Method and apparatus with hardware logic for pre-processing LiDAR data

Abstract

An apparatus is disclosed that includes an input interface. The input interface is configured to obtain input sensor data including light detection and ranging (LiDAR) data indicative of an environment. The apparatus includes a preprocessor communicatively coupled to the input interface. The preprocessor includes parser logic. The preprocessor includes a decoder communicatively coupled to the parser logic. The parser logic is configured to parse input sensor data. The decoder is configured to decode the parsed input sensor data. The preprocessor is configured to provide the decoded input sensor data and the parsed input sensor data to the processor for fusion.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Application No. 63 / 334,740, filed April 26, 2022, entitled “METHODS AND APPARATUS WITH HARDWARE LOGIC FOR PRE-PROCESSING LIDAR DATA,” which is incorporated herein by reference in its entirety for all purposes. Pursuant to 37 CFR 1.57, any and all applications for which a foreign or domestic priority claim is identified in the Application Data Sheet filed with the present application are incorporated herein by reference. Background Art

[0003] LiDAR data processing such as point cloud fusion is performed in software for object detection. This requires the software to cope with a large amount of real-time point data from the LiDAR sensor. This causes delays in the processing of the LiDAR data. BRIEF DESCRIPTION OF THE DRAWINGS

[0004] Reference numerals are repeated throughout the drawings to indicate corresponding relationships between referenced elements.The drawings are provided to illustrate embodiments of the inventive subject matter described herein and not to limit the scope thereof.

[0005] Figure 1 is an example environment in which a vehicle including one or more components of an autonomous system may be implemented;

[0006] Figure 2 is a diagram of one or more systems of a vehicle including an autonomous system;

[0007] Figure 3 yes Figure 1 and Figure 2 a diagram of one or more devices and / or components of one or more systems;

[0008] Figure 4 is a diagram of some components of an autonomous system;

[0009] Figure 5 is a diagram of an example apparatus for preprocessing LiDAR data according to the present disclosure; and

[0010] Figure 6 is a diagram of an example apparatus for preprocessing LiDAR data according to the present disclosure;

[0011] Figure 7 is a diagram of an example apparatus for preprocessing LiDAR data according to the present disclosure;

[0012] FIG. 8A to FIG. 8Bis a diagram illustrating sector-wise data collection according to the present disclosure;

[0013] Fig. 9 is a flow chart of a process for preprocessing LiDAR data according to the present disclosure; and

[0014] Fig.10 is a block diagram of an example chip layout for a computing unit of an autonomous robotic system according to the present disclosure. DETAILED DESCRIPTION

[0015] 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.

[0016] In the accompanying drawings, for ease of description, the specific arrangement or order of schematic elements (such as those representing systems, devices, modules, instruction blocks and / or data elements, etc.) is illustrated. However, those skilled in the art will understand that, unless explicitly described, the specific order or arrangement of schematic elements in the accompanying drawings is not intended to mean that a specific processing order or sequence, or separation of processing is required. In addition, unless explicitly described, the inclusion of schematic elements in the accompanying drawings is not intended to mean that such elements are required in all embodiments, nor is it intended to mean that the features represented by such elements cannot be included in some embodiments or cannot be combined with other elements in some embodiments.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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, etc.) to communicate with another unit, this means that the unit is able to 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 can refer to a network packet (e.g., a data packet, etc.) including data.

[0021] 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."

[0022] 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.

[0023] General Overview

[0024] In some aspects and / or embodiments, the device described herein includes an input interface. The input interface is configured to obtain input sensor data. For example, the input sensor data includes light detection and ranging (LiDAR) data indicating an environment. The device includes a preprocessor communicatively coupled to the input interface. The preprocessor optionally includes a parser logic. The preprocessor includes a decoder communicatively coupled to the parser logic. The parser logic is configured to parse the input sensor data. The decoder is configured to decode the parsed input sensor data. The preprocessor is configured to provide the decoded input sensor data and the parsed input sensor data to the processor for fusion.

[0025] The present disclosure relates to methods and devices for providing hardware logic for multi-purpose decoding of LiDAR data. The hardware logic provides custom logic (such as custom register transfer level (RTL) logic, etc.) for increasing the parallelism of decoding LiDAR data. LiDAR data and / or image data can be collected for each partition of the LiDAR (related to the azimuth of the LiDAR). This allows the disclosed device to perform partitioned data collection. The collected data is preprocessed by the disclosed device in a partitioned manner and provided to a processor for point merging and / or point fusion. In other words, in some embodiments, the device can divide the data obtained from the sensor system based on the partition of the field of view of the sensor that obtained the data. For example, if the sensor system (e.g., a LiDAR sensor) has a 360-degree field of view, the device can divide the field of view into four 90-degree segments. The device can associate data obtained by measurements made by the sensor system within a specific 90-degree segment or data corresponding to the measurement with the segment during storage and / or processing of the data (e.g., preprocessing or point merging, etc.). The hardware logic may include a partition manager and a synchronizer for synchronizing LiDAR data and image data.

[0026] By means of implementation of the systems, methods, and computer program products described herein, techniques for pre-processing LiDAR data can benefit from improved latency in the overall processing of "Raw" LiDAR data (e.g., unprocessed LiDAR data). The disclosed device is configured to reduce latency in processing "Raw" sensor data (e.g., LiDAR data and image data) from multiple sensors (e.g., data output by sensors, etc.) by providing decoded input sensor data and parsed input sensor data to a processor for fusion, data integration, or joint analysis. The disclosed device supports the collection and processing of point clouds or point cloud data by partition. In some examples, advantageously, the LiDAR data and image data are synchronized by having the LiDAR act as a master or leader of the image sensor, which can act as a slave or follower. The disclosed device provides a multi-purpose decoder and architecture that reduces computational redundancy in processing LiDAR data to be provided to a neural network (e.g., a semantic network and / or a range view image method (RVIM)) by providing a custom hardware architecture that does not require conversion from polar coordinates to Cartesian coordinates and back to polar coordinates.

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

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

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

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

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

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

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

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

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

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

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

[0038] Reference now Figure 2 , vehicle 200 (which can be Figure 1 102) includes, or is associated with, autonomous system 202, powertrain control system 204, steering control system 206, and braking system 208. In some embodiments, vehicle 200 is similar to vehicle 102 (see Figure 1). In some embodiments, the autonomous system 202 is configured to give the vehicle 200 autonomous driving capabilities (e.g., implementing at least one of the following driving automatic or maneuvering-based functions, features, and / or devices, etc., which enable the vehicle 200 to partially or completely operate without human intervention, including but not limited to fully autonomous vehicles (e.g., vehicles that abandon reliance on human intervention, such as Level 5 ADS-operated vehicles, etc.), highly autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in certain situations, such as Level 4 ADS-operated vehicles, etc.), and / or conditionally autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in limited situations). , such as Level 3 ADS operated vehicles, etc.). In one embodiment, the autonomous system 202 includes the operational or tactical functionality required to enable the vehicle 200 to operate in on-road traffic and continuously perform part or all of the dynamic driving tasks (DDT). In another embodiment, the autonomous system 202 includes an advanced driver assistance system (ADAS) including driver support features. The autonomous system 202 supports various levels of driving automation ranging from no driving automation (e.g., Level 0) to full driving automation (e.g., Level 5). For a detailed description of fully autonomous vehicles and highly autonomous vehicles, reference may be made to SAE International's standard J3016: Taxonomy and Definitions for TermsRelated to On-Road Motor Vehicle Automated Driving Systems, the entire contents of which are incorporated by reference. In some embodiments, the vehicle 200 is associated with an autonomous queue manager and / or a ride-sharing company.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0052] In some embodiments, vehicle 200 includes at least one platform sensor (not explicitly illustrated) for measuring or inferring a property of a state or condition of vehicle 200. In some examples, vehicle 200 includes platform sensors such as a global positioning system (GPS) receiver, an inertial measurement unit (IMU), wheel rate sensors, wheel brake pressure sensors, wheel torque sensors, engine torque sensors, and / or steering angle sensors. Although 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 .

[0053] Reference now Figure 3 , a schematic diagram illustrating apparatus 300. As illustrated, apparatus 300 includes a processor 304, a memory 306, a storage component 308, an input interface 310, an output interface 312, a communication interface 314, and a bus 302. In some embodiments, apparatus 300 corresponds to: at least one apparatus of vehicle 102 (e.g., at least one apparatus of a system of vehicle 102); Figures 1 to 3At least one device in vehicle 102; and / or one or more devices in network 112 (e.g., one or more devices in a system of network 112). In some embodiments, one or more devices in vehicle 102 (e.g., one or more devices in a system of vehicle 102), and / or one or more devices in network 112 (e.g., one or more devices in a system of network 112) include at least one device 300 and / or at least one component of device 300. Figure 3 As shown, apparatus 300 includes a bus 302 , a processor 304 , a memory 306 , a storage component 308 , an input interface 310 , an output interface 312 , and a communication interface 314 .

[0054] The bus 302 includes components 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.

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

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

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

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

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

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

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

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

[0063] Reference now Figure 4, 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.).

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

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

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

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

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

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

[0070] Database 410 stores data transmitted to, received from, and / or updated by perception system 402, planning system 404, positioning system 406, and / or control system 408. In some examples, database 410 includes a storage component (e.g., a storage component) for storing data and / or software related to operations and using at least one system of autonomous vehicle software 400. Figure 3In some embodiments, database 410 stores data associated with a 2D and / or 3D map of at least one area. In some examples, database 410 stores data associated with a 2D and / or 3D map of a portion of a city, portions of multiple cities, multiple cities, counties, states, and / or states (e.g., countries), etc. In such examples, a vehicle (e.g., a vehicle that is the same or similar to vehicle 102 and / or vehicle 200) may 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. The field of view may refer to an area scanned or otherwise sensed by a LiDAR sensor. The field of view may include a geographic area where light may be emitted by a LiDAR sensor and / or light may be reflected back to the LiDAR sensor. In some cases, the field of view may be determined for a specific period of time, and the field of view may change over time and / or based on the movement of the LiDAR sensor.

