System and method for updating high definition maps

Abstract

A system and method for determining HD map update information based on a vehicle. A sensor-equipped vehicle can determine the position of various detected objects relative to the vehicle. The vehicle can also determine the position of a reference object relative to the vehicle, where the position of the reference object in an absolute coordinate system is also known. The absolute coordinates of various detected objects can then be determined based on the absolute position of the reference object and the positions of other objects relative to the reference object. The newly determined absolute positions of the detected objects can then be transmitted to the HD map service for updating.

Description

Background Art

[0001] Embodiments of the present disclosure generally relate to vehicles and more particularly to vehicle-based high-definition map updates. Summary of the invention

[0002] Modern autonomous vehicles often use high-definition (HD) maps to assist with various driving functions, such as autonomous navigation, path planning, and obstacle avoidance. Such HD maps typically describe the locations of various road-related features to the centimeter-level accuracy required for navigation.

[0003] However, vehicles adopting HD maps face significant challenges. As road conditions and objects change (often sporadically) due to construction, natural phenomena, etc., HD maps must be updated regularly or frequently. Updating HD maps to centimeter-level accuracy traditionally requires the use of high-precision positioning systems such as Global Navigation Satellite System (GNSS) receivers, which are expensive and bulky and not suitable for use in consumer-purchased vehicles. As a result, HD map updates require special vehicles equipped with GNSS receivers, which comes with significant costs, and their limited deployment naturally limits the coverage and frequency of updates due to cost.

[0004] As an alternative, some HD map update services accept relative coordinates of objects. The vehicle can determine the position or coordinates of the object relative to the vehicle itself, and the map update service can use the determined vehicle position to convert these relative coordinates into coordinates in an absolute system, such as World Geodetic System 1984 (WGS84). Unfortunately, relative coordinates are not widely accepted by such map update services.

[0005] Thus, described herein are systems and methods for updating an HD map using absolute coordinates rather than relative coordinates. The absolute coordinates are determined by relative coordinates calculated by the vehicle, which are sent to an HD map update service where they are more readily accepted than relative coordinates. The vehicle can determine the relative position of objects using various on-board services, and can retrieve the absolute positions of reference objects whose relative positions are also known. The absolute coordinates of any detected object can then be determined from the absolute position of the reference object and the calculated positions of these other objects relative to the reference object. The newly determined absolute positions of the detected objects can then be transmitted to the HD map service for updating.

[0006] The vehicle may determine the relative position of an object using any suitable sensor input of any type. In some embodiments of the present disclosure, an autonomous vehicle equipped with a camera or other sensor may determine the relative position of an object based on the sensor data. For example, a camera-equipped vehicle may capture images of objects within its field of view, and the position of those objects relative to the vehicle may be determined from those images. Object positions may also be determined from other sensors, such as light detection and ranging (LiDAR) data, ultrasonic or other proximity sensors, and the like.

[0007] The vehicle may also determine the relative position of an object using any suitable method or approaches. As one example, the vehicle may determine multiple different estimates of the relative position of an object, which estimates may then be averaged in some suitable manner to determine a position value. For example, a single vehicle may pass by an object multiple times, each time determining an estimate of the relative position of the object. As another example, multiple vehicles may pass by the same object, with each vehicle determining an estimate of the position of the object. Regardless of the manner in which multiple estimates of the position of an object are determined, the multiple estimates may then be averaged in some manner to determine a single position value for the object. Thus, more accurate position information may be determined, and any movement of the object over time may be accounted for.

[0008] Some embodiments of the present disclosure also adopt a local absolute coordinate system that is different from the HD map absolute coordinate system. Therefore, the vehicle can first determine the position of the object in the local absolute coordinate system and then send the local absolute coordinate position to the HD map service, or convert the local absolute coordinates into HD map absolute coordinates and send these converted coordinates to the HD map service. For example, the vehicle can maintain a local roadside map in which the detected objects are placed, wherein the roadside map uses an absolute coordinate system, which may be the same as the coordinate system used by the remote HD map or may be different. That is, the vehicle can determine the coordinates of the objects detected in the absolute road coordinate system based on the relative coordinates determined by them and the position of the reference object in the road coordinate system. Objects placed in the road coordinate system can be transmitted to the HD map service, for example by converting their positions in the road coordinate system to the global coordinate system of the HD map and then sending the converted coordinates to the HD map service.

[0009] It may also be noted that the systems and methods of embodiments of the present disclosure may be employed to confirm or modify known object locations. That is, the absolute location of an object may be determined and compared to an already established object location. If the newly determined location differs too much from the established location (e.g., exceeds a threshold), the established location may be modified in any manner. Over time, successive revisions may more accurately locate the object. This may also help account for roadside objects that may have moved over time, such as lamp posts moved by new construction, fallen tree branches, etc.

[0010] Therefore, embodiments of the present disclosure propose systems and methods for determining the absolute coordinates of an object based on a vehicle that may be in motion. The position of the object relative to the vehicle is determined by the vehicle as it moves, and these relative positions are converted to absolute coordinates using one or more reference objects detected by the vehicle and whose absolute coordinates are known. The calculated absolute coordinates can then be transmitted to, for example, an HD map service to update the HD map. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other objects and advantages of the present disclosure will become apparent upon consideration of the following detailed description in conjunction with the accompanying drawings, wherein like reference numerals refer to like parts throughout, and wherein:

[0012] Figure 1 conceptually illustrates an exemplary system and method for vehicle-based HD map updating according to an embodiment of the present disclosure;

[0013] Figure 2 shows an exemplary HD semantic map for an autonomous vehicle according to an embodiment of the present disclosure;

[0014] Figure 3 is a block diagram of an example semantic mapping system for use with an autonomous vehicle according to an embodiment of the present disclosure;

[0015] Figure 4A is an illustration of an example autonomous vehicle according to some embodiments of the present disclosure;

[0016] Figure 4B For use according to some embodiments of the present disclosure Figure 4A Examples of autonomous vehicle camera positions and fields of view;

[0017] Figure 4C For use according to some embodiments of the present disclosure Figure 4A a block diagram of an example system architecture for an example autonomous vehicle;

[0018] Figure 4D According to some embodiments of the present disclosure Figure 4A A system diagram of communication between a cloud-based server and an example autonomous vehicle;

[0019] Figure 5 is a block diagram of an example computing device suitable for implementing some embodiments of the present disclosure;

[0020] Figure 6 The training and deployment of the machine learning model of the present disclosure embodiment is shown; and

[0021] Figure 7is a flowchart illustrating an exemplary process of vehicle-based HD map updating according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] In one embodiment, the present disclosure relates to systems and methods for determining HD map update information based on a vehicle. A vehicle equipped with sensors can determine the position of various detected objects relative to the vehicle. The vehicle can also determine the position of a reference object relative to the vehicle, where the position of the reference object in an absolute coordinate system is also known. The absolute coordinates of various detected objects can then be determined based on the absolute position of the reference object and the positions of other objects relative to the reference object. The newly determined absolute positions of the detected objects can then be transmitted to the HD map service for updating.

[0023] Figure 1 An exemplary system and method for vehicle-based HD map updates according to an embodiment of the present disclosure is conceptually illustrated. The vehicle 100 may be a vehicle equipped with sensors, such as the autonomous vehicle 400 described further below, including a camera, and one or more processors. The processor is programmed to perform a number of functions, including building and maintaining a semantic map, and autonomous driving functions, such as using the semantic map for route and path planning, controlling the vehicle for navigation, and detecting and avoiding objects. The construction and use of semantic maps in vehicle navigation and other functions are further described below.

[0024] In operation, the vehicle 100 may capture images of objects and determine their positions relative to the vehicle 100. More specifically, an object 130, which may be a stop sign, may be captured by the vehicle's front camera when the object 150 is within the field of view 110 of the vehicle's front camera. Similarly, another object 150, such as a street light, may be captured by the vehicle's side camera when the other object 150 is within the field of view 120 of the vehicle's side camera. In some embodiments of the present disclosure, the captured images may be visible light images, although in alternative embodiments of the present disclosure, the cameras may capture images of any other wavelength of light.

[0025] The vehicle 100 can determine the position and shape of these objects 130, 150 based on these captured images and other sensors (if necessary). The position information can be determined in any suitable manner for determining the position of the object in one or more captured images relative to the one or more cameras that captured the images. Such a position determination method includes determining the distance and orientation relative to the vehicle 100. Distance determination can be accomplished by any suitable method, such as by a range finding system that determines the distance of the object based on the flight time of the signal reflected from those objects. Such a system can use any signal, such as a LiDAR beam, a radar signal, an ultrasonic wave, a radio signal, etc. Distance determination can also be accomplished by captured images, such as by a monocular or stereoscopic image capture system that uses triangulation of objects within images taken by one or more cameras. Similarly, orientation determination can be accomplished by any suitable method, such as by a monocular or stereoscopic image capture system that determines the relative orientation of the object based on a reference point within the captured image of one or more cameras.

[0026] Once the relative positions of objects such as objects 130, 150 are determined, the vehicle 100 can determine their absolute positions. In particular, the vehicle 100 can maintain a semantic map that includes, in one or more of its map layers, the position of at least one reference object whose absolute position is known. In addition, the vehicle 100 determines its own absolute position when each image is captured, such as through an onboard global positioning system (GPS). The absolute positions of the objects 130, 150 can then be determined based on the relative positions of the objects 130, 150, the absolute positions of one or more reference objects, and the absolute position of the vehicle 100 determined, for example, by its GPS.

[0027] As an example, the vehicle 100 may determine its location via GPS and may also store in its semantic map the absolute location of reference objects, such as a stop sign 160 that it recently passed. The location of the street light 150 relative to the stop sign 160 may then be determined using one or more images capturing the stop sign 160 and the street light 150. Such relative distances between these objects 150, 160 may be determined in any manner, and include determining such relative distances based on the relative distances between these objects and the vehicle 100 determined from the captured images and the GPS-determined location of the vehicle 100 when each image was captured. Similarly, the relative distances between these objects 150, 160 may be determined from a captured image containing both objects 150 and 160 within its field of view (or a plurality of such captured images whose composite images contain both objects 150, 160 therein).

[0028] Once the positions of the objects relative to the reference object 160 are determined, their absolute positions may also be determined based on the absolute position of the reference object 160. In this manner, the absolute position of any object may be determined by the vehicle 100. In particular, objects such as street boundaries 110 (which may be any boundary, such as a sidewalk boundary, a sidewalk, a lane marking, etc.), crosswalk markings 140 or other road markings, or any other object detectable by a camera or any other sensor, may have their shapes and positions determined by embodiments of the present disclosure.

[0029] The vehicle 100 can maintain a locally stored semantic map, such as a roadside map, for example, for navigation. Detected objects, their locations, shapes, and classifications can be stored in the appropriate layer of the roadside map. In addition, at different times, objects stored or placed in the roadside map can be transmitted to other map services to update their HD maps. In this way, the vehicle 100 can update any HD map stored locally or otherwise stored with accurate absolute position information of any detected object. The vehicle 100 semantic map can be maintained by synchronizing with the HD map service, i.e., downloading HD map features regularly or at other times and placing them in their roadside maps. For example, the vehicle 100 can download features within a specified distance of its GPS location. Retrieval of features can occur at any time, such as at predetermined intervals, when the vehicle is started, once the vehicle has traveled a predetermined distance, etc.

[0030] According to some embodiments of the present disclosure, attention is now turned to semantic maps and exemplary autonomous vehicles (AVs) that can use such maps. HD semantic maps and their centimeter-level accuracy are very useful for AVs to generate real-time knowledge of the environment and machine learning-driven decision-making capabilities. When analyzing its functions, semantic maps generally include three main layers: geometry layer, lane geometry layer, and semantic features and map prior layer.

[0031] The geometry layer or base layer can be described as a more complex navigation system. This layer may include road segments, intersections, and interconnections. The lane geometry layer may contain data about how many lanes there are, the direction of travel on the road, and how the roads connect to each other. The geometry layer is also typically where an AV or any mapping-capable vehicle localizes itself on a semantic map.

[0032] The lane geometry layer, or intermediate layer, typically requires high precision. The lane geometry layer can store features that help define the vehicle's path, down to centimeter-level accuracy. This intermediate layer contains features such as individual lane markings, street-level rules (such as yield, slow, or warn), and the proximity of neighboring cars. While the geometry layer handles how to get from point A to point B, the lane geometry layer handles the detailed path planning to get to your destination safely.

