Security decomposition for path determination in autonomous systems

Abstract

A safety decomposition for path determination in autonomous systems. An architecture can use multiple data sources to generate lane graphics or path determinations for devices such as robots or autonomous vehicles, while meeting applicable requirements and operational regulations. The system can fuse data useful for determining localization from multiple sources. To ensure safety compliance, this fused data is compared with data from systems where safety is trusted, and the fused localization data can be used and verified as compliant with safety regulations as long as at least two comparators agree on the fused localization data. The system can also fuse available information useful for lane perception. This fused data is compared with data from systems where safety is trusted, and the fused lane graphics data can be provided for navigation and verified as compliant as long as at least two comparators used by these safety-compliant systems agree on the fused lane graphics data.

Description

[0001] Cross-references to related applications

[0002] This application claims priority to U.S. Provisional Application No. 63 / 111,250, entitled “Secure Decomposition for Autonomous Vehicle Path Detection,” filed November 9, 2020, which is hereby incorporated herein by reference in its entirety for all purposes. Background Technology

[0003] As various industries move towards increased automation, whether for vehicles, robots, or other automated systems, there is a corresponding need to improve the safety of operations performed by such automation. This can include improving the accuracy of motion or navigation, such as determining the optimal path for an automated machine under the current conditions of its environment. For many automated systems, various sensors and sources may exist that provide data for determining such paths. To ensure that such path determination methods meet specific safety requirements or regulations, individual determinations are made to ensure compliance with those requirements or regulations. In most cases, this approach can provide safe operation, but may offer a less than ideal riding experience; for example, for passengers in an autonomous vehicle, it provides a safe ride, but is not particularly smooth or consistently pleasant. Attached Figure Description

[0004] Various embodiments according to this disclosure will be described with reference to the accompanying drawings, in which:

[0005] Figure 1A and 1B Examples of multiple components of an autonomous vehicle according to at least one embodiment are shown;

[0006] Figure 2A , Figure 2B and Figure 2C A view of a lane graphics generation architecture available according to at least one embodiment is shown;

[0007] Figure 3 The diagram illustrates a combined lane graphic determination architecture available according to at least one embodiment;

[0008] Figure 4 An example process for determining a navigation path according to at least one embodiment is shown;

[0009] Figure 5 An example process for path determination using fusion propagation, according to at least one embodiment, is shown;

[0010] Figure 6 Components of a system for determining a navigation path according to at least one embodiment are shown;

[0011] Figure 7A The inference and / or training logic according to at least one embodiment is illustrated;

[0012] Figure 7B The inference and / or training logic according to at least one embodiment is illustrated;

[0013] Figure 8 An example data center system according to at least one embodiment is shown;

[0014] Figure 9 A computer system according to at least one embodiment is shown;

[0015] Figure 10 A computer system according to at least one embodiment is shown;

[0016] Figure 11 At least a portion of a graphics processor according to one or more embodiments is shown;

[0017] Figure 12 At least a portion of a graphics processor according to one or more embodiments is shown;

[0018] Figure 13 This is an example data flow diagram of an advanced computing pipeline according to at least one embodiment;

[0019] Figure 14 This is a system diagram of an example system for training, adapting, instantiating, and deploying machine learning models in an advanced computing pipeline, according to at least one embodiment; and

[0020] Figure 15A and 15B A data flow diagram of a process for training a machine learning model according to at least one embodiment is shown, as well as a client-server architecture for enhancing annotation tools using a pre-trained annotation model;

[0021] Figure 16A and 16B Components that can be used with a vehicle system according to at least one embodiment are shown. Detailed Implementation

[0022] The methods according to various embodiments can provide the generation of navigation paths, graphics, or trajectories for automated or semi-automated devices, such as vehicles or robots. Specifically, the various embodiments fuse or otherwise consider information from multiple sources to determine optimal positioning and lane graphics determination that also meet applicable requirements or regulations (such as those potentially involving operational safety). For autonomous (or semi-autonomous) vehicles or robots, this may involve determining the optimal lane graphics for guiding navigation or movement based on, for example, a combination of map data and real-time perception data. In at least one embodiment, the system (e.g., an electronic control unit (ECU)) may fuse together sensor data that can help determine positioning, such as data from radar or camera systems. To provide safety compliance, this fused data can be compared with data from individual systems where safety is trusted (such as from a safety-compliant camera or radar system). The fused positioning data can be used and can be verified as compliant with safety regulations, provided that at least two comparators or "voters" for these safety-compliant systems agree on the fused positioning data. This fused positioning data can be provided as input to a lane perception system.

[0023] In a similar manner, such a system (e.g., an ECU) can fuse available and reliable information for lane perception and graphics generation, or otherwise consider available and reliable information for lane perception and graphics generation, such as relating it to HD maps and real-time lane perception data, as determined using cameras or LiDAR systems, and fused positioning data. To provide safety compliance, the fused lane graphics data can also be compared with data from systems where safety is trusted, such as data generated using safety-compliant cameras or LiDAR systems. The fused lane graphics data can be provided for navigation and can be verified as compliant with safety regulations, provided that at least two comparators used by these safety-compliant systems agree on the fusion of the lane graphics data. If the fused lane graphics data does not receive at least two votes from these comparators, or is otherwise determined not to meet one or more other safety criteria, then another lane graphics or navigation determination can be used. Such a system can provide switching between navigation determinations to ensure the vehicle always operates safely, even if one or more systems on the vehicle become unavailable or unreliable.

[0024] This system can also utilize at least some amount of dead reckoning, where historical data can be used during finite time periods when real-time sensing data is unavailable or at least unreliable. Furthermore, the selection of inputs to be fused together for positioning or real-time sensing data can be switched over time based on confidence and availability. One or more arbitrators can analyze the fused data, and if unavailable or unreliable, one of the safety-compliant signals can be replaced to ensure the vehicle can continue to operate safely. In some embodiments, at least two ECUs, such as one for vehicle data and one for satellite data, can also be used, where real-time switching can be performed to operate on lane graphics from either or both ECUs to ensure the vehicle can continue to operate safely even if one signal is unavailable or unreliable.

[0025] consider Figure 1A The autonomous vehicle 100 shown is illustrated. In at least one embodiment, vehicle 100 may be, but is not limited to, a passenger vehicle, such as a car, truck, bus, and / or another type of vehicle that carries one or more passengers. In at least one embodiment, vehicle 100 may be a semi-trailer truck for hauling cargo. In at least one embodiment, vehicle 100 may be an aircraft, robotic vehicle, or other type of vehicle that may or may not be designed to carry passengers or cargo, but may be designed to perform one or more tasks that require at least some amount of movement or motion of the vehicle.

[0026] Autonomous vehicles can be described in terms of their level of automation, as defined by the National Highway Traffic Safety Administration (“NHTSA”), the U.S. Department of Transportation’s division, and the Society of Automotive Engineers (“SAE”) in its “Classification and Definition of Terms Related to Driving Automation Systems for Highway Motor Vehicles” (e.g., standard number J3016-201806, published June 15, 2018; standard number J3016-201609, published September 30, 2016; and previous and future versions of this standard). In one or more embodiments, vehicle 100 may be able to have one or more of the functions according to Level 1 through Level 5 of autonomous driving. For example, in at least one embodiment, depending on the embodiment, vehicle 100 may be able to perform conditional automation (Level 3), high automation (Level 4), and / or full automation (Level 5).

[0027] In at least one embodiment, vehicle 100 may include, but is not limited to, components such as chassis, body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other vehicle components. In at least one embodiment, vehicle 100 may include, but is not limited to, propulsion system 150, such as an internal combustion engine, a hybrid power station, an all-electric motor, and / or another type of propulsion system. In at least one embodiment, propulsion system 150 may be connected to the drivetrain of vehicle 100, which may include, but is not limited to, a transmission, to enable propulsion of vehicle 100. In at least one embodiment, propulsion system 150 may be controlled in response to receiving a signal from throttle / accelerator 152.

[0028] In at least one embodiment, a steering system 154, including but not limited to a steering wheel, is used to steer the vehicle 100 (e.g., along a desired path or route) when the propulsion system 150 is operating (e.g., when the vehicle is in motion). In at least one embodiment, the steering system 154 may receive signals from one or more steering actuators 156. The steering wheel may be optional for fully automated (level 5) functionality. In at least one embodiment, a brake sensor system 146 may be used to operate vehicle braking in response to receiving signals from one or more brake actuators 148 and / or brake sensors.

[0029] In at least one embodiment, there may be, but is not limited to, one or more system-on-chips (“SoCs”). Figure 1A A controller 136, comprising (not shown) and / or (one or more) graphics processing units (“(one or more) GPUs”), provides signals (e.g., signals indicating commands) to one or more components and / or systems of vehicle 100. For example, in at least one embodiment, controller 136 may send signals to operate vehicle braking via brake actuator 148, steering system 154 via steering actuator 156, and / or propulsion system 150 via throttle / accelerator 152. Controller 136 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 indicating commands) to enable autonomous driving and / or assisted human driving of vehicle 100. In at least one embodiment, controller 136 may include a first controller 136 for autonomous driving functions, a second controller 136 for functional safety functions, a third controller 136 for artificial intelligence functions (e.g., computer vision), a fourth controller 136 for infotainment functions, a fifth controller 136 for redundancy in emergency situations, and / or other controllers. In at least one embodiment, a single controller 136 may handle two or more of the above functions, and two or more controllers 136 may handle a single function, and / or any combination thereof.

[0030] In at least one embodiment, controller 136 provides signals for controlling one or more components and / or systems of vehicle 100 in response to sensor data (e.g., sensor inputs) received from one or more sensors. In at least one embodiment, sensor data may be received from, for example, but not limited to, the following devices: one or more Global Navigation Satellite System (“GNSS”) sensors 158 (e.g., one or more Global Positioning System sensors), one or more RADAR (radar) sensors 160, one or more ultrasonic sensors 162, one or more LiDAR sensors 164, one or more Inertial Measurement Unit (“IMU”) sensors 166 (e.g., one or more accelerometers, one or more gyroscopes, one or more magnetic compasses, one or more magnetometers, etc.), microphone 196, stereo camera 168, wide-angle camera 170 (e.g., fisheye camera), one or more infrared camera 172, one or more surround camera 174 (e.g., 360-degree camera), long-range camera (… Figure 1A (not shown in the image), one or more medium-range cameras ( Figure 1A (not shown), one or more speed sensors 144 (e.g., for measuring the speed of vehicle 100), vibration sensors 142, steering sensors 140, brake sensors (e.g., as part of brake sensor system 146), and / or other sensor types.

[0031] In at least one embodiment, one or more controllers 136 may receive input (e.g., represented by input data) from the instrument panel 132 of the vehicle 100 and provide output (e.g., represented by output data, display data, etc.) via a human-machine interface (“HMI”) display 134, an audible signal, a speaker, and / or via other components of the vehicle 100. In at least one embodiment, the output may include information such as vehicle speed, velocity, time, map data (e.g., high-definition map). Figure 1A (not shown in the image) Location data (e.g., the location of vehicle 100, as on a map), direction, the location of other vehicles (e.g., occupied grid), information about objects as perceived by controller 136, and the state of those objects, etc. For example, in at least one embodiment, HMI display 134 may display information about the presence of one or more objects (e.g., street signs, caution signs, traffic light changes, etc.), and / or information about driving maneuvers that the vehicle has performed, is performing, or will perform (e.g., changing lanes now, exiting exit 34B within two miles, etc.).

[0032] In at least one embodiment, vehicle 100 also includes a network interface 124, which can communicate over one or more networks using wireless antenna 126 and / or a modem. For example, in at least one embodiment, network interface 124 may be able to communicate via Long Term Evolution (“LTE”), Wideband Code Division Multiple Access (“WCDMA”), Universal Mobile Telecommunications System (“UMTS”), Global System for Mobile Communications (“GSM”), IMT-CDMA Multicarrier (“CDMA2000”), etc. In at least one embodiment, wireless antenna 126 may also use one or more local area networks (such as Bluetooth, Bluetooth Low Energy (“LE”), Z-Wave, ZigBee, etc.) and / or one or more low-power wide area networks (“LPWAN”) (such as LoRaWAN, SigFox, etc.) for communication between objects in the environment (e.g., vehicles, mobile devices, etc.).

[0033] Figure 1B The illustration shows an embodiment according to at least one of the embodiments. Figure 1A Examples of camera positions and fields of view for the autonomous vehicle 100. In at least one embodiment, the camera and corresponding field of view are exemplary embodiments and are not intended to be limiting. For example, in at least one embodiment, additional and / or alternative cameras may be included and / or the cameras may be located at different positions on the vehicle 100.

[0034] In at least one embodiment, the camera type may include, but is not limited to, a digital camera suitable for use with components and / or systems of vehicle 100. In at least one embodiment, one or more cameras may operate at Vehicle Safety Integrity Level (“ASIL”) B and / or another ASIL. In at least one embodiment, depending on the embodiment, the camera type may have any image capture rate, such as 60 frames per second (fps), 120 fps, 240 fps, etc. In at least one embodiment, the camera may be able to use a rolling shutter, a global shutter, another type of shutter, or a combination thereof. In at least one embodiment, the color filter array may include a red transparent transparent transparent (“RCCC”) color filter array, a red transparent transparent blue (“RCCB”) color filter array, a red blue green transparent (“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 at least one embodiment, a transparent pixel camera (such as a camera with an array of RCCC, RCCB and / or RBGC color filters) may be used to attempt to increase photosensitivity.

[0035] In at least one embodiment, one or more cameras may be used to perform advanced driver assistance system (“ADAS”) functions (e.g., as part of a redundancy or fail-safe design). For example, in at least one embodiment, a multi-functional single camera may be mounted to provide functions including lane departure warning, traffic sign assist, and intelligent headlight control. In at least one embodiment, one or more cameras (e.g., all cameras) may simultaneously record and provide image data (e.g., video).

[0036] In at least one embodiment, one or more cameras may be mounted in a mounting assembly, such as a custom-designed (3D-printed) assembly, to remove stray light and reflections from inside the vehicle (e.g., reflections from the dashboard in the windshield mirror), which may interfere with the camera's image data capture capabilities. Referring to a wing mirror mounting assembly, in at least one embodiment, the wing mirror assembly may be custom-3D printed such that the camera mounting plate matches the shape of the wing mirror. In at least one embodiment, one or more cameras may be integrated into the wing mirror. For side-view cameras, in at least one embodiment, one or more cameras may also be integrated into the four pillars at each corner.

[0037] In at least one embodiment, a camera having a field of view including a portion of the environment in front of vehicle 100 (e.g., a front-facing camera) can be used to surround the field of view to help identify forward-facing paths and obstacles, and, with the assistance of controller 136 and / or one or more control SoCs, to help provide information crucial for generating an occupancy grid and / or determining a preferred vehicle path. In at least one embodiment, the front-facing camera can be used to perform many of the same ADAS functions as LIDAR, including but not limited to emergency braking, pedestrian detection, and collision avoidance. In at least one embodiment, the front-facing camera can also be used for ADAS functions and systems, including but not limited to lane departure warning (“LDW”), autonomous cruise control (“ACC”), and / or other functions such as traffic sign recognition.

[0038] In at least one embodiment, multiple cameras can be used in a front-mounted configuration, including, for example, a monocular camera platform including a CMOS (“complementary metal-oxide-semiconductor”) color imager. In at least one embodiment, a wide-angle camera 170 can be used to sense objects entering the field of view from the periphery (e.g., pedestrians, intersecting traffic, or bicycles). Although Figure 1BOnly one wide-angle camera 170 is shown, but in other embodiments, any number (including zero) of wide-angle cameras 170 may be present on vehicle 100. In at least one embodiment, any number (one or more) of remote cameras 198 (e.g., a pair of long-view stereo cameras) may be used for depth-based object detection, especially for objects for which neural networks have not yet been trained. In at least one embodiment, the remote cameras 198 may also be used for object detection and classification, as well as basic object tracking.

[0039] In at least one embodiment, any number of stereo cameras 168 may be included in the front-mounted configuration. In at least one embodiment, one or more stereo cameras 168 may include an integrated control unit that includes a scalable processing unit that can provide programmable logic (“FPGA”) and a multi-core microprocessor with an integrated controller area network (“CAN”) or Ethernet interface on a single chip. In at least one embodiment, such a unit can be used to generate a 3D map of the environment of the vehicle 100, including distance estimates for all points in the image. In at least one embodiment, one or more of the stereo cameras 168 may include, but are not limited to, one or more compact stereo vision sensors, which may include, but are not limited to, two camera lenses (one on each side) and an image processing chip that can measure the distance from the vehicle 100 to a target object and use the generated information (e.g., metadata) to activate autonomous emergency braking and lane departure warning functions. In at least one embodiment, other types of stereo cameras 168 may be used in addition to those stereo cameras described herein or alternatively.