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

[0072] Reference now Figure 5 , illustrates a diagram of a device 500 for preprocessing LiDAR data. In some embodiments, the device 500 is a system disclosed herein (e.g., Figure 2 200 vehicles, Figure 2 Autonomous systems 202, Figure 2 Autonomous Vehicle (AV) Computing 202f, Figure 3 The device 300 and / or Figure 4 AV software 400) or implemented by the system disclosed herein. In one or more examples and embodiments, the device 500 includes such as Figure 2AV computing 202f and the like (such as AV software and / or AV hardware, etc.). In some examples or embodiments, the device 500 is a SoC (e.g., Fig.10 The SoC) described above may be implemented by the SoC.

[0073] In one or more embodiments or examples, the device 500 includes a preprocessor 502 and an input interface 504 communicatively coupled to the preprocessor 502. In some embodiments, the input interface 504 is configured to obtain input sensor data 546a. For example, the input sensor data 546a includes light detection and ranging (LiDAR) data indicating the environment. The input interface 504 may include one or more embodiments of the embodiments described with respect to the input interface 310. In addition, the input interface 504 may include any type of circuit for receiving sensor data associated with a sensor (e.g., sensor 546). For example, the input interface may be or may include a data pin, a receiver, an input filter, a multiplexer, or any other input circuit system that can receive and / or preprocess (e.g., filter) sensor data received from a sensor. The preprocessor 502 optionally includes a parser logic, such as a parser logic 514. The parser logic is communicatively coupled to the input interface. The preprocessor 502 includes a decoder, such as a decoder 524. The decoder is optionally communicatively coupled to the parser logic. The parser logic (e.g., parser logic 514) is configured to parse input sensor data (e.g., input sensor data 504a, which may be the same as or different from input sensor data 546a). The decoder (e.g., decoder 524) is configured to decode the parsed input sensor data (e.g., parsed input sensor data 514a). The preprocessor 502 is configured to provide the decoded input sensor data (e.g., decoded input sensor data 524a) and the parsed input sensor data (e.g., parsed input sensor data 514a) to a processor (e.g., processor 538) for fusion. The processor 538 may be a processor such as Figure 4 AV software 400 or Figure 2 AV Computing 202f AV Computing or Figure 3 The processor 304. In this application, the terms "communicatively coupled" may be used interchangeably with "operatively coupled" and / or "operatively connected".

[0074] In one or more embodiments or examples, the pre-processor 502 includes a plurality of parser logics 514, 516, 518, 520, 522, a plurality of decoders 524, 526, 528, 530, 532, and optional data capture logic 534, and optional motion compensation and point cloud transformer (MC&PCT) 536. In one or more embodiments or examples, the pre-processor 502 is communicatively coupled to a plurality of sensors 546, 548, 550, 552, 554, a memory 556, and a processor 538.

[0075] In one or more embodiments or examples, the input sensor data indicates the environment around the autonomous vehicle (e.g., Figure 1 environment), such as for determining a trajectory of the autonomous vehicle. In some examples, the input sensor data indicates an object, a drivable surface, a non-drivable surface, a pedestrian, an animal, a tree, a vehicle, or any other type of potential obstacle located in the environment. In one or more embodiments or examples, the input interface 504 is configured to obtain input sensor data 546a. For example, the input interface 504 can be one or more of an Ethernet interface, a bus interface, an optical interface, and a coaxial interface. In one or more embodiments or examples, the input sensor data (e.g., the input sensor data 546a) includes information indicating the environment (e.g., Figure 1 The input sensor data includes light detection and ranging (LiDAR) data from a LiDAR sensor. Figure 5 In the present invention, for example, a LiDAR sensor provides LiDAR data by performing a full scan such as a 360-degree scan of the environment.

[0076] In one or more embodiments or examples, the input interface 504 is configured to obtain input sensor data from one or more sensors (such as, sensor 546 and / or sensor 548, etc.). For example, the input interface is communicatively coupled to the one or more sensors. In one or more embodiments or examples, the one or more sensors are associated with an autonomous vehicle. The autonomous vehicle may include one or more sensors that may be configured to monitor the environment in which the autonomous vehicle operates, for example, via a first sensor 546 through first input sensor data 546a. In one or more embodiments or examples, the one or more sensors include a first sensor 546, a second sensor 548, a third sensor 550, a fourth sensor 552, and / or a fifth sensor 554. For example, the one or more sensors may be Figure 2One or more of the illustrated sensors. For example, any one of sensors 546, 548, 550, 552, 554 may be Figure 2 Any of the illustrated sensors. In some embodiments, any of sensors 546, 548, 550, 552, 554 may be one or more of a LiDAR sensor, a Radar sensor, an image sensor, a microphone, and an infrared sensor. In one or more embodiments or examples, each of sensors 546, 548, 550, 552, 554 is a LiDAR sensor. In one or more embodiments or examples, sensor 546 is a primary LiDAR sensor or a main LiDAR sensor, and each of sensors 548, 550, 552, 554 is an additional or secondary LiDAR sensor. In some embodiments, sensor 546 is a long-range LiDAR sensor, and each of sensors 548, 550, 552, 554 is a short-range LiDAR sensor.

[0077] In one or more embodiments, the pre-processor 502 is configured to connect to the Fig.10 The processor 538 of the SoC or chiplet.

[0078] In one or more embodiments or examples, the preprocessor 502 includes a parser logic (e.g., parser logic 514, 516, 518, 520, 522) and a decoder (e.g., decoders 524, 526, 528, 530, 532) communicatively coupled to the parser logic. The parser logic may include any circuit system configured to parse (e.g., separate, segment, etc.) the input sensor data. For example, the parser logic may include a filter circuit that can parse the input sensor data and can filter a portion of the input sensor data to, for example, remove noise or identify specific data within the input sensor data. For example, the parser logic may be configured to remove header data from the input sensor data and provide the input sensor data without the header. In addition, parsing the input sensor data may include extracting specific data from the input sensor data. For example, parsing the input sensor data may include extracting obstacle data corresponding to obstacles detected by the sensor (e.g., LiDAR) during operation. As another example, the parser logic can be configured to transform input sensor data obtained in one format (e.g., a format with an Ethernet header) into input sensor data in another format (e.g., a format without an Ethernet header). In some cases, the parsed input sensor data includes polar coordinate data obtained from the LiDAR data. The parser logic provides the parsed input sensor data to a processor (e.g., processor 538) and / or a memory (e.g., memory 556). In one or more embodiments or examples, the parser logic is configured to parse the input sensor data and form (one or more) data packets based on the input sensor data. In one or more embodiments or examples, the parser logic includes a data parser configured to obtain and parse input sensor data from an input interface. In one or more embodiments or examples, the parser logic is configured to provide the parsed input sensor data, optionally as one or more data packets.

[0079] The decoder 524 is configured to decode or transform or convert the parsed input sensor data 514a. For example, the decoder 524 can convert or transform the data from polar coordinates (e.g., points associated with a point cloud represented in a polar coordinate grid) to Cartesian coordinates, and / or vice versa. The decoded input sensor data includes, for example, Cartesian coordinates associated with LiDAR data. The decoder 524 can provide the decoded input sensor data to a processor (e.g., processor 538) and / or a memory (e.g., memory 556).

[0080] The preprocessor 502 may be configured to provide the decoded input sensor data and the parsed input sensor data to the processor for fusion or at least partially parallel processing. For example, the preprocessor 502 may be configured to provide the decoded input sensor data 524a and the parsed input sensor data 514a to the processor 538, for example, to perform fusion of LiDAR data and image sensor data. For example, the preprocessor 502 is configured to provide the decoded input sensor data 524a and the parsed input sensor data 514a to the processor 538 in parallel for fusion. In some examples where the sensor 546 is a LiDAR sensor, the LiDAR sensor may provide the preprocessor 502 with LiDAR data based on a full sweep (e.g., a 360-degree sweep, a full range of motion, or a full field of view). Furthermore, the pre-processor 502 can provide the decoded input sensor data 524a (e.g., Cartesian coordinates) and the resolved input sensor data 514a (e.g., polar coordinates) to the processor 538 for fusion, for example, to perform a range view image method (RVIM) and a bird's eye view method (BEVM), respectively. Advantageously, in certain embodiments, by providing the processor 538 with both the input sensor data 524a in Cartesian coordinates and the resolved sensor data 514a in polar coordinates, the processor 538 can perform both RVIM (or range imaging) and BEVM at least partially in parallel, thereby reducing computational redundancy and latency.

[0081] In one or more embodiments or examples, the device 500 includes multiple input interfaces 504, 506, 508, 510, 512. Each interface 504, 506, 508, 510, 512 in the multiple interfaces can be configured to obtain input sensor data 504a, 506a, 508a, 510a, 512a from corresponding sensors 546, 548, 550, 552, 554, respectively. For example, each interface can be associated with a sensor. For example, a first input interface (e.g., input interface 504) obtains LiDAR data from a first LiDAR sensor (e.g., sensor 546). For example, a second input interface (e.g., input interface 506) obtains LiDAR data from a second LiDAR sensor (e.g., sensor 548). In some embodiments, the first input interface obtains image data from a first image sensor. In some embodiments, each input sensor data 504a, 506a, 508a, 510a, 512a is configured to generate and / or provide light detection and ranging (LiDAR) data. In one or more embodiments or examples, two sensors (e.g., sensors 546 and 548) are configured to provide input sensor data 546a, 548b to an input interface (e.g., input interface 504). In some embodiments, sensor 548 can provide input sensor data (e.g., input sensor data 548a, 548b, 548c, 548d, 548e) to an input interface (e.g., input interfaces 504, 506, 508, 510, 512, respectively).

[0082] In one or more embodiments or examples, each of the plurality of parser logics 514, 516, 518, 520, 522 is configured to parse input sensor data 504a, 506a, 508a, 510a, 512a from the corresponding input interfaces 504, 506, 508, 510, 512, respectively (depending on the corresponding input interfaces, the input sensor data 504a, 506a, 508a, 510a, 512a and the input sensor data 546a, 548a, 550a, 552a, 554a may be the same or different). In one or more embodiments or examples, at least one of the parser logics 514, 516, 518, 520, 522 is communicatively coupled to at least one of the corresponding input interfaces 504, 506, 508, 510, 512. In one or more embodiments or examples, each parser logic 514, 516, 518, 520, 522 is communicatively coupled to a corresponding input interface 504, 506, 508, 510, 512. Data parsing of one parser logic is performed in parallel with data parsing of at least one other parser logic in the plurality of parser logics. For example, parsing of input sensor data 504a by parser logic 514 is performed in parallel with parsing of input sensor data 506a by parser logic 516. For example, parsing of input sensor data 504a by parser logic 514 is performed in parallel with parsing of corresponding input sensor data 506a, 508a, 510a, 512a by parser logic 516, 518, 520, 522.