[0033] The semantic features and map priors layer or topmost layer can be divided into two parts. The semantic features portion of this layer may contain features corresponding to static or essentially static objects in the environment, including traffic lights, crosswalks, and road signs, which, together with the information provided by the lane geometry and geometry layers, provide the AV with more context to understand its environment in order to make accurate navigation decisions. The map priors portion of this layer may address the domain of probabilities associated with people expecting a certain observed behavior. For example, this may include the likelihood of a child crossing a particular street near a neighboring middle school between 7 am and 8 am and between 3 pm and 4:30 pm. Map priors can be encoded into the software of the map. In some embodiments, map priors may represent the more nuanced decision-making capabilities necessary for the AV to effectively interpret human navigation behavior.

[0034] Convolutional neural networks (CNNs), which exemplify deep learning strategies, are often used with semantic mapping. In some embodiments, a camera on an AV can produce an RGB image in real time while the AV is running. The CNN parses the pixel matrix of the RGB image by applying smaller matrices, or kernels, to generate a convolution matrix. Successive layers of kernels can extract image features that are used by successive parts of a CNN or other neural network to classify or identify objects in the image.

[0035] AVs typically use certain sensors to communicate with semantic Figure 1 These may include GPS sensors, inertial measurement units (IMUs), LiDAR systems, visible light cameras, etc. Any one or more of these sensors may be used to generate a 3-D representation of the AV's environment. In addition, in order to continuously update and improve the semantic map, an iterative process of using multiple AVs on the road is often useful. Such a configuration may employ a remote server that maintains a central HD semantic map and each AV continuously communicates with the remote server via a communication medium such as a secure network in order to store and update the semantic map. Alternatively or additionally, a local hard drive on each AV may store and maintain the semantic map.

[0036] Figure 2The functionality of an HD semantic map for various functions, such as navigation and route planning, in an autonomous vehicle (AV) is illustrated. In an exemplary embodiment, a geometry layer, also referred to as a base layer, may be depicted as roads, lanes, and intersections upon which other layers of an HD semantic map may be built. In an exemplary embodiment, the base layer may include more basic aspects of the map, which may include features such as walls 203 and obstacles 204. In an exemplary embodiment, lane geometry or an intermediate layer may address lane markings and street level rules, which may include bike lanes 206, lane arrows 207, and lane widths 208. In an exemplary embodiment, semantic features and map priors—or the topmost layer—may include traffic lights 200, crosswalks 201, stop lines 202, and utility poles 205, as well as other information such as the probabilities of risks or obstacles (e.g., the local likelihood of pedestrians crossing the street) used by the AV when planning the best path to a destination. For example, if this intersection is near a school or dog park, the topmost layer, particularly the map priors, may take into account probabilities specific to these locations when drawing the path.

[0037] Figure 3 is a block diagram of an example semantic mapping system for use with an autonomous vehicle according to an embodiment of the present disclosure. In an exemplary embodiment, a camera 300 and a LiDAR 307 (if employed) create an RGB image 301 and a point cloud 306, respectively. In some embodiments, an RGB image can be a matrix of pixels with corresponding weights or bits that determine the number of colors they display, and a point cloud is a collection of data points in space generated by, for example, a laser from a LiDAR reflected from an object. A neural network, such as a convolutional neural network (CNN), can be used to extract features of the RGB image, as described above, to generate a semantic segmentation 302 containing pixel-by-pixel labels. In some embodiments, features can then be identified by a CNN or other neural network to perform semantic mapping 305, where the location and determined shape of the identified object (via, for example, a point cloud 306 or RGB image 301) is placed in the appropriate semantic map layer. This can produce a 2.5D semantic mesh map 304, or an updated local or roadside HD map, which can be used by neural networks and other processes to plan vehicle paths 303.

[0038] The semantic mapping 305 may be used to update a locally stored AV's roadside map as described above, and / or may be transmitted for updating a remotely stored HD map. In the latter example, multiple AVs may transmit their semantic mapping 305 to update a remotely stored HD map. In this manner, a central HD map may be maintained and updated in real time by multiple AVs detecting and determining various road features while driving.

[0039] Figure 4A4 is an illustration of an example autonomous vehicle 400 according to some embodiments of the present disclosure. Autonomous vehicle 400 (also referred to herein as “vehicle 400”) may include, but is not limited to, a passenger vehicle, such as a car, a truck, a bus, an emergency vehicle, a shuttle, an electric or motorized bicycle, a motorcycle, a fire truck, a police car, an ambulance, a boat, an engineering vehicle, an underwater vehicle, a drone, a robot (e.g., a factory robot or a personal assistant robot), a robotic platform, and / or other types of vehicles (e.g., unmanned and / or accommodating one or more passengers). Autonomous vehicles are generally described according to the automation levels defined by the National Highway Traffic Safety Administration (NHTSA), a division of the U.S. Department of Transportation, and the Society of Automotive Engineers (SAE), “Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles” (Standard No. J3016-201806, issued on June 15, 2018, Standard No. J3016-201609, issued on September 30, 2016, and previous and future versions of the standard). The vehicle 400 may be capable of implementing functions consistent with one or more of levels 3-5 of autonomous driving levels. For example, depending on the embodiment, the vehicle 400 may be capable of conditional automation (level 3), high automation (level 4), and / or full automation (level 5).

[0040] The vehicle 400 may include components such as a chassis, a body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other components of the vehicle. The vehicle 400 may include a propulsion system 450, such as an internal combustion engine, a hybrid power plant, an all-electric engine, and / or another type of propulsion system. The propulsion system 450 may be connected to a drive train of the vehicle 400, which may include a transmission, to achieve propulsion of the vehicle 400. The propulsion system 450 may be controlled in response to receiving a signal from a throttle / accelerator 452.

[0041] A steering system 454, which may include a steering wheel, may be used to steer vehicle 400 (e.g., along a desired path or route) when propulsion system 450 is operating (e.g., when the vehicle is in motion). Steering system 454 may receive signals from steering actuator 456. For fully automated (Level 5) functionality, a steering wheel may be optional.

[0042] Brake sensor system 446 may be used to operate vehicle brakes in response to receiving signals from brake actuator 448 and / or brake sensors.

[0043] The system may include one or more CPUs, one or more system on chip (SoC) 404 ( Figure 4C ) and / or one or more controllers 436 of one or more GPUs may provide signals (e.g., representing commands) to one or more components and / or systems of the vehicle 400. For example, the one or more controllers may send signals to operate vehicle brakes via one or more brake actuators 448, to operate a steering system 454 via one or more steering actuators 456, and / or to operate a propulsion system 450 via one or more throttles / accelerators 452. The one or more controllers 436 may include one or more onboard (e.g., integrated) computing devices (e.g., supercomputers) that process sensor signals and output operating commands (e.g., signals representing commands) to enable autonomous driving and / or assist a human driver in driving the vehicle 400. The one or more controllers 436 may include a first controller 436 for autonomous driving functions, a second controller 436 for functional safety functions, a third controller 436 for artificial intelligence functions (e.g., computer vision), a fourth controller 436 for infotainment functions, a fifth controller 436 for redundancy in emergency situations, and / or other controllers. In some examples, a single controller 436 may handle two or more of the above functions, two or more controllers 436 may handle a single function, and / or any combination thereof.

[0044] The one or more controllers 436 may provide signals for controlling one or more components and / or systems of the vehicle 400 in response to sensor data (e.g., sensor input) received from one or more sensors. The sensor data may be received from, for example and without limitation, a global navigation satellite system sensor 458 (e.g., a global positioning system sensor), a RADAR sensor 460, an ultrasonic sensor 462, a LIDAR sensor 464, an inertial measurement unit (IMU) sensor 466 (e.g., an accelerometer, a gyroscope, a magnetic compass, a magnetometer, etc.), a microphone 496, a stereo camera 468, a wide angle camera 470 (e.g., a fisheye camera), an infrared camera 472, a surround camera 474 (e.g., a 360 degree camera), a long-range and / or mid-range camera 498, a speed sensor 444 (e.g., for measuring the velocity of the vehicle 400), a vibration sensor 442, a steering sensor 440, a brake sensor (e.g., as part of a brake sensor system 446), and / or other sensor types.

[0045] One or more of the controllers 436 may receive inputs (e.g., represented by input data) from the instrument cluster 432 of the vehicle 400 and provide outputs (e.g., represented by output data, display data, etc.) via a human machine interface (HMI) display 434, an audible annunciator, a speaker, and / or via other components of the vehicle 400. These outputs may include information such as vehicle speed, velocity, time, map data (e.g., Figure 4C The HMI display 434 may include information such as the HD map 422 of the vehicle 400, location data (e.g., the location of the vehicle 400 on the map), directions, locations of other vehicles (e.g., occupancy grid), information about objects and states of objects as sensed by the controller 436, etc. For example, the HMI display 434 may display information about the presence of one or more objects (e.g., street signs, warning signs, traffic light changes, etc.) and / or information about driving maneuvers that the vehicle has made, is making, or will make (e.g., changing lanes now, leaving 34B in two miles, etc.).

[0046] The vehicle 400 further includes a network interface 424 that can communicate via one or more networks using one or more wireless antennas 426 and / or a modem. For example, the network interface 424 may be capable of communicating via LTE, WCDMA, UMTS, GSM, CDMA2000, etc. The one or more wireless antennas 426 may also enable communication between objects in the environment (e.g., vehicles, mobile devices, etc.) using one or more local area networks such as Bluetooth, Bluetooth LE, Z-wave, ZigBee, etc. and / or one or more low power wide area networks (LPWANs) such as LoRaWAN, SigFox, etc.

[0047] Figure 4B For use according to some embodiments of the present disclosure Figure 4A An example of camera positions and fields of view for an example autonomous vehicle 400 is shown. The cameras and respective fields of view are an example embodiment and are not intended to be limiting. For example, additional and / or replaceable cameras may be included, and / or the cameras may be located at different locations on the vehicle 400.

[0048] The camera type for the camera may include, but is not limited to, a digital camera that may be suitable for use with components and / or systems of the vehicle 400. The camera may operate at an automotive safety integrity level (ASIL) B and / or at another ASIL. The camera type may have any image capture rate, such as 60 frames per second (fps), 120fps, 240fps, etc., depending on the embodiment. The camera may be capable of using a rolling shutter, a global shutter, another type of shutter, or a combination thereof. In some examples, the color filter array may include a red-white-white-white (RCCC) color filter array, a red-white-white-blue (RCCB) color filter array, a red-blue-green-white (RBGC) color filter array, a Foveon X3 color filter array, a Bayer sensor (RGGB) color filter array, a monochrome sensor color filter array, and / or another type of color filter array. In some embodiments, a clear pixel camera such as a camera with an RCCC, RCCB, and / or RBGC color filter array may be used in an effort to improve light sensitivity.

[0049] In some examples, one or more of the cameras can be used to perform advanced driver assistance system (ADAS) functions (e.g., as part of a redundant or fail-safe design). For example, a multi-function monocular camera can be installed to provide functions including lane departure warning, traffic sign assistance, and intelligent headlight control. One or more of the cameras (e.g., all of the cameras) can simultaneously record and provide image data (e.g., video).

[0050] One or more of the cameras may be mounted in a mounting assembly such as a custom designed (3-D printed) assembly to cut off stray light and reflections from within the car (e.g., reflections from the dashboard reflected in the windshield mirror) that may interfere with the camera's image data capture capabilities. With respect to the wing mirror mounting assembly, the wing mirror assembly may be custom 3-D printed so that the camera mounting plate matches the shape of the wing mirror. In some examples, one or more cameras may be integrated into the wing mirror. For side view cameras, one or more cameras may also be integrated into the four pillars at each corner of the cab.

[0051] A camera (e.g., a front-facing camera) having a field of view that includes a portion of the environment in front of the vehicle 400 can be used for surround view to help identify the forward path and obstacles, as well as assist in providing information critical to generating an occupancy grid and / or determining a preferred vehicle path with the help of one or more controllers 436 and / or control SoCs. The front-facing camera can be used to perform many of the same ADAS functions as LIDAR, including emergency braking, pedestrian detection, and collision avoidance. The front-facing camera can also be used for ADAS functions and systems, including lane departure warning ("LDW"), autonomous cruise control ("ACC"), and / or other functions such as traffic sign recognition.

[0052] A variety of cameras may be used in the front-facing configuration, including, for example, a monocular camera platform including a CMOS (complementary metal oxide semiconductor) color imager. Another example may be a wide-angle camera 470, which may be used to sense objects (e.g., pedestrians, intersection traffic, or bicycles) entering the field of view from the periphery. Figure 4B Only one wide-angle camera is shown in the figure, but there may be any number of wide-angle cameras 470 on the vehicle 400. In addition, long-range cameras 498 (e.g., a long-view stereo camera pair) may be used for depth-based object detection, especially for objects for which a neural network has not been trained. Long-range cameras 498 may also be used for object detection and classification and basic object tracking.