[0040] In at least one embodiment, a camera (e.g., a side-view camera) having a field of view including a portion of the environment on the side of the vehicle 100 can be used for surround view, providing information for creating and updating occupancy grids, and for generating side impact collision warnings. For example, in at least one embodiment, one or more surround cameras 174 (e.g., such as...) Figure 1B The four surround cameras 174 shown may be positioned on vehicle 100. In at least one embodiment, the surround cameras 174 may be any number and combination of, but not limited to, wide-angle cameras 170, fisheye cameras, 360-degree cameras, and / or the like. For example, in at least one embodiment, the four fisheye cameras may be located at the front, rear, and sides of vehicle 100. In at least one embodiment, vehicle 100 may use three surround cameras 174 (e.g., left, right, and rear), and may use one or more other cameras (e.g., front-facing cameras) as a fourth surround-view camera.

[0041] In at least one embodiment, a camera (e.g., a rear-view camera) having a field of view including a portion of the rear environment of vehicle 100 can be used for parking assistance, surround view, rear collision warning, and creating and updating occupancy grids. In at least one embodiment, a wide variety of cameras can be used, including but not limited to cameras that are also suitable as front-facing cameras (e.g., one or more long-range cameras 198 and / or one or more mid-range cameras 176, one or more stereo cameras 168, one or more infrared cameras 172, etc.), as described herein.

[0042] To enable such a vehicle to perform a task (such as navigating to a destination), the control system can determine one or more actions to be taken at any given time corresponding to that task. For autonomous vehicles, this may include, for example, making one or more adjustments to the steering or braking systems to maneuver the vehicle in a determined manner along a determined path (e.g., a path that allows the vehicle to navigate down the current lane of the road within lane markings) while avoiding collisions and operating at an appropriate speed. To determine the appropriate actions to be taken, it may be advantageous in at least some systems to first determine the path or trajectory the vehicle should follow. Determining the navigation path may include the step of determining information about the environment in which the vehicle is currently operating (such as a high-definition (HD) map, model, or representation of the road and nearby objects), which may be obtained from a source that has previously modeled the environment, or may be generated in real time using sensors or other mechanisms for perceiving information about the environment. In at least some embodiments, it may also be advantageous to determine relevant positioning data or data indicating where the vehicle is in the environment with an accuracy sufficient to meet any safety or operational requirements.

[0043] However, as mentioned, in order to provide a pleasant experience for passengers, it may be desirable to define a path or series of operations that are optimized, at least to some extent, for smoothness or comfort, in addition to safety. In many cases, this involves a balance between safe operation and smooth operation, as safe driving is of paramount importance, but within the bounds of safe operation, it may be desirable to define a path that provides the most pleasant or consistent ride, such as one without rapid adjustments in direction, acceleration, or deceleration.

[0044] For at least some types of automation, what further complicates matters is that the operation of automated or semi-automated devices or systems (such as autonomous vehicles) is often subject to very specific or stringent safety requirements or regulations. While it may be desirable to combine data from different sources to determine the optimal path, it can be difficult to prove that such determination meets those requirements or regulations. Furthermore, for safety reasons, information from different sources is often kept separate so that a failure of a given sensor or subsystem does not affect the overall operation of the vehicle, such as potentially leading to suboptimal path determination. Thus, from a safety perspective, it may be desirable to keep information from different sensors, subsystems, or sources in separate, parallel, or redundant data streams. However, from an optimal operational perspective, it is preferable to combine as much available information as possible to make the best possible decision at any given time.

[0045] For example, autonomous vehicles can utilize Automated Driving Systems (ADS) that perform Complex Dynamic Driving Tasks (DDT). For instance, SAE Automation Levels 3-5 currently require collision avoidance capabilities to enable tasks where a human driver does not need to take over driving during a malfunction event. Analysis of hazardous events and their risks can be performed using Hazard Analysis and Risk Assessment (HARA) according to the ISO 26262 standard. One of the key functions of an example ADS system strongly relevant to safe operation involves path detection. The path detection module identifies drivable paths and can provide information about road geometry and topology. This can include, for example, data on the center path and / or boundaries of road lanes, which can be determined using data from sources such as images or videos captured by cameras on the vehicle, LiDAR data, GPS data, IMU data, or HD map data obtained from a reliable source. A malfunction in the path detection module can lead to hazardous events, such as leaving the road or entering the path of oncoming vehicles. HARA analysis indicates that such hazardous events could be fatal, thus requiring prevention at the highest Automotive Safety Integrity Level (ASIL), such as ASIL D. To ensure that the system can protect against these and other such hazardous events with a sufficiently high level of integrity, any system, service, or process that relies on any of the aforementioned functional components needs to be verified and validated to strict ASIL D compliance, which may require significant development work and costs.

[0046] Therefore, the methods according to various embodiments can attempt to simultaneously optimize both safety and performance, while ensuring the safety compliance of any resulting outcomes. In at least one embodiment, the path detection module of the ADS can provide safety compliance, such as ASIL D compliance, which can significantly reduce the amount of effort required for development, verification, and validation, while providing improved flexibility and robustness in the design. Such a system can provide fusion of path detection sources, as well as safety (e.g., ASIL D) monitoring of the fused path generated by the system. Such a system can provide runtime switching between different combinations of localization and real-time path awareness, while providing high availability. Such a system can also provide propagation fusion of fused localization and fused path based at least in part on processes such as dead reckoning. If a vehicle cannot detect lane markings at the current time, but those markings have been previously detected and information about those markings is stored, the vehicle can use information about its speed and heading, for example, along with that historical data, to infer perception data over at least a period of time (such as a few seconds), for example, a few seconds depending on factors such as speed and environment.

[0047] Figure 2A An overview 200 of example path detection functional safety architectures, according to various embodiments, for use with automated or semi-automated systems, devices, or processes, is shown. This architecture enables automated safety systems to achieve ADS at SAE Level 3 and above. As will be noted, components of this architecture can provide ASIL D (highest level of vehicle hazard avoidance), ASIL B(D) (lower level), and QM(D) compliance, where QM compliance indicates a hazard for which no safety requirements are specified. As mentioned, it may be important for such systems to avoid sending incorrect or unsafe path information (such as paths that cause the vehicle to deviate from the road) to the vehicle's controller, which could lead to serious accidents or damage. Therefore, such path determination systems should ensure that the path information sent provides safety integrity, such as being ASIL D compliant path data. To avoid single points of failure in ASIL D systems that directly lead to incorrect paths, system-level redundancy can be employed so that the failure of a single component does not result in dangerous behavior or incorrect operation.

[0048] like Figure 2AThe architecture shown can utilize independently and distinctly generated lane graphics to monitor and ensure that each final lane graphic used for driving is safe, as with ASIL D guarantees. In at least one embodiment, lane graphics can be generated independently by different modules (such as perception data source 212) based on data provided or utilized from HD maps or real-time lane perception sources (such as one or more cameras or LiDAR sources on the vehicle). In at least one embodiment, the ASIL D requirement of ADS can be achieved by ensuring that the lane graphics generated using combined data, or the "combined" or "fused" lane graphics, are consistent with both the real-time lane perception and the corresponding map data, at least within acceptable tolerances or variations. This can be referred to as an ASIL decomposition scheme between independent component pairs, where, for example, ASIL D is segmented and assigned to ASIL B(D) consistent with ISO 26262 for the perception lane graphics comparator and the map lane graphics comparator in a set of lane graphics comparators 216.

[0049] Such a system can switch between multiple different modules at runtime to ensure the integrity of the final output, such as ASIL D integrity, which depends at least in part on the availability of those modules. The ability to switch at runtime provides benefits including increased system availability, as such a system may not be able to guarantee continued safe operation unless at least two different modules are available to confirm that the final output meets safety and other relevant requirements. A path determination system's ability to detect whether a combined path is correct (e.g., with ASIL D integrity) may not always be sufficient for safety. In some cases where the driver cannot be used as a backup, such a system should guarantee that it can continue to generate the correct path until the system has reached a safe state, such as when the vehicle has stopped in a safe location. In at least one embodiment, one or more relatively simple and lightweight software components can be used, which has advantages, at least compared to more complex software components that can provide lower safety integrity and higher latency.

[0050] Figure 2A The high-level architecture 200 is divided into two parts: a part related to localization (upper part of the figure) and a part related to lane graphics generation (lower part of the figure). These parts are respectively in Figure 2B and 2CThe following is illustrated in more detail. Localization may involve determining the at least current location of an object (such as a vehicle or robot) in an environment (such as a road) based on available and reliable information. In this example, multiple localization data sources 202 may exist, such as cameras (e.g., a front-facing wide-angle camera) and radar sensors, among other such options. Data from each of these sources can be used to generate a corresponding localization determination, such as a camera-based localization determination and a radar-based localization determination, where each determination may involve analyzing captured image or sensor data for the purpose of making the corresponding determination. In this example, these separate determinations may be passed to a localization fusion component 204, which may generate a “combined” or “fused” localization determination based at least in part on the available individual localization determinations (at least those generated with at least minimum confidence). Different algorithms or methods may be used to combine these determinations, such as weighted or spatial averaging, neural networks to infer optimal values ​​based on available inputs or complex fusion functions, and other such options. Where weights are used, these weights may be based at least in part on relative confidence values ​​in the individual determinations. As mentioned, combining or fusing data from different subsystems or sources can be beneficial because there may be obstacles or other events that could render at least a portion of the view from a camera or sensor unreliable, and this data could be reliably determined or filled in from another source while retaining reliable data obtained from that camera or sensor. Using only a single system can be problematic if part of the view is obstructed, as it may not be possible to make reliable determinations with sufficient confidence.

[0051] One or more positioning comparators 206 can be used to compare individual positioning determinations with fused determinations. These comparators may also utilize additional inputs, such as positioning determinations from other systems, such as satellite-based systems, as discussed in more detail later herein. The use of satellite systems can be beneficial in situations such as when a problem occurs on a vehicle and the vehicle needs to be safely towed to the side of the road and stopped, and a path can be generated based on satellite data to perform this task. Satellite systems may be unaffected by problems on a single vehicle and may have additional sensors and processing available to provide additional reliable inputs. The results produced by the positioning comparators can be provided as input to a given location reliability monitor 208, which can continuously determine location reliability values ​​210 for at least the fused positioning data. This may involve comparing the fused positioning determination with the individual positioning determination at each time point to determine if there is sufficient consistency. If the fused positioning is consistent with the ASIL D individual positioning determination, the fused positioning data can also be used and verified to meet ASIL D (or other such requirements).

[0052] As mentioned, there may be instances where individual sensors or sources fail to provide accurate positioning, such as when the view is blurred, the sensor is saturated, or when factors affecting data reliability occur. In such cases, the reliability of the positioning data may be lower, or at least below the reliability required by relevant safety standards or requirements. Figure 2A In the illustrated architecture 200, multiple sources can be dynamically compared, combined, or selected from any of many different combinations, such that any inconsistent sources can be ignored or discarded at least at that point in time. In at least one embodiment, it is sufficient for at least two of these ASIL D-compliant sources to agree on using a corresponding location determination from one of these sources. Further, if at least two of those sources agree on a fused location determination, the fused location determination can be utilized. As described above, one or more comparators 206 can be used to perform corresponding comparisons, and the results of these comparisons can be analyzed by a monitor 208 to determine which location determination is reliable and should be used. In at least one embodiment, these comparators 206 can be considered as voters, as they vote, for example, on whether two or more inputs are within an acceptable range or threshold. The corresponding monitor can then analyze the results of these voters, and if at least two voters agree that the fused location determination is reliable, the fused location determination can be utilized. In this example, a confidence or health determination 210 can be provided for review, for example, to ensure the correct operation of the system. It can also provide health determination 210 and fusion (or individual) positioning determination as input to one or more perception data sources (such as HD mapping sources) for the purpose of determining appropriate lane graphics based at least in part on the positioning determination.

[0053] The lane graphics portion of architecture 200 operates somewhat similarly to the localization portion. Utilizing one or more perception data sources 212, which may include one or more HD lane map images and one or more perception sources, such as cameras or LiDAR, data can be generated that can be analyzed to attempt to make a real-time or real-time determination of the lane graphics based on this real-time perception data, or content perceived by vehicle-mounted (or vehicle-associated) subsystems as appropriate lane graphics based on available information (such as image or sensor data). These proposed lane graphics can be provided to a lane graphics fusion module 214, which can generate a fused or combined lane graphics determination based at least in part on fusing or combining these individual lane graphics determinations (or predictions). A set of comparators 216 can also compare these individual and fused lane graphics to determine if they are sufficiently consistent. The results of these comparisons can be provided to a lane graphics confidence monitor 218, which can determine whether the fused lane graphics determination is consistent with two or more of these individual determinations with sufficient confidence and can output a determination 220 of confidence at least in the fused lane graphics. If the confidence level in the fusion determination is high enough, such as meeting at least a minimum confidence threshold or value, the fused lane graph 222 can be provided to the control system for, for example, vehicle navigation. If the confidence level in the fused lane graph is not high enough, lane graphs from one of the individual or other fusion sources can be utilized to provide continuous safe operation, such as by selecting the individual lane graph with the highest confidence level or health score. As mentioned, operation using individual lane graphs can provide safe operation, but may not be optimal from a performance perspective.

[0054] Figure 2B and 2C More detailed views of the positioning and lane graphic determination sections 230 and 260 of this example architecture 200 are shown respectively. Figure 2BThe diagram illustrates that positioning data sources (such as angle radar 232 and front wide-angle camera 236) can provide data to corresponding positioning modules 234, 238, which can generate individual positioning determinations. These individual positioning determinations can be provided to positioning fusion module 240 to generate fused positioning determinations. The fused positioning data, along with the corresponding individual positioning determinations, can be provided to separate camera positioning components 242 and radar positioning components 244 for comparison, and can be passed to positioning confidence (or health) monitor 242. The confidence monitor may include a primary positioning monitor 244 and a secondary positioning monitor 246 that can generate corresponding confidence determinations, which can be analyzed by the positioning monitor to generate individual positioning confidence values. In at least some embodiments, these confidence determinations may be binary, thereby indicating whether the fused determination is reliable. In at least some embodiments, a confidence score relative to a threshold can be used to determine reliability. This health information can be passed to a propagation positioning module, which can be used by the positioning fusion module for subsequent fusion determinations. The fused information and fused location data can also be transmitted to the location arbitrator 246.

[0055] exist Figure 2CIn the diagram, portion 260 of the architecture for lane pattern determination is shown to receive fused (or individual) location determinations and confidence values ​​or health determinations into a map lane pattern 262, which can use this information with HD map data, for example, to generate lane patterns for the current location and environmental conditions or state. One or more components (such as a front-facing wide-angle camera) can provide image data that can be utilized by a lane-aware graphics component 266 to generate lane patterns based at least in part on the identification of objects (such as lane markings and vehicles) in the captured image or video data. Lane pattern determinations for these individuals can be provided to a lane pattern fusion module 274, which can generate a fused lane pattern determination based at least in part on the lane pattern determinations for these or other provided individuals. The merged lane pattern determination and the individual lane pattern determination can be provided to comparators 268 and 270 for comparison information and the results are provided to a lane pattern health monitor. The lane pattern health monitor may include a main monitor 278 and a secondary monitor 280 for analyzing the comparison data and providing health or confidence levels to the merged lane pattern monitor module or component 284. The merged lane pattern monitor module or component 284 can determine and output the health or confidence data of the merged lane pattern, thereby ensuring that the merged lane pattern meets all safety requirements. This health information can also be provided to the propagation merged lane pattern component to provide data to the lane pattern fusion module 274 for subsequent merged lane pattern determination, and to the positioning arbitrator 272 to determine whether the merged lane pattern data is available and reliable; otherwise, one or more individual lane pattern determinations should be used.

[0056] As shown, to improve the accuracy of the lane graphics function, the lane graphics fusion module 274 can utilize lane graphics data from different sources that can be fused together, such as lane graphics data based on HD maps and perception-based lane graphics data. In at least one embodiment of this architecture, this function can be evaluated as QM(D) and can utilize any suitable fusion function to achieve and maintain the most accurate possible lane graphics output. The positioning fusion module 240 can operate in a similar manner, wherein the positioning function can provide QM(D) fusion of multiple positioning sources (such as radar-based positioning sources and camera-based positioning sources) to achieve optimal accuracy of the positioning function. To achieve the ASIL D integrity required for fused lane graphics and fused positioning, the fused lane graphics and fused positioning can be compared with two independent and distinct ASIL B(D) sources (such as lane graphics based on HD maps and perception-based lane graphics with camera and radar positioning), and the results are sent to an ASIL D monitor that checks the status of the two comparisons. Therefore, an example decomposition scheme can be given by the following equation:

[0057] ASIL D→ASIL B(D)+ASIL B(D)

[0058] Fusion positioning camera positioning comparator radar positioning comparator

[0059] ASIL D→ASIL B(D)+ASIL B(D)

[0060] Fusion of lane graphics: HD map-based lane graphics and perception-based lane graphics.