[0083] In one or more embodiments or examples, at least one of the plurality of decoders 524, 526, 528, 530, 532 is communicatively coupled to a corresponding parser logic of the plurality of parser logics 514, 516, 518, 520, 522. In one or more embodiments or examples, each of the plurality of decoders 524, 526, 528, 530, 532 is communicatively coupled to a corresponding parser logic of the plurality of parser logics 514, 516, 518, 520, 522. In one or more embodiments or examples, a decoder (e.g., decoder 524, 526, 528, 530, 532) is configured to decode parsed input sensor data (e.g., parsed input sensor data 514a, 516a, 518a, 520a, 522a, respectively) from a corresponding parser logic (e.g., parser logic 514, 516, 518, 520, 522). In one or more embodiments or examples, decoding of parsed input sensor data (e.g., parsed input sensor data 514a) is performed in parallel with at least one other decoding of at least one other decoder of the plurality of decoders (e.g., decoding of parsed input sensor data 516a by decoder 526). For example, decoding of parsed input sensor data 514a is performed in parallel with at least one other decoding of parsed input sensor data 516a, 518a, 520a, and / or 522a. In one or more embodiments or examples, decoders (e.g., decoders 524, 526, 528, 530, 532) are configured to decode parsed input sensor data (e.g., parsed input sensor data 514a, 516a, 518a, 520a, 522a, respectively) and provide decoded input sensor data (e.g., decoded input sensor data 524a, 526a, 528a, 530a, 532a, respectively). The preprocessor 502 provides the decoded input sensor data (e.g., decoded input sensor data 524a, 526a, 528a, 530a, 532a) and the parsed input sensor data (e.g., parsed input sensor data 514a, 516a, 518a, 520a, 522a) to, for example, a memory 556 and / or a processor 548. The disclosed hardware logic can increase parallelism and improve latency of "Raw" data processing of LiDAR data. Advantageously, this can avoid the device 500 from having to perform multiple data conversions. It will be appreciated that in some embodiments, one or more sensors 546, 548, 550, 552, 554 are LiDAR sensors that provide LiDAR data based on a full sweep (e.g., a 360-degree sweep) as input sensor data.It will be appreciated that in some embodiments, LiDAR data from a full sweep is preprocessed in parallel by the preprocessor disclosed herein.

[0084] In one or more embodiments or examples, the device 500 includes a memory 556. In one or more embodiments or examples, the pre-processor 502 is configured to store the decoded input sensor data (e.g., one or more of the decoded input sensor data 524a, 526a, 528a, 530a, 532a) and the parsed input sensor data (e.g., one or more of the parsed input sensor data 514a, 516a, 518a, 520a, 522a) in the memory 556. In one or more embodiments or examples, the preprocessor 502 is configured to store the decoded input sensor data (e.g., one or more of the decoded input sensor data 524a, 526a, 528a, 530a, 532a) in a first portion of the memory and store the parsed input sensor data (e.g., one or more of the parsed input sensor data 514a, 516a, 518a, 520a, 522a) in a second portion of the memory. In one or more examples, the first portion is different from the second portion.

[0085] In one or more embodiments or examples, the pre-processor 502 includes data capture logic 534, and an optional motion compensation and point cloud transformer (MC&PCT) 536. In one or more embodiments or examples, the data capture logic 534 is configured to obtain decoded input sensor data (e.g., one or more of the decoded input sensor data 524a, 526a, 528a, 530a, 532a) and parsed input sensor data (e.g., one or more of the parsed input sensor data 514a, 516a, 518a, 520a, 522a) from the memory 556. For example, the memory 556 can provide the memory data 556a to the data capture logic. For example, the memory data 556a may include decoded input sensor data (e.g., one or more of the decoded input sensor data 524a, 526a, 528a, 530a, 532a) and parsed input sensor data (e.g., one or more of the parsed input sensor data 514a, 516a, 518a, 520a, 522a). In one or more embodiments or examples, the data capture logic 534 is communicatively coupled to the decoder (e.g., one or more of the decoders 524, 526, 528, 530, 532). For example, the data capture logic 534 may be a data grabber. In one or more embodiments or examples, the data capture logic is configured to obtain decoded input sensor data (e.g., one or more of decoded input sensor data 524a, 526a, 528a, 530a, 532a) from a decoder (e.g., one or more of decoded input sensor data 524a, 526a, 528a, 530a, 532a, respectively) and parsed input sensor data (e.g., one or more of parsed input sensor data 514a, 516a, 518a, 520a, 522a) from a parser logic (e.g., one or more of parsed input sensor data 514a, 516a, 518a, 520a, 522a). For example, the decoded input sensor data includes Cartesian coordinates to be provided for a range view image method (RVIM). For example, the parsed input sensor data includes polar coordinates to be provided for a bird's eye view method (BEVM). In one or more embodiments or examples, the data capture logic is configured to store the decoded input sensor data in a first portion of the memory 556 and store the parsed input sensor data in a second portion of the memory 556. For example, the data capture logic 534 provides data 534b including parsed input sensor data (e.g., one or more of the parsed input sensor data 514a, 516a, 518a, 520a, 522a) representing polar coordinates to the processor 538.

[0086] In one or more embodiments or examples, the motion compensation and point cloud transformer (MC&PCT) 536 is configured to obtain the capture logic data 534a from the data capture logic 534 and provide data 536b to the processor 538. The motion compensation and point cloud transformer (MC&PCT) 536 is configured, for example, to compensate for vehicle motion. For example, when the autonomous vehicle is moving, it may be advantageous to repair the delay between the time when the sensor data is obtained (e.g., from the sensors 546, 568, 550, 552, 554) and the time when it is calculated. The motion compensator can be configured to synchronize the longitudinal movement of the autonomous vehicle with the longitudinal movement of the obtained data, such as via calculating a timing offset.

[0087] For example, the motion compensation and point cloud transformer (MC&PCT) 536 is configured to transform the point cloud. For example, the MC&PCT 536 is configured to increase the density of points in the point cloud while maintaining the accuracy of the point cloud.

[0088] For example, capture logic data 534a includes decoded input sensor data (e.g., one or more of decoded input sensor data 524a, 526a, 528a, 530a, 532a) and parsed input sensor data (e.g., one or more of parsed input sensor data 514a, 516a, 518a, 520a, 522a). For example, data 534b includes parsed input sensor data (e.g., one or more of parsed input sensor data 514a, 516a, 518a, 520a, 522a) representing polar coordinates. For example, data 536b includes the result of motion compensation of decoded input sensor data (e.g., one or more of decoded input sensor data 524a, 526a, 528a, 530a, 532a).

[0089] In one or more embodiments or examples, the MC&PCT 536 is configured to obtain memory data 536a from the memory 556. For example, the memory data 536a includes decoded input sensor data (e.g., one or more of the decoded input sensor data 524a, 526a, 528a, 530a, 532a) and parsed input sensor data (e.g., one or more of the parsed input sensor data 514a, 516a, 518a, 520a, 522a). In one or more embodiments or examples, the MC&PCT 536 is configured to provide data 536a to the memory 556. For example, the data 536a is the result of motion compensation of the decoded input sensor data (e.g., one or more of the decoded input sensor data 524a, 526a, 528a, 530a, 532a).

[0090] In one or more embodiments or examples, the processor 538 is configured to process data using a range view image method (RVIM (or RVN 540)) and / or a bird's eye view method (BEVM (or LSN 544)). The RVIM takes as input Cartesian coordinates provided by decoded input sensor data (e.g., via data 536b). The BEVM takes as input polar coordinates provided by parsed input sensor data (e.g., via data 534b).

[0091] Reference now Figure 6 , Figure 6 600 is a diagram illustrating an apparatus 600 for preprocessing LiDAR data. In some embodiments, the apparatus 600 is or includes Figure 5 In some embodiments, the device 600 is a system (e.g., Figure 2 200 vehicles, Figure 2 Autonomous systems 202 Figure 2 Autonomous Vehicle (AV) Computing 202f, Figure 3 The device 300 and / or Figure 4 In some examples or embodiments, the device 600 is a SoC (e.g., Fig.10 Advantageously, the device 600 can provide partitioned point cloud data collection in real time to further optimize latency.

[0092] The device 600 includes an input interface 604 and a preprocessor 602 and an optional memory 646. In one or more embodiments or examples, the preprocessor 602 is or includes Figure 5In some embodiments, the apparatus 600 includes (e.g., with Figure 5 ) input interfaces 604, 606, 608, 610, 612, (e.g., Figure 5 ) parser logic 614, 616, 618, 620, 622, (e.g., Figure 5 ) decoders 624, 626, 628, 630, and 632 (e.g., Figure 5 ) data capture logic 634, (e.g., Figure 5 Motion compensation and point cloud transformer (MC&PCT) 642) and / or including (for example, Figure 5 RVIM and BEVM are the same or similar) distance view image method (RVIM (or RVN 662)) and bird's eye view method (BEVM (or LSN 664)) (for example, with Figure 5 processor 660) that is the same as or similar to the processor of the present invention.

[0093] In one or more embodiments or examples, the preprocessor 602 includes a plurality of input interfaces (e.g., input interfaces 604, 606, 608, 610, 612). In one or more embodiments or examples, each of the plurality of input interfaces (e.g., input interfaces 604, 606, 608, 610, 612) is configured to obtain input sensor data (e.g., input sensor data 650a, 652a, 654a, 656a, 658a) from a corresponding sensor. For example, each of the plurality of input interfaces 604, 606, 608, 610, 612 obtains input sensor data 650a, 652a, 654a, 656a, 658a from sensors 650, 652, 654, 656, 658, respectively. In one or more embodiments or examples, two sensors (e.g., sensors 650 and 652) are configured to provide input sensor data 650a, 652b to an input interface (e.g., input interface 604). In some embodiments, sensor 652 may provide input sensor data (e.g., input sensor data 652a, 652b, 652c, 652d, 652e) to an input interface (e.g., to input interfaces 604, 606, 608, 610, and 612, respectively). For example, each of the plurality of input interfaces 604, 606, 608, 610, 612 outputs input sensor data 604a, 606a, 608a, 610a, 612a (which may be the same as input sensor data 650a, 652a, 654a, 656a, 658a).