[0053] One or more stereo cameras 468 may also be included in the front configuration. The stereo camera 468 may include an integrated control unit including an expandable processing unit that may provide a multi-core microprocessor and programmable logic (e.g., FPGA) with an integrated CAN or Ethernet interface on a single chip. Such a unit may be used to generate a 3-D map of the vehicle environment, including distance estimates for all points in the image. An alternative stereo camera 468 may include a compact stereo vision sensor that may include two camera lenses (one on each side) and an image processing chip that may measure the distance from the vehicle to the target object and use the generated information (e.g., metadata) to activate autonomous emergency braking and lane departure warning functions. Other types of stereo cameras 468 may be used in addition to or alternatively to those described herein.

[0054] A camera (e.g., a side view camera) having a field of view of a portion of the environment including the sides of the vehicle 400 can be used for surround viewing, providing information used to create and update the occupancy grid and generate side impact collision warnings. Figure 4B The four surround cameras 474 shown in FIG. 4 can be placed around the vehicle 400. The surround cameras 474 can include wide-angle cameras 470, fisheye cameras, 360-degree cameras, and / or the like. For example, four fisheye cameras can be placed in front, behind, and on the sides of the vehicle. In an alternative arrangement, the vehicle can use three surround cameras 474 (e.g., left, right, and rear), and can utilize one or more other cameras (e.g., a forward-facing camera) as a fourth surround camera.

[0055] A camera having a field of view that includes a portion of the environment behind the vehicle 400 (e.g., a rear view camera) can be used to assist with parking, surround view, rear collision warning, and creating and updating an occupancy grid. A variety of cameras can be used, including but not limited to cameras that are also suitable as front-facing cameras as described herein (e.g., long-range and / or mid-range cameras 498, stereo cameras 468, infrared cameras 472, etc.).

[0056] A camera having a field of view including a portion of the interior or cabin of the vehicle 400 may be used to monitor one or more states of a driver, passenger, or object in the cabin. Any type of camera may be used, including but not limited to one or more cabin cameras 441, which may be any type of camera described herein and may be placed anywhere on or in the vehicle 400 to provide a view of the cabin or its interior. For example, one or more cabin cameras 441 may be placed in or on portions of the vehicle 400 dashboard, rearview mirrors, sideview mirrors, seats, or doors, and may be oriented to capture images of any driver, passenger, or any other object or portion of the vehicle 400.

[0057] Figure 4C For use according to some embodiments of the present disclosure Figure 4A Block diagram of an example system architecture for an example autonomous vehicle 400. It should be understood that this arrangement and other arrangements described herein are set forth merely as examples. Other arrangements and elements (e.g., machines, interfaces, functions, sequences, functional groupings, etc.) may be used in addition to or in place of those shown, and some elements may be omitted entirely. Further, many of the elements described herein are functional entities that may be implemented as discrete or distributed components or in combination with other components, and in any appropriate combination and location. The various functions described herein as being performed by an entity may be implemented by hardware, firmware, and / or software. For example, the various functions may be implemented by a processor executing instructions stored in a memory.

[0058] Figure 4C Each of the components, features, and systems of vehicle 400 is illustrated as being connected via bus 402. Bus 402 may include a controller area network (CAN) data interface (alternatively, referred to herein as a "CAN bus"). CAN may be a network inside vehicle 400 that assists in controlling various features and functions of vehicle 400, such as actuation of brakes, acceleration, braking, steering, windshield wipers, and the like. The CAN bus may be configured to have tens or even hundreds of nodes, each with its own unique identifier (e.g., CAN ID). The CAN bus may be read to find steering wheel angle, ground speed, engine speed per minute (RPM), button position, and / or other vehicle status indicators. The CAN bus may be ASIL B compliant.

[0059] Although bus 402 is described as a CAN bus here, this is not intended to be limiting. For example, in addition to or alternatively to the CAN bus, FlexRay and / or Ethernet can be used. In addition, although bus 402 is represented by a single line, this is not intended to be limiting. For example, there can be any number of buses 402, which may include one or more CAN buses, one or more FlexRay buses, one or more Ethernet buses, and / or one or more other types of buses using different protocols. In some examples, two or more buses 402 can be used to perform different functions, and / or can be used for redundancy. For example, a first bus 402 can be used for a collision avoidance function, and a second bus 402 can be used for drive control. In any example, each bus 402 can communicate with any component of the vehicle 400, and two or more buses 402 can communicate with the same component. In some examples, each SoC 404, each controller 436, and / or each computer in the vehicle can have access to the same input data (e.g., input from a sensor of the vehicle 400), and can be connected to a common bus such as a CAN bus.

[0060] The vehicle 400 may include one or more controllers 436, such as those described herein. Figure 4A Those controllers described. Controller 436 can be used for a variety of functions. Controller 436 can be coupled to any other different components and systems of vehicle 400, and can be used for control of vehicle 400, artificial intelligence of vehicle 400, infotainment for vehicle 400, and / or the like.

[0061] The vehicle 400 may include one or more system on chip (SoC) 404. The SoC 404 may include a CPU 406, a GPU 408, a processor 410, a cache 412, an accelerator 414, a data store 415, and / or other components and features not shown. The SoC 404 may be used to control the vehicle 400 in a variety of platforms and systems. For example, one or more SoCs 404 may be combined with an HD map 422 in a system (e.g., a system of the vehicle 400), and the HD map may be downloaded from one or more servers (e.g., a server) via a network interface 424. Figure 4D one or more servers 478) to obtain map refreshes and / or updates.

[0062] CPU 406 may include a CPU cluster or CPU complex (alternatively, referred to herein as "CCPLEX"). CPU 406 may include multiple cores and / or L2 caches. For example, in some embodiments, CPU 406 may include eight cores in a coherent multiprocessor configuration. In some embodiments, CPU 406 may include four dual-core clusters, each of which has a dedicated L2 cache (e.g., 2MB L2 cache). CPU 406 (e.g., CCPLEX) may be configured to support simultaneous cluster operations, so that any combination of clusters of CPU 406 can be active at any given time.

[0063] CPU 406 may implement power management capabilities including one or more of the following features: each hardware block may be automatically clock gated when idle to save dynamic power; each core clock may be gated when the core is not actively executing instructions due to the execution of WFI / WFE instructions; each core may be independently power gated; each core cluster may be independently clock gated when all cores are clock gated or power gated; and / or each core cluster may be independently power gated when all cores are power gated. CPU 406 may further implement an enhanced algorithm for managing power states, in which allowed power states and expected wake-up times are specified, and hardware / microcode determines the optimal power state to enter for the core, cluster, and CCPLEX. The processing core may support a simplified power state entry sequence in software, with the work being offloaded to the microcode.

[0064] The GPU 408 may include an integrated GPU (alternatively, referred to herein as an "iGPU"). The GPU 408 may be programmable and efficient for parallel workloads. In some examples, the GPU 408 may use an enhanced tensor instruction set. The GPU 408 may include one or more streaming microprocessors, each of which may include an L1 cache (e.g., an L1 cache with at least 96KB storage capacity), and two or more of these streaming microprocessors may share an L2 cache (e.g., an L2 cache with 512KB storage capacity). In some embodiments, the GPU 408 may include at least eight streaming microprocessors. The GPU 408 may use a computer-based application programming interface (API). In addition, the GPU 408 may use one or more parallel computing platforms and / or programming models (e.g., NVIDIA's CUDA).

[0065] In the case of automotive and embedded use, GPU 408 can be power optimized to achieve optimal performance. For example, GPU 408 can be manufactured on fin field effect transistors (FinFETs). However, this is not intended to be limiting, and GPU 408 can be manufactured using other semiconductor manufacturing processes. Each streaming microprocessor can merge several mixed precision processing cores divided into multiple blocks. For example and without limitation, 64 PF32 cores and 32 PF64 cores can be divided into four processing blocks. In such an example, each processing block can be allocated 16 FP32 cores, 8 FP64 cores, 16 INT32 cores, two mixed precision NVIDIA tensor cores for deep learning matrix arithmetic, L0 instruction cache, thread beam (warp) scheduler, dispatch unit and / or 64KB register file. In addition, the streaming microprocessor can include independent parallel integer and floating point data paths to provide efficient execution of workloads using a mix of calculations and addressing calculations. The streaming microprocessor may include independent thread scheduling capabilities to allow for finer-grained synchronization and collaboration between parallel threads. A streaming microprocessor may include a combined L1 data cache and shared memory unit to increase performance while simplifying programming.

[0066] GPU 408 can include high bandwidth memory (HBM) and / or a 16GB HBM2 memory subsystem that provides a peak memory bandwidth of approximately 900 GB / s in some examples. In some examples, synchronous graphics random access memory (SGRAM), such as fifth generation graphics double data rate synchronous random access memory (GDDR5), can be used in addition to or in lieu of HBM memory.

[0067] The GPU 408 may include unified memory technology that includes access counters to allow memory pages to be more accurately migrated to the processor that accesses them most frequently, thereby improving the efficiency of memory ranges shared between processors. In some examples, address translation service (ATS) support may be used to allow the GPU 408 to directly access the CPU 406 page table. In such an example, when the GPU 408 memory management unit (MMU) experiences a miss, an address translation request may be transmitted to the CPU 406. In response, the CPU 406 may look up the virtual-physical mapping for the address in its page table and transmit the translation back to the GPU 408. In this way, the unified memory technology may allow a single unified virtual address space to be used for memory of both the CPU 406 and the GPU 408, thereby simplifying GPU 408 programming and porting applications to the GPU 408.

[0068] In addition, GPU 408 may include access counters that can track how often GPU 408 accesses the memory of other processors. The access counters can help ensure that memory pages are moved to the physical memory of the processor that accesses them most frequently.

[0069] SoC 404 may include any number of caches 412, including those described herein. For example, cache 412 may include an L3 cache available to both CPU 406 and GPU 408 (e.g., connected to both CPU 406 and GPU 408). Cache 412 may include a write-back cache that may track the state of a line, for example, by using a cache coherence protocol (e.g., MEI, MESI, MSI, etc.). Depending on the embodiment, the L3 cache may include 4MB or more, although smaller cache sizes may also be used.

[0070] The SoC 404 may include an arithmetic logic unit (ALU) that may be used to perform processing for any of a variety of tasks or operations with respect to the vehicle 400 - such as processing a DNN. In addition, the SoC 404 may include a floating point unit (FPU) - or other math coprocessor or digital coprocessor type - for performing mathematical operations within the system. For example, the SoC 104 may include one or more FPUs integrated as execution units within the CPU 406 and / or GPU 408.

[0071] SoC 404 may include one or more accelerators 414 (e.g., hardware accelerators, software accelerators, or combinations thereof). For example, SoC 404 may include a hardware acceleration cluster, which may include optimized hardware accelerators and / or large on-chip memory. The large on-chip memory (e.g., 4MB SRAM) may enable the hardware acceleration cluster to accelerate neural networks and other calculations. The hardware acceleration cluster may be used to supplement GPU 408 and offload some tasks of GPU 408 (e.g., free up more cycles of GPU 408 for performing other tasks). As an example, accelerator 414 may be used for targeted workloads (e.g., perception, convolutional neural networks (CNNs), etc.) that are sufficiently stable to be easily controlled for acceleration. When used herein, the term "CNN" may include all types of CNNs, including region-based or regional convolutional neural networks (RCNNs) and fast RCNNs (e.g., for object detection).

[0072] Accelerator 414 (e.g., hardware acceleration cluster) may include a deep learning accelerator (DLA). DLA may include one or more tensor processing units (TPUs) that may be configured to provide an additional 10 trillion operations per second for deep learning applications and reasoning. TPU may be an accelerator configured to perform image processing functions (e.g., for CNN, RCNN, etc.) and optimized for performing image processing functions. DLA may be further optimized for a specific set of neural network types and floating point operations and reasoning. The design of DLA may provide higher performance per millimeter than a general-purpose GPU, and far exceeds the performance of a CPU. TPU may perform several functions, including a single instance convolution function, support, for example, INT8, INT16, and FP16 data types for both features and weights, and post-processor functions.