[0061] ASIL D monitors 242 and 276 provide a simple check of the lane pattern comparator or positioning comparator inputs to ensure that both comparators are valid simultaneously, thereby ensuring that the lane pattern or positioning is ASIL D compliant.

[0062] One of the most important aspects of an ADS system performing complex dynamic driving tasks is its availability in the event of a failure. For ADS systems rated at SAE Automation Levels 3-5, the system should be designed to detect failures and continue operating using other available components and signals that are still functioning correctly. The propagation fusion lane graphics component 282 can generate a representation of lane lines on the road based on the last good value of the fused lane graphics, checked, for example, by both a perception lane graphics comparator and a map lane graphics comparator. This module can take the last good value of the fused lane graphics and estimate new lane lines around its own vehicle, for example, using relative motion (e.g., vehicle direction, speed, and acceleration / deceleration). In some embodiments, such a method may be referred to as dead reckoning because it relies at least in part on historical data when current data may not be sufficiently reliable. In this embodiment, the propagation fusion lane graphics module 282 conforms to ASIL D and can generate lane lines independently. In at least some embodiments, the propagated fusion lane graphics will be accurate only for a finite time period, at least in part due to the self-motion nature of error accumulation. Similarly, the propagation fusion positioning module 248 can generate its own vehicle's precise six-degree-of-freedom (DOF) position and orientation, at least in part, based on the last good value of the fused positioning, checked by, for example, both camera and radar positioning comparators. This module takes the last good value of the fused positioning and uses relative motion to estimate the vehicle's new 6-DOF position and orientation. This module is also ASIL D compliant and can independently generate 6-DOF positioning values. This propagation positioning may also be accurate only for a finite time period, at least in part, due to the self-motion nature of error accumulation. Therefore, dead reckoning can be used to transform the ASIL D-assured lane map and maintain ASIL D integrity for a finite time period in the absence of available monitors.

[0063] The propagation fusion used for both localization and lane map fusion can be combined with runtime switching between different variants to further improve system availability. For example, if the system is running on radar-based localization and camera-based path awareness, and the camera-based path awareness is lost for a few seconds, the system can continue that variant on the propagated fused lane map for a period of time before switching to the camera-based and radar-based localization variant.

[0064] In this example, the positioning arbitrator 246 can determine when the fused positioning data is unavailable or sufficiently reliable, and can provide this data to the comparator and confidence monitor, thereby allowing the system to determine, at least for an instance or time period, to operate using the individual positioning determination instead of the fused positioning determination. This architecture allows the fusion of the positioning source and the lane graphics source to occur at QM(D). This may cause the fusion function to be unavailable for driving when the signal is not accurate enough to pass the comparator's test. When the fused signal is unavailable, the arbitrator can use the ASIL B(D) positioning and lane graphics signals. With this approach, the ASIL D output is still maintained because the comparator independently checks the inputs from it, and the ASIL D monitor still checks two independent ASIL B(D) values.

[0065] In at least one embodiment, the ability to dynamically switch between different combinations of location and real-time path awareness can be further advantageous. Figure 3 An example architecture 300 is shown that provides this dynamic switching. This could include, for example, a runtime switch between two positioning determinations instead of between positioning and real-time path awareness. In this example architecture, map-based lane graphics are used as ASIL D components, and positioning can be performed using the same ASIL D decomposition variables interpreted in previous architectures. This runtime switch between real-time awareness / positioning and positioning-only lane graphics can provide a significant amount of flexibility and can significantly improve usability. Other types of dynamic switching are also possible, such as based on two independent positioning sources (e.g., camera positioning and radar positioning) for map-based lane graphics, and based on two independent real-time perception sources (e.g., camera lane awareness and LiDAR lane awareness) for real-time awareness-based lane graphics. Other dynamic switching can be performed between map-based lane graphics and real-time awareness-based lane graphics.

[0066] Figure 3 The architecture 300 also allows for at least two electronic control units (ECUs) (such as...) Figure 3The architecture dynamically switches between the main ECU in the vehicle (shown in the upper part) and the satellite-based ECU (shown in the lower part). As shown, each ECU may include similar components for acquiring corresponding positioning data 302, 320, generating fused positioning using fusion components 304, 322, comparing the fused positioning with individual positioning using comparators 306, 324, and providing this information to a given location confidence monitor 308. In this example, at least some positioning data from the satellite ECU may be provided as input to the comparator 306 of the main ECU, which helps determine consistency between additional positioning determinations. As shown, each ECU may take positioning determinations and use them with map lane graphics modules 312, 328, and may use data from one or more perception data sources 310, 330 to determine a fused lane graphics using corresponding fusion modules 316, 332. The fused lane graphics may be compared with individual lane graphics by corresponding comparators 314, 334 to have a confidence determination made by the corresponding lane graphics confidence monitors 318, 336. The confidence values ​​of these lane patterns can be used to determine which lane pattern to utilize. In at least some embodiments, the lane pattern from the master ECU will be utilized unless it does not provide sufficient confidence, in which case the lane pattern from the satellite ECU is determined to be usable. Extending functionality between two ECUs by transmitting positioning and path detection signals across ECUs can further improve availability during normal operation, which can compensate for degraded operation or wear and tear of one of the ECUs.

[0067] In some situations, it may be desirable to perform at least some degree of lane awareness switching, at least in part, based on location within the environment. For example, if a vehicle is traveling on a highway with very clear lane markings, real-time lane awareness can produce a very accurate lane map. However, there may be situations where the vehicle is at a large intersection where there are no clear markings about how to proceed. In such cases, real-time lane awareness may not provide a reliable lane map, but a map-based lane map may be reliable enough. Then, during the journey, it may be possible to switch between real-time lane awareness mode and location mode multiple times based solely on road conditions, status, or type to continue driving safely.

[0068] For autonomous vehicles, where decisions need to be made very quickly to ensure safety, many of these components and processes can be performed on the vehicle itself to avoid latency and connectivity issues. Therefore, the vehicle can have one or more processors (e.g., CPU or GPU) to perform at least some of these processes. Parts of this functionality that may not be time-critical (such as map data updates) can be performed by a remote server or in the cloud, among other options. For autonomous vehicles, path determination may need to be made on the order of milliseconds, making it desirable for independent path determination modules to produce results quickly and easily, relying on these results if the fusion process lags behind or cannot produce results on at least the same timescale over a period of time.

[0069] Figure 4 Example process 400 for determining a navigation path (such as a lane graph) that can be utilized according to various embodiments is shown. It should be understood that, for this and other processes discussed herein, additional, fewer, or alternative steps may exist, performed in a similar or alternative order or at least partially in parallel, within the scope of different embodiments, unless specifically stated otherwise. Furthermore, although this example is described with reference to an autonomous vehicle operating within a given lane, it should be understood that other types of safety compliance paths or graphs can be determined for other autonomous or semi-autonomous vehicles, devices, robots, processes, or systems according to various embodiments.

[0070] In this example, location data is obtained from at least two independent and trusted sources (402). This may include, for example, using different cameras or sensors to capture image or sensor data, and making independent location determinations based on the corresponding types of data. Trusted data in this example may refer to data obtained from a system or process that has been determined to comply with one or more regulations or requirements (such as those potentially involving security) and has been determined at least at a minimum confidence level. A fused location determination may be generated based at least in part on the independent location determinations from these independent and trusted sources (404). As mentioned, the fused determination may be made based on any combination or subset of these individual determinations, such as at least in part on factors such as confidence and availability. The fused and individual location determinations may be provided to one or more comparators to determine consistency between at least the independent determinations and the fused location determination (406). In at least one embodiment, each comparator compares the corresponding independent location determination with the fused determination. The results of these comparators can be used to determine the confidence or health of the fusion localization determination (408), such as a binary decision on whether the fusion determination should be used based on a confidence that meets at least a minimum confidence threshold, or otherwise a different localization determination will be selected.

[0071] The fused lane map, or alternative positioning determinations if not selected based on the health of the confidence information, can be generated from real-time perception data from at least two independent and trusted sources to produce independent lane maps (410). The fused lane map can be generated at least in part based on these individual lane maps from these independent sources (412). The fused lane map determination and the individual lane map determinations can be provided to one or more comparators for comparison between the fused lane map and the individual lane maps (414). The confidence in the fused lane map can be determined at least in part based on the number of consistent values ​​from the comparators (416). If the confidence or health of the fused lane map is determined to at least meet a minimum confidence threshold or criterion (418), then the fused lane map can be provided to an operational component (420), such as a drive unit or other device, system, service, or process, for navigation or operation of the corresponding vehicle. If the confidence level in the fused lane graph does not meet at least the minimum confidence threshold or standard, an alternative lane graph may be selected to provide navigation or operation (422), wherein the alternative lane graph may be any individual lane graph, or a fused lane graph from another system or control unit, or other such options.

[0072] Figure 5 Another example process 500, according to various embodiments, can utilize dead reckoning in positioning or lane mapping determination. In this example, real-time perception data is obtained from at least two independent and trusted sources (502). The real-time perception data received from each of these independent sources can be analyzed to determine whether the data meets at least a minimum confidence criterion (504), such as being necessary to ensure compliance with one or more safety regulations or requirements. If perception data is not received from one of these independent sources for a period of time, then that source can be treated similarly to one of these sources with insufficient confidence. If it is determined that the perception data from at least one of these independent sources does not meet the corresponding criterion (506), then a dead reckoning process can be initiated. This may involve accessing recently fused and / or independent positioning data (508), and determining the current motion (e.g., self-motion) or state of the vehicle (510). Based at least in part on the recent positioning data and the vehicle's motion or state, positioning data can be inferred for at least a period of time when inference is determined to be reliable (512). In different embodiments, this may involve inferring one or more individual positioning determinations, or inferring fused positioning. If all location data is redefined to meet all relevant criteria, then real-time sensed data can be used to determine an individual's location (514). If more than two location determinations are available, a fused location determination can be generated at least in part based on the available location determinations (516), where data such as information about... Figure 4The described process analyzes the fused location information for safety and compliance purposes. If the location determination based solely on inference is available, it can be used for route determination, at least enabling the vehicle to operate safely or move to a safe location until reliable data becomes available again.

[0073] The different methods presented in this paper are lightweight enough to be executed in real time on different types of devices or systems, such as intelligent vehicles, robots, servers, personal computers, or automated devices. Such processing can be performed using data captured or generated on the device or system or received from an external source, such as sensor or map data received via at least one network. This source can be any suitable source, such as a separate client device, a device management system, or a third-party data provider, among other options. In some instances, the processing and / or use of this data can be performed by one of these other devices, systems, or entities and then provided to the vehicle (or another such receiver) for use.

[0074] As an example, Figure 6An example network configuration 600 is shown that can be used to provide, generate, modify, encode, and / or transmit data. In at least one embodiment, a vehicle 602, or other automated or semi-automated device or system, can use components of a status monitoring application 604 on vehicle 602 and data locally stored on the vehicle to generate or receive data. In at least one embodiment, a vehicle monitoring application 624 executing on a data or content server 620 (e.g., a cloud server or edge server) can initiate a session associated with at least vehicle 602, such as by utilizing a session manager and user data stored in a vehicle database 634, and can allow content related to routes, locations, or operations to be determined or managed by a data manager 626. A route generation module 628 may attempt to provide map, route, or location data to vehicle 602 using data from different sources, using information that can be locally stored in a map database 632, received from a third-party service 660, or received from satellite 670, and other such options. A security module 630 may be used to determine the compliance of this data with one or more security requirements. In at least some embodiments, this data may be designed to be at least redundant with the data determined on vehicle 602. At least a portion of the data can be transmitted to vehicle 602 via a suitable transmission manager 622 for transmission through download, streaming, or another such transmission channel. An encoder can be used to encode and / or compress the data before transmission to vehicle 602. In at least one embodiment, vehicle 602 receiving the data can provide it to a corresponding status monitor 604, which may (or alternatively) include a path generator 608 and a safety compliance module 6104 for analyzing data received to or captured by vehicle 602 and generating or selecting appropriate operational instructions or determinations to provide to vehicle control system 606, such as enabling vehicle navigation along a determined path. A decoder can also be used, as appropriate, to decode data received via network 640. In at least one embodiment, at least some of the data (e.g., map data or historical perception data) may already be stored on, generated on, or accessible by vehicle 602, such that at least that portion of the data does not require transmission via network 640, such as in cases where the data may have been previously downloaded or stored locally on a hard disk or optical disc. In at least one embodiment, a transport mechanism such as data streaming may be used to transmit the data from server 620. In at least one embodiment, at least a portion of the data may be obtained, determined, or streamed from another source (such as a separate client device 650 or a third-party service 660 that may also include functions for making relevant determinations). In at least one embodiment, a portion of the function may be performed using multiple computing devices or multiple processors within one or more computing devices (such as a combination of CPU and GPU).

[0075] In this example, the device may include any suitable device with computing or sensing capabilities, such as a vehicle, robot, automation system, desktop computer, laptop computer, set-top box, streaming device, game console, smartphone, tablet computer, smart vehicle, robotic-assisted machine, VR headset, AR goggles, wearable computer, or smart TV. Each device may be able to transmit and receive requests across at least one wired or wireless network (such as the Internet, Ethernet, Local Area Network (LAN), or cellular network, and other such options). In this example, these requests may be submitted to or received from an address associated with a cloud provider that can operate or control one or more electronic resources in the cloud provider environment, such as data centers or server clusters. In at least one embodiment, requests may be received or processed by at least one edge server located at the network edge and outside at least one security layer associated with the cloud provider environment. In this way, latency can be reduced by enabling client devices to interact with servers closer to the network, while also improving the security of resources in the cloud provider environment.

[0076] In at least one embodiment, such a system can be used for automated operations. In other embodiments, such a system can be used for other purposes, such as providing image or video content to test or validate autonomous machine applications, or for performing deep learning operations. In at least one embodiment, such a system can be implemented using edge devices, or it can be combined with one or more virtual machines (VMs). In at least one embodiment, such a system can be implemented at least partially in a data center or at least partially using cloud computing resources.

[0077] Reasoning and training logic

[0078] Figure 7A Inference and / or training logic 715 is shown for performing inference and / or training operations associated with one or more embodiments. The following is in conjunction with... Figure 7A and / or Figure 7B Provide details about reasoning and / or training logic 715.

[0079] In at least one embodiment, inference and / or training logic 715 may include, but is not limited to, code and / or data storage 701 for storing forward and / or output weights and / or input / output data, and / or other parameters configuring neurons or layers of a neural network trained for and / or used for inference in one or more embodiments. In at least one embodiment, training logic 715 may include or be coupled to code and / or data storage 701 for storing graph code or other software to control timing and / or sequence, wherein weight and / or other parameter information is loaded to configure logic, including integer and / or floating-point units (collectively, arithmetic logic units (ALUs)). In at least one embodiment, code (such as graph code) loads weight or other parameter information into the processor ALU based on the architecture of the neural network to which the code corresponds. In at least one embodiment, code and / or data storage 701 stores weight parameters and / or input / output data of each layer of a neural network trained or used in one or more embodiments during forward propagation of input / output data and / or weight parameters during training and / or inference using one or more embodiments. In at least one embodiment, any portion of the code and / or data storage 701 may be included within other on-chip or off-chip data storage, including the processor's L1, L2, or L3 cache or system memory.

[0080] In at least one embodiment, any portion of the code and / or data storage 701 may be internal or external to one or more processors or other hardware logic devices or circuits. In at least one embodiment, the code and / or data storage 701 may be a cache memory, dynamic random-addressable memory (“DRAM”), static random-addressable memory (“SRAM”), non-volatile memory (e.g., flash memory), or other storage. In at least one embodiment, the choice of whether the code and / or data storage 701 is internal or external to the processor, for example, or composed of DRAM, SRAM, flash memory, or some other storage type, may depend on the available on-chip or off-chip storage space, the latency requirements of the training and / or inference functions being performed, the batch size of the data used in the inference and / or training of the neural network, or some combination of these factors.

