause 8: The vehicle of any preceding clause, wherein the at least one camera comprises at least one electronic camera.

[0117] Clause 9: A vehicle according to any one of the preceding clauses, wherein the raw image data is obtained by controlling the acquisition time of the at least one camera.

[0118] Clause 10: A vehicle as described in any of the preceding clauses, wherein the raw image data includes an approximation of photon quantities.

[0119] Clause 11: A method comprising performing an operation as specified in any of the preceding clauses.

[0120] Clause 12: A non-transitory computer-readable storage medium comprising at least one program executed by one or more processors of a first device, the at least one program comprising instructions which, when executed by the one or more processors, cause the first device to perform a method according to the preceding clause.

[0121] Item 13: An integrated circuit comprising: a first hardware component configured to obtain an image from at least one camera and provide the image to an image and signal processing pipeline; a second hardware component comprising the image and signal processing pipeline, wherein the second hardware component is configured to identify steps in the image and signal processing pipeline to be omitted in processing the image and process the image while omitting the steps identified to be omitted; and a third hardware component configured to provide the processed image to a system of a vehicle to operate the vehicle based at least in part on information obtained from analysis of the processed image.

[0122] In the previous description, aspects and embodiments of the present disclosure have been described with reference to many specific details, which may vary depending on the implementation. Therefore, the description and the accompanying drawings should be regarded as illustrative, not restrictive. The only and exclusive indication of the scope of the invention, and the applicant's expectation that the content of the scope of the invention is the literal and equivalent scope of the claims issued from this application in the specific form of the claims, including any subsequent amendments. Any definition of the terms used to be included in such claims that are clearly set forth herein should be based on the meaning of such terms as used in the claims. In addition, when the term "also includes" is used in the previous description or the attached claims, the following of the phrase may be an additional step or entity, or a sub-step / sub-entity of the previously described step or entity.

Claims

1. A vehicle, comprising: at least one computer-readable medium storing computer-executable instructions; At least one processor is communicatively coupled to at least one camera and is configured to execute the computer executable instructions, the execution of which includes the following operations: obtaining, using at least one SoC, raw data associated with a raw image from at least one camera; generating, using the at least one SoC, an updated image based on an original image from the at least one camera, wherein the updated image represents photons received by the at least one camera; providing the updated image to an image and signal processing pipeline using the at least one SoC; processing the updated image using the image and signal processing pipeline using the at least one SoC; and Instructions are transmitted that cause the vehicle to perform operations based at least in part on information obtained from the analysis of the processed images.

2. The vehicle of claim 1, wherein the operation comprises: Steps in the image and signal processing pipeline are identified that are to be omitted in processing of the updated image.

3. The vehicle according to claim 2, wherein: The processing of the updated image omits the steps identified as omitted.

4. The vehicle according to claim 2, wherein: Identifying steps in the image and signal processing pipeline to be omitted in processing of the updated image includes omitting one or more hardware components.

5. The vehicle according to claim 2, wherein: Identifying the image and steps of the signal processing pipeline to be omitted in processing the updated image is determined based on the state of the vehicle.

6. A vehicle according to any one of the preceding claims, wherein: The image and signal processing pipeline includes one or more hardware components.

7. A vehicle according to any one of the preceding claims, wherein: The image and signal processing pipeline includes one or more SoCs.

8. A vehicle according to any one of the preceding claims, wherein: The at least one camera includes at least one electronic camera.

9. A vehicle according to any one of the preceding claims, wherein: The raw image data is obtained by controlling an acquisition time of the at least one camera.

10. A vehicle according to any one of the preceding claims, wherein: The raw image data includes an approximation of the photon quantity.

11. A method comprising performing the operations specified in any one of the preceding claims.

12. A non-transitory computer-readable storage medium comprising at least one program executed by one or more processors of a first device, the at least one program comprising instructions that, when executed by the one or more processors, cause the first device to perform the method according to claim 8.

13. An integrated circuit comprising: a first hardware component configured to obtain images from at least one camera and provide the images to an image and signal processing pipeline; a second hardware component comprising an image and signal processing pipeline, wherein the second hardware component is configured to identify steps in the image and signal processing pipeline to be omitted in processing of the image and to process the image with the steps identified to be omitted omitted omitted; as well as A third hardware component is configured to provide the processed image to a system of a vehicle to operate the vehicle based at least in part on information obtained from analysis of the processed image.

Images