[0094] In one or more embodiments or examples, the input sensor data (e.g., input sensor data 650a) includes information indicating the environment (e.g., Figure 1 The input sensor data includes light detection and ranging (LiDAR) data from a LiDAR sensor. Figure 6 In one or more embodiments or examples, the input sensor data includes LiDAR data from a LiDAR operating with a field of view. In one or more embodiments or examples, the input interface is configured to obtain a first LiDAR data set. For example, the first LiDAR data set corresponds to a first portion of the field of view (e.g., (e.g., (e.g., (e.g., (e.g., (e.g., (e.g., (e.g., (e.g., (e.g., (e.g., (e.g., (e.g., (e.g., (e.g., (e.g., (e.g., (e.g., (e.g., (e. Fig. 8AIn other words, for example, a portion is a partition of the field of view. A partition can be considered as a portion or part of the field of view of the LiDAR (e.g., less than 360 degrees). For example, input sensor data collection is performed by partition of the field of view or for each part of the field of view. In one or more embodiments or examples, the input interface is configured to obtain a second LiDAR data set, which is associated with a second part of the field of view. The LiDAR data may include information related to the part of the field of view. In other words, the LiDAR data may identify the corresponding field of view associated with the LiDAR data. In one or more embodiments, the input interface may include a first input interface and a second input interface. For example, a first LiDAR data set is obtained via the first input interface, and a second LiDAR data set is obtained via the second input interface.

[0095] The pre-processor 602 includes a plurality of parser logics 614, 616, 618, 620, 622, a plurality of decoders 624, 626, 628, 630, 632, and optional data capture logic 634, a data reader 636, a frame buffer 638, a partition manager 640, a motion compensation and point cloud transformer (MP&PCT) 642, and a synchronizer 644 (e.g., a camera and LiDAR synchronizer). The pre-processor 602 is communicatively coupled to a processor 660, one or more sensors (e.g., one or more of sensors 650, 652, 654, 656, 658), and a memory 646.

[0096] In one or more embodiments or examples, each of the plurality of parser logics 614, 616, 618, 620, 622 is configured to parse input sensor data (e.g., corresponding input sensor data 604a, 606a, 608a, 610a, 612a) from the corresponding input interface 604, 606, 608, 610, 612, respectively. The data parsing of each parser logic is performed in parallel with at least one other data parsing of other parser logics in the plurality of parser logics.

[0097] In one or more embodiments or examples, the parser logic (e.g., the parser logic 614, 616, 618, 620, 622) includes a partition counter (e.g., the partition counter 648, 650, 652, 654, 656). In one or more embodiments or examples, the partition counter is configured to determine information (e.g., information 648a, 650a, 652a, 654a, 656a) indicating a portion of the field of view (e.g., a partition of the field of view). For example, the information may include angle information, such as an azimuth angle of a LiDAR such as an azimuth counter, etc. The information may be used to associate the LiDAR data with the image sensor data. The information is provided to, for example, the data capture logic 634, to, for example, the partition manager 640 for data collection by partition, and / or to, for example, the synchronizer 644. For example, the implementation of the azimuth counter within the parser logic may be used to control data collection by partition based on the azimuth value (e.g., the azimuth counter value).

[0098] In one or more embodiments or examples, the sensor data also includes image data from an image sensor (e.g., sensor 672). The image sensor can be a camera or any type of system capable of capturing an image. In some embodiments, there can be multiple image sensors or cameras. In one or more embodiments or examples, the preprocessor 602 is configured to synchronize the image data with the LiDAR data. In one or more embodiments or examples, the preprocessor 602 includes a synchronizer 644 configured to synchronize the image data with the LiDAR data.

[0099] In one or more embodiments or examples, the preprocessor 602 is configured to synchronize the image data with the LiDAR data by activating an image sensor (e.g., sensor 672) to obtain image data based on information (e.g., information 648a, 650a, 652a, 654a, and 656a). For example, the synchronizer 644 activates the image sensor 672 via signal 644a based on the information to obtain image data. For example, the LiDAR sensor acts as a master device (or main system) for determining the timing of collecting sensor data for a particular sensor, thereby enabling synchronization between sensors. In some embodiments, for example, due to the continuous rotation of the field of view around the LiDAR, the LiDAR is a favorable candidate for acting as a timing master device or main system to perform partition synchronization between the LiDAR data and the camera data. In addition, it may be easier for the LiDAR system to provide changes in rotation speed and / or operation to the camera system or image sensor than for the camera system to detect changes in the rotation of the LiDAR.

[0100] In some embodiments, the synchronizer 644 may activate a specific image sensor 672 of the plurality of image sensors based on the partition of the LiDAR sensor (e.g., based on the direction the LiDAR sensor is facing). Thus, the synchronizer 644 may synchronize the LiDAR sensor with the image sensor 672. Alternatively, the plurality of image sensors may each be active, but the synchronizer 644 may synchronize the specific image sensor with the LiDAR sensor based at least in part on the direction of the LiDAR sensor during a sweep in which the LiDAR sensor rotates a specific number of degrees (e.g., 360 degrees). Synchronizing the LiDAR sensor with the image sensor may include synchronizing data captured by the LiDAR sensor with data captured by the image sensor. In some cases, the partition manager 640 may divide the field of view of the LiDAR sensor (e.g., a long-range LiDAR sensor) into partitions. In some such cases, the synchronizer 644 may synchronize the image sensor with the corresponding partition of the LiDAR sensor. By synchronizing the LiDAR sensor partitions with the image sensor, and by processing data obtained from the LiDAR sensor in parallel with data obtained from the image sensor, latency may be reduced and parallel processing may be increased. Furthermore, by synchronizing the LiDAR sensor with the image sensor using synchronizer 644, the system can be adaptive because the synchronizer can maintain synchronization with the image sensor when the rotation speed of the LiDAR sensor is adjusted, and can automatically adjust the exposure timing of the image sensor to maintain synchronization with the LiDAR sensor. In some embodiments, synchronizer 644 can maintain synchronization based on an orientation value.

[0101] In one or more embodiments or examples, each of the plurality of decoders 624, 626, 628, 630, 632 is communicatively coupled to a corresponding parser logic in the plurality of parser logics. In one or more embodiments or examples, each of the plurality of decoders 624, 626, 628, 630, 632 is configured to decode the parsed input sensor data 614a, 616a, 618a, 620a, 622a, respectively. The decoding of each parsed input sensor data 614a, 616a, 618a, 620a can be performed in parallel with at least one other decoding of one of the plurality of decoders. In one or more embodiments or examples, the preprocessor 602 provides the decoded input sensor data 624a, 626a, 628a, 630a, 632a and the parsed input sensor data 614a, 616a, 618a, 620a, 622a to the memory 646.

[0102] In one or more embodiments or examples, the data capture logic 634 is configured to obtain decoded input sensor data 624a, 626a, 628a, 630a, 632a from a plurality of decoders, parsed input sensor data 614a, 616a, 618a, 620a, 622a from a plurality of parser logics, and information 648a, 650a, 652a, 654a, 656a determined by corresponding partition counters 648, 650, 652, 654, 656. In one or more embodiments or examples, the data capture logic 634 is configured to store the decoded input sensor data 624a, 626a, 628a, 630a, 632a in a first portion of the memory 646 and store the parsed input sensor data 614a, 616a, 618a, 620a, 622a in a second portion of the memory 646. In one or more embodiments or examples, the partition manager 640 obtains data 634b from the data capture logic, the data 634b including decoded input sensor data and parsed input sensor data associated with information related to the portion of the field of view.

[0103] In one or more embodiments or examples, the preprocessor 602 includes a partition manager 640, which is configured to obtain information (e.g., information 648a, 650a, 652a, 654a, 656a) from (one or more) partition counters and, optionally, perform partition-by-partition data collection from a memory 646 via data capture logic 634.

[0104] In one or more embodiments or examples, the partition manager 640 is configured to obtain corresponding information 648a, 650a, 652a, 654a, 656a provided by the plurality of partition counters 648, 650, 652, 654, 656, and perform partition-by-partition data collection from the data capture logic 634. In one or more embodiments or examples, the partition manager 640 obtains the information 648a, 650a, 652a, 654a, 656a in the form of data 668. In one or more embodiments or examples, the data capture logic 634 obtains the information 648a, 650a, 652a, 654a, 656a in the form of data 666.

[0105] In one or more embodiments or examples, the synchronizer 644 is configured to synchronize input sensor data including image data from an image sensor (e.g., image sensor 672) with input sensor data including LiDAR data using information 648a, 650a, 652a, 654a, 656a. In one or more embodiments or examples, the synchronizer provides a trigger signal 644a to the image sensor 672.

[0106] In one or more embodiments or examples, the preprocessor 602 stores the decoded input sensor data 624a, 626a, 628a, 630a, 632a and the parsed input sensor data 614a, 616a, 618a, 620a, 622a in a memory 646, for example, in a first portion of the memory 646 and a second portion of the memory 646.

[0107] In one or more embodiments or examples, the data reader 636 obtains data 646a from the memory 646. In one or more embodiments or examples, the data 646a includes decoded input sensor data 624a, 626a, 628a, 630a, 632a and / or parsed input sensor data 614a, 616a, 618a, 620a, 622a.

[0108] In one or more embodiments or examples, the MC&PCT 642 obtains decoded input sensor data 624a, 626a, 628a, 630a, 632a and / or parsed input sensor data 614a, 616a, 618a, 620a, 622a from the data reader 636 as part of the data 636b. In one or more embodiments or examples, the MC&PCT 642 is configured to process the data 636b for compensating for the motion of the autonomous vehicle and to provide compensation results, such as from the data 642a, etc., and optional information to the frame buffer 638. For example, the MC&PCT 642 can store multiple frames and can transmit the processed frames to the frame buffer 638. In one or more embodiments or examples, the decoded input sensor data 624a, 626a, 628a, 630a, 632a includes Cartesian coordinates. In one or more embodiments or examples, the parsed input sensor data 614a, 616a, 618a, 620a, 622a includes polar coordinates.

[0109] In one or more embodiments or examples, the frame buffer 638 provides or sends data 638a to the memory 646 and / or provides or sends data 638b to the processor 660. The data 638a and the data 638b may be provided in frames.