[0081] In at least one embodiment, the inference and / or training logic 715 may include, but is not limited to, code and / or data storage 705 to store backpropagation and / or output weights and / or input / output data neural networks corresponding to neurons or layers of a neural network trained and / or used for inference in one or more embodiments. In at least one embodiment, during training and / or inference using one or more embodiments, the code and / or data storage 705 stores weight parameters and / or input / output data for each layer of a neural network trained or used in one or more embodiments during backpropagation of input / output data and / or weight parameters. In at least one embodiment, the training logic 715 may include or be coupled to code and / or data storage 705 for storing graph code or other software to control timing and / or sequence, wherein weight and / or other parameter information is loaded to configure logic including integer and / or floating-point units (collectively, an arithmetic logic unit (ALU)). In at least one embodiment, code (such as graph code) causes weight or other parameter information to be loaded into the processor ALU based on the architecture of the neural network corresponding to the code. In at least one embodiment, any portion of the code and / or data storage 705 may be included together with other on-chip or off-chip data storage, including the processor's L1, L2, or L3 cache or system memory. In at least one embodiment, any portion of the code and / or data storage 705 may be internal or external to one or more processors or other hardware logic devices or circuits. In at least one embodiment, the code and / or data storage 705 may be cache memory, DRAM, SRAM, non-volatile memory (e.g., flash memory), or other storage. In at least one embodiment, the choice between the code and / or data storage 705 being internal or external to the processor, for example, whether it consists of DRAM, SRAM, flash memory, or some other type of storage, depends on whether the available storage is on-chip or off-chip, the latency requirements of the training and / or inference functions being performed, the data batch size used in the inference and / or training of the neural network, or some combination of these factors.

[0082] In at least one embodiment, code and / or data storage 701 and code and / or data storage 705 may be separate storage structures. In at least one embodiment, code and / or data storage 701 and code and / or data storage 705 may be the same storage structure. In at least one embodiment, code and / or data storage 701 and code and / or data storage 705 may have partially identical storage structures and partially separate storage structures. In at least one embodiment, any portion of code and / or data storage 701 and code and / or data storage 705 may be included with other on-chip or off-chip data storage, including the processor's L1, L2, or L3 cache or system memory.

[0083] In at least one embodiment, the inference and / or training logic 715 may include, but is not limited to, one or more arithmetic logic units (“ALUs”) 710 (including integer and / or floating-point units) for performing logical and / or mathematical operations at least in part based on or instructed by training and / or inference code (e.g., graph code), the results of which may produce activations (e.g., output values ​​from layers or neurons within a neural network) stored in activation storage 720, which are functions of input / output and / or weight parameter data stored in code and / or data storage 701 and / or code and / or data storage 705. In at least one embodiment, activation is activated in response to execution instructions or other code, and linear algebraic and / or matrix-based mathematical generation performed by ALU 710 is stored in activation storage 720, wherein weight values ​​stored in code and / or data storage 705 and / or code and / or data storage 701 are used as operands with other values, such as bias values, gradient information, momentum values, or other parameters or hyperparameters, and any or all of these can be stored in code and / or data storage 705 or code and / or data storage 701 or other on-chip or off-chip storage.

[0084] In at least one embodiment, one or more processors or other hardware logic devices or circuits include one or more ALUs 710, while in another embodiment, one or more ALUs 710 may be located outside the processor or other hardware logic device or the circuitry using them (e.g., a coprocessor). In at least one embodiment, one or more ALUs 710 may be included within an execution unit of a processor, or otherwise included in a group of ALUs accessible by the execution unit of the processor, which may be within the same processor or distributed among different processors of different types (e.g., a central processing unit, a graphics processing unit, a fixed-function unit, etc.). In at least one embodiment, code and / or data storage 701, code and / or data storage 705, and activation storage 720 may be the same processor or other hardware logic device or circuitry, while in another embodiment, they may be in different processors or other hardware logic devices or circuitry, or some combination of the same and different processors or other hardware logic devices or circuitry. In at least one embodiment, any portion of activation storage 720 may be included together with other on-chip or off-chip data storage, including the processor's L1, L2, or L3 cache or system memory. Furthermore, inference and / or training code may be stored together with other code accessible to the processor or other hardware logic or circuitry, and may be retrieved and / or processed using the processor’s fetch, decode, schedule, execute, exit, and / or other logic circuitry.

[0085] In at least one embodiment, the active memory 720 may be a cache memory, DRAM, SRAM, non-volatile memory (e.g., flash memory), or other memory. In at least one embodiment, the active memory 720 may be wholly or partially located inside or outside one or more processors or other logic circuits. In at least one embodiment, the choice of whether the active memory 720 is internal to or external to the processor may depend on the available on-chip or off-chip storage, the latency requirements for training and / or inference functions, the batch size of data used in inference and / or training the neural network, or some combination of these factors. For example, it may include DRAM, SRAM, flash memory, or other memory types. In at least one embodiment, Figure 7A The inference and / or training logic 715 shown can be used in conjunction with an application-specific integrated circuit (“ASIC”), such as those from Google. Processing unit, from Graphcore TM Inference processing units (IPUs) or from Intel Corp. (e.g., "Lake Crest") processor. In at least one embodiment, Figure 7A The inference and / or training logic 715 shown can be used in conjunction with central processing unit (“CPU”) hardware, graphics processing unit (“GPU”) hardware, or other hardware such as field programmable gate array (“FPGA”)

[0086] Figure 7B Inference and / or training logic 715 according to at least one or more embodiments is illustrated. In at least one embodiment, the inference and / or training logic 715 may include, but is not limited to, hardware logic, wherein computational resources are dedicated or otherwise uniquely used in conjunction with weight values ​​or other information corresponding to one or more layers of neurons within a neural network. In at least one embodiment, Figure 7B The inference and / or training logic 715 shown can be used in conjunction with an application-specific integrated circuit (ASIC), such as those from Google. Processing unit, from Graphcore TM Inference processing units (IPUs) or from Intel Corp. (e.g., "Lake Crest") processor. In at least one embodiment, Figure 7BThe inference and / or training logic 715 shown can be used in conjunction with central processing unit (CPU) hardware, graphics processing unit (GPU) hardware, or other hardware (e.g., field-programmable gate array (FPGA)). In at least one embodiment, the inference and / or training logic 715 includes, but is not limited to, code and / or data storage 701 and code and / or data storage 705, which can be used to store code (e.g., graph code), weight values, and / or other information, including bias values, gradient information, momentum values, and / or other parameter or hyperparameter information. Figure 7B In at least one embodiment shown, each of code and / or data storage 701 and code and / or data storage 705 is associated with dedicated computing resources (e.g., computing hardware 702 and computing hardware 706), respectively. In at least one embodiment, each of computing hardware 702 and computing hardware 706 includes one or more ALUs that perform mathematical functions (e.g., linear algebraic functions) only on the information stored in code and / or data storage 701 and code and / or data storage 705, respectively, and the results of the function execution are stored in activation storage 720.

[0087] In at least one embodiment, each of the code and / or data storage 701 and 705 and the corresponding computing hardware 702 and 706 corresponds to a different layer of the neural network, such that activation obtained from one “store / computation pair 701 / 702” of the code and / or data storage 701 and computing hardware 702 provides input as input to the next “store / computation pair 705 / 706” of the code and / or data storage 705 and computing hardware 706, in order to reflect the conceptual organization of the neural network. In at least one embodiment, each store / computation pair 701 / 702 and 705 / 706 may correspond to more than one neural network layer. In at least one embodiment, additional store / computation pairs (not shown) may be included in the inference and / or training logic 715 after or in parallel with the store / computation pairs 701 / 702 and 705 / 706.

[0088] Data Center

[0089] Figure 8 An example data center 800 that can be used with at least one embodiment is shown. In at least one embodiment, the data center 800 includes a data center infrastructure layer 810, a framework layer 820, a software layer 830, and an application layer 840.

[0090] In at least one embodiment, such as Figure 8As shown, the data center infrastructure layer 810 may include a resource coordinator 812, packet computing resources 814, and node computing resources (“nodes CR”) 816(1)-816(N), where “N” represents any positive integer. In at least one embodiment, nodes CR 816(1)-816(N) may include, but are not limited to, any number of central processing units (“CPUs”) or other processors (including accelerators, field-programmable gate arrays (FPGAs), graphics processors, etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid-state drives or disk drives), network input / output (“NW I / O”) devices, network switches, virtual machines (“VMs”), power modules, and cooling modules, etc. In at least one embodiment, one or more nodes CR 816(1)-816(N) may be servers having one or more of the aforementioned computing resources.

[0091] In at least one embodiment, the grouped computing resource 814 may include individual groups (not shown) of node CRs housed within one or more racks, or a plurality of racks (also not shown) housed within data centers in various geographic locations. The individual groups of node CRs within the grouped computing resource 814 may include computing, networking, memory, or storage resources that can be configured or allocated to support groups of one or more workloads. In at least one embodiment, several node CRs, including CPUs or processors, may be grouped within one or more racks to provide computing resources to support one or more workloads. In at least one embodiment, the one or more racks may also include any number of power modules, cooling modules, and network switches, in any combination.

[0092] In at least one embodiment, resource coordinator 812 may configure or otherwise control one or more nodes CR816(1)-816(N) and / or grouped computing resources 814. In at least one embodiment, resource coordinator 812 may include a software design infrastructure (“SDI”) management entity for data center 800. In at least one embodiment, resource coordinator may include hardware, software, or some combination thereof.

[0093] In at least one embodiment, such as Figure 8As shown, framework layer 820 includes a job scheduler 822, a configuration manager 824, a resource manager 826, and a distributed file system 828. In at least one embodiment, framework layer 820 may include a framework of software 832 supporting software layer 830 and / or one or more applications 842 supporting application layer 840. In at least one embodiment, software 832 or application 842 may respectively include web-based service software or applications, such as services or applications provided by Amazon Web Services, Google Cloud, and Microsoft Azure. In at least one embodiment, framework layer 820 may be, but is not limited to, a free and open-source software web application framework, such as Apache Spark, which can utilize distributed file system 828 for large-scale data processing (e.g., "big data"). TM (Hereinafter referred to as "Spark"). In at least one embodiment, the job scheduler 832 may include a Spark driver to facilitate the scheduling of workloads supported by various layers of the data center 800. In at least one embodiment, the configuration manager 824 may be able to configure different layers, such as the software layer 830 and the framework layer 820, which includes Spark and a distributed file system 828 for supporting large-scale data processing. In at least one embodiment, the resource manager 826 is able to manage cluster or group computing resources mapped to or allocated to support the distributed file system 828 and the job scheduler 822. In at least one embodiment, the cluster or group computing resources may include group computing resources 814 on the data center infrastructure layer 810. In at least one embodiment, the resource manager 826 may coordinate with the resource coordinator 812 to manage these mapped or allocated computing resources.

[0094] In at least one embodiment, the software 832 included in the software layer 830 may include software used by at least a portion of the nodes CR816(1)-816(N), the grouped computing resources 814, and / or the distributed file system 828 of the framework layer 820. One or more types of software may include, but are not limited to, Internet web page search software, email virus scanning software, database software, and streaming video content software.

[0095] In at least one embodiment, one or more applications 842 included in application layer 840 may include one or more types of applications used by at least a portion of nodes CR816(1)-816(N), grouped computing resources 814, and / or the distributed file system 828 of framework layer 820. One or more types of applications may include, but are not limited to, any number of genomics applications, cognitive computing and machine learning applications, including training or inference software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.), or other machine learning applications used in conjunction with one or more embodiments.

[0096] In at least one embodiment, any of the configuration manager 824, resource manager 826, and resource coordinator 812 can perform any number and type of self-modification actions based on any amount and type of data acquired in any technically feasible manner. In at least one embodiment, self-modification actions can mitigate potentially poor configuration decisions by data center operators of data center 800 and can prevent underutilization and / or poor performance of the data center.

[0097] In at least one embodiment, data center 800 may include tools, services, software, or other resources to train one or more machine learning models or to use one or more machine learning models to predict or infer information according to one or more embodiments described herein. For example, in at least one embodiment, a machine learning model can be trained by calculating weight parameters based on a neural network architecture using the software and computing resources described above with respect to data center 800. In at least one embodiment, information can be inferred or predicted using trained machine learning models corresponding to one or more neural networks using the resources described above with respect to data center 800 by using weight parameters calculated through one or more training techniques described herein.

[0098] In at least one embodiment, the data center may use a CPU, application-specific integrated circuit (ASIC), GPU, FPGA, or other hardware to utilize the aforementioned resources to perform training and / or inference. Furthermore, one or more of the aforementioned software and / or hardware resources may be configured as a service to allow a user to train or perform information inference, such as image recognition, speech recognition, or other artificial intelligence services.

[0099] Inference and / or training logic 715 is used to perform inference and / or training operations associated with one or more embodiments. The following is combined with... Figure 7A and / or Figure 7B Details are provided regarding the inference and / or training logic 715. In at least one embodiment, the inference and / or training logic 715 can be implemented in the system. Figure 8 Used in this context for inference or prediction operations based at least in part on weight parameters computed using neural network training operations, neural network functions and / or architectures, or neural network use cases described herein.

[0100] These components can be used to synthesize a compositional image into a single representation using parameters determined from one or more quality assessment values.

[0101] Computer System

[0102] Figure 9 This is a block diagram illustrating an exemplary computer system according to at least one embodiment. The exemplary computer system may be a system of interconnected devices and components, a system-on-a-chip (SoC), or some combination thereof formed with a processor, which may include an execution unit to execute instructions. In at least one embodiment, according to this disclosure, such as the embodiments described herein, computer system 900 may include, but is not limited to, components such as processor 902, whose execution unit includes logic to execute algorithms for process data. In at least one embodiment, computer system 900 may include a processor, such as one available from Intel Corporation of Santa Clara, California. Processor family, Xeon TM , XScale TM and / or StrongARM TM , Core TM or Nervana TM A microprocessor may be used, although other systems (including PCs, engineering workstations, set-top boxes, etc.) with other microprocessors may also be used. In at least one embodiment, computer system 900 may execute a version of the Windows operating system available from Microsoft Corporation of Redmond, Washington, although other operating systems (such as UNIX and Linux), embedded software, and / or graphical user interfaces may also be used.

[0103] The embodiments can be used in other devices, such as handheld devices and embedded applications. Some examples of handheld devices include cellular phones, Internet Protocol (IP) devices, digital cameras, personal digital assistants (“PDAs”), and handheld PCs. In at least one embodiment, the embedded application may include a microcontroller, a digital signal processor (“DSP”), a system-on-a-chip (SoC), a network computer (“NetPC”), a set-top box, a network hub, a wide area network (“WAN”) switch, or any other system that can execute one or more instructions according to at least one embodiment.

[0104] In at least one embodiment, the computer system 900 may include, but is not limited to, a processor 902, which may include, but is not limited to, one or more execution units 908, to perform machine learning model training and / or inference according to the techniques described herein. In at least one embodiment, the computer system 900 is a single-processor desktop or server system, but in another embodiment, the computer system 900 may be a multiprocessor system. In at least one embodiment, the processor 902 may include, but is not limited to, a Complex Instruction Set Computer (“CISC”) microprocessor, a Reduced Instruction Set Computing (“RISC”) microprocessor, a Very Long Instruction Word (“VLIW”) microprocessor, a processor implementing instruction set combination, or any other processor device, such as a digital signal processor. In at least one embodiment, the processor 902 may be coupled to a processor bus 910, which can transmit data signals between the processor 902 and other components in the computer system 900.

[0105] In at least one embodiment, processor 902 may include, but is not limited to, a Level 1 (“L1”) internal cache memory (“cache”) 904. In at least one embodiment, processor 902 may have a single internal cache or multiple levels of internal cache. In at least one embodiment, the cache memory may reside external to processor 902. Depending on specific implementation and requirements, other embodiments may also include a combination of internal and external caches. In at least one embodiment, register file 906 may store different types of data in various registers, including but not limited to integer registers, floating-point registers, status registers, and instruction pointer registers.

[0106] In at least one embodiment, an execution unit 908, including but not limited to logic for performing integer and floating-point operations, is also located within the processor 902. In at least one embodiment, the processor 902 may further include a microcode (“ucode”) read-only memory (“ROM”) for storing microcode of certain macro instructions. In at least one embodiment, the execution unit 908 may include logic for processing a packaged instruction set 909. In at least one embodiment, by including the packaged instruction set 909 in the instruction set of the general-purpose processor 902, along with the associated circuitry for executing the instructions, packaged data in the general-purpose processor 902 can be used to perform operations used by many multimedia applications. In one or more embodiments, many multimedia applications can be executed more quickly and efficiently by using the full width of the processor's data bus to perform operations on the packaged data, which may eliminate the need to transfer smaller data units on the processor's data bus to perform one or more operations on one data element at a time.

[0107] In at least one embodiment, execution unit 908 may also be used in a microcontroller, embedded processor, graphics device, DSP, and other types of logic circuitry. In at least one embodiment, computer system 900 may include, but is not limited to, memory 920. In at least one embodiment, memory 920 may be implemented as a dynamic random access memory (“DRAM”) device, a static random access memory (“SRAM”) device, a flash memory device, or another storage device. In at least one embodiment, memory 920 may store instructions 919 and / or data 921 represented by data signals that can be executed by processor 902.