[0073] The DLA can quickly and efficiently execute neural networks, particularly CNNs, on processed or unprocessed data for any of a wide variety of functions, such as, but not limited to: a CNN for object recognition and detection using data from a camera sensor; a CNN for distance estimation using data from a camera sensor; a CNN for emergency vehicle detection and identification and detection using data from a microphone; a CNN for facial recognition and vehicle owner identification using data from a camera sensor; and / or a CNN for safety and / or security-related events.

[0074] The DLA can perform any function of the GPU 408, and by using an inference accelerator, for example, the designer can target any function to either the DLA or the GPU 408. For example, the designer can focus the processing of CNNs and floating point operations on the DLA, and leave other functions to the GPU 408 and / or other accelerators 414.

[0075] The accelerator 414 (e.g., a hardware acceleration cluster) may include a programmable vision accelerator (PVA), which may be alternatively referred to herein as a computer vision accelerator. The PVA may be designed and configured to accelerate computer vision algorithms for advanced driver assistance systems (ADAS), autonomous driving, and / or augmented reality (AR) and / or virtual reality (VR) applications. The PVA may provide a balance between performance and flexibility. For example, each PVA may include, for example and without limitation, any number of reduced instruction set computer (RISC) cores, direct memory access (DMA), and / or any number of vector processors.

[0076] The RISC core can interact with an image sensor (e.g., an image sensor of any camera described herein), an image signal processor, and / or the like. Each of these RISC cores can include any number of memories. Depending on the embodiment, the RISC core can use any of a number of protocols. In some examples, the RISC core can execute a real-time operating system (RTOS). The RISC core can be implemented using one or more integrated circuit devices, application specific integrated circuits (ASICs), and / or storage devices. For example, the RISC core can include an instruction cache and / or a tightly coupled RAM.

[0077] The DMA may enable components of the PVA to access system memory independently of the CPU 406. The DMA may support any number of features used to provide optimizations for the PVA, including, but not limited to, support for multi-dimensional addressing and / or circular addressing. In some examples, the DMA may support addressing in up to six or more dimensions, which may include block width, block height, block depth, horizontal block stepping, vertical block stepping, and / or depth stepping.

[0078] The vector processor can be a programmable processor that can be designed to efficiently and flexibly perform programming for computer vision algorithms and provide signal processing capabilities. In some examples, the PVA can include a PVA core and two vector processing subsystem partitions. The PVA core can include a processor subsystem, one or more DMA engines (e.g., two DMA engines), and / or other peripherals. The vector processing subsystem can operate as the main processing engine of the PVA, and can include a vector processing unit (VPU), an instruction cache, and / or a vector memory (e.g., VMEM). The VPU core can include a digital signal processor, such as, for example, a single instruction multiple data (SIMD), a very long instruction word (VLIW) digital signal processor. The combination of SIMD and VLIW can enhance throughput and rate.

[0079] Each of the vector processors may include an instruction cache and may be coupled to a dedicated memory. As a result, in some examples, each of the vector processors may be configured to execute independently of the other vector processors. In other examples, the vector processors included in a particular PVA may be configured to employ data parallelism. For example, in some embodiments, multiple vector processors included in a single PVA may execute the same computer vision algorithm, but on different regions of an image. In other examples, the vector processors included in a particular PVA may execute different computer vision algorithms simultaneously on the same image, or even execute different algorithms on sequence images or portions of an image. Among other things, any number of PVAs may be included in a hardware acceleration cluster, and any number of vector processors may be included in each of these PVAs. In addition, the PVA may include additional error correction code (ECC) memory to enhance overall system security.

[0080] The accelerator 414 (e.g., a hardware acceleration cluster) may include an on-chip computer vision network and SRAM to provide high bandwidth, low latency SRAM for the accelerator 414. In some examples, the on-chip memory may include at least 4MB of SRAM consisting of, for example and not limited to, eight field-configurable memory blocks that can be accessed by both the PVA and the DLA. Each pair of memory blocks may include an advanced peripheral bus (APB) interface, configuration circuitry, a controller, and a multiplexer. Any type of memory may be used. The PVA and DLA may access the memory via a backbone that provides high-speed memory access to the PVA and DLA. The backbone may include an on-chip computer vision network that interconnects the PVA and DLA to the memory (e.g., using APB).

[0081] The on-chip computer vision network may include an interface that determines that both the PVA and DLA provide ready and valid signals before transmitting any control signals / addresses / data. Such an interface may provide separate phases and separate channels for transmitting control signals / addresses / data, as well as burst communication for continuous data transmission. This type of interface may comply with ISO 26262 or IEC 61508 standards, but other standards and protocols may also be used.

[0082] In some examples, SoC 404 may include a real-time ray tracing hardware accelerator such as described in U.S. Patent Application No. 16 / 101,232 filed on August 10, 2018. The real-time ray tracing hardware accelerator may be used to quickly and efficiently determine the position and range of objects (e.g., within a world model) in order to generate real-time visualization simulations for RADAR signal interpretation, for sound propagation synthesis and / or analysis, for SONAR system simulations, for general wave propagation simulations, for comparison with LIDAR data for the purpose of positioning and / or other functions, and / or for other purposes. In some embodiments, one or more tree traversal units (TTUs) may be used to perform one or more ray tracing related operations.

[0083] The accelerator 414 (e.g., a hardware accelerator cluster) has a wide range of uses in autonomous driving. The PVA can be a programmable visual accelerator that can be used in key processing stages in ADAS and autonomous vehicles. The capabilities of the PVA are a good match for algorithmic domains that require predictable processing, low power, and low latency. In other words, the PVA performs well on semi-dense or dense rule computations, and even on small data sets that require predictable runtimes with low latency and low power. Therefore, in the context of a platform for autonomous vehicles, the PVA is designed to run classic computer vision algorithms because they are efficient at object detection and integer math.

[0084] For example, according to one embodiment of the technology, PVA is used to perform computer stereo vision. In some examples, algorithms based on semi-global matching can be used, but this is not intended to be limiting. Many applications for level 3-5 autonomous driving require instant motion estimation / stereo matching (e.g., structure from motion, pedestrian recognition, lane detection, etc.). PVA can perform computer stereo vision functions on input from two monocular cameras.

[0085] In some examples, PVA can be used to perform dense optical flow. For example, PVA can be used to process raw RADAR data (e.g., using a 4D fast Fourier transform) to provide a processed RADAR signal before transmitting the next RADAR pulse. In other examples, PVA is used for time-of-flight depth processing, for example by processing raw time-of-flight data to provide processed time-of-flight data.

[0086] DLA can be used to run any type of network to enhance control and driving safety, including, for example, a neural network that outputs a confidence measure for each object detection. Such a confidence value can be interpreted as a probability, or as providing a relative "weight" of each detection compared to other detections. This confidence value enables the system to make further decisions about which detections should be considered true positive detections rather than false positive detections. For example, the system can set a threshold for confidence and only consider detections that exceed the threshold as true positive detections. In an automatic emergency braking (AEB) system, a false positive detection will cause the vehicle to automatically perform emergency braking, which is obviously undesirable. Therefore, only the most confident detection should be considered a trigger for AEB. DLA can run a neural network for regressing confidence values. The neural network can take at least some subset of parameters as its input, such as bounding box dimensions, ground plane estimates obtained (e.g., from another subsystem), inertial measurement unit (IMU) sensor 466 output related to vehicle 400 orientation and distance, 3D position estimates of objects obtained from neural networks and / or other sensors (e.g., LIDAR sensor 464 or RADAR sensor 460), etc.

[0087] SoC 404 may include one or more data stores 416 (e.g., memory). Data store 416 may be on-chip memory of SoC 404 that may store neural networks to be executed on the GPU and / or DLA. In some examples, data store 416 may be large enough to store multiple instances of a neural network for redundancy and safety. Data store 412 may include an L2 or L3 cache 412. References to data store 416 may include references to memory associated with a PVA, DLA, and / or other accelerator 414 as described herein.

[0088] SoC 404 may include one or more processors 410 (e.g., embedded processors). Processor 410 may include a boot and power management processor, which may be a dedicated processor and subsystem for handling boot power and management functions and related safety implementations. The boot and power management processor may be part of the SoC 404 boot sequence and may provide runtime power management services. The boot power and management processor may provide clock and voltage programming, auxiliary system low power state transitions, SoC 404 thermal and temperature sensor management, and / or SoC 404 power state management. Each temperature sensor may be implemented as a ring oscillator whose output frequency is proportional to the temperature, and SoC 404 may use the ring oscillator to detect the temperature of CPU 406, GPU 408, and / or accelerator 414. If it is determined that the temperature exceeds a threshold, the boot and power management processor may enter a temperature fault routine and place SoC 404 in a lower power state and / or place vehicle 400 in a driver safety parking mode (e.g., parking vehicle 400 safely).

[0089] Processor 410 may further include a set of embedded processors that may be used as an audio processing engine. The audio processing engine may be an audio subsystem that allows for full hardware support for multi-channel audio through multiple interfaces and a wide range of flexible audio I / O interfaces. In some examples, the audio processing engine is a dedicated processor core having a digital signal processor with dedicated RAM.

[0090] The processor 410 may further include an always-on processor engine that may provide the necessary hardware features to support low-power sensor management and wake-up use cases. The always-on processor engine may include a processor core, tightly coupled RAM, supporting peripherals (e.g., timers and interrupt controllers), various I / O controller peripherals, and routing logic.

[0091] The processor 410 may further include a safety cluster engine, which includes a dedicated processor subsystem that handles safety management of automotive applications. The safety cluster engine may include two or more processor cores, tightly coupled RAM, supporting peripherals (e.g., timers, interrupt controllers, etc.), and / or routing logic. In safety mode, the two or more cores may operate in lockstep mode and act as a single core with comparison logic to detect any differences between their operations.

[0092] Processor 410 may further include a real-time camera engine, which may include a dedicated processor subsystem for handling real-time camera management.

[0093] Processor 410 may further include a high dynamic range signal processor, which may include an image signal processor, which is a hardware engine that is part of the camera processing pipeline.

[0094] The processor 410 may include a video image compositer, which may be a processing block (e.g., implemented on a microprocessor) that implements video post-processing functions required for the video playback application to generate a final image for the player window. The video image compositer may perform lens distortion correction for the wide-angle camera 470, the surround camera 474, and / or for an in-cab surveillance camera sensor. The in-cab surveillance camera sensor is preferably monitored by a neural network running on another instance of the advanced SoC, configured to recognize in-cab events and respond accordingly. The in-cab system may perform lip reading to activate mobile phone service and place a call, dictate an email, change a vehicle destination, activate or change the vehicle's infotainment system and settings, or provide voice-activated web surfing. Certain functions are only available to the driver when the vehicle is operating in autonomous mode and are disabled in other circumstances.

[0095] The video image compositer may include enhanced temporal noise reduction for both spatial and temporal noise reduction. For example, in the case of motion in the video, the noise reduction appropriately weights the spatial information and reduces the weight of information provided by adjacent frames. In the case where an image or portion of an image does not include motion, the temporal noise reduction performed by the video image compositer may use information from previous images to reduce noise in the current image.

[0096] The video image compositor may also be configured to perform stereoscopic rectification on the input stereoscopic footage frames. The video image compositor may further be used for user interface composition when the operating system desktop is in use and the GPU 408 does not need to continuously render new surfaces. Even when the GPU 408 is powered on and active, actively performing 3D rendering, the video image compositor may be used to offload the GPU 408 to improve performance and responsiveness.

[0097] SoC 404 may further include a mobile industry processor interface (MIPI) camera serial interface for receiving video and input from a camera, a high-speed interface, and / or a video input block that may be used for camera and related pixel input functions. SoC 404 may further include an input / output controller that may be controlled by software and may be used to receive I / O signals that are not committed to a specific role.

[0098] SoC 404 may further include a wide range of peripheral interfaces to enable communication with peripherals, audio codecs, power management and / or other devices. SoC 404 may be used to process data from cameras (connected via Gigabit multimedia serial link and Ethernet), sensors (e.g., LIDAR sensor 464, RADAR sensor 460, etc., which may be connected via Ethernet), data from bus 402 (e.g., speed of vehicle 400, steering wheel position, etc.), data from GNSS sensor 458 (connected via Ethernet or CAN bus). SoC 404 may further include dedicated high-performance mass storage controllers, which may include their own DMA engines, and which may be used to free up CPU 406 from routine data management tasks.

[0099] SoC 404 can be an end-to-end platform with a flexible architecture that spans automation levels 3-5, providing a comprehensive functional safety architecture that leverages and efficiently uses computer vision and ADAS technologies to achieve diversity and redundancy, together with deep learning tools to provide a platform for a flexible and reliable driving software stack. SoC 404 can be faster, more reliable, and even more energy efficient and space efficient than conventional systems. For example, when combined with CPU 406, GPU 408, and data storage 45, accelerator 414 can provide a fast and efficient platform for level 3-5 autonomous vehicles.