[0110] Reference now Figure 7 , illustrates a diagram of a device 700 for preprocessing LiDAR data. In some embodiments, the device 700 is associated with Figure 5 The device 500 and / or Figure 6 The device 600 of the embodiment described in the embodiment is the same as or includes one or more embodiments. In some embodiments, the device 700 is a system disclosed herein (e.g., Figure 2 200 vehicles, Figure 2 Autonomous systems 202 Figure 2 Autonomous Vehicle (AV) Computing 202f, Figure 3 The device 300 and / or Figure 4 AV software 400) or implemented by the system disclosed in this article.

[0111] In some embodiments, the device 700 includes one or more sensors 750, 752, 754, 756, 758, a pre-processor 702, one or more input interfaces 704, 706, 708, 710, 712, one or more parser logic 714, 716, 718, 720 and 722, one or more decoders 724, 726, 728, 730, 732, data capture logic 734, 736, motion compensation (MC) for polar coordinates 738, motion compensation (MC) for Cartesian coordinates 740, memory 750, and / or a processor 742 including a range view image method (RVIM or RVN 746) and / or a bird's eye view method (BEVM or LSN 748). Some of these components are similarly integrated in Figure 5 The device 500 and / or Figure 6 In the device 600, such as multiple sensors (such as Figure 5 and / or Figure 6 sensors, etc.), preprocessors (such as Figure 5 and / or Figure 6 preprocessors, etc.), input interfaces (such as Figure 5 and / or Figure 6 Input interface, etc.), parser logic (such as Figure 5 and / or Figure 6 parser logic, etc.), decoders (such as Figure 5 and / or Figure 6 decoders, etc.), memory (such as Figure 5 and / or Figure 6 memory, etc.), data capture logic (such as Figure 5 and / or Figure 6 data capture logic, etc.) and processors (such as Figure 5 and / or Figure 6 The device 600 may be viewed as an example of integrating the motion compensation (MC) 738 for polar coordinates and the motion compensation (MC) 740 for Cartesian coordinates.

[0112] In one or more embodiments or examples, the preprocessor 702 includes a plurality of input interfaces 704, 706, 708, 710, 712, which are configured to obtain input sensor data 750a, 752a, 752b, 752c, 752d, 752e, 754a, 756a, 758a from corresponding sensors 750, 752, 754, 756, 758, respectively. The input sensor data 750a, 752a, 752b, 752c, 752d, 752e, 754a, 756a, 758a includes light detection and ranging (LiDAR) data. The acquisition of the input sensor data by the input interface does not necessarily have to be directional. In one or more embodiments or examples, a sensor such as sensor 752 may provide input sensor data 752a, 752b, 752c, 752d, 752e to input interfaces 704, 706, 708, 710, 712. In one or more embodiments or examples, a sensor such as sensor 750 may provide input sensor data 750a to input interface 704.

[0113] In one or more embodiments or examples, the preprocessor 702 includes a plurality of parser logic 714, 716, 718, 720, 722, a plurality of decoders 724, 726, 728, 730, 732, and optional data capture logic 734, 736 and MC for polar and Cartesian coordinates. In one or more embodiments or examples, the preprocessor 702 is communicatively coupled to one or more sensors 750, 752, 754, 756, 758 and / or a memory 750 and / or a processor 742.

[0114] In one or more embodiments or examples, a plurality of parser logics 714, 716, 718, 720, 722 are configured to parse input sensor data 704a, 706a, 708a, 710a, 712a from corresponding input interfaces 704, 706, 708, 710, 710, 712, respectively. In one or more embodiments or examples, the input sensor data 704a, 706a, 708a, 710a, 712a may be the same as the input sensor data 750a, 752a, 752b, 752c, 752d, 752e, 754a, 756a, 758a, or may be based on the input sensor data 750a, 752a, 752b, 752c, 752d, 752e, 754a, 756a, 758a. In one or more embodiments or examples, data parsing by each parser logic is performed in parallel with data parsing by at least one other parser logic of the plurality of parser logics.

[0115] In one or more embodiments or examples, a plurality of decoders 724, 726, 728, 730, 732 are configured to decode the parsed input sensor data 714a, 716a, 718a, 720a, 722a from the corresponding parser logic 714, 716, 718, 720, and 722, respectively. The decoding of each parsed input sensor data 714a, 716a, 718a, 720a, 722a is performed in parallel with the decoding of at least one other decoder in the plurality of decoders. In one or more embodiments or examples, the preprocessor 702 provides the decoded input sensor data 724a, 726a, 728a, 730a, 732a and the parsed input sensor data 714a, 716a, 718a, 720a, 722a to the memory 750.

[0116] In one or more embodiments or examples, the data capture logic 734 is configured to obtain the decoded input sensor data 724a, 726a, 728a, 730a, 732a and the parsed input sensor data 714a, 716a, 718a, 720a, 722a from the memory 750. In one or more embodiments or examples, the memory 750 provides the memory data 750a to the data capture logic. In one or more embodiments or examples, the memory data 750a includes the decoded input sensor data 724a, 726a, 728a, 730a, 732a and the parsed input sensor data 714a, 716a, 718a, 720a, 722a.

[0117] In one or more embodiments or examples, the MC 738 is configured to obtain data 734a from the data capture logic 734 and provide decoded input sensor data 724a, 726a, 728a, 730a, 732a to the processor 742. In one or more embodiments or examples, the MC 738 is configured to obtain data 738a from the memory 750. In one or more embodiments or examples, the motion compensation (MC) 738 provides the decoded input sensor data 724a, 726a, 728a, 730a, 732a to the RVN 746 integrated in the processor 742. In one or more embodiments or examples, the decoded input sensor data 724a, 726a, 728a, 730a, 732a includes Cartesian coordinates. In one or more embodiments or examples, the processor 742 obtains the decoded input sensor data 724a, 726a, 728a, 730a, 732a via the data 738b.

[0118] In one or more embodiments or examples, the MC 740 is configured to obtain the capture logic data 736a from the data capture logic 736 and provide the parsed input sensor data 714a, 716a, 718a, 720a, 722a to the LSN 748 integrated in the processor 742. In one or more embodiments or examples, the parsed input sensor data 714a, 716a, 718a, 720a, 722a includes polar coordinates. In one or more embodiments or examples, the processor 742 obtains the parsed input sensor data 714a, 716a, 718a, 720a, 722a via the data 740a. In one or more embodiments or examples, the memory 750 can provide the decoded input sensor data 724a, 726a, 728a, 730a, 732a to the data capture logic 736 via the data 738c.

[0119] Devices 500, 600, 700 may be implemented as a system on a chip (SoC) for processing input sensor data. Devices 500, 600, 700 may be Fig.10 A portion of a system on chip (SoC) provided in the system for processing input sensor data.

[0120] Fig. 8AA vehicle 802 is shown that includes a long range LiDAR 804, short range LiDARs 838, 842, 846, 854, 858, cameras 814, 816, 818, 820, and portions of the field of view of the long range LiDAR (e.g., LiDAR sectors) 806, 808, 810, and 812. For each portion of the field of view, LiDAR data 822, 824, 826, 828 and image data 830, 832, 834, 836 are collected.

[0121] Figure 8B Portions of the field of view of the long range LiDAR (e.g., LiDAR sectors) 806, 808, 810, 812, cameras 814, 816, 818, 820, and image data 830, 832, 834, 836 are shown. In one or more embodiments or examples, a sector manager (e.g., sector manager 640) is configured to generate a corresponding sector counter (e.g., Figure 6 The information provided by the partition counters 648, 650, 652, 654, 656) (e.g., Figure 6 In one or more embodiments or examples, from the data capture logic (e.g., Figure 5 Data capture logic 534, Figure 6 The data capture logic 634 or Figure 7 The synchronizer is configured to obtain image data by activating the image sensor according to the information provided by the partition counter (e.g., Figure 8B 830, 832, 834 and 836), to make the image data (e.g., FIG. 8A to FIG. 8B image data 830, 832, 834, 836) and LiDAR data (e.g., Fig. 8A In other words, the image data is synchronized with the LiDAR data by activating the LiDAR partitions (e.g., LiDAR partitions 806, 808, 810, 812) based on the information provided by the partition counter, and the LiDAR partitions cause the camera to initiate the collection of image data 830, 832, 834, 836 by providing trigger information 806a, 808a, 810a, 812a.

[0122] Reference now Fig. 9, which illustrates a flow chart of a process 900 for preprocessing LiDAR data. In some embodiments, one or more of the operations described with respect to the process or method 900 is performed by Figure 5 Equipment 500, Figure 6 Device 600 and / or Figure 7 Additionally or alternatively, in some embodiments, one or more operations described with respect to process 900 are performed by a processor 700 (e.g., completely and / or partially, etc.). Figure 5 Equipment 500, Figure 6 Device 600 and / or Figure 7 The device 700 separates or includes Figure 5 Equipment 500, Figure 6 Device 600 and / or Figure 7 other devices or groups of devices of apparatus 700 (e.g., completely and / or partially, etc.).

[0123] In one or more examples, the process 900 includes receiving input sensor data by a preprocessor at block 902. In one or more examples, the input sensor data includes light detection and ranging (LiDAR) data. In one or more examples, the process 900 includes parsing the input sensor data by the preprocessor at block 904. In one or more examples, the process 900 includes decoding the parsed input sensor data by the preprocessor at block 906. In one or more examples, the process 900 includes sending the decoded input sensor data and the parsed input sensor data to the processor by the preprocessor at block 908.

[0124] In one or more examples, receiving input sensor data by the preprocessor at box 902 includes receiving input sensor data from a corresponding sensor. Each interface is associated with a sensor. For example, a first input interface obtains LiDAR data from a first LiDAR. For example, a second input interface obtains LiDAR data from a second LiDAR. For example, a first interface obtains image data from a first image sensor. In one or more examples, two sensors feed data into one input interface. In one or more examples, parsing the input sensor data by the preprocessor at box 904 includes parsing a portion of the input sensor data and at least one other portion of the input sensor data in parallel. In one or more examples, decoding the parsed input sensor data by the preprocessor at box 906 includes decoding a portion of the parsed input sensor data and at least one other portion of the parsed input sensor data in parallel.

[0125] In one or more examples, the input sensor data includes LiDAR data from a LiDAR operating with the field of view. In one or more examples, receiving the input sensor data by the preprocessor at block 902 includes receiving a first LiDAR data set by the preprocessor. In one or more examples, the first LiDAR data set is associated with a first portion of the field of view.