[0108] In at least one embodiment, the system logic chip may be coupled to the processor bus 910 and the memory 920. In at least one embodiment, the system logic chip may include, but is not limited to, a memory controller hub (“MCH”) 916, and the processor 902 may communicate with the MCH 916 via the processor bus 910. In at least one embodiment, the MCH 916 may provide a high-bandwidth memory path 918 to the memory 920 for instruction and data storage, as well as for storage of graphics commands, data, and textures. In at least one embodiment, the MCH 916 may initiate data signals between the processor 902, the memory 920, and other components in the computer system 900, and bridge data signals between the processor bus 910, the memory 920, and the system I / O interface 922. In at least one embodiment, the system logic chip may provide a graphics port for coupling to a graphics controller. In at least one embodiment, the MCH 916 may be coupled to the memory 920 via the high-bandwidth memory path 918, and the graphics / video card 912 may be coupled to the MCH 916 via an Accelerated Graphics Port (“AGP”) interconnect 914.

[0109] In at least one embodiment, computer system 900 may use system I / O interface 922, i.e., a proprietary hub interface bus, to couple MCH 916 to I / O controller hub (“ICH”) 930. In at least one embodiment, ICH 930 may provide direct connectivity to certain I / O devices via a local I / O bus. In at least one embodiment, the local I / O bus may include, but is not limited to, a high-speed I / O bus for connecting peripheral devices to memory 920, chipset, and processor 902. Examples may include, but are not limited to, audio controller 929, firmware hub (“Flash BIOS”) 928, wireless transceiver 926, data storage 924, a conventional I / O controller 923 including user input and keyboard interfaces, serial expansion port 927 (e.g., a Universal Serial Bus (USB) port), and network controller 934. Data storage 924 may include hard disk drives, floppy disk drives, CD-ROM devices, flash memory devices, or other mass storage devices.

[0110] In at least one embodiment, Figure 9 A system including interconnected hardware devices or "chips" is shown, while in other embodiments, Figure 9 A system-on-a-chip (“SoC”) may be shown. In at least one embodiment, Figure 9The devices shown can be interconnected with proprietary interconnects, standardized interconnects (e.g., PCIe), or some combination thereof. In at least one embodiment, one or more components of the computer system 900 are interconnected using a Compute Fast Link (CXL) interconnect.

[0111] The inference and / or training logic 715 is used to perform inference and / or training operations associated with one or more embodiments. The following is in conjunction with... Figure 7A 7B and / or 7B provide details about the inference and / or training logic 715. In at least one embodiment, the inference and / or training logic 715 can be in the system Figure 9 The operation is used to infer or predict based at least in part on weight parameters calculated using the neural network training operations, neural network functions and / or architecture or neural network usage described herein.

[0112] These components can be used to synthesize a compositional image into a single representation using parameters determined from one or more quality assessment values.

[0113] Figure 10 This is a block diagram illustrating an electronic device 1000 utilizing a processor 1010 according to at least one embodiment. In at least one embodiment, the electronic device 1000 may be, for example, but not limited to, a laptop, tower server, rack server, blade server, laptop computer, desktop computer, tablet computer, mobile device, telephone, embedded computer, or any other suitable electronic device.

[0114] In at least one embodiment, system 1000 may include, but is not limited to, processor 1010, which is communicatively coupled to any suitable number or type of components, peripherals, modules, or devices. In at least one embodiment, processor 1010 is coupled using a bus or interface (such as an I2C bus), system management bus (“SMBus”), low pin count (LPC) bus, serial peripheral interface (“SPI”), high definition audio (“HDA”) bus, serial advanced technology accessory (“SATA”) bus, universal serial bus (“USB”) (versions 1, 2, 3), or universal asynchronous receiver / transmitter (“UART”) bus. In at least one embodiment, Figure 10 One embodiment is shown that includes interconnected hardware devices or "chips," while in other embodiments, Figure 10 An exemplary system-on-a-chip (“SoC”) may be illustrated. In at least one embodiment, Figure 10 The devices shown can be interconnected with dedicated interconnects, standardized interconnects (e.g., PCIe), or some combination thereof. In at least one embodiment, a compute fast link (CXL) interconnect is used for interconnection. Figure 10 One or more components.

[0115] In at least one embodiment, Figure 10 It may include a display 1024, a touch screen 1025, a touchpad 1030, a near field communication unit (“NFC”) 1045, a sensor hub 1040, a thermal sensor 1046, a fast chipset (“EC”) 1035, a trusted platform module (“TPM”) 1038, a BIOS / firmware / flash memory (“BIOS, FW flash”) 1022, a DSP 1060, a drive 1020 (such as a solid-state drive (“SSD”) or a hard disk drive (“HDD”)), a wireless local area network unit (“WLAN”) 1050, a Bluetooth unit 1052, a wireless wide area network unit (“WWAN”) 1056, a global positioning system (GPS) 1055, a camera (“USB 3.0 camera”) 1054 (e.g., a USB 3.0 camera), and / or a low-power double data rate (“LPDDR”) memory unit (“LPDDR3”) 1015 implemented therein, such as the LPDDR3 standard. These components can each be implemented in any suitable manner.

[0116] In at least one embodiment, other components may be communicatively coupled to processor 1010 via the components discussed above. In at least one embodiment, accelerometer 1041, ambient light sensor (“ALS”) 1042, compass 1043, and gyroscope 1044 may be communicatively coupled to sensor hub 1040. In at least one embodiment, thermal sensor 1039, fan 1037, keyboard 1046, and touchpad 1030 may be communicatively coupled to EC 1035. In at least one embodiment, speaker 1063, headset 1064, and microphone (“mic”) 1065 may be communicatively coupled to audio unit (“audio codec and Class D amplifier”) 1062, which in turn may be communicatively coupled to DSP 1060. In at least one embodiment, audio unit 1064 may include (e.g., but not limited to) audio encoder / decoder (“codec”) and Class D amplifier. In at least one embodiment, SIM card (“SIM”) 1057 may be communicatively coupled to WWAN unit 1056. In at least one embodiment, components such as WLAN unit 1050, Bluetooth unit 1052, and WWAN unit 1056 may be implemented in a next-generation form factor (“NGFF”).

[0117] The inference and / or training logic 715 is used to perform inference and / or training operations associated with one or more embodiments. The following is in conjunction with... Figure 7A and / or Figure 7B Details are provided regarding the inference and / or training logic 715. In at least one embodiment, the inference and / or training logic 715 can be... Figure 10The system is used to infer or predict operations based at least in part on weight parameters calculated using neural network training operations, neural network functions and / or architecture or neural network usage as described herein.

[0118] These components can be used to synthesize a compositional image into a single representation using parameters determined from one or more quality assessment values.

[0119] Figure 11 This is a block diagram of a processing system according to at least one embodiment. In at least one embodiment, system 1100 includes one or more processors 1102 and one or more graphics processors 1108, and may be a single-processor desktop system, a multi-processor workstation system, or a server system having a large number of processors 1102 or processor cores 1107. In at least one embodiment, system 1100 is a processing platform incorporated within a system-on-a-chip (SoC) integrated circuit for use in mobile, handheld, or embedded devices.

[0120] In at least one embodiment, system 1100 may include or be integrated into a game console (including game and media consoles), mobile game console, handheld game console, or online game console in a server-based gaming platform. In at least one embodiment, system 1100 is a mobile phone, smartphone, tablet computing device, or mobile internet device. In at least one embodiment, processing system 1100 may also include a wearable device (such as a smartwatch, smart glasses, augmented reality, or virtual reality device), coupled to or integrated within the wearable device. In at least one embodiment, processing system 1100 is a television or set-top box device having one or more processors 1102 and a graphical interface generated by one or more graphics processors 1108.

[0121] In at least one embodiment, one or more processors 1102 each include one or more processor cores 1107 for processing instructions that, when executed, perform operations for system and user software. In at least one embodiment, each of the one or more processor cores 1107 is configured to process a particular instruction set 1109. In at least one embodiment, the instruction set 1109 may facilitate Complex Instruction Set Computing (CISC), Reduced Instruction Set Computing (RISC), or computation via Very Long Instruction Word (VLIW). In at least one embodiment, the processor cores 1107 may each process different instruction sets 1109, which may include instructions that facilitate the emulation of other instruction sets. In at least one embodiment, the processor cores 1107 may also include other processing devices, such as digital signal processors (DSPs).

[0122] In at least one embodiment, processor 1102 includes cache memory 1104. In at least one embodiment, processor 1102 may have a single internal cache or multi-level internal caches. In at least one embodiment, the cache memory is shared among different components of processor 1102. In at least one embodiment, processor 1102 also uses an external cache (e.g., a Level 3 (L3) cache or a last-level cache (LLC)) (not shown), which can be shared among processor cores 1107 using known cache coherence techniques. In at least one embodiment, register file 1106 is additionally included in processor 1102, which may include different types of registers (e.g., integer registers, floating-point registers, status registers, and instruction pointer registers) for storing different types of data. In at least one embodiment, register file 1106 may include general-purpose registers or other registers.

[0123] In at least one embodiment, one or more processors 1102 are coupled to one or more interface buses 1110 to transmit communication signals, such as address, data, or control signals, between the processors 1102 and other components in the system 1100. In at least one embodiment, the interface bus 1110 may be a processor bus, such as a version of the Direct Media Interface (DMI) bus. In at least one embodiment, the interface 1110 is not limited to the DMI bus and may include one or more peripheral component interconnect buses (e.g., PCI, PCI Express), memory buses, or other types of interface buses. In at least one embodiment, the processor 1102 includes an integrated memory controller 1116 and a platform controller hub 1130. In at least one embodiment, the memory controller 1116 facilitates communication between memory devices and other components of the system 1100, while the platform controller hub (PCH) 1130 provides connectivity to I / O devices via a local I / O bus.

[0124] In at least one embodiment, memory device 1120 may be a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, a flash memory device, a phase-change memory device, or some other memory device with performance suitable for use as process memory. In at least one embodiment, memory device 1120 may operate as system memory of system 1100 to store data 1122 and instructions 1121 for use when one or more processors 1102 execute an application or process. In at least one embodiment, memory controller 1116 is also coupled to an optional external graphics processor 1112, which may communicate with one or more graphics processors 1108 of processor 1102 to perform graphics and media operations. In at least one embodiment, display device 1111 may be connected to (one or more) processors 1102. In at least one embodiment, display device 1111 may include one or more internal display devices, such as in a mobile electronic device or laptop device attached via a display interface (e.g., DisplayPort, etc.) or an external display device. In at least one embodiment, the display device 1111 may include a head-mounted display (HMD), such as a stereoscopic display device for virtual reality (VR) or augmented reality (AR) applications.

[0125] In at least one embodiment, the platform controller hub 1130 enables peripheral devices to connect to the memory device 1120 and the processor 1102 via a high-speed I / O bus. In at least one embodiment, the I / O peripheral devices include, but are not limited to, an audio controller 1146, a network controller 1134, a firmware interface 1128, a wireless transceiver 1126, a touch sensor 1125, and a data storage device 1124 (e.g., a hard disk drive, flash memory, etc.). In at least one embodiment, the data storage device 1124 may be connected via a storage interface (e.g., SATA) or via a peripheral bus (such as a peripheral component interconnect bus (e.g., PCI, PCI Express)). In at least one embodiment, the touch sensor 1125 may include a touchscreen sensor, a pressure sensor, or a fingerprint sensor. In at least one embodiment, the wireless transceiver 1126 may be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile network transceiver, such as a 3G, 4G, or LTE transceiver. In at least one embodiment, the firmware interface 1128 enables communication with the system firmware and may be, for example, a Unified Extensible Firmware Interface (UEFI). In at least one embodiment, network controller 1134 can enable network connectivity to a wired network. In at least one embodiment, a high-performance network controller (not shown) is coupled to interface bus 1110. In at least one embodiment, audio controller 1146 is a multi-channel high-definition audio controller. In at least one embodiment, system 1100 includes an optional legacy I / O controller 1140 for coupling legacy (e.g., Personal System 2 (PS / 2)) devices to the system. In at least one embodiment, platform controller hub 1130 may also be connected to one or more Universal Serial Bus (USB) controllers 1142 to connect input devices, such as keyboard and mouse combinations 1143, cameras 1144, or other USB input devices.

[0126] In at least one embodiment, instances of the memory controller 1116 and platform controller hub 1130 may be integrated into a discrete external graphics processor (such as external graphics processor 1112). In at least one embodiment, the platform controller hub 1130 and / or the memory controller 1116 may be external to one or more processors 1102. For example, in at least one embodiment, system 1100 may include an external memory controller 1116 and platform controller hub 1130, which may be configured as a system chipset-based memory controller hub and peripheral controller hub to communicate with processor 1102.

[0127] The inference and / or training logic 715 is used to perform inference and / or training operations associated with one or more embodiments. The following is in conjunction with... Figure 7A7B and / or 7B provide details regarding the inference and / or training logic 715. In at least one embodiment, some or all of the inference and / or training logic 715 may be incorporated into the graphics processor 1500. For example, in at least one embodiment, the training and / or inference techniques described herein may use one or more ALUs implemented in the graphics processor. Furthermore, in at least one embodiment, the inference and / or training operations described herein may use, in addition to Figure 7A Alternatively, it may be accomplished using logic other than that shown in 7B. In at least one embodiment, the weight parameters may be stored in on-chip or off-chip memory and / or registers (shown or not shown), which configure the graphics processor's ALU for executing one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

[0128] These components can be used to synthesize a compositional image into a single representation using parameters determined from one or more quality assessment values.

[0129] Figure 12 This is a block diagram of a processor 1200 according to at least one embodiment, the processor having one or more processor cores 1202A-1202N, an integrated memory controller 1214, and an integrated graphics processor 1208. In at least one embodiment, the processor 1200 may include up to and including additional cores 1202N, indicated by dashed boxes. In at least one embodiment, each of the processor cores 1202A-1202N includes one or more internal cache units 1204A-1204N. In at least one embodiment, each processor core may also access one or more shared cache units 1206.

[0130] In at least one embodiment, internal cache units 1204A-1204N and shared cache unit 1206 represent a cache memory hierarchy within processor 1200. In at least one embodiment, cache memory units 1204A-1204N may include at least one level of instruction and data cache memory within each processor core, and one or more levels of shared intermediate cache memory, such as Level 2 (L2), Level 3 (L3), Level 4 (L4), or other levels of cache, wherein the highest level of cache preceding external memory is classified as LLC. In at least one embodiment, cache coherence logic maintains coherence between different cache units 1206 and 1204A-1204N.

[0131] In at least one embodiment, the processor 1200 may further include a collection of one or more bus controller units 1216 and system agent cores 1210. In at least one embodiment, one or more bus controller units 1216 manage a set of peripheral buses, such as one or more PCI or PCI Fast buses. In at least one embodiment, the system agent core 1210 provides management functions for different processor components. In at least one embodiment, the system agent core 1210 includes one or more integrated memory controllers 1214 for managing access to different external memory devices (not shown).

[0132] In at least one embodiment, one or more of the processor cores 1202A-1202N include support for simultaneous multithreading. In at least one embodiment, the system agent core 1210 includes components for coordinating and operating the cores 1202A-1202N during multithreaded processing. In at least one embodiment, the system agent core 1210 may additionally include a power control unit (PCU) including logic and components for regulating one or more power states of the processor cores 1202A-1202N and the graphics processor 1208.

[0133] In at least one embodiment, processor 1200 further includes a graphics processor 1208 for performing graphics processing operations. In at least one embodiment, graphics processor 1208 is coupled to a shared cache unit 1206 and a system proxy core 1210 (including one or more integrated memory controllers 1214). In at least one embodiment, system proxy core 1210 further includes a display controller 1211 for driving graphics processor output to one or more coupled displays. In at least one embodiment, display controller 1211 may also be a separate module coupled to graphics processor 1208 via at least one interconnect or may be integrated within graphics processor 1208.

[0134] In at least one embodiment, the ring-based interconnect unit 1212 is used to couple internal components of the processor 1200. In at least one embodiment, alternative interconnect units, such as point-to-point interconnects, switched interconnects, or other technologies, may be used. In at least one embodiment, the graphics processor 1208 is coupled to the ring interconnect 1212 via I / O link 1213.

[0135] In at least one embodiment, I / O link 1213 represents at least one of a variety of I / O interconnects, including on-package I / O interconnects that facilitate communication between different processor components and high-performance embedded memory modules 1218 (such as eDRAM modules). In at least one embodiment, each of the processor cores 1202A-1202N and the graphics processor 1208 uses the embedded memory module 1218 as a shared final-level cache.

[0136] In at least one embodiment, processor cores 1202A-1202N are homogeneous cores executing a common instruction set architecture. In at least one embodiment, processor cores 1202A-1202N are heterogeneous in terms of instruction set architecture (ISA), wherein one or more processor cores 1202A-1202N execute a common instruction set, while one or more other cores of processor cores 1202A-1202N execute a common instruction set or a subset of a different instruction set. In at least one embodiment, processor cores 1202A-1202N are heterogeneous in terms of microarchitecture, wherein one or more cores with relatively high power consumption are coupled to one or more power cores with lower power consumption. In at least one embodiment, processor 1200 can be implemented on one or more chips or as a SoC integrated circuit.