[0100] The technology thus provides capabilities and functionality that cannot be achieved by conventional systems. For example, computer vision algorithms can be executed on CPUs, which can be configured using high-level programming languages ​​such as the C programming language to execute a wide variety of processing algorithms across a wide variety of visual data. However, CPUs often fail to meet the performance requirements of many computer vision applications, such as those related to, for example, execution time and power consumption. In particular, many CPUs are unable to execute complex object detection algorithms in real time, which is a requirement for in-vehicle ADAS applications and a requirement for practical Level 3-5 autonomous vehicles.

[0101] In contrast to conventional systems, by providing a CPU complex, a GPU complex, and a hardware acceleration cluster, the techniques described herein allow multiple neural networks to be executed simultaneously and / or sequentially, and the results to be combined together to achieve Level 3-5 autonomous driving functions. For example, a CNN executed on a DLA or dGPU (e.g., GPU 420) may include text and word recognition, allowing the supercomputer to read and understand traffic signs, including signs for which a neural network has not been specifically trained. The DLA may further include a neural network capable of recognizing, interpreting, and providing semantic understanding of the signs, and passing that semantic understanding to a path planning module running on the CPU complex.

[0102] As another example, as required for Level 3, 4, or 5 driving, multiple neural networks may be running simultaneously. For example, a warning sign consisting of "Caution: Flashing Lights Indicate Icing Conditions" along with electric lights may be interpreted by several neural networks independently or collectively. The sign itself may be recognized as a traffic sign by a deployed first neural network (e.g., a trained neural network), and the text "Flashing Lights Indicate Icing Conditions" may be interpreted by a deployed second neural network that informs the vehicle's path planning software (preferably executing on a CPU complex) that icing conditions exist when the flashing lights are detected. The flashing lights may be identified by operating a deployed third neural network over multiple frames that informs the vehicle's path planning software of the presence (or absence) of the flashing lights. All three neural networks may be running simultaneously, for example, within the DLA and / or on the GPU 408.

[0103] In some examples, a CNN for facial recognition and owner recognition can use data from the camera sensor to identify the presence of an authorized driver and / or owner of the vehicle 400. The always-on sensor processing engine can be used to unlock the vehicle and turn on the lights when the owner approaches the driver's door, and in security mode, disable the vehicle when the owner leaves the vehicle. In this way, the SoC 404 provides security against theft and / or carjacking.

[0104] In another example, a CNN for emergency vehicle detection and identification can use data from microphone 496 to detect and identify emergency vehicle sirens. In contrast to conventional systems that use general classifiers to detect sirens and manually extract features, SoC 404 uses CNN to classify environmental and urban sounds and to classify visual data. In a preferred embodiment, the CNN running on the DLA is trained to identify the relative closing rate of emergency vehicles (e.g., by using the Doppler effect). The CNN can also be trained to identify emergency vehicles specific to the local area in which the vehicle is operating, as identified by the GNSS sensor 458. Thus, for example, when operating in Europe, the CNN will seek to detect European sirens, and when in the United States, the CNN will seek to identify only North American sirens. Once an emergency vehicle is detected, with the assistance of the ultrasonic sensor 462, the control program can be used to execute emergency vehicle safety routines to slow the vehicle, drive to the side of the road, stop the vehicle, and / or idle the vehicle until the emergency vehicle passes.

[0105] The vehicle may include a CPU 418 (e.g., a discrete CPU or dCPU) that may be coupled to the SoC 404 via a high-speed interconnect (e.g., PCIe). The CPU 418 may include, for example, an X86 processor. The CPU 418 may be used to perform any of a variety of functions, including, for example, arbitrating potentially inconsistent results between ADAS sensors and the SoC 404, and / or monitoring the status and health of the controller 436 and / or the infotainment SoC 430.

[0106] The vehicle 400 may include a GPU 420 (e.g., a discrete GPU or dGPU) that may be coupled to the SoC 404 via a high-speed interconnect (e.g., NVIDIA's NVLINK). The GPU 420 may provide additional artificial intelligence functionality, for example, by executing redundant and / or different neural networks, and may be used to train and / or update the neural network based on inputs (e.g., sensor data) from sensors of the vehicle 400.

[0107] The vehicle 400 may further include a network interface 424, which may include one or more wireless antennas 426 (e.g., one or more wireless antennas for different communication protocols, such as cellular antennas, Bluetooth antennas, etc.). The network interface 424 can be used to enable wireless connections with the cloud (e.g., with a server 478 and / or other network devices), with other vehicles, and / or with computing devices (e.g., a passenger's client device) via the Internet. In order to communicate with other vehicles, a direct link can be established between the two vehicles, and / or an indirect link can be established (e.g., across a network and through the Internet). The direct link can be provided using a vehicle-to-vehicle communication link. The vehicle-to-vehicle communication link can provide the vehicle 400 with information about vehicles approaching the vehicle 400 (e.g., vehicles in front of, to the side of, and / or behind the vehicle 400). This functionality can be part of the cooperative adaptive cruise control functionality of the vehicle 400.

[0108] The network interface 424 may include a SoC that provides modulation and demodulation functions and enables the controller 436 to communicate over a wireless network. The network interface 424 may include a radio frequency front end for up-conversion from baseband to radio frequency and down-conversion from radio frequency to baseband. The frequency conversion may be performed by a known process and / or may be performed using a super-heterodyne process. In some examples, the radio frequency front end function may be provided by a separate chip. The network interface may include a wireless function for communicating via LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-wave, ZigBee, LoRaWAN, and / or other wireless protocols. The vehicle 400 may further include a data storage 428 that may include an off-chip (e.g., outside the SoC 404) storage device. The data storage 428 may include one or more storage elements, including RAM, SRAM, DRAM, VRAM, flash memory, hard disk, and / or other components and / or devices that can store at least one bit of data.

[0109] The vehicle 400 may further include a GNSS sensor 458 (e.g., GPS and / or assisted GPS sensor) for assisting with mapping, perception, occupancy grid generation, and / or path planning functions. Any number of GNSS sensors 458 may be used, including, for example and without limitation, GPS using a USB connector with an Ethernet to serial (RS-232) bridge.

[0110] The vehicle 400 may further include a RADAR sensor 460. The RADAR sensor 460 may be used by the vehicle 400 for remote vehicle detection even in darkness and / or inclement weather conditions. The RADAR functional safety level may be ASIL B. The RADAR sensor 460 may use CAN and / or bus 402 (e.g., to transmit data generated by the RADAR sensor 460) for control and access to object tracking data, accessing Ethernet in some examples to access raw data. A variety of RADAR sensor types may be used. For example and without limitation, the RADAR sensor 460 may be suitable for front, rear, and side RADAR use. In some examples, a pulse Doppler RADAR sensor is used.

[0111] The RADAR sensor 460 may include different configurations, such as long-range with a narrow field of view, short-range with a wide field of view, short-range side coverage, and the like. In some examples, the long-range RADAR may be used for adaptive cruise control functions. The long-range RADAR system may provide a wide field of view (e.g., within a range of 250m) achieved by two or more independent scans. The RADAR sensor 460 may help distinguish between static and moving objects and may be used by the ADAS system for emergency braking assistance and forward collision warnings. The long-range RADAR sensor may include a single-station multimode RADAR with multiple (e.g., six or more) fixed RADAR antennas and high-speed CAN and FlexRay interfaces. In an example with six antennas, the central four antennas may create a focused beam pattern designed to record the surroundings of the vehicle 400 at a higher rate with minimal traffic interference from adjacent lanes. The other two antennas may expand the field of view, making it possible to quickly detect vehicles entering or leaving the lane of the vehicle 400.

[0112] As an example, a medium-range RADAR system may include a range of up to 460m (front) or 80m (rear) and a field of view of up to 42 degrees (front) or 450 degrees (rear). A short-range RADAR system may include, but is not limited to, a RADAR sensor designed to be mounted on both ends of a rear bumper. When mounted on both ends of a rear bumper, such a RADAR sensor system may create two beams that continuously monitor the blind spots behind and beside the vehicle.

[0113] Short-range RADAR systems can be used in ADAS systems for blind spot detection and / or lane change assistance.

[0114] The vehicle 400 may further include ultrasonic sensors 462. The ultrasonic sensors 462, which may be placed on the front, rear, and / or sides of the vehicle 400, may be used for parking assistance and / or creating and updating occupancy grids. A variety of ultrasonic sensors 462 may be used, and different ultrasonic sensors 462 may be used for different detection ranges (e.g., 2.5 m, 4 m). The ultrasonic sensors 462 may operate at a functional safety level of ASIL B.

[0115] The vehicle 400 may include a LIDAR sensor 464. The LIDAR sensor 464 may be used for object and pedestrian detection, emergency braking, collision avoidance, and / or other functions. The LIDAR sensor 464 may be ASIL B for functional safety level. In some examples, the vehicle 400 may include multiple LIDAR sensors 464 (e.g., two, four, six, etc.) that may use Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

[0116] In some examples, LIDAR sensor 464 may be able to provide a list of objects and their distances for a 360-degree field of view. Commercially available LIDAR sensor 464 may have, for example, an advertised range of approximately 100m, an accuracy of 2cm-3cm, and support for 100Mbps Ethernet connections. In some examples, one or more non-protruding LIDAR sensors 464 may be used. In such examples, LIDAR sensor 464 may be implemented as a small device that can be embedded in the front, back, side, and / or corner of vehicle 400. In such examples, LIDAR sensor 464 may provide a field of view of up to 120 degrees horizontally and 35 degrees vertically, with a range of 200m, even for low reflectivity objects. Front-mounted LIDAR sensor 464 may be configured for a horizontal field of view between 45 and 135 degrees.

[0117] In some examples, LIDAR technologies such as 3D flash LIDAR may also be used. 3D flash LIDAR uses flashes of laser as an emission source to illuminate the vehicle's surroundings up to about 200m. The flash LIDAR unit includes a receiver that records the laser pulse transmission time and reflected light on each pixel, which in turn corresponds to the range from the vehicle to the object. Flash LIDAR can allow highly accurate and distortion-free images of the surrounding environment to be generated with each laser flash. In some examples, four flash LIDAR sensors may be deployed, one on each side of the vehicle 400. Available 3D flash LIDAR systems include solid-state 3D staring array LIDAR cameras (e.g., non-scanning LIDAR devices) with no moving parts other than fans. The flash LIDAR device can use 5 nanosecond Class I (eye-safe) laser pulses per frame, and can capture the reflected laser light in the form of a 3D range point cloud and co-registered intensity data. By using flash LIDAR, and because flash LIDAR is a solid-state device with no moving components, the LIDAR sensor 464 may be less susceptible to motion blur, vibration, and / or shock.

[0118] The vehicle may further include an IMU sensor 466. In some examples, the IMU sensor 466 may be located at the center of the rear axle of the vehicle 400. The IMU sensor 466 may include, for example and without limitation, an accelerometer, a magnetometer, a gyroscope, a magnetic compass, and / or other sensor types. In some examples, for example, in a six-axis application, the IMU sensor 466 may include an accelerometer and a gyroscope, and in a nine-axis application, the IMU sensor 466 may include an accelerometer, a gyroscope, and a magnetometer.

[0119] In some embodiments, IMU sensor 466 can be implemented as a miniature high-performance GPS-assisted inertial navigation system (GPS / INS) that combines micro-electromechanical system (MEMS) inertial sensors, high-sensitivity GPS receivers, and advanced Kalman filter algorithms to provide estimates of position, velocity, and attitude. Thus, in some examples, IMU sensor 466 can enable vehicle 400 to estimate heading by directly observing and correlating velocity changes from GPS to IMU sensor 466 without input from a magnetic sensor. In some examples, IMU sensor 466 and GNSS sensor 458 can be combined into a single integrated unit.

[0120] The vehicle may include microphones 496 positioned in and / or around the vehicle 400. The microphones 496 may be used for, among other things, emergency vehicle detection and identification.