[0126] In one or more examples, receiving input sensor data by the pre-processor at block 902 includes receiving, by the pre-processor, a second LiDAR dataset. In one or more examples, the second LiDAR dataset is associated with a second portion of the field of view.

[0127] In one or more examples, process 900 includes determining information indicating the portion. For example, the information may be angle information, such as an azimuth of a LiDAR or azimuth counter, etc. The information may be used to associate the LiDAR data with the sensor data.

[0128] In one or more examples, the sensor data also includes image data from an image sensor.In one or more examples, process 900 includes synchronizing the image data with the LiDAR data.

[0129] In one or more examples, synchronizing the image data with the LiDAR data includes activating the image sensor according to the information to obtain the image data. For example, the LiDAR acts as a master device for timing to synchronously collect input sensor data by partition.

[0130] In one or more examples, process 900 includes storing decoded input sensor data in a first portion of memory and storing parsed input sensor data in a second portion of memory.

[0131] In one or more examples, the second portion of the memory is different from the first portion of the memory.

[0132] In one or more examples, process 900 includes optionally providing the parsed input sensor data in the form of one or more data packets.

[0133] Fig.101 is a block diagram of a chip layout of a computing unit 1300 for an autonomous robotic system according to one or more embodiments. The computing unit 1300 can be implemented in, for example, AV computing (e.g., AV computing 202f). The computing unit 1300 includes a sensor multiplexer (Mux) 1301, a main computing cluster 1302-1 to 1302-5, a failover computing cluster 1302-6, and an Ethernet switch 1302. The Ethernet switch 1302 includes a plurality of Ethernet transceivers for sending commands 1315 to a vehicle 1303, wherein the commands 1315 are transmitted by, for example, Figure 2 One or more of the DBW system 202h, safety controller 202g, braking system 208, powertrain control system 204, and / or steering control system 206 are shown receiving.

[0134] The first master computing cluster 1302-1 includes a SoC 1303-1, volatile memories 1305-1, 1305-2, a power management integrated circuit (PMIC) 1304-1, and a flash boot 1311-1. The second master computing cluster 1302-2 includes a SoC 1303-2, volatile memories 1306-1, 1306-2 (e.g., DRAM), a PMIC 1304-2, and a flash operating system (OS) 1312-2. The third master computing cluster 1302-3 includes a SoC 1303-3, volatile memories 1307-1, 1307-2, a PMIC 1304-3, and a flash OS memory 1312-1. The fourth master computing cluster 1302-4 includes a SoC 1303-5, volatile memories 1308-1, 1308-2, a PMIC 1304-5, and a flash boot memory 1311-2. The fifth primary computing cluster 1302-4 includes SoC 1303-4, volatile memories 1309-1, 1309-2, PMIC 1304-4, and flash boot memory 1311-3. The failover computing cluster 1302-6 includes SoC 1303-6, volatile memories 1310-1, 1310-2, PMIC 1304-6, and flash OS memory 1312-3.

[0135] Each of the SoCs 1303-1 to 1303-6 may be a multi-processor SoC (MPSoC). Each of the SoCs may function as the above-mentioned Figures 5 to 7 Any one of or consisting of the pre-processors 502, 602, 702 and / or processors 538, 660, 742 discussed.

[0136] In some embodiments, PMICs 1304-1 to 1304-6 monitor relevant signals on a bus (e.g., a PCIe bus) and communicate with a corresponding memory controller (e.g., a memory controller in a DRAM chip) to notify the memory controller of a power mode change such as a change from a normal mode to a low power mode or a change from a low power mode to a normal mode. In an embodiment, PMICs 1304-1 to 1304-6 also receive communication signals from their corresponding memory controllers that are monitoring the bus and operate to prepare the memory for a lower power mode. When the memory chip is ready to enter a low power mode, the memory controller communicates with its corresponding slave PMIC to instruct the slave PMIC to initiate the lower power mode.

[0137] In some embodiments, the sensor multiplexer 1301 receives and multiplexes sensor data (e.g., video data, LiDAR point cloud, Radar data) from the sensor bus through the sensor interface 1313. In some embodiments, the sensor interface 1313 is a low voltage differential signaling (LVDS) interface. In an embodiment, the sensor multiplexer 1301 guides the video data channel (e.g., mobile industry processor interface A copy of the camera serial interface (CSI) channel) is sent to the failover cluster 1302-6. When one or more primary computing clusters 1302-1 fail, the failover cluster 1302-6 provides backup to the primary computing cluster using the video data to operate the AV.

[0138] Computing unit 1300 is an example of a high-performance computing unit for an autonomous robotic system, such as AV computing, etc., and other embodiments may include more or fewer clusters, and each cluster may have more or fewer SoCs, volatile memory chips, non-volatile memory chips, NPUs, GPUs, and Ethernet switches / transceivers.

[0139] A non-transitory computer-readable medium is disclosed, the medium comprising instructions stored thereon, which, when executed by at least one processor, causes the at least one processor to perform operations according to one or more of the methods disclosed herein.

[0140] Furthermore, methods, non-transitory computer-readable media, and systems according to any of the following clauses are disclosed:

[0141] Clause 1. A device comprising:

[0142] an input interface configured to receive sensor data from a sensor, wherein the sensor comprises a light detection and ranging sensor (LiDAR sensor), and wherein the sensor data comprises LiDAR data associated with an environment scanned by the LiDAR sensor; and

[0143] a preprocessor communicatively coupled to the input interface,

[0144] wherein the preprocessor comprises a parser logic and a decoder communicatively coupled to the parser logic,

[0145] wherein the parser logic is configured to parse the sensor data to obtain parsed sensor data,

[0146] wherein the decoder is configured to decode the parsed sensor data to obtain decoded sensor data, and

[0147] Wherein, the preprocessor is configured to provide the decoded sensor data and the parsed sensor data to a processor, and the processor is configured to perform range imaging and bird's-eye view imaging based at least in part on at least one of the decoded sensor data and the parsed sensor data.

[0148] Clause 2. The apparatus of clause 1, wherein the decoder is configured to decode the parsed sensor data by converting the parsed sensor data from a first coordinate space to a second coordinate space, and wherein the decoded sensor data is in the second coordinate space.

[0149] Clause 3. The apparatus of clause 2, wherein providing the decoded sensor data and the parsed sensor data to the processor causes the processor to process the sensor data in the first coordinate space and the second coordinate space at least partially in parallel.

[0150] Clause 4. A device according to any of the preceding clauses, wherein the input interface is one of a plurality of input interfaces of the device, wherein each of the plurality of input interfaces is configured to obtain input sensor data from a corresponding sensor of a plurality of sensors, and wherein the pre-processor comprises:

[0151] a plurality of parser logics, wherein each parser logic of the plurality of parser logics is communicatively coupled to a corresponding input interface of the plurality of input interfaces, wherein each parser logic is configured to parse sensor data from the corresponding input interface in parallel with at least one other parser logic of the plurality of parser logics; and

[0152] A plurality of decoders, wherein each decoder of the plurality of decoders is communicatively coupled to a corresponding parser logic of the plurality of parser logics, wherein each decoder is configured to decode parsed sensor data from the corresponding parser logic in parallel with at least one other decoder of the plurality of decoders.

[0153] Clause 5. The device of clause 4, wherein at least one of the plurality of sensors is an image sensor.

[0154] Clause 6. The apparatus of any preceding clause, wherein the LiDAR sensor operates within a field of view of the environment, and wherein the LiDAR data comprises a first LiDAR data set associated with a first portion of the field of view.

[0155] Clause 7. The apparatus of clause 6, wherein the input interface is configured to obtain a second LiDAR data set associated with a second portion of the field of view.

[0156] Clause 8. The apparatus of clause 6 or 7, wherein the parser logic comprises a partition counter configured to determine information indicative of a first portion of the field of view.

[0157] Clause 9. The apparatus of clause 8, wherein the sensor data further comprises image data from an image sensor, wherein the preprocessor is configured to synchronize the image data with the LiDAR data.

[0158] Clause 10. The apparatus of clause 9, wherein the preprocessor is configured to synchronize the image data with the LiDAR data by controlling activation of the image sensor based on information indicative of a first portion of a field of view of the LiDAR sensor.

[0159] Clause 11. The apparatus of any preceding clause, further comprising a memory, and wherein the pre-processor is configured to store the decoded sensor data in a first portion of the memory and the parsed sensor data in a second portion of the memory.

[0160] Clause 12. The apparatus of clause 11, wherein the pre-processor comprises data capture logic communicatively coupled to the decoder, and wherein the data capture logic is configured to:

[0161] obtaining decoded sensor data from the decoder and parsed sensor data from the parser logic; and

[0162] The decoded sensor data is stored in a first portion of the memory, and the parsed sensor data is stored in a second portion of the memory.

[0163] Clause 13. The apparatus of clause 11 or 12, wherein the second portion of the memory is different from the first portion of the memory.

[0164] Clause 14. The apparatus of any preceding clause, wherein the parser logic is configured to provide the parsed sensor data as at least one data packet.

[0165] Clause 15. The apparatus of any preceding clause, wherein the decoded sensor data and the parsed sensor data cause the processor to perform a fusion of the decoded sensor data and the parsed sensor data.

[0166] Clause 16. A method comprising:

[0167] Receiving sensor data by a preprocessor, wherein the sensor data includes LiDAR data obtained from a light detection and ranging sensor (LiDAR sensor) by scanning an environment;

[0168] parsing the sensor data by the preprocessor to obtain parsed sensor data;

[0169] decoding, by the preprocessor, the parsed sensor data to obtain decoded sensor data; and

[0170] The decoded sensor data and the parsed sensor data are transmitted by the pre-processor to a processor, wherein the processor is configured to perform range imaging and bird's-eye view imaging based at least in part on the sensor data.

[0171] Clause 17. The method of Clause 16, wherein decoding the parsed sensor data comprises converting the parsed sensor data from a first coordinate space to a second coordinate space, and wherein the decoded sensor data is in the second coordinate space.

[0172] Clause 18. The method of Clause 17, wherein transmitting the decoded sensor data and the parsed sensor data to the processor causes the processor to process the sensor data in the first coordinate space and the second coordinate space at least partially in parallel.