[0137] The inference and / or training logic 715 is used to perform inference and / or training operations associated with one or more embodiments. The following is in conjunction with... Figure 7A and / or Figure 7B Details regarding the inference and / or training logic 715 are provided. In at least one embodiment, some or all of the inference and / or training logic 715 may be incorporated into the processor 1200. For example, in at least one embodiment, the training and / or inference techniques described herein may be used in the graphics processor 1512, (one or more) graphics cores 1202A-1202N, or... Figure 12 One or more ALUs embodied in other components. Furthermore, in at least one embodiment, the inference and / or training operations described herein can use, except... Figure 7A Alternatively, it may be accomplished using logic other than that shown in 7B. In at least one embodiment, the weight parameters may be stored in on-chip or off-chip memory and / or registers (shown or not shown), which configure the ALU of the graphics processor 1200 for executing one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

[0138] These components can be used to synthesize a compositional image into a single representation using parameters determined from one or more quality assessment values.

[0139] Virtualization computing platform

[0140] Figure 13 This is an example data flow diagram of process 1300 for generating and deploying an image processing and inference pipeline according to at least one embodiment. In at least one embodiment, process 1300 can be deployed for use with imaging devices, processing devices, and / or other device types at one or more facilities 1302. Process 1300 can be executed within training system 1304 and / or deployment system 1306. In at least one embodiment, training system 1304 can be used to train, deploy, and implement machine learning models (e.g., neural networks, object detection algorithms, computer vision algorithms, etc.) for use in deployment system 1306. In at least one embodiment, deployment system 1306 can be configured to offload processing and computing resources between distributed computing environments to reduce infrastructure requirements at facility 1302. In at least one embodiment, one or more applications in the pipeline can use or invoke services of deployment system 1306 (e.g., inference, visualization, computation, AI, etc.) during application execution.

[0141] In at least one embodiment, some applications in the advanced processing and inference pipeline may use machine learning models or other AI to perform one or more processing steps. In at least one embodiment, the machine learning model may be trained at facility 1302 using data 1308 (such as imaging data) generated at facility 1302 (and stored on one or more Picture Archiving and Communication System (PACS) servers at facility 1302), or may be trained using imaging or sorting data 1308 or a combination thereof from another (one or more) facility. In at least one embodiment, training system 1304 may be used to provide applications, services, and / or other resources for generating jobs, deployable machine learning models, for deployment system 1306.

[0142] In at least one embodiment, the model registry 1324 may be supported by an object storage that supports versioning and object metadata. In at least one embodiment, the object storage may be, for example, cloud storage (e.g., Figure 14 The cloud platform (1426)-compatible application programming interface (API) is accessed from within the cloud platform. In at least one embodiment, machine learning models in the model registry 1324 can be uploaded, listed, modified, or deleted by the developer or partner of the system interacting with the API. In at least one embodiment, the API can provide access to methods that allow a user with appropriate credentials to associate a model with an application, enabling the model to be executed as part of the containerized instantiation of the application.

[0143] In at least one embodiment, training pipeline 1404 ( Figure 14This could include a scenario where facility 1302 is training its own machine learning model, or has an existing machine learning model that needs to be optimized or updated. In at least one embodiment, imaging data 1308 generated by one or more imaging devices, sequencing devices, and / or other device types can be received. In at least one embodiment, once the imaging data 1308 is received, AI-assisted annotation 1310 can be used to help generate annotations corresponding to the imaging data 1308 for use as ground-based data for machine learning models. In at least one embodiment, AI-assisted annotation 1310 can include one or more machine learning models (e.g., convolutional neural networks (CNNs)) that can be trained to generate annotations corresponding to certain types of imaging data 1308 (e.g., from certain devices). In at least one embodiment, AI-assisted annotation 1310 can then be used directly, or it can be adjusted or fine-tuned using annotation tools to generate ground-based data. In at least one embodiment, AI-assisted annotation 1310, labeled clinic data 1312, or a combination thereof can be used as ground-based data for training machine learning models. In at least one embodiment, the trained machine learning model may be referred to as output model 1316 and may be used by deployment system 1306 as described herein.

[0144] In at least one embodiment, training pipeline 1404 ( Figure 14This could include scenarios where facility 1302 requires a machine learning model to perform one or more processing tasks for one or more applications in deployment system 1306, but facility 1302 may not currently have such a machine learning model (or may not have an optimized, efficient, or effective model for such a purpose). In at least one embodiment, an existing machine learning model can be selected from model registry 1324. In at least one embodiment, model registry 1324 can include machine learning models trained to perform various inference tasks on imaging data. In at least one embodiment, the machine learning models in model registry 1324 may have already been trained on imaging data from facilities other than facility 1302 (e.g., remote facilities). In at least one embodiment, the machine learning model may have already been trained on imaging data from one location, two locations, or any number of locations. In at least one embodiment, when training on imaging data from a particular location, training may occur at that location, or at least in a manner that protects the confidentiality of the imaging data or restricts the imaging data from being transmitted in the field. In at least one embodiment, once the model has been trained or partially trained at a location, the machine learning model can be added to model registry 1324. In at least one embodiment, the machine learning model can then be retrained or updated at any number of other facilities, and the retrained or updated model can be made available in model registry 1324. In at least one embodiment, the machine learning model can then be selected from model registry 1324—and referred to as output model 1316—and can be used in deployment system 1306 to perform one or more processing tasks for one or more applications of the deployment system.

[0145] In at least one embodiment, training pipeline 1404 ( Figure 14The scenario may include facility 1302, which requires a machine learning model to perform one or more processing tasks for one or more applications in deployment system 1306, but facility 1302 may not currently have such a machine learning model (or may not have an optimized, efficient, or effective model for such purposes). In at least one embodiment, the machine learning model selected from model registry 1324 may not be fine-tuned or optimized for the imaging data 1308 generated at facility 1302 due to population differences, robustness of training data used to train the machine learning model, anomalous diversity of training data, and / or other problems with the training data. In at least one embodiment, AI-assisted annotation 1310 may be used to help generate annotations corresponding to imaging data 1308, which is used as ground-based data for retraining or updating the machine learning model. In at least one embodiment, labeled data 1312 may be used as ground-based data for training the machine learning model. In at least one embodiment, retraining or updating the machine learning model may be referred to as model training 1314. In at least one embodiment, model training 1314 (e.g., AI-assisted annotation 1310, labeled clinic data 1312, or a combination thereof) can be used as ground-based real-world data for retraining or updating the machine learning model. In at least one embodiment, the trained machine learning model may be referred to as output model 1316 and may be used by deployment system 1306 as described herein.

[0146] In at least one embodiment, deployment system 1306 may include software 1318, service 1320, hardware 1322, and / or other components, features, and functions. In at least one embodiment, deployment system 1306 may include a software "stack" such that software 1318 can be built on top of service 1320 and can use service 1320 to perform some or all of the processing tasks, service 1320 and software 1318 can be built on top of hardware 1322 and use hardware 1322 to perform processing, storage, and / or other computational tasks of deployment system 1306. In at least one embodiment, software 1318 may include any number of different containers, each of which can perform application instantiation. In at least one embodiment, each application can perform one or more processing tasks (e.g., inference, object detection, feature detection, segmentation, image enhancement, calibration, etc.) in high-level processing and inference pipelines. In at least one embodiment, the high-level processing and inference pipeline may be defined based on the selection of different containers desired or required for processing imaging data 1308, in addition to containers that receive and configure imaging data for use by each container and / or for use by facility 1302 after processing through the pipeline (e.g., converting the output back to a usable data type). In at least one embodiment, a combination of containers within software 1318 (e.g., a combination of containers that make up the pipeline) may be referred to as a virtual tool (as described in more detail herein), and the virtual tool may utilize service 1320 and hardware 1322 to perform some or all of the processing tasks of an application instantiated in a container.

[0147] In at least one embodiment, the data processing pipeline may receive input data (e.g., imaging data 1308) in a specific format in response to an inference request (e.g., a request from a user of deployment system 1306). In at least one embodiment, the input data may represent one or more images, videos, and / or other data representations generated by one or more imaging devices. In at least one embodiment, the data may undergo preprocessing as part of the data processing pipeline to prepare the data for processing by one or more applications. In at least one embodiment, postprocessing may be performed on the output of one or more inference tasks or other processing tasks of the pipeline to prepare output data for the next application and / or to prepare output data for user transmission and / or use (e.g., in response to an inference request). In at least one embodiment, the inference task may be performed by one or more machine learning models, such as trained or deployed neural networks, which may include the output model 1316 of training system 1304.

[0148] In at least one embodiment, the tasks of the data processing pipeline can be encapsulated in containers, each representing a discrete, fully functional instantiation of an application capable of referencing a machine learning model and a virtualized computing environment. In at least one embodiment, containers or applications can be published to a private (e.g., restricted access) area of ​​a container registry (described in more detail herein), and trained or deployed models can be stored in model registry 1324 and associated with one or more applications. In at least one embodiment, an image of the application (e.g., a container image) can be available in the container registry, and once selected by a user from the container registry for deployment in the pipeline, the image can be used to generate containers to provide instantiations of the application for use by the user's system.

[0149] In at least one embodiment, a developer (e.g., a software developer, clinician, physician, etc.) may develop, publish, and store applications (e.g., as containers) for performing image processing and / or inference on the provided data. In at least one embodiment, development, publication, and / or storage may be performed using a software development kit (SDK) associated with the system (e.g., to ensure that the developed applications and / or containers are compatible with the system). In at least one embodiment, at least some of the services 1320 may be utilized as a system (e.g., Figure 14 The system 1400 uses its SDK to test the developed application locally (e.g., at the first facility, with data from the first facility). In at least one embodiment, because DICOM objects can contain anywhere from one to hundreds of images or other data types, and due to variations in data, the developer can be responsible for managing (e.g., setting up construction, incorporating preprocessing into the application, etc.) the extraction and preparation of input data. In at least one embodiment, once verified by the system 1400 (e.g., for accuracy), the application can be made available in the container registry for user selection and / or implementation to perform one or more processing tasks with respect to data at the user's facility (e.g., a second facility).

[0150] In at least one embodiment, the developer can then share the application or container over the network for the system (e.g., Figure 14The system 1400 allows for user access and use. In at least one embodiment, completed and validated applications or containers may be stored in a container registry, and associated machine learning models may be stored in a model registry 1324. In at least one embodiment, a requesting entity providing an inference or image processing request may browse the container registry and / or model registry 1324 for applications, containers, datasets, machine learning models, etc., select desired combinations of elements to include in the data processing pipeline, and submit an imaging processing request. In at least one embodiment, the request may include input data necessary to perform the request (and, in some examples, associated patient data), and / or may include the selection of one or more applications and / or machine learning models to be performed in the processing request. In at least one embodiment, the request may then be passed to one or more components of the deployment system 1306 (e.g., the cloud) to perform processing in the data processing pipeline. In at least one embodiment, the processing performed by the deployment system 1306 may include referencing the selected elements (e.g., applications, containers, models, etc.) from the container registry and / or model registry 1324. In at least one embodiment, once the results are generated by the pipeline, the results can be returned to the user for reference (e.g., for viewing in a viewing application suite executed locally, on a field workstation, or on a terminal).

[0151] In at least one embodiment, service 1320 may be utilized to assist in processing or executing applications or containers in the pipeline. In at least one embodiment, service 1320 may include computing services, artificial intelligence (AI) services, visualization services, and / or other service types. In at least one embodiment, service 1320 may provide functionality shared by one or more applications in software 1318, thus the functionality may be abstracted into services that can be invoked or utilized by applications. In at least one embodiment, the functionality provided by service 1320 can operate dynamically and more efficiently, while also allowing applications to process data in parallel (e.g., using parallel computing platform 1430). Figure 14To scale well. In at least one embodiment, service 1320 can be shared between and among different applications, rather than requiring each application sharing the same functionality provided by service 1320 to have a corresponding instance of service 1320. In at least one embodiment, as a non-limiting example, the service may include an inference server or engine that can be used to perform detection or segmentation tasks. In at least one embodiment, a model training service may be included, which can provide machine learning model training and / or retraining capabilities. In at least one embodiment, a data augmentation service may also be included, which can provide GPU-accelerated data (e.g., DICOM, RIS, CIS, REST-compatible, RPC, raw, etc.) extraction, resizing, scaling, and / or other enhancements. In at least one embodiment, a visualization service may be used, which can add image rendering effects—such as ray tracing, rasterization, denoising, sharpening, etc.—to add realism to two-dimensional (2D) and / or three-dimensional (3D) models. In at least one embodiment, a virtual instrument service may be included that provides beamforming, segmentation, inference, imaging, and / or support for other applications within the virtual instrument pipeline.

[0152] In at least one embodiment, where service 1320 includes an AI service (e.g., an inference service), one or more machine learning models can be executed by invoking (e.g., as an API call) the inference service (e.g., an inference server) to perform the machine learning models or their processing as part of application execution. In at least one embodiment, where another application includes one or more machine learning models for segmentation tasks, the application can execute one or more machine learning models for performing processing operations associated with the segmentation tasks based on inference service invoking. In at least one embodiment, the software 1318 implementing the high-level processing and inference pipelines including the segmentation application and the anomaly detection application can be streamlined because each application can invoke the same inference service to perform one or more inference tasks.

[0153] In at least one embodiment, hardware 1322 may include a GPU, CPU, graphics card, AI / deep learning system (e.g., an AI supercomputer, such as NVIDIA's DGX), cloud platform, or a combination thereof. In at least one embodiment, different types of hardware 1322 may be used to provide efficient, specifically designed support for software 1318 and services 1320 in deployment system 1306. In at least one embodiment, the use of GPU processing may be implemented for processing locally (e.g., at facility 1302), within an AI / deep learning system, in a cloud system, and / or in other processing components of deployment system 1306 to improve the efficiency, accuracy, and power of image processing and generation. In at least one embodiment, as a non-limiting example, software 1318 and / or services 1320 may be optimized for GPU processing related to deep learning, machine learning, and / or high-performance computing. In at least one embodiment, at least some of the computing environments of deployment system 1306 and / or training system 1304 may execute one or more supercomputers or high-performance computing systems in a data center, wherein GPU-optimized software (e.g., the hardware and software combination of NVIDIA's DGX system) may be used. In at least one embodiment, hardware 1322 may include any number of GPUs, as described herein, that can be invoked to perform data processing in parallel. In at least one embodiment, the cloud platform may also include GPU processing for GPU-optimized execution of deep learning tasks, machine learning tasks, or other computational tasks. In at least one embodiment, the cloud platform (e.g., NVIDIA's NGC) may be executed using an AI / deep learning supercomputer and / or GPU-optimized software (e.g., provided on NVIDIA's DGX systems) as a hardware abstraction and scaling platform. In at least one embodiment, the cloud platform may integrate application container cluster systems or orchestration systems (e.g., Kubernetes) across multiple GPUs to achieve seamless scaling and load balancing.

[0154] Figure 14 This is a system diagram of an example system 1400 for generating and deploying an imaging deployment pipeline according to at least one embodiment. In at least one embodiment, system 1400 can be used to implement... Figure 13 The process 1300 and / or other processes including advanced processing and inference pipelines. In at least one embodiment, system 1400 may include training system 1304 and deployment system 1306. In at least one embodiment, training system 1304 and deployment system 1306 may be implemented using software 1318, service 1320 and / or hardware 1322, as described herein.

[0155] In at least one embodiment, system 1400 (e.g., training system 1304 and / or deployment system 1306) may be implemented in a cloud computing environment (e.g., using cloud 1426). In at least one embodiment, system 1400 may be implemented locally relative to a healthcare service facility, or implemented as a combination of cloud computing resources and local computing resources. In at least one embodiment, access to APIs in cloud 1426 may be restricted to authorized users through established security measures or protocols. In at least one embodiment, the security protocol may include a web token that can be signed by an authentication service (e.g., AuthN, AuthZ, Gluecon, etc.) and carries appropriate authorization. In at least one embodiment, the API of the virtual tool (described herein) or other instantiations of system 1400 may be restricted to a set of public IPs that have been reviewed or authorized for interaction.

[0156] In at least one embodiment, the various components of system 1400 may communicate with each other and with each other using any of a variety of different network types, including but not limited to local area networks (LANs) and / or wide area networks (WANs) via wired and / or wireless communication protocols. In at least one embodiment, communication between facilities and components of system 1400 (e.g., for transmitting inference requests, for receiving the results of inference requests, etc.) may be transmitted via a data bus, a wireless data protocol (Wi-Fi), a wired data protocol (e.g., Ethernet), etc.