[0121] The vehicle may further include any number of camera types, including a stereo camera 468, a wide angle camera 470, an infrared camera 472, a surround camera 474, a long-range and / or mid-range camera 498, and / or other camera types. These cameras can be used to capture image data around the entire periphery of the vehicle 400. The type of camera used depends on the embodiment and the requirements of the vehicle 400, and any combination of camera types can be used to provide the necessary coverage around the vehicle 400. In addition, the number of cameras can vary depending on the embodiment. For example, the vehicle may include six cameras, seven cameras, ten cameras, twelve cameras, and / or another number of cameras. As an example and not limitation, the cameras can support Gigabit Multimedia Serial Link (GMSL) and / or Gigabit Ethernet. Each of the cameras described herein may be capable of supporting Gigabit Multimedia Serial Link (GMSL) and / or Gigabit Ethernet. Figure 4A and Figure 4B Described in more detail.

[0122] The vehicle 400 may further include a vibration sensor 442. The vibration sensor 442 may measure vibrations of components of the vehicle, such as an axle. For example, changes in vibration may indicate changes in the road surface. In another example, when two or more vibration sensors 442 are used, the difference between the vibrations may be used to determine friction or slip of the road surface (e.g., when there is a vibration difference between a powered drive shaft and a free-spinning shaft).

[0123] The vehicle 400 may include an ADAS system 438. In some examples, the ADAS system 438 may include a SoC. The ADAS system 438 may include autonomous / adaptive / automatic cruise control (ACC), cooperative adaptive cruise control (CACC), forward collision warning (FCW), automatic emergency braking (AEB), lane departure warning (LDW), lane keeping assist (LKA), blind spot warning (BSW), rear cross traffic warning (RCTW), collision warning system (CWS), lane centering (LC), and / or other features and functions.

[0124] The ACC system may use a RADAR sensor 460, a LIDAR sensor 464, and / or a camera. The ACC system may include a longitudinal ACC and / or a lateral ACC. The longitudinal ACC monitors and controls the distance to the vehicle immediately in front of the vehicle 400, and automatically adjusts the vehicle speed to maintain a safe distance from the vehicle in front. The lateral ACC performs distance keeping and suggests that the vehicle 400 change lanes when necessary. The lateral ACC is related to other ADAS applications such as LC and CWS.

[0125] CACC uses information from other vehicles, which can be received indirectly from other vehicles via a wireless link or through a network connection (e.g., through the Internet) via a network interface 424 and / or a wireless antenna 426. A direct link can be provided by a vehicle-to-vehicle (V2V) communication link, while an indirect link can be an infrastructure-to-vehicle (I2V) communication link. Typically, the V2V communication concept provides information about the vehicle immediately ahead (e.g., the vehicle immediately ahead of the vehicle 400 and in the same lane as it), while the I2V communication concept provides information about traffic farther ahead. The CACC system may include either or both of the I2V and V2V information sources. Given information about the vehicle ahead of the vehicle 400, CACC can be more reliable, and it is possible to improve the smoothness of traffic flow and reduce road congestion.

[0126] The FCW system is designed to alert the driver to hazards so that the driver can take corrective action. The FCW system uses a front camera and / or RADAR sensor 460 coupled to a dedicated processor, DSP, FPGA and / or ASIC, which is electrically coupled to driver feedback such as a display, speaker and / or vibration component. The FCW system can provide warnings in the form of, for example, sound, visual warnings, vibrations and / or rapid brake pulses.

[0127] The AEB system detects an impending front collision with another vehicle or other object and can automatically apply the brakes if the driver does not take corrective action within specified time or distance parameters. The AEB system can use a front camera and / or RADAR sensor 460 coupled to a dedicated processor, DSP, FPGA and / or ASIC. When the AEB system detects a hazard, it typically first alerts the driver to take corrective action to avoid the collision, and if the driver does not take corrective action, the AEB system can automatically apply the brakes in an effort to prevent or at least mitigate the effects of the predicted collision. The AEB system may include technologies such as dynamic brake support and / or collision approach braking.

[0128] The LDW system provides visual, audible, and / or tactile warnings, such as steering wheel or seat vibrations, to alert the driver when the vehicle 400 crosses a lane marking. When the driver indicates an intention to leave the lane, by activating a turn signal, the LDW system is not activated. The LDW system may use a front-facing camera coupled to a dedicated processor, DSP, FPGA, and / or ASIC that is electrically coupled to driver feedback such as a display, speaker, and / or vibration assembly.

[0129] The LKA system is a variation of the LDW system. If the vehicle 400 begins to leave the lane, the LKA system provides steering input or braking to correct the vehicle 400.

[0130] The BSW system detects and warns the driver of vehicles in the car's blind spot. The BSW system can provide visual, audible, and / or tactile alerts to indicate that merging or changing lanes is unsafe. The system can provide additional warnings when the driver uses a turn signal. The BSW system can use a rear-facing camera and / or RADAR sensor 460 coupled to a dedicated processor, DSP, FPGA, and / or ASIC that is electrically coupled to driver feedback such as a display, speaker, and / or vibration component.

[0131] The RCTW system may provide visual, audible, and / or tactile notifications when an object is detected outside the range of the rear camera while the vehicle 400 is in reverse. Some RCTW systems include AEB to ensure that the vehicle brakes are applied to avoid a crash. The RCTW system may use one or more rear RADAR sensors 460 coupled to a dedicated processor, DSP, FPGA, and / or ASIC that is electrically coupled to driver feedback such as a display, speaker, and / or vibration assembly.

[0132] Conventional ADAS systems may be prone to false positive results, which may annoy and distract the driver, but are typically not catastrophic because the ADAS system alerts the driver and allows the driver to decide whether the safety condition really exists and take action accordingly. However, in the autonomous vehicle 400, in the case of conflicting results, the vehicle 400 itself must decide whether to pay attention to the results from the main computer or the auxiliary computer (e.g., the first controller 436 or the second controller 436). For example, in some embodiments, the ADAS system 438 can be a backup and / or auxiliary computer for providing perception information to the backup computer rationality module. The backup computer rationality monitor can run redundant and diverse software on hardware components to detect faults in perception and dynamic driving tasks. The output from the ADAS system 438 can be provided to the supervisory MCU. If the outputs from the main computer and the auxiliary computer conflict, the supervisory MCU must determine how to coordinate the conflict to ensure safe operation.

[0133] In some examples, the master computer can be configured to provide a confidence score to the supervisory MCU, indicating the master computer's confidence in the selected result. If the confidence score exceeds a threshold, the supervisory MCU can follow the direction of the master computer, regardless of whether the auxiliary computer provides conflicting or inconsistent results. In the case where the confidence score does not meet the threshold and the master computer and the auxiliary computer indicate different results (e.g., conflicts), the supervisory MCU can arbitrate between these computers to determine the appropriate result.

[0134] The supervisory MCU may be configured to run a neural network that is trained and configured to determine conditions under which the auxiliary computer provides a false alarm based on outputs from the primary computer and the auxiliary computer. Thus, the neural network in the supervisory MCU may learn when the output of the auxiliary computer may be trusted and when it may not. For example, when the auxiliary computer is a RADAR-based FCW system, the neural network in the supervisory MCU may learn when the FCW system is identifying a metal object that is not actually dangerous, such as a drainage grate or manhole cover that triggers an alarm. Similarly, when the auxiliary computer is a camera-based LDW system, the neural network in the supervisory MCU may learn to ignore the LDW when a cyclist or pedestrian is present and lane departure is actually the safest strategy. In an embodiment that includes a neural network running on the supervisory MCU, the supervisory MCU may include at least one of a DLA or a GPU suitable for running the neural network with associated memory. In a preferred embodiment, the supervisory MCU may include a component of SoC 404 and / or be included as a component of SoC 404.

[0135] In other examples, the ADAS system 438 may include an auxiliary computer that uses traditional computer vision rules to perform ADAS functions. In this way, the auxiliary computer can use classic computer vision rules (if-then), and the presence of a neural network in the supervisory MCU can improve reliability, safety, and performance. For example, diverse implementations and intentional non-identity make the entire system more fault-tolerant, especially for failures caused by software (or software-hardware interface) functions. For example, if there is a software vulnerability or error in the software running on the main computer and the non-identical software code running on the auxiliary computer provides the same overall result, the supervisory MCU can be more confident that the overall result is correct and that the vulnerability in the software or hardware used by the main computer does not cause a substantial error.

[0136] In some examples, the output of the ADAS system 438 can be fed to the perception block of the main computer and / or the dynamic driving task block of the main computer. For example, if the ADAS system 438 indicates a forward collision warning due to an object immediately ahead, the perception block can use this information in identifying the object. In other examples, the auxiliary computer can have its own neural network that is trained and thus reduces the risk of false positives as described herein.

[0137] The vehicle 400 may further include an infotainment SoC 430 (e.g., an in-vehicle infotainment system (IVI)). Although illustrated and described as an SoC, an infotainment system may not be an SoC and may include two or more discrete components. The infotainment SoC 430 may include a combination of hardware and software that may be used to provide audio (e.g., music, personal digital assistant, navigation instructions, news, radio, etc.), video (e.g., TV, movies, streaming, etc.), phone (e.g., hands-free calling), network connectivity (e.g., LTE, WiFi, etc.), and / or information services (e.g., navigation system, rear parking assistance, radio data system, vehicle-related information such as fuel level, total distance covered, brake fuel level, oil level, door open / closed, air filter information, etc.) to the vehicle 400. For example, the infotainment SoC 430 may include a radio, a disc player, a navigation system, a video player, USB and Bluetooth connections, an onboard computer, onboard entertainment, WiFi, steering wheel audio controls, hands-free voice controls, a head-up display (HUD), an HMI display 434, a telematics device, a control panel (e.g., for controlling and / or interacting with various components, features and / or systems), and / or other components. The infotainment SoC 430 may further be used to provide information (e.g., visual and / or auditory) to a user of the vehicle, such as information from an ADAS system 438, autonomous driving information such as planned vehicle maneuvers, trajectories, surrounding environment information (e.g., intersection information, vehicle information, road information, etc.), and / or other information.

[0138] The infotainment SoC 430 may include GPU functionality. The infotainment SoC 430 may communicate with other devices, systems, and / or components of the vehicle 400 via a bus 402 (e.g., a CAN bus, Ethernet, etc.). In some examples, the infotainment SoC 430 may be coupled to a supervisory MCU so that in the event of a failure of a master controller 436 (e.g., a main and / or backup computer of the vehicle 400), the GPU of the infotainment system may perform some self-driving functions. In such an example, the infotainment SoC 430 may place the vehicle 400 in a driver-safe parking mode as described herein.

[0139] The vehicle 400 may further include an instrument cluster 432 (e.g., a digital instrument panel, an electronic instrument cluster, a digital instrument panel, etc.). The instrument cluster 432 may include a controller and / or a supercomputer (e.g., a separate controller or a supercomputer). The instrument cluster 432 may include a set of instruments, such as a speedometer, a fuel level, an oil pressure, a tachometer, an odometer, a turn indicator, a shift position indicator, a seat belt warning light, a parking brake warning light, an engine fault light, an airbag (SRS) system information, lighting controls, safety system controls, navigation information, etc. In some examples, information may be displayed and / or shared between the infotainment SoC 430 and the instrument cluster 432. In other words, the instrument cluster 432 may be included as part of the infotainment SoC 430, or vice versa.

[0140] Figure 4D For a cloud-based server and Figure 4A 4. System diagram of communication between example autonomous vehicles 400. System 476 may include server 478, network 490, and vehicles including vehicle 400. Server 478 may include multiple GPUs 484 (A)-1284 (H) (collectively referred to as GPUs 484 here), PCIe switches 482 (A)-482 (H) (collectively referred to as PCIe switches 482 here), and / or CPUs 480 (A)-480 (B) (collectively referred to as CPUs 480 here). GPUs 484, CPUs 480, and PCIe switches may be interconnected with high-speed interconnects and / or PCIe connections 486 such as, for example and without limitation, NVLink interfaces 488 developed by NVIDIA. In some examples, GPUs 484 are connected via NVLink and / or NVSwitch SoCs, and GPUs 484 and PCIe switches 482 are connected via PCIe interconnects. Although eight GPUs 484, two CPUs 480, and two PCIe switches are illustrated, this is not intended to be limiting. Depending on the embodiment, each of the servers 478 may include any number of GPUs 484, CPUs 480, and / or PCIe switches. For example, each of the servers 478 may include eight, sixteen, thirty-two, and / or more GPUs 484.

[0141] Server 478 may receive image data over network 490 and from a vehicle, the image data representing images showing unexpected or changed road conditions, such as recently begun road work. Server 478 may transmit neural network 492, updated neural network 492, and / or map information 494, including information about traffic and road conditions, over network 490 and to the vehicle. Updates to map information 494 may include updates to HD map 422, as described herein, such as absolute coordinates of detected objects, information about construction sites, potholes, curves, flooding, or other obstacles. In some examples, neural network 492, updated neural network 492, and / or map information 494 may have been generated from new training and / or data received from any number of vehicles in the environment and / or based on experience of training performed at a data center (e.g., using server 478 and / or other servers).