[0173] Clause 19. The method of any preceding clause, wherein the LiDAR sensor is one of a plurality of sensors, and wherein receiving the sensor data comprises receiving sensor data from the plurality of sensors;

[0174] wherein parsing the sensor data comprises parsing in parallel a portion of the sensor data received from a first sensor of the plurality of sensors and at least one other portion of the sensor data received from a second sensor of the plurality of sensors; and

[0175] Wherein, decoding the parsed sensor data includes decoding a portion of the parsed sensor data corresponding to a portion of the sensor data received from the first sensor and at least one other portion of the parsed sensor data corresponding to a portion of the sensor data received from the second sensor in parallel.

[0176] Clause 20. The method of clause 19, wherein at least one of the plurality of sensors is an image sensor.

[0177] Clause 21. The method of any of the preceding clauses, wherein the LiDAR sensor operates within a field of view of an environment, and wherein receiving the sensor data comprises receiving a first LiDAR data set associated with a first portion of the field of view.

[0178] Clause 22. The method of clause 21, wherein receiving the sensor data comprises receiving a second LiDAR data set associated with a second portion of the field of view.

[0179] Clause 23. The method of clause 21 or 22, wherein the method further comprises determining information indicative of the first portion of the field of view using a partition counter.

[0180] Clause 24. The method of clause 23, wherein the sensor data further comprises image data from an image sensor, wherein the method further comprises synchronizing the image data with the LiDAR data.

[0181] Clause 25. The method of clause 24, wherein synchronizing the image data with the LiDAR data comprises activating the image sensor based on information indicative of a first portion of a field of view of the LiDAR sensor.

[0182] Clause 26. The method of any preceding clause, further comprising storing the decoded sensor data in a first portion of a memory and storing the parsed sensor data in a second portion of the memory.

[0183] Clause 27. The method according to clause 26, further comprising:

[0184] receiving decoded sensor data from the decoder and parsed sensor data from the parser logic; and

[0185] The decoded sensor data is stored in a first portion of the memory, and the parsed sensor data is stored in a second portion of the memory.

[0186] Clause 28. The method of clause 26 or 27, wherein the second portion of the memory is different from the first portion of the memory.

[0187] Clause 29. The method of any preceding clause, wherein transmitting the parsed sensor data further comprises transmitting the parsed sensor data as at least one data packet.

[0188] Clause 30. The method of any preceding clause, wherein the decoded sensor data and the parsed sensor data are transmitted to the processor for fusion of the decoded sensor data and the parsed sensor data.

[0189] Clause 31. A non-transitory computer-readable medium comprising computer-executable instructions that, when executed by a preprocessor, cause the preprocessor to:

[0190] Receiving sensor data, wherein the sensor data includes LiDAR data obtained from a light detection and ranging sensor (LiDAR sensor) by scanning an environment;

[0191] parsing the sensor data to obtain parsed sensor data;

[0192] decoding the parsed sensor data to obtain decoded sensor data; and

[0193] The decoded sensor data and the parsed sensor data are transmitted to a processor, wherein the processor is configured to perform range imaging and bird's-eye view imaging based at least in part on the sensor data.

[0194] Clause 32. The non-transitory computer-readable medium of Clause 31, wherein decoding the parsed sensor data comprises converting the parsed sensor data from a first coordinate space to a second coordinate space, and wherein the decoded sensor data is in the second coordinate space.

[0195] Clause 33. A non-transitory computer-readable medium as described in Clause 32, wherein transmitting the decoded sensor data and the parsed sensor data to the processor enables the processor to process the sensor data in the first coordinate space and the second coordinate space at least partially in parallel.

[0196] Clause 34. The non-transitory computer-readable medium of any of the preceding clauses, wherein the LiDAR sensor is one of a plurality of sensors, and wherein receiving the sensor data comprises receiving sensor data from the plurality of sensors;

[0197] wherein parsing the sensor data comprises parsing in parallel a portion of the sensor data received from a first sensor of the plurality of sensors and at least one other portion of the sensor data received from a second sensor of the plurality of sensors; and

[0198] Wherein, decoding the parsed sensor data includes decoding a portion of the parsed sensor data corresponding to a portion of the sensor data received from the first sensor and at least one other portion of the parsed sensor data corresponding to a portion of the sensor data received from the second sensor in parallel.

[0199] Clause 35. The non-transitory computer-readable medium of Clause 34, wherein at least one of the plurality of sensors is an image sensor.

[0200] Clause 36. A non-transitory computer-readable medium as described in any of the preceding clauses, wherein the LiDAR sensor operates within a field of view of an environment, and wherein receiving the sensor data includes receiving a first LiDAR data set associated with a first portion of the field of view.

[0201] Clause 37. The non-transitory computer-readable medium of Clause 36, wherein receiving the sensor data comprises receiving a second LiDAR data set associated with a second portion of the field of view.

[0202] Clause 38. The non-transitory computer-readable medium of clause 36 or 37, wherein the computer-executable instructions further cause the pre-processor to determine information indicative of the first portion of the field of view using a partition counter.

[0203] Clause 39. The non-transitory computer-readable medium of clause 38, wherein the sensor data further comprises image data from an image sensor, wherein the computer-executable instructions further cause the preprocessor to synchronize the image data with the LiDAR data.

[0204] Clause 40. The non-transitory computer-readable medium of Clause 39, wherein synchronizing the image data with the LiDAR data comprises activating the image sensor based on information indicative of a first portion of a field of view of the LiDAR sensor.

[0205] Clause 41. A non-transitory computer-readable medium as described in any of the preceding clauses, wherein the computer-executable instructions further cause the preprocessor to store the decoded sensor data in a first portion of a memory and to store the parsed sensor data in a second portion of the memory.

[0206] Clause 42. The non-transitory computer-readable medium of Clause 41, wherein the computer-executable instructions further cause the preprocessor to:

[0207] receiving decoded sensor data from the decoder and parsed sensor data from the parser logic; and

[0208] The decoded sensor data is stored in a first portion of the memory, and the parsed sensor data is stored in a second portion of the memory.

[0209] Clause 43. The non-transitory computer-readable medium of Clause 41 or 42, wherein the second portion of the memory is different from the first portion of the memory.

[0210] Clause 44. The non-transitory computer-readable medium of any of the preceding clauses, wherein transmitting the parsed sensor data further comprises transmitting the parsed sensor data as at least one data packet.

[0211] Clause 45. The non-transitory computer-readable medium of any of the preceding clauses, wherein the decoded sensor data and the parsed sensor data are transmitted to the processor for fusion of the decoded sensor data and the parsed sensor data.

[0212] Additional Embodiments

[0213] All methods and tasks described herein can be performed and fully automated by a computer system. A computer system may include, in some cases, a plurality of different computers or computing devices (e.g., physical servers, workstations, storage arrays, cloud computing resources, etc.), which communicate and interoperate through a network to perform the functions described. Each such computing device typically includes a processor (or multiple processors) that executes program instructions or modules stored in a memory or other non-transient computer-readable storage medium or device (e.g., a solid-state storage device, a disk drive, etc.). The various functions disclosed herein may be embodied in such program instructions, or may be implemented in a dedicated circuit system (e.g., an ASIC or FPGA) of a computer system. In the case where a computer system includes a plurality of computing devices, these devices may be, but need not be, located in the same location. The results of the disclosed methods and tasks may be persistently stored by transforming a physical storage device such as a solid-state memory chip or a disk into different states. In some embodiments, the computer system may be a cloud-based computing system whose processing resources are shared by a plurality of different business entities or other users.

[0214] The processing described herein or illustrated in the drawings of the present disclosure may be initiated in response to an event (such as on a predetermined or dynamically determined schedule, on demand when initiated by a user or system administrator, or in response to some other event). When such processing is initiated, a set of executable program instructions stored on one or more non-transitory computer-readable media (e.g., hard drive, flash memory, removable media, etc.) may be loaded into a memory (e.g., RAM) of a server or other computing device. These executable instructions may then be executed by a hardware-based computer processor of the computing device. In some embodiments, such processing or a portion thereof may be implemented serially or in parallel on multiple computing devices and / or multiple processors.

[0215] According to the present embodiment, certain actions, events or functions of any process or algorithm described herein may be performed in a different sequence, may be added, combined or omitted entirely (e.g., not all described operations or events are necessary for the practice of the algorithm). In addition, in some embodiments, operations or events may be performed simultaneously, for example, through multithreading, interrupt processing, or multiple processors or processor cores or on other parallel architectures, rather than sequentially.

[0216] The various exemplary logic blocks, modules, routines, and algorithm steps described in conjunction with the embodiments disclosed herein may be implemented as electronic hardware (e.g., ASIC or FPGA devices), computer software running on computer hardware, or a combination of the two. In addition, the various exemplary logic blocks and modules described in conjunction with the embodiments disclosed herein may be implemented or performed by: a machine, such as a processor device, a digital signal processor ("DSP"), an application-specific integrated circuit ("ASIC"), a field programmable gate array ("FPGA"), or other programmable logic device; discrete gate or transistor logic; discrete hardware components; or any combination thereof designed to perform the functions described herein. The processor device may be a microprocessor, but in an alternative, the processor device may be a controller, a microcontroller, or a state machine, or a combination thereof, etc. The processor device may include a circuit system configured to process computer executable instructions. In another embodiment, the processor device includes an FPGA or other programmable device that performs logical operations without processing computer executable instructions. The processor device may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor), multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration. Although the present invention is primarily described with respect to digital techniques, the processor device may also primarily include analog components. For example, some or all of the rendering techniques described herein may be implemented in analog circuitry or in a hybrid analog and digital circuitry. The computing environment may include any type of computer system, including but not limited to a microprocessor-based computer system, a mainframe computer, a digital signal processor, a portable computing device, a device controller, or a computing engine within an appliance (to name a few examples).

[0217] The elements of the methods, processes, routines or algorithms described in conjunction with the embodiments disclosed herein may be directly embodied in hardware, software modules executed by a processor device, or a combination of the two. The software module may reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of non-transient computer-readable storage medium. An exemplary storage medium may be coupled to a processor device so that the processor device may read information from the storage medium and write information to the storage medium. In an alternative, the storage medium may be integrated with the processor device. The processor device and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor device and the storage medium may reside in a user terminal as discrete components.