[0157] In at least one embodiment, the training system 1304 may execute the training pipeline 1404, similar to that described herein. Figure 13 Those described. In at least one embodiment, wherein one or more machine learning models will be used by deployment system 1306 in deployment pipeline 1410, training pipeline 1404 may be used to train or retrain one or more (e.g., pre-trained) models, and / or implement one or more pre-trained models 1406 (e.g., without retraining or updating). In at least one embodiment, as a result of training pipeline 1404, one or more output models 1316 may be generated. In at least one embodiment, training pipeline 1404 may include any number of processing steps, such as, but not limited to, imaging data (or other input data) transformation or adaptation. In at least one embodiment, different training pipelines 1404 may be used for different machine learning models used by deployment system 1306. In at least one embodiment, similar to the description of... Figure 13 The training pipeline 1404 of the first example described can be used for a first machine learning model, similar to that described above. Figure 13 The training pipeline 1404 of the second example described can be used for a second machine learning model, and is similar to that described above. Figure 13The training pipeline 1404 of the third example described can be used for a third machine learning model. In at least one embodiment, any combination of tasks within the training system 1304 can be used depending on what each corresponding machine learning model requires. In at least one embodiment, one or more machine learning models may have already been trained and are ready for deployment, so the machine learning models may not undergo any processing by the training system 1304 and can be implemented by the deployment system 1306.

[0158] In at least one embodiment, depending on the implementation or embodiment, one or more output models 1316 and / or one or more pre-trained models 1406 may include any type of machine learning model. In at least one embodiment, but not limited to, the machine learning model used by system 1400 may include one or more machine learning models using linear regression, logistic regression, decision trees, support vector machines (SVM), primitive Bayes, k-nearest neighbors (Knn), K-means clustering, random forests, dimensionality reduction algorithms, gradient boosting algorithms, neural networks (e.g., autoencoders, convolutions, recursion, perceptrons, long / short-term memory (LSTM), Hopfield, Boltzmann, deep belief, deconvolution, adversarial generation, liquid machines, etc.), and / or other types of machine learning models.

[0159] In at least one embodiment, the training pipeline 1404 may include AI-assisted annotations, such as those provided herein in relation to at least... Figure 15BDescribed in more detail. In at least one embodiment, the labeled data 1312 (e.g., conventional annotations) can be generated using any number of techniques. In at least one embodiment, labels or other annotations can be generated within a drawing program (e.g., annotating program), a computer-aided design (CAD) program, a labeling program, another type of program suitable for generating annotations or labels for ground reality, and / or can be hand-drawn in some examples. In at least one embodiment, the ground reality data can be synthetically generated (e.g., generated from a computer model or rendering), realistically generated (e.g., designed and generated from real-world data), machine-automated (e.g., extracting features from the data using feature analysis and learning and then generating labels), manually annotated (e.g., labelers or annotation experts defining the placement of labels), and / or a combination thereof. In at least one embodiment, for each instance of imaging data 1308 (or other data types used by a machine learning model), there may be corresponding ground reality data generated by the training system 1304. In at least one embodiment, AI-assisted annotation can be performed as part of the deployment pipeline 1410; as a supplement to or alternative to AI-assisted annotation included in the training pipeline 1404. In at least one embodiment, system 1400 may include a multi-layer platform that may include a software layer (e.g., software 1318) capable of performing one or more medical imaging and diagnostic functions for diagnostic applications (or other application types). In at least one embodiment, system 1400 may be communicatively coupled to (e.g., via an encrypted link) a network of PACS servers in one or more facilities. In at least one embodiment, system 1400 may be configured to access and reference data from PACS servers to perform operations such as training machine learning models, deploying machine learning models, image processing, inference, and / or other operations.

[0160] In at least one embodiment, the software layer may be implemented as a secure, encrypted, and / or certified API through which an application or container can be invoked (e.g., called) from one or more external environments (e.g., facility 1302). In at least one embodiment, the application may then invoke or execute one or more services 1320 to perform computational, AI, or visualization tasks associated with the respective application, and the software 1318 and / or service 1320 may utilize the hardware 1322 to perform processing tasks in an efficient and effective manner.

[0161] In at least one embodiment, deployment system 1306 may execute deployment pipeline 1410. In at least one embodiment, deployment pipeline 1410 may include any number of applications that may be applied sequentially, non-sequentially, or otherwise to imaging data (and / or other data types) generated by imaging devices, sequencing devices, genomics devices, etc., including AI-assisted annotation as described above. In at least one embodiment, as described herein, deployment pipeline 1410 for an individual device may be referred to as a virtual instrument for the device (e.g., a virtual ultrasound instrument, a virtual CT scanner, a virtual sequencing instrument, etc.). In at least one embodiment, for a single device, more than one deployment pipeline 1410 may exist depending on the information required from the data generated by the device. In at least one embodiment, a first deployment pipeline 1410 may exist when it is desired to detect an abnormality from an MRI machine, and a second deployment pipeline 1410 may exist when it is desired to perform image enhancement from the output of the MRI machine.

[0162] In at least one embodiment, the image generation application may include processing tasks that include using a machine learning model. In at least one embodiment, a user may expect to use their own machine learning model or select a machine learning model from the model registry 1324. In at least one embodiment, a user may implement their own machine learning model or select a machine learning model to include in the application used to perform the processing task. In at least one embodiment, the application may be optional and customizable, and by defining the construction of the application, the deployment and implementation of the application for a particular user is presented as a more seamless user experience. In at least one embodiment, by leveraging other features of system 1400—such as service 1320 and hardware 1322—the deployment pipeline 1410 may be even more user-friendly, providing easier integration and producing more accurate, efficient, and timely results.

[0163] In at least one embodiment, deployment system 1306 may include user interface 1414 (e.g., graphical user interface, web interface, etc.) which can be used to select applications to be included in deployment pipeline 1410, schedule applications during setup and / or deployment, modify or change applications or their parameters or configurations, use deployment pipeline 1410 and interact with deployment pipeline 1410, and / or otherwise interact with deployment system 1306. In at least one embodiment, although not shown relative to training system 1304, user interface 1414 (or different user interfaces) may be used to select models to be used in deployment system 1306, to select models to be used for training or retraining in training system 1304, and / or to otherwise interact with training system 1304.

[0164] In at least one embodiment, in addition to the application orchestration system 1428, a pipeline manager 1412 may be used to manage interactions between applications or containers deploying pipeline 1410 and services 1320 and / or hardware 1322. In at least one embodiment, the pipeline manager 1412 may be configured to facilitate interactions from application to application, from application to service 1320, and / or from application or service to hardware 1322. In at least one embodiment, although shown as included in software 1318, this is not intended to be limiting, and in some examples (e.g., as...) Figure 12 As shown in the diagram (cc), pipeline manager 1412 may be included in service 1320. In at least one embodiment, application orchestration system 1428 (e.g., Kubernetes, DOCKER, etc.) may include a container orchestration system that can group applications into containers as logical units for coordination, management, scaling, and deployment. In at least one embodiment, by associating applications (e.g., refactored applications, split applications, etc.) from deployment pipeline 1410 with separate containers, each application can execute in a self-contained environment (e.g., at the kernel level) to improve speed and efficiency.

[0165] In at least one embodiment, each application and / or container (or its image) can be developed, modified, and deployed independently (e.g., a first user or developer can develop, modify, and deploy a first application, while a second user or developer can develop, modify, and deploy a second application separate from the first user or developer). This allows focus on and attention to the tasks of individual applications and / or containers without being hindered by the tasks of other applications or containers. In at least one embodiment, communication and collaboration between different containers or applications can be facilitated by the pipeline manager 1412 and the application orchestration system 1428. In at least one embodiment, as long as the system (e.g., based on the application or container's architecture) knows the expected inputs and / or outputs of each container or application, the application orchestration system 1428 and / or the pipeline manager 1412 can facilitate communication between and between them, as well as resource sharing between and between each application or container. In at least one embodiment, because one or more applications or containers in the deployment pipeline 1410 can share the same services and resources, the application orchestration system 1428 can orchestrate, load balance, and determine the sharing of services or resources between and among different applications or containers. In at least one embodiment, the scheduler can be used to track the resource requirements of applications or containers, the current or planned use of these resources, and resource availability. In at least one embodiment, the scheduler can thus allocate resources to different applications and distribute resources among and between applications based on the system's needs and availability. In some instances, the scheduler (and / or other components of the application orchestration system 1428) can determine resource availability and distribution based on constraints imposed on the system (e.g., user constraints), such as Quality of Service (QoS), the urgency of data output requirements (e.g., determining whether to perform real-time processing or delayed processing), etc.

[0166] In at least one embodiment, services 1320 utilized and shared by applications or containers in deployment system 1306 may include computing services 1416, AI services 1418, visualization services 1420, and / or other service types. In at least one embodiment, an application may invoke (e.g., execute) one or more of services 1320 to perform processing operations of the application. In at least one embodiment, computing service 1416 may be utilized by an application to perform supercomputing or other high-performance computing (HPC) tasks. In at least one embodiment, computing services 1416 may be fully utilized to perform parallel processing (e.g., using parallel computing platform 1430) for processing data substantially simultaneously through one or more tasks of an application and / or one or more tasks of a single application. In at least one embodiment, parallel computing platform 1430 (e.g., NVIDIA's CUDA) may enable general-purpose computing on a GPU (GPGPU) (e.g., GPU 1422). In at least one embodiment, the software layer of parallel computing platform 1430 may provide access to the GPU's virtual instruction set and parallel computing elements to execute computing kernels. In at least one embodiment, the parallel computing platform 1430 may include memory, and in some embodiments, memory may be shared between and within multiple containers and / or between and within different processing tasks within a single container. In at least one embodiment, inter-process communication (IPC) calls may be generated for multiple containers and / or for multiple processes within a container to use the same data from a shared segment of memory from the parallel computing platform 1430 (e.g., in cases where multiple applications are processing the same information at multiple different stages of an application). In at least one embodiment, instead of copying data and moving it to different locations in memory (e.g., read / write operations), the same data in the same location of memory is used for any number of processing tasks (e.g., simultaneously, at different times, etc.). In at least one embodiment, since new data is generated using data as a result of processing, this information about the new location of the data can be stored and shared between different applications. In at least one embodiment, the location of the data and the location of updated or modified data may be part of the definition of how the payload is understood within the container.

[0167] In at least one embodiment, AI service 1418 may be used to perform an inference service for executing machine learning models associated with an application (e.g., tasks assigned to perform one or more processing tasks of the application). In at least one embodiment, AI service 1418 may utilize AI system 1424 to execute machine learning models (e.g., neural networks, such as CNNs) for segmentation, reconstruction, object detection, feature detection, classification, and / or other inference tasks. In at least one embodiment, applications of deployment pipeline 1410 may use one or more of the output models 1316 from training system 1304 and / or other models of the application to perform inference on imaging data. In at least one embodiment, two or more examples of inference using application orchestration system 1428 (e.g., scheduler) may be available. In at least one embodiment, a first category may include a high-priority / low-waittime path that implements a higher service level agreement, such as for performing inference on urgent requests during emergencies or on radiologists during diagnosis. In at least one embodiment, a second category may include a standard priority path that may be used for requests that are not urgent or in which analysis can be performed later. In at least one embodiment, the application orchestration system 1428 may allocate resources (e.g., service 1320 and / or hardware 1322) based on priority paths for different inference tasks for the AI ​​service 1418.

[0168] In at least one embodiment, shared storage may be installed within AI service 1418 of system 1400. In at least one embodiment, shared storage may operate as a cache (or other storage device type) and may be used to process inference requests from applications. In at least one embodiment, when an inference request is submitted, the request may be received by a set of API instances deployed on system 1306, and one or more instances may be selected (e.g., for best fit, for load balancing, etc.) to process the request. In at least one embodiment, to process the request, the request may be fed into a database, and if not already in the cache, a machine learning model may be located from model registry 1324. A verification step may ensure that an appropriate machine learning model is loaded into the cache (e.g., shared storage), and / or a copy of the model may be saved to the cache. In at least one embodiment, a scheduler (e.g., of pipeline manager 1412) may be used to launch the application referenced in the request if the application is not yet running or if there are not enough instances of the application. In at least one embodiment, an inference server may be launched if an inference server has not yet been started to execute the model. Any number of inference servers may be launched for each model. In at least one embodiment, in a clustered pull model where the inference server is located, the model can be cached whenever load balancing is favorable. In at least one embodiment, the inference server can be statically loaded into the corresponding distributed server.

[0169] In at least one embodiment, an inference server running in a container can be used to perform inference. In at least one embodiment, an instance of the inference server can be associated with a model (and optionally multiple versions of the model). In at least one embodiment, if an instance of the inference server does not exist when a request to perform inference on the model is received, a new instance can be loaded. In at least one embodiment, when the inference server is started, a model can be passed to the inference server so that the same container can be used to serve different models as long as the inference server runs as different instances.

[0170] In at least one embodiment, during application execution, an inference request for a given application may be received, and a container (e.g., an instance of a hosted inference server) may be loaded (if not already loaded), and a start procedure may be invoked. In at least one embodiment, preprocessing logic within the container may load, decode incoming data, and / or perform any additional preprocessing on the incoming data (e.g., using a CPU and / or GPU). In at least one embodiment, once the data is ready for inference, the container may perform inference on the data as needed. In at least one embodiment, this may include a single inference call for an image (e.g., a hand X-ray) or may request inference on hundreds of images (e.g., a chest CT scan). In at least one embodiment, the application may summarize the results before completion, which may include, but is not limited to, a single confidence score, pixel-level segmentation, voxel-level segmentation, generating visualizations, or generating text to summarize the findings. In at least one embodiment, different models or applications may be assigned different priorities. For example, some models may have a real-time (TAT < 1 minute) priority, while others may have a lower priority (e.g., TAT < 10 minutes). In at least one embodiment, model execution time can be measured from the requesting agency or entity, and model execution time can include partner network traversal time, as well as execution on the inference service.

[0171] In at least one embodiment, the transmission of requests between service 1320 and the inference application can be hidden behind a software development kit (SDK) and robust transmission can be provided via queues. In at least one embodiment, requests are placed in queues via an API with a unique application / tenant ID combination, and the SDK pulls requests from the queues and delivers them to the application. In at least one embodiment, the name of the queue can be provided in the environment from which the SDK will pick up the queue. In at least one embodiment, asynchronous communication via queues can be useful because it allows any instance of the application to pick up work as it becomes available. Results can be transmitted back via queues to ensure no data loss. In at least one embodiment, queues can also provide the ability to partition work, as the highest priority work can be routed to a queue with a majority of instances of the application connected to it, while the lowest priority work can be routed to a queue with a single instance connected to it, which processes tasks in the order they are received. In at least one embodiment, the application can run on GPU-accelerated instances generated in cloud 1426, and the inference service can perform inference on the GPU.

[0172] In at least one embodiment, visualization service 1420 may be used to generate visualizations for viewing the output of application and / or deployment pipeline 1410. In at least one embodiment, GPU 1422 may be utilized by visualization service 1420 to generate visualizations. In at least one embodiment, rendering effects (such as ray tracing) may be implemented by visualization service 1420 to generate higher quality visualizations. In at least one embodiment, visualizations may include, but are not limited to, 2D image rendering, 3D volumetric rendering, 3D volumetric reconstruction, 2D tomographic slicing, virtual reality displays, augmented reality displays, etc. In at least one embodiment, a virtualized environment may be used to generate a virtual interactive display or environment (e.g., a virtual environment) for interaction by users of the system (e.g., doctors, nurses, radiologists, etc.). In at least one embodiment, visualization service 1420 may include an internal visualizer, dynamic image and / or other rendering or image processing capabilities or functions (e.g., ray tracing, rasterization, internal optics, etc.).

[0173] In at least one embodiment, hardware 1322 may include GPU 1422, AI system 1424, cloud 1426, and / or any other hardware for performing training system 1304 and / or deployment system 1306. In at least one embodiment, GPU 1422 (e.g., NVIDIA's TESLA and / or QUADRO GPUs) may include any number of GPUs that can be used to perform processing tasks of any of the features or functions of computing service 1416, AI service 1418, visualization service 1420, other services, and / or software 1318. For example, with respect to AI service 1418, GPU 1422 may be used to perform preprocessing on imaging data (or other data types used by machine learning models), postprocessing on the output of machine learning models, and / or to perform inference (e.g., for executing machine learning models). In at least one embodiment, cloud 1426, AI system 1424, and / or other components of system 1400 may use GPU 1422. In at least one embodiment, cloud 1426 may include a GPU-optimized platform for deep learning tasks. In at least one embodiment, AI system 1424 may use a GPU, and one or more AI systems 1424 may be used to perform cloud 1426—or a task that is at least part of deep learning or inference. Accordingly, although hardware 1322 is shown as discrete components, this is not intended to be limiting, and any component of hardware 1322 may be combined with or utilized by any other component of hardware 1322.