[0142] Server 478 can be used to train a machine learning model (e.g., a neural network) based on training data. The training data can be generated by the vehicle, and / or can be generated in a simulation (e.g., using a game engine). In some examples, the training data is labeled (e.g., when the neural network benefits from supervised learning) and / or undergoes other preprocessing, while in other examples, the training data is not labeled and / or preprocessed (e.g., when the neural network does not require supervised learning). Training can be performed according to any one or more classes of machine learning techniques, including but not limited to the following classes: supervised training, semi-supervised training, unsupervised training, self-learning, reinforcement learning, joint learning, transfer learning, feature learning (including principal components and cluster analysis), multilinear subspace learning, manifold learning, representation learning (including alternate dictionary learning), rule-based machine learning, anomaly detection, and any variants or combinations thereof. Once the machine learning model is trained, the machine learning model can be used by the vehicle (e.g., transmitted to the vehicle via network 490), and / or the machine learning model can be used by server 478 to remotely monitor the vehicle.

[0143] In some examples, server 478 can receive data from the vehicle and apply the data to the latest real-time neural network for real-time intelligent reasoning. Server 478 may include a deep learning supercomputer and / or a dedicated AI computer powered by GPU 484, such as DGX and DGX Station machines developed by NVIDIA. However, in some examples, server 478 may include a deep learning infrastructure of a data center powered only by CPUs.

[0144] The deep learning infrastructure of server 478 may be capable of rapid real-time inference, and may use this capability to assess and verify the health of the processors, software, and / or associated hardware in vehicle 400. For example, the deep learning infrastructure may receive periodic updates from vehicle 400, such as a sequence of images and / or objects located in the sequence of images that vehicle 400 has located (e.g., via computer vision and / or other machine learning object classification techniques). The deep learning infrastructure may run its own neural network to identify objects and compare them to the objects identified by vehicle 400, and if the results do not match and the infrastructure concludes that the AI ​​in vehicle 400 has failed, server 478 may transmit a signal to vehicle 400 instructing the fail-safe computer of vehicle 400 to take control, notify passengers, and complete a safe parking maneuver.

[0145] For reasoning, the server 478 may include a GPU 484 and one or more programmable reasoning accelerators (e.g., NVIDIA's TensorRT 3). The combination of GPU-powered servers and reasoning acceleration can make real-time responses possible. In other examples, such as when performance is not so important, CPU, FPGA, and other processor-powered servers can be used for reasoning.

[0146] Figure 5 5 is a block diagram of an example computing device 500 suitable for implementing some embodiments of the present disclosure. The computing device 500 may include an interconnect system 502 that directly or indirectly couples the following devices: memory 504, one or more central processing units (CPUs) 506, one or more graphics processing units (GPUs) 508, communication interfaces 510, I / O ports 512, input / output components 514, power supplies 516, one or more presentation components 518 (e.g., displays), and one or more logic units 520.

[0147] although Figure 5 The various blocks of are shown as being connected via an interconnect system 502 having wires, but this is not intended to be limiting and is merely for clarity. For example, in some embodiments, a presentation component 518 such as a display device may be considered an I / O component 514 (e.g., if the display is a touch screen). As another example, CPU 506 and / or GPU 508 may include memory (e.g., memory 504 may represent a storage device in addition to the memory of GPU 508, CPU 506, and / or other components). In other words, Figure 5The computing devices in the description are merely illustrative. No distinction is made between categories such as "workstations," "servers," "laptops," "desktops," "tablets," "client devices," "mobile devices," "handheld devices," "game consoles," "electronic control units (ECUs)," "virtual reality systems," "augmented reality systems," and / or other device or system types, as all of these are considered Figure 5 within the range of computing devices.

[0148] The interconnection system 502 may represent one or more links or buses, such as an address bus, a data bus, a control bus, or a combination thereof. The interconnection system 502 may include one or more bus types, such as an industry standard architecture (ISA) bus, an extended industry standard architecture (EISA) bus, a video electronics standard association (VESA) bus, a peripheral component interconnect (PCI) bus, a peripheral component interconnect express (PCIe) bus, and / or another type of bus or link. In some embodiments, there is a direct connection between components. As an example, the CPU 506 may be directly connected to the memory 504. In addition, the CPU 506 may be directly connected to the GPU 508. In the case where there is a direct or point-to-point connection between components, the interconnection system 502 may include a PCIe link to perform the connection. In these examples, the computing device 500 does not need to include a PCI bus.

[0149] Memory 504 may include any of a variety of computer-readable media. Computer-readable media may be any available media that can be accessed by computing device 500. Computer-readable media may include volatile and nonvolatile media and removable and non-removable media. By way of example and not limitation, computer-readable media may include computer storage media and communication media.

[0150] Computer storage media may include volatile and nonvolatile media and / or removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, and / or other data types. For example, memory 504 may store computer-readable instructions (e.g., representing programs and / or program elements, such as an operating system).

[0151] Computer storage media may include, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by the computing device 500. As used herein, computer storage media does not include the signals themselves.

[0152] Computer storage media may contain computer readable instructions, data structures, program modules, and / or other data types in a modulated data signal such as a carrier wave or other transmission mechanism, and include any information delivery media. The term "modulated data signal" may refer to a signal that has one or more of its characteristics set or changed in such a way that information is encoded into the signal. By way of example and not limitation, computer storage media may include wired media such as a wired network or a direct wired connection, and wireless media such as sound, RF, infrared, and other wireless media. Any combination of the above should also be included within the scope of computer readable media.

[0153] The CPU 506 may be configured to execute at least some computer-readable instructions in order to control one or more components of the computing device 500 to perform one or more of the methods and / or processes described herein. Each of the CPUs 506 may include one or more cores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.) capable of simultaneously processing a large number of software threads. The CPU 506 may include any type of processor, and may include different types of processors, depending on the type of computing device 500 implemented (e.g., a processor with fewer cores for mobile devices and a processor with more cores for servers). For example, depending on the type of computing device 500, the processor may be an Advanced RISC Machine (ARM) processor implemented using Reduced Instruction Set Computing (RISC) or an x86 processor implemented using Complex Instruction Set Computing (CISC). The computing device 500 may also include one or more CPUs 506 in addition to one or more microprocessors or supplementary coprocessors such as math coprocessors.

[0154] In addition to or in lieu of CPU 506 , GPU 508 may be configured to execute at least some computer-readable instructions to control one or more components of computing device 500 to perform one or more methods and / or processes described herein. One or more GPUs 508 may be an integrated GPU (e.g., with one or more CPUs 506) and / or one or more GPUs 508 may be a discrete GPU. In embodiments, one or more GPUs 508 may be a coprocessor of one or more CPUs 506. The computing device 500 may use the GPU 508 to render graphics (e.g., 3D graphics) or perform general-purpose computing. For example, the GPU 508 may be used for general-purpose computing on a GPU (GPGPU). The GPU 508 may include hundreds or thousands of cores capable of processing hundreds or thousands of software threads simultaneously. The GPU 508 may generate pixel data for outputting an image in response to a rendering command (e.g., a rendering command received from the CPU 506 via a host interface). The GPU 508 may include graphics memory, such as display memory, for storing pixel data or any other suitable data, such as GPGPU data. The display memory may be included as part of the memory 504. The GPU 508 may include two or more GPUs operating in parallel (e.g., via a link). The link may connect the GPUs directly (e.g., using NVLINK) or may connect the GPUs through a switch (e.g., using NVSwitch). When combined together, each GPU 508 may generate pixel data or GPGPU data for a different portion of the output or for different outputs (e.g., a first GPU for a first image and a second GPU for a second image). Each GPU may include its own memory or may share memory with other GPUs.

[0155] In addition to or in lieu of the CPU 506 and / or the GPU 508, the logic unit 520 may be configured to execute at least some computer-readable instructions to control one or more components of the computing device 500 to perform one or more methods and / or processes described herein. In embodiments, the CPU 506, the GPU 508, and / or the logic unit 520 may perform any combination of methods, processes, and / or portions thereof, either separately or in conjunction. The one or more logic units 520 may be a part of and / or integrated with one or more of the CPU 506 and / or the GPU 508, and / or the one or more logic units 520 may be a discrete component or otherwise external to the CPU 506 and / or the GPU 508. In embodiments, the one or more logic units 520 may be a coprocessor to the one or more CPUs 506 and / or the one or more GPUs 508.

[0156] Examples of logic unit 520 include one or more processing cores and / or components thereof, such as a tensor core (TC), a tensor processing unit (TPU), a pixel vision core (PVC), a vision processing unit (VPU), a graphics processing cluster (GPC), a texture processing cluster (TPC), a streaming multiprocessor (SM), a tree traversal unit (TTU), an artificial intelligence accelerator (AIA), a deep learning accelerator (DLA), an arithmetic logic unit (ALU), an application specific integrated circuit (ASIC), a floating point unit (FPU), an I / O element, a peripheral component interconnect (PCI) or a peripheral component interconnect express (PCIe) element, etc.

[0157] The communication interface 510 may include one or more receivers, transmitters, and / or transceivers that enable the computing device 500 to communicate with other computing devices via an electronic communication network, including wired and / or wireless communications. The communication interface 510 may include components and functionality that enable communication over any of a number of different networks, such as a wireless network (e.g., Wi-Fi, Z-wave, Bluetooth, Bluetooth LE, ZigBee, etc.), a wired network (e.g., via Ethernet or Infiniband communications), a low power wide area network (e.g., LoRaWAN, SigFox, etc.), and / or the Internet.

[0158] The I / O ports 512 can enable the computing device 500 to be logically coupled to other devices including I / O components 514, presentation components 518, and / or other components, some of which can be built into (e.g., integrated into) the computing device 500. Illustrative I / O components 514 include microphones, mice, keyboards, joysticks, game pads, game controllers, satellite dishes, scanners, printers, wireless devices, and the like. The I / O components 514 can provide a natural user interface (NUI) that processes air gestures, voice, or other physiological inputs generated by a user. In some instances, the input can be transmitted to an appropriate network element for further processing. The NUI can implement any combination of voice recognition, stylus recognition, facial recognition, biometric recognition, gesture recognition on and adjacent to the screen, air gestures, head and eye tracking, and touch recognition associated with the display of the computing device 500 (as described in more detail below). The computing device 500 may include a depth camera such as a stereo camera system, an infrared camera system, an RGB camera system, touch screen technology, and combinations of these for gesture detection and recognition. Additionally, computing device 500 may include an accelerometer or gyroscope to enable motion detection (e.g., as part of an inertial measurement unit (IMU)). In some examples, the output of the accelerometer or gyroscope may be used by computing device 500 to render immersive augmented reality or virtual reality.

[0159] The power supply 516 may include a hardwired power supply, a battery power supply, or a combination thereof. The power supply 516 may provide power to the computing device 500 to enable the components of the computing device 500 to operate.

[0160] The presentation component 518 may include a display (e.g., a monitor, a touch screen, a television screen, a head-up display (HUD), other display types, or a combination thereof), a speaker, and / or other presentation components. The presentation component 518 may receive data from other components (e.g., GPU 508, CPU 506, etc.) and output the data (e.g., as an image, video, sound, etc.).

[0161] The present disclosure may be described in the general context of machine-usable instructions or computer codes executed by a computer or other machine such as a personal digital assistant or other handheld device, including computer-executable instructions such as program modules. Typically, program modules including routines, programs, objects, components, data structures, etc. refer to codes that perform specific tasks or implement specific abstract data types. The present disclosure may be practiced in a variety of system configurations, including handheld devices, consumer electronics, general-purpose computers, more specialized computing devices, etc. The present disclosure may also be practiced in a distributed computing environment in which tasks are performed by remote processing devices linked through a communication network.

[0162] Figure 6 The training and deployment of the machine learning model of the present disclosure embodiment are illustrated. In at least one embodiment, the machine learning model may include a neural network such as a CNN. An untrained neural network 606 is trained using a training data set 602, which in some embodiments of the present disclosure may be a set of images of an object assuming various head postures. In at least one embodiment, the training framework 604 is a PyTorch framework, while in other embodiments, the training framework 604 is TensorFlow, Boost, Caffe, Microsoft Cognitive Toolkit / CNTK, MXNet, Chainer, Keras, Deeplearning4j or other training frameworks. The training framework 604 uses the processing resources described herein to train the untrained neural network 606 to generate a trained neural network 608. In at least one embodiment, the initial weights may be randomly selected or selected by pre-training using a deep belief network. Training may be performed in a supervised, partially supervised, or unsupervised manner.