[0218] 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 device comprising: an input interface configured to receive sensor data from a sensor, wherein the sensor comprises a light detection and ranging sensor (LiDAR sensor), and wherein the sensor data comprises LiDAR data associated with an environment scanned by the LiDAR sensor; and a preprocessor communicatively coupled to the input interface, wherein the preprocessor comprises a parser logic and a decoder communicatively coupled to the parser logic, wherein the parser logic is configured to parse the sensor data to obtain parsed sensor data, wherein the decoder is configured to decode the parsed sensor data to obtain decoded sensor data, and Wherein, the preprocessor is configured to provide the decoded sensor data and the parsed sensor data to a processor, and the processor is configured to perform range imaging and bird's-eye view imaging based at least in part on at least one of the decoded sensor data and the parsed sensor data.

2. The device according to claim 1, wherein The decoder is configured to decode the parsed sensor data by converting the parsed sensor data from a first coordinate space to a second coordinate space, and wherein the decoded sensor data is in the second coordinate space.

3. The device according to claim 2, wherein: Providing the decoded sensor data and the parsed sensor data to the processor causes the processor to process the sensor data in the first coordinate space and the second coordinate space at least partially in parallel.

4. The device according to any one of the preceding claims, wherein: The input interface is one of a plurality of input interfaces of the device, wherein each of the plurality of input interfaces is configured to obtain input sensor data from a corresponding sensor of a plurality of sensors, and wherein the preprocessor comprises: a plurality of parser logics, wherein each parser logic of the plurality of parser logics is communicatively coupled to a corresponding input interface of the plurality of input interfaces, wherein each parser logic is configured to parse sensor data from the corresponding input interface in parallel with at least one other parser logic of the plurality of parser logics; and A plurality of decoders, wherein each decoder of the plurality of decoders is communicatively coupled to a corresponding parser logic of the plurality of parser logics, wherein each decoder is configured to decode parsed sensor data from the corresponding parser logic in parallel with at least one other decoder of the plurality of decoders.

5. The device according to claim 4, wherein: At least one of the plurality of sensors is an image sensor.

6. Apparatus according to any one of the preceding claims, wherein: The LiDAR sensor operates within a field of view of the environment, and wherein the LiDAR data includes a first LiDAR data set associated with a first portion of the field of view.

7. The device according to claim 6, wherein: The input interface is configured to obtain a second LiDAR data set associated with a second portion of the field of view.

8. The device according to claim 6 or 7, wherein: The parser logic includes a partition counter configured to determine information indicative of a first portion of the field of view.

9. The device according to claim 8, wherein: The sensor data also includes image data from an image sensor, wherein the pre-processor is configured to synchronize the image data with the LiDAR data.

10. The device according to claim 9, wherein: The pre-processor is configured to synchronize the image data with the LiDAR data by controlling activation of the image sensor based on information indicative of a first portion of a field of view of the LiDAR sensor.

11. The device according to any one of the preceding claims, further comprising a memory, and wherein: The pre-processor is configured to store the decoded sensor data in a first portion of the memory and the parsed sensor data in a second portion of the memory.

12. The device according to claim 11, wherein The pre-processor comprises data capture logic communicatively coupled to the decoder, and wherein the data capture logic is configured to: obtaining decoded sensor data from the decoder and parsed sensor data from the parser logic; and The decoded sensor data is stored in a first portion of the memory, and the parsed sensor data is stored in a second portion of the memory.

13. The device according to claim 11 or 12, wherein: The second portion of the memory is different from the first portion of the memory.

14. Apparatus according to any one of the preceding claims, wherein: The parser logic is configured to provide the parsed sensor data as at least one data packet.

15. Apparatus according to any one of the preceding claims, wherein: The decoded sensor data and the parsed sensor data enable the processor to perform fusion of the decoded sensor data and the parsed sensor data.

16. A method comprising: Receiving sensor data by a preprocessor, wherein the sensor data includes LiDAR data obtained from a light detection and ranging sensor (LiDAR sensor) by scanning an environment; parsing the sensor data by the preprocessor to obtain parsed sensor data; decoding, by the preprocessor, the parsed sensor data to obtain decoded sensor data; and The decoded sensor data and the parsed sensor data are transmitted by the pre-processor to a processor, wherein the processor is configured to perform range imaging and bird's-eye view imaging based at least in part on the sensor data.

17. The method according to claim 16, wherein: Decoding the parsed sensor data includes converting the parsed sensor data from a first coordinate space to a second coordinate space, and wherein the decoded sensor data is in the second coordinate space.

18. The method according to claim 17, wherein: Transmitting the decoded sensor data and the parsed sensor data to the processor causes the processor to process the sensor data in the first coordinate space and the second coordinate space at least partially in parallel.

19. A method according to any one of the preceding claims, wherein: The LiDAR sensor is one of a plurality of sensors, and wherein receiving the sensor data comprises receiving sensor data from the plurality of sensors; wherein parsing the sensor data comprises parsing in parallel a portion of the sensor data received from a first sensor of the plurality of sensors and at least one other portion of the sensor data received from a second sensor of the plurality of sensors; and Wherein, decoding the parsed sensor data includes decoding a portion of the parsed sensor data corresponding to a portion of the sensor data received from the first sensor and at least one other portion of the parsed sensor data corresponding to a portion of the sensor data received from the second sensor in parallel.

20. The method according to claim 19, wherein: At least one of the plurality of sensors is an image sensor.

21. A method according to any one of the preceding claims, wherein: The LiDAR sensor operates within a field of view of an environment, and wherein receiving the sensor data includes receiving a first LiDAR data set associated with a first portion of the field of view.

22. The method according to claim 21, wherein: Receiving the sensor data includes receiving a second LiDAR data set associated with a second portion of the field of view.

23. The method according to claim 21 or 22, wherein: The method also includes determining information indicative of a first portion of the field of view using a partition counter.

24. The method according to claim 23, wherein: The sensor data also includes image data from an image sensor, wherein the method further includes synchronizing the image data with the LiDAR data.

25. The method according to claim 24, wherein: Synchronizing the image data with the LiDAR data includes activating the image sensor based on information indicative of a first portion of a field of view of the LiDAR sensor.

26. The method according to any one of the preceding claims, further comprising: The decoded sensor data is stored in a first portion of a memory, and the parsed sensor data is stored in a second portion of the memory.

27. The method according to claim 26, further comprising: receiving decoded sensor data from the decoder and parsed sensor data from the parser logic; as well as The decoded sensor data is stored in a first portion of the memory, and the parsed sensor data is stored in a second portion of the memory.

28. The method according to claim 26 or 27, wherein: The second portion of the memory is different from the first portion of the memory.

29. A method according to any one of the preceding claims, wherein: Transmitting the parsed sensor data further includes transmitting the parsed sensor data as at least one data packet.

30. A method according to any one of the preceding claims, wherein: The decoded sensor data and the parsed sensor data are transmitted to the processor for fusion of the decoded sensor data and the parsed sensor data.

31. A non-transitory computer readable medium comprising computer executable instructions that, when executed by a pre-processor, cause the pre-processor to: Receive sensor data, where The sensor data includes LiDAR data obtained from a light detection and ranging sensor, i.e., a LiDAR sensor, by scanning an environment; parsing the sensor data to obtain parsed sensor data; decoding the parsed sensor data to obtain decoded sensor data; as well as The decoded sensor data and the parsed sensor data are transmitted to a processor, wherein the processor is configured to perform range imaging and bird's-eye view imaging based at least in part on the sensor data.

32. The non-transitory computer readable medium of claim 31 , wherein: Decoding the parsed sensor data includes converting the parsed sensor data from a first coordinate space to a second coordinate space, and wherein the decoded sensor data is in the second coordinate space.

33. The non-transitory computer readable medium of claim 32, wherein: Transmitting the decoded sensor data and the parsed sensor data to the processor enables the processor to process the sensor data in the first coordinate space and the second coordinate space at least partially in parallel.

34. The non-transitory computer readable medium of any preceding claim, wherein: The LiDAR sensor is one of a plurality of sensors, and wherein receiving the sensor data comprises receiving sensor data from the plurality of sensors; wherein parsing the sensor data comprises parsing in parallel a portion of the sensor data received from a first sensor of the plurality of sensors and at least one other portion of the sensor data received from a second sensor of the plurality of sensors; and Wherein, decoding the parsed sensor data includes decoding a portion of the parsed sensor data corresponding to a portion of the sensor data received from the first sensor and at least one other portion of the parsed sensor data corresponding to a portion of the sensor data received from the second sensor in parallel.

35. The non-transitory computer readable medium of claim 34, wherein: At least one of the plurality of sensors is an image sensor.

36. The non-transitory computer readable medium of any preceding claim, wherein: The LiDAR sensor operates within a field of view of an environment, and wherein receiving the sensor data includes receiving a first LiDAR data set associated with a first portion of the field of view.

37. The non-transitory computer readable medium of claim 36, wherein: Receiving the sensor data includes receiving a second LiDAR data set associated with a second portion of the field of view.

38. The non-transitory computer readable medium of claim 36 or 37, wherein: The computer-executable instructions further cause the pre-processor to determine information indicative of a first portion of the field of view using a partition counter.

39. The non-transitory computer readable medium of claim 38, wherein: The sensor data also includes image data from an image sensor, wherein the computer-executable instructions further cause the pre-processor to synchronize the image data with the LiDAR data.

40. The non-transitory computer readable medium of claim 39, wherein: Synchronizing the image data with the LiDAR data includes activating the image sensor based on information indicative of a first portion of a field of view of the LiDAR sensor.

41. The non-transitory computer readable medium of any preceding claim, wherein: The computer-executable instructions further cause the pre-processor to store the decoded sensor data in a first portion of a memory and to store the parsed sensor data in a second portion of the memory.

42. The non-transitory computer readable medium of claim 41, wherein: The computer executable instructions further cause the preprocessor to: receiving decoded sensor data from the decoder and parsed sensor data from the parser logic; as well as The decoded sensor data is stored in a first portion of the memory, and the parsed sensor data is stored in a second portion of the memory.

43. The non-transitory computer readable medium of claim 41 or 42, wherein: The second portion of the memory is different from the first portion of the memory.

44. The non-transitory computer readable medium of any preceding claim, wherein: Transmitting the parsed sensor data further includes transmitting the parsed sensor data as at least one data packet.

45. The non-transitory computer readable medium of any preceding claim, wherein: The decoded sensor data and the parsed sensor data are transmitted to the processor for fusion of the decoded sensor data and the parsed sensor data.

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