[0174] In at least one embodiment, AI system 1424 may include a dedicated computing system (e.g., a supercomputer or HPC) configured for inference, deep learning, machine learning, and / or other artificial intelligence tasks. In at least one embodiment, AI system 1424 (e.g., NVIDIA's DGX) may include GPU-optimized software (e.g., a software stack) that can be executed using multiple GPUs 1422, in addition to CPUs, RAM, storage devices, and / or other components, features, or functionalities. In at least one embodiment, one or more AI systems 1424 may be implemented in a cloud 1426 (e.g., in a data center) to perform some or all of the AI-based processing tasks of system 1400.

[0175] In at least one embodiment, cloud 1426 may include GPU-accelerated infrastructure (e.g., NVIDIA's NGC) that provides a GPU-optimized platform for performing processing tasks of system 1400. In at least one embodiment, cloud 1426 may include AI system 1424 for performing one or more AI-based tasks of system 1400 (e.g., as a hardware abstraction and scaling platform). In at least one embodiment, cloud 1426 may be integrated with application orchestration system 1428 utilizing multiple GPUs to enable seamless scaling and load balancing between and within applications and services 1320. In at least one embodiment, the tasks of cloud 1426 may be performing at least some of the services 1320 of system 1400, including computing service 1416, AI service 1418, and / or visualization service 1420, as described herein. In at least one embodiment, cloud 1426 may perform mini-batch inference and mass inference (e.g., perform NVIDIA's TENSORRT), provide accelerated parallel computing APIs and platform 1430 (e.g., NVIDIA's CUDA), perform application orchestration system 1428 (e.g., KUBERNETES), provide graphics rendering APIs and platform (e.g., for ray tracing, 2D graphics, 3D graphics, and / or other rendering techniques to produce higher quality cinematography), and / or provide other functionalities for system 1400.

[0176] Figure 15A A data flow diagram of a process 1500 for training, retraining, or updating a machine learning model according to at least one embodiment is shown. In at least one embodiment, as a non-limiting example, the following can be used: Figure 14System 1400 performs processing 1500. In at least one embodiment, process 1500 may utilize services 1320 and / or hardware 1322 of system 1400, as described herein. In at least one embodiment, the refined model 1512 generated by process 1500 may be executed by deployment system 1306 for one or more containerized applications in deployment pipeline 1410.

[0177] In at least one embodiment, model training 1314 may include retraining or updating the initial model 1504 (e.g., a pre-trained model) using new training data (e.g., new input data, such as customer dataset 1506 and / or new ground truth data associated with the input data). In at least one embodiment, to retrain or update the initial model 1504, the output or lost layer of the initial model 1504 may be reset, deleted, and / or replaced with an updated or new output or lost layer. In at least one embodiment, the initial model 1504 may have previously fine-tuned parameters (e.g., weights and / or biases) that remain from previous training, so training or retraining 1314 may not take as long as training the model from scratch or require as much processing. In at least one embodiment, during model training 1314, by resetting or replacing the output or lost layer of the initial model 1504, when generating new predictions, the parameters may be updated and retuned for the new dataset based on loss calculations associated with the accuracy of the output or (one or more) lost layers, such as customer dataset 1506 (e.g., customer dataset 1506 ... Figure 13 Image data 1308).

[0178] In at least one embodiment, the pre-trained model 1406 may be stored in a data storage or registry (e.g., Figure 13The model registry 1324 is used for training. In at least one embodiment, the pre-trained model 1406 may have been trained at least partially at one or more facilities other than the facility performing process 1500. In at least one embodiment, to protect the privacy and permissions of patients, subjects, or clients at different facilities, the pre-trained model 1406 may be trained on-site using client or patient data generated within the facility. In at least one embodiment, the pre-trained model 1406 may be trained using cloud 1426 and / or other hardware 1322, but confidential, privacy-protected patient data may not be transferred to any component of cloud 1426 (or other off-site hardware), used by any component of cloud 1426, or accessed by any component of cloud 1426 (or other off-site hardware). In at least one embodiment, when training the pre-trained model 1406 using patient data from more than one facility, the pre-trained model 1406 may be trained separately for each facility before training on patient or client data from another facility. In at least one embodiment, such as where customer or patient data has been published for privacy reasons (e.g., by waiting, for experimental use, etc.), or where customer or patient data is included in a public dataset, customer or patient data from any number of facilities can be used to train pre-trained models 1406 on-site and / or off-site, such as in data centers or other cloud computing infrastructures.

[0179] In at least one embodiment, when selecting an application to use in deployment pipeline 1410, the user can also select the machine learning model to be used for the specific application. In at least one embodiment, the user may not have a model available for use, so the user can select a pre-trained model 1406 to use with the application. In at least one embodiment, the pre-trained model 1406 may not be optimized to generate accurate results on the user's facility's customer dataset 1506 (e.g., based on patient diversity, demographics, type of medical imaging equipment used, etc.). In at least one embodiment, the pre-trained model 1406 may be updated, retrained, and / or fine-tuned for use at the appropriate facility before being deployed to deployment pipeline 1410 for use with the application.

[0180] In at least one embodiment, the user can select a pre-trained model 1406 to be updated, retrained, and / or fine-tuned, and the pre-trained model 1406 can be referred to as the initial model 1504 of the training system 1304 within process 1500. In at least one embodiment, the customer dataset 1506 (e.g., imaging data, genomics data, sequencing data, or other data types generated by facilities) can be used to perform model training 1314 (which may include, but is not limited to, transfer learning) on ​​the initial model 1504 to generate a refined model 1512. In at least one embodiment, ground-based data corresponding to the customer dataset 1506 can be generated by the training system 1304. In at least one embodiment, the ground-based data can be generated at least in part by clinicians, scientists, physicians, or practicing physicians at the facility (e.g., such as...). Figure 13 Clinical data marked with markers (1312).

[0181] In at least one embodiment, AI-assisted annotation 1310 may be used in some examples to generate ground condition data. In at least one embodiment, AI-assisted annotation 1310 (e.g., implemented using an AI-assisted annotation SDK) may leverage machine learning models (e.g., neural networks) to generate suggested or predicted ground condition data for a customer dataset. In at least one embodiment, user 1510 may use annotation tools within a user interface (graphical user interface (GUI)) on computing device 1508.

[0182] In at least one embodiment, user 1510 can interact with the GUI via computing device 1508 to edit or fine-tune (automatic) annotations. In at least one embodiment, polygon editing features can be used to move the vertices of a polygon to more accurate or finely adjusted positions.

[0183] In at least one embodiment, once the customer dataset 1506 has associated ground-based data, the ground-based data (e.g., annotations from AI-assisted sources, manual labeling, etc.) can be used to generate a refined model 1512 during model training 1314. In at least one embodiment, the customer dataset 1506 can be applied to the initial model 1504 an arbitrary number of times, and the ground-based data can be used to update the parameters of the initial model 1504 until an acceptable level of accuracy is achieved for the refined model 1512. In at least one embodiment, once the refined model 1512 is generated, it can be deployed within one or more deployment pipelines 1410 at a facility for performing one or more processing tasks related to medical imaging data.

[0184] In at least one embodiment, the refined model 1512 can be uploaded to the pre-trained model 1406 in the model registry 1324 for selection by another facility. In at least one embodiment, this process can be performed at any number of facilities, allowing the refined model 1512 to be further refined any number of times on new datasets to generate a more general model.

[0185] Figure 15B This is an example illustration of a client-server architecture 1532 for enhancing annotation tools using a pre-trained annotation model, according to at least one embodiment. In at least one embodiment, an AI-assisted annotation tool 1536 can be instantiated based on the client-server architecture 1532. In at least one embodiment, the annotation tool 1536 in an imaging application can help radiologists, for example, identify organs and abnormalities. In at least one embodiment, the imaging application may include software tools that help user 1510 identify (as a non-limiting example) several extreme points on a specific organ of interest in an original image 1534 (e.g., in a 3D MRI or CT scan) and receive automatic annotation results of all 2D slices of the specific organ. In at least one embodiment, the results can be stored in a data store as training data 1538 and used (e.g., but not limited to) ground-based real-world data for training. In at least one embodiment, when computing device 1508 sends extreme points for AI-assisted annotation 1310, a deep learning model can, for example, receive this data as input and return inference results for segmented organs or abnormalities. In at least one embodiment, a pre-instantiated annotation tool (such as...) Figure 15B The AI-assisted annotation tool 1536B can be enhanced by making API calls (e.g., API call 1544) to a server (such as an annotation assistant server 1540, which may, for example, include a set of pre-trained models 1542 stored in an annotation model registry). In at least one embodiment, the annotation model registry may store pre-trained models 1542 (e.g., machine learning models, such as deep learning models) that have been pre-trained to perform AI-assisted annotation on specific organs or abnormalities. These models can be further updated using the training pipeline 1404. In at least one embodiment, the pre-installed annotation tool can be improved over time as new tagged clinic data 1312 is added.

[0186] These components can be used to synthesize a compositional image into a single representation using parameters determined from one or more quality assessment values.

[0187] Automation technology

[0188] Figure 16A The illustration shows an embodiment according to at least one of the embodiments. Figure 16AA block diagram of an example system architecture for an autonomous vehicle 1600. In at least one embodiment, Figure 16A Each of one or more components, one or more features, and one or more systems of vehicle 1600 is shown as connected via bus 1602. In at least one embodiment, bus 1602 may include, but is not limited to, a CAN data interface (which may alternatively be referred to herein as “CAN bus”). In at least one embodiment, the CAN bus may be a network within vehicle 1600 used to help control various features and functions of vehicle 1600, such as brake actuation, acceleration, braking, steering, windshield wipers, etc. In one embodiment, bus 1602 may be configured to have dozens or even hundreds of nodes, each node having its own unique identifier (e.g., CAN ID). In at least one embodiment, bus 1602 can be read to find steering wheel angle, ground speed, engine revolutions per minute (“RPM”), button positions, and / or other vehicle status indicators. In at least one embodiment, bus 1602 may be an ASIL B compliant CAN bus.

[0189] In at least one embodiment, FlexRay and / or Ethernet may be used in addition to or from CAN. In at least one embodiment, there may be any number of buses 1602, which may include, but are not limited to, zero or more CAN buses, zero or more FlexRay buses, zero or more Ethernet buses, and / or zero or more other types of buses using other protocols. In at least one embodiment, two or more buses may be used to perform different functions and / or may be used for redundancy. For example, a first bus may be used for a collision avoidance function, and a second bus may be used for actuation control. In at least one embodiment, each bus 1602 may communicate with any component of vehicle 1600, and two or more buses 1602 may communicate with the same component. In at least one embodiment, each of any number of system-on-chip (“SoC”) 1604, each of one or more controllers 1636, and / or each computer within the vehicle may access the same input data (e.g., input from sensors of vehicle 1600) and may be connected to a common bus, such as a CAN bus.

[0190] In at least one embodiment, vehicle 1600 may include one or more controllers 1636, such as those described herein. Figure 1A The controller 1636 can be used for a variety of functions. In at least one embodiment, the controller 1636 can be coupled to any of the various other components and systems of the vehicle 1600 and can be used to control the vehicle 1600, the artificial intelligence of the vehicle 1600, the infotainment and / or other functions of the vehicle 1600.

[0191] In at least one embodiment, vehicle 1600 may include any number of SoCs 1604. Each of the SoCs 1604 may include, but is not limited to, a central processing unit (“one or more CPUs”) 1606, a graphics processing unit (“one or more GPUs”) 1608, one or more processors 1610, one or more caches 1612, one or more accelerators 1614, one or more data storage 1616, and / or other components and features not shown. In at least one embodiment, one or more SoCs 1604 may be used to control vehicle 1600 on various platforms and systems. For example, in at least one embodiment, one or more SoCs 1604 may be combined with a high-definition (“HD”) map 1622 in a system (e.g., the system of vehicle 1600), the high-definition map 1622 being available from one or more servers via a network interface 1624. Figure 16A (Not shown in the image) Get map refresh and / or update.

[0192] In at least one embodiment, one or more CPUs 1606 may include CPU clusters or CPU complexes (which may alternatively be referred to herein as “CCPLEX”). In at least one embodiment, one or more CPUs 1606 may include multiple cores and / or a secondary (“L2”) cache. For example, in at least one embodiment, one or more CPUs 1606 may include eight cores in an intercoupled multiprocessor configuration. In at least one embodiment, one or more CPUs 1606 may include four dual-core clusters, each cluster having a dedicated L2 cache (e.g., 2MB L2 cache). In at least one embodiment, one or more CPUs 1606 (e.g., CCPLEX) may be configured to support simultaneous cluster operation, such that any combination of clusters of one or more CPUs 1606 can be active at any given time.

[0193] In at least one embodiment, one or more CPUs 1606 may implement power management functions, including but not limited to one or more of the following features: automatic clock gating of individual hardware modules to conserve dynamic power when idle; clock gating of each core when the core is not actively executing instructions due to executing Wait for Interrupt (“WFI”) / Event Wait (“WFE”) instructions; independent power supply for each core; independent clock gating for each core cluster when all cores are clock-gated or power-gated; and / or independent power gating for each core cluster when all cores are power-gated. In at least one embodiment, one or more CPUs 1606 may further implement an enhanced algorithm for managing power states, wherein allowed power states and expected wake-up times are specified, and the hardware / microcode determines the optimal power state for cores, clusters, and CCPLEX inputs. In at least one embodiment, the processing core may support a simplified power state input sequence in software, wherein the work is offloaded to the microcode.

[0194] In at least one embodiment, one or more GPUs 1608 may include integrated GPUs (or "iGPUs" herein). In at least one embodiment, one or more GPUs 1608 may be programmable and efficient for parallel workloads. In at least one embodiment, one or more GPUs 1608 may use an enhanced tensor instruction set. In at least one embodiment, one or more GPUs 1608 may include one or more streaming microprocessors, wherein each streaming microprocessor may include a Level 1 ("L1") cache (e.g., an L1 cache with at least 96KB of storage capacity), and two or more streaming microprocessors may share an L2 cache (e.g., an L2 cache with 512KB of storage capacity). In at least one embodiment, one or more GPUs 1608 may include at least eight streaming microprocessors. In at least one embodiment, one or more GPUs 1608 may use a computational application programming interface (API). In at least one embodiment, one or more GPUs 1608 may use one or more parallel computing platforms and / or programming models (e.g., NVIDIA's CUDA).

[0195] In at least one embodiment, one or more GPU 1608s may be power-optimized for optimal performance in automotive and embedded use cases. For example, in one embodiment, one or more GPU 1608s may be fabricated on a FinFET (“FinFET”). In at least one embodiment, each streaming microprocessor may include multiple mixed-precision processing cores divided into multiple blocks. For example, but not limited to, 64 PF32 cores and 32 PF64 cores may be divided into four processing blocks. In at least one embodiment, each processing block may be allocated 16 FP32 cores, 8 FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA Tensor cores for deep learning matrix arithmetic, a level-zero (“L0”) instruction cache, a thread bundle scheduler, a dispatch unit, and / or a 64KB register file. In at least one embodiment, the streaming microprocessor may include independent parallel integer and floating-point data paths to provide efficient execution of workloads that mix computation and addressing operations. In at least one embodiment, the streaming microprocessor may include independent thread scheduling capabilities to enable finer-grained synchronization and cooperation between parallel threads. In at least one embodiment, the streaming microprocessor may include a combined L1 data cache and shared memory unit to improve performance while simplifying programming.

[0196] In at least one embodiment, one or more GPUs 1608 may include high-bandwidth memory (“HBM”) and / or a 16GB HBM2 memory subsystem to provide a peak storage bandwidth of approximately 900GB / s in some examples. In at least one embodiment, in addition to or instead of HBM memory, synchronous graphics random access memory (“SGRAM”) may be used, such as graphics double data rate type five synchronous random access memory (“GDDR5”).

[0197] In at least one embodiment, one or more GPUs 1608 may include unified memory technology. In at least one embodiment, address translation service (“ATS”) support can be used to allow one or more GPUs 1608 to directly access the page tables of one or more CPUs 1606. In at least one embodiment, when one or more GPUs 1608 memory management units (“MMUs”) experience a miss, an address translation request can be sent to one or more CPUs 1606. In response, in at least one embodiment, two CPUs of the one or more CPUs 1606 can look up the virtual-physical mapping of the address in their page tables and transfer the translation back to one or more GPUs 1608. In at least one embodiment, unified memory technology can allow a single unified virtual address space to be used for the memory of both one or more CPUs 1606 and one or more GPUs 1608, thereby simplifying the programming of one or more GPUs 1608 and the porting of applications to one or more GPUs 1608.

[0198] In at least one embodiment, one or more GPUs 1608 may include any number of access counters that can track the frequency of memory accesses by one or more GPUs