[0163] In at least one embodiment, for example when a regression classifier is used, supervised learning can be used to train the untrained neural network 606, where the training data set 602 includes inputs paired with desired outputs, or where the training data set 602 includes inputs with known outputs and the outputs of the neural network are manually graded. In at least one embodiment, the untrained neural network 606 is trained in a supervised manner. The training framework 604 processes the inputs from the training data set 602 and compares the resulting outputs to a set of expected or desired outputs. In at least one embodiment, the error is then propagated back through the untrained neural network 606. The training framework 604 adjusts the weights that control the untrained neural network 606. The training framework 604 may include tools for monitoring how the untrained neural network 606 converges to a model, such as a trained neural network 608, suitable for generating the correct answer, such as in the result 614, based on known input data, such as new data 612. In at least one embodiment, the training framework 604 repeatedly trains the untrained neural network 606 while adjusting the weights to refine the output of the untrained neural network 606 using a loss function and an adjustment process (e.g., stochastic gradient descent). In at least one embodiment, training framework 604 trains untrained neural network 606 until untrained neural network 606 reaches a desired accuracy. Trained neural network 608 can then be deployed to implement any number of machine learning operations.

[0164] In at least one embodiment, the untrained neural network 606 can be trained using unsupervised learning, where the untrained neural network 606 attempts to train itself using unlabeled data. In at least one embodiment, the unsupervised learning training data set 602 can include input data without any associated output data or "ground truth" data. The untrained neural network 606 can learn the groupings within the training data set 602 and can determine how the individual inputs are related to the untrained data set 602. In at least one embodiment, unsupervised training can be used to generate a self-organizing map, which is a trained neural network network 608 that is capable of performing operations useful for reducing the dimensionality of the new data 612. Unsupervised training can also be used to perform anomaly detection, which allows identification of data points in the new data set 612 that deviate from normal or existing patterns of the new data set 612.

[0165] In at least one embodiment, semi-supervised learning can be used, which is a technique in which the training data set 602 includes a mixture of labeled and unlabeled data. The training framework 604 can therefore be used to perform incremental learning, such as through transfer learning techniques. This incremental learning enables the trained neural network 608 to adapt to new data 612 without forgetting the knowledge instilled in the network during initial training.

[0166] Figure 7 is a flow chart illustrating an exemplary process of vehicle-based HD map updating according to an embodiment of the present disclosure. Here, autonomous vehicle 400 may capture images of one or more objects in the field of view of its various sensors (step 700), such as Figure 1 As shown. The image may be an image from a visible light camera or any other sensor, including sensors such as LiDAR systems. The relative coordinates of these objects or the positions of these objects relative to the vehicle 400 are then determined (step 710). The determination of the relative position of the object can be accomplished in any manner, such as by triangulating multiple images captured at known distances apart from each other. As an example, multiple different cameras can capture images of the same object at the same time, where the relative position of the object can be determined by triangulating using the captured images and the known distances between the cameras. As another example, a single camera can capture multiple images of the same object, where a GPS sensor or other position detection system determines the position of the vehicle 400 at each captured image. Triangulation can then be performed using the captured image and the difference in the position of the vehicle 400 between the two images. As another example, the position can be determined as above using any ranging system, single vision or stereo vision system.

[0167] The vehicle 400 also determines the absolute coordinates of at least one reference object (step 720). In particular, the vehicle 400 can determine the absolute coordinates of the object, and the relative coordinates of the object can also be determined. For example, the vehicle 400 can select an object within the field of view of one of its sensors, which is also an object contained in its roadside map or HD map, and the vehicle 400 can access the HD map and the HD map can be used to place the object in its roadside map. Therefore, objects can be detected and identified as described above, and their shapes and relative positions are compared with corresponding semantic map objects to determine whether there is a match. Matching objects can therefore be determined as objects that already exist on the semantic map, and their absolute coordinates are known. The match between the detected object and the semantic map object can be determined in any manner, such as by comparing the object position and size / shape with any specified one or more metrics. For example, objects classified as the same object type and within a predetermined threshold distance from each other can be considered to be the same object. Similarly, objects whose sizes match within any one or more predetermined size tolerances and are within a predetermined threshold distance from each other can be considered to be the same object.

[0168] When the detected object is deemed to match an object already stored in the semantic map of the vehicle 400 or in some other semantic map that the vehicle 400 can access, such as a semantic map maintained by a remote service with which the vehicle 400 can communicate electronically, the absolute coordinates of the object are retrieved. The object can then serve as a reference object whose absolute coordinates can be used to determine the absolute coordinates of other detected objects. That is, the vehicle 400 can then calculate the absolute coordinates of other detected objects based on the absolute coordinates of the reference object, the absolute coordinates of the vehicle 400 (e.g., GPS coordinates), and the determined position of the detected object relative to the vehicle 400 (step 730).

[0169] The vehicle 400 may then update its roadside map with the calculated absolute coordinates of any detected objects (step 740). That is, the detected objects may be placed in one or more appropriate layers of the semantic roadside map of the vehicle 400. In this manner, it may be observed that embodiments of the present disclosure allow vehicles 400 to update their roadside maps with the shapes, absolute coordinates, and classifications or identities of objects detected in real time as the vehicle 400 traverses any path.

[0170] In addition, vehicle 400 can transmit the absolute coordinates of any objects input into its roadside map for use in updating the remote HD map. That is, vehicle 400 can send information about any detected objects to a remote HD map service at any desired time so that the remote HD map can be updated to include these objects. Accordingly, vehicle 400 can check to determine whether it should transmit updates to such a remote service (step 750). If not, the process can return to step 700 and the vehicle can continue to detect objects and input the information it calculates into its roadside map. If vehicle 400 instead determines that it should send its update, vehicle 400 can send the absolute coordinates and any other required information - such as the corresponding object shape / contour and identification / classification - to the remote HD map service (step 760).

[0171] Note that multiple vehicles 400 can send update information to the remote HD map service. That is, the HD map can be continuously updated by multiple different remote vehicles 400. It should also be noted that the vehicle 400 can detect and determine the absolute coordinates of any object, including objects that already exist in the semantic map of the vehicle 400 or the remote HD map. The vehicle 400 can therefore determine that certain objects have absolute coordinate values ​​that are different from those already present in their on-board or roadside maps. For example, this may occur when the object has been moved, such as by construction, or may be caused by inaccuracies, such as inaccurate GPS readings due to poor satellite signal reception conditions. The vehicle 400 can handle this position difference in any way. As an example, the vehicle 400 can simply update the objects to their newly determined positions, the position of the object can be modified to any average of the old coordinates and the new coordinates, the old coordinates can be selected until the new coordinates are confirmed by another pass of the object, and the new coordinates can be discarded if inaccuracies such as poor GPS readings are detected. Detecting objects multiple times at new locations can also determine that the objects have moved, discarding the old coordinates and replacing them with newly determined coordinates.

[0172] Similarly, a remote HD mapping service may receive object coordinates that are different from the existing stored coordinates of the object. The mapping service may handle such differences in any manner. As an example, the absolute coordinates of any object may be accumulated or stored as a record of the received coordinates of the object, and the position of the object may be determined as a certain average of the accumulated position values. For example, the position of an object may be determined as the arithmetic mean of a predetermined number of recent samples received from multiple different vehicles 400, or the arithmetic mean of samples received within a recent predetermined time period (e.g., the past 24 hours, the past week, etc.). The position of an object or feature may also be updated or modified only when the newly received position value differs by more than a predetermined amount. That is, in some embodiments, received measurements that are too similar to values ​​already stored may be discarded. Embodiments of the present disclosure contemplate determining a position from multiple different received position values ​​in any manner, which may employ any averaging or estimation process.

[0173] It should also be noted that the coordinate system of the local or vehicle 400 roadside map may be different from the coordinate system of the remote HD map service, even though both are absolute coordinate systems and either can be used to update the other. Embodiments of the present disclosure contemplate converting between these coordinate systems in a known manner using known transformations, etc. One or both of the vehicle 400 and the remote HD map service may perform the coordinate transformation as appropriate.

[0174] For the purpose of explanation, the foregoing description uses specific nomenclature to provide a thorough understanding of the present disclosure. However, it is obvious to those skilled in the art that specific details are not required to practice the methods and systems of the present disclosure. Therefore, the foregoing descriptions of specific embodiments of the present invention are presented for the purpose of illustration and description. They are not intended to be exhaustive or limit the present invention to the precise form disclosed. In view of the above teachings, many modifications and variations are possible. For example, any sensor, camera, or other means can be used to detect an object. The object absolute coordinates can be determined in any way and can be stored in any semantic map layer. In addition, the semantic map position can be modified or updated in any way to resolve the differences between the object positions in any way. The embodiments are selected and described in order to best explain the principles of the present invention and its practical application, so that other technicians in the field can best utilize the methods and systems of the present disclosure and various embodiments with various modifications suitable for the intended specific purposes. In addition, the different features of the various embodiments disclosed or otherwise disclosed can be mixed and matched or otherwise combined to create further embodiments contemplated by the present disclosure.

Claims

1. A computer-implemented method comprising: determining a first absolute position of a first object; determining a second absolute position of the machine; Determining first position information of one or more second objects relative to the first object using: second position information of the first object relative to the machine, third position information of the one or more second objects relative to the machine, and a second absolute position of the machine; determining a third absolute position of the one or more second objects according to the first position information of the one or more second objects relative to the first object and according to the first absolute position of the first object; as well as One or more control operations for the machine are performed based at least on the third absolute position of the one or more second objects. 2 . The method of claim 1 , wherein the third position information is determined using at least one image of the one or more second objects.

3. The method of claim 2, wherein the at least one image is captured using a camera corresponding to the machine.

4. The method of claim 1, wherein the second absolute position of the machine is determined using a global positioning system (GPS) of the machine.

5. The method according to claim 1, wherein the method further comprises: One or more absolute positions of the one or more second objects in the first absolute coordinate system are determined based at least in part on the third absolute position in the second absolute coordinate system. 6 . The method of claim 1 , further comprising sending the third absolute position of the one or more second objects to a mapping service external to the machine. 7 . The method of claim 6 , further comprising receiving updated position information of the one or more second objects from the map service, and revising the third absolute position of the one or more second objects according to the updated position information.

8. A computer-implemented method comprising: Calculate position information of the second object relative to the first object based at least on positions of the first object and the second object relative to the machine and a first absolute position of the machine; Calculating a third absolute position of the second object according to the position information of the second object relative to the first object and according to the second absolute position of the first object; as well as A map of an environment is updated based on at least the third absolute position of the second object. 9 . The method of claim 8 , wherein the position information of the second object relative to the first object is determined based on one or more images of the first object and the second object.

10. A processor, comprising: One or more processing units, the one or more processing units being configured to: Calculate position information of the second object relative to the first object based at least on positions of the first object and the second object relative to the machine and a first absolute position of the machine; Calculating a third absolute position of the second object according to the position information of the second object relative to the first object and according to the second absolute position of the first object; as well as Based at least on the third absolute position of the second object, one or more control operations are performed for the machine.

11. The processor of claim 10, wherein the position of the second object relative to the machine is determined using at least one image of the second object.

12. The processor of claim 11, wherein the at least one image is captured using a camera corresponding to the machine.

13. A processor according to claim 10, wherein the one or more processing units are further used to determine one or more absolute positions of the second object in the first absolute coordinate system at least in part based on the third absolute position in the second absolute coordinate system, and to send the one or more absolute positions to a map service outside the machine.

14. The processor of claim 10, wherein the one or more processing units are further configured to update a local map using the third absolute position of the second object. 15 . The processor of claim 10 , wherein the one or more processing units are further configured to receive updated position information of the second object from a map service, and to correct the third absolute position according to the updated position information.

16. A processor, comprising: One or more processing units, the one or more processing units being configured to: Calculate position information of the second object relative to the first object based at least on positions of the first object and the second object relative to the machine and a first absolute position of the machine; calculating a third absolute position of the second object according to the position information of the second object relative to the first object and according to the second absolute position of the first object; and A map of an environment is updated using the third absolute position of the second object. 17 . The processor of claim 16 , wherein the position information of the second object relative to the first object is further determined based on one or more images of the first object and the second object.

18. The processor of claim 16, wherein the updating of the map comprises sending the third absolute position of the second object to a mapping service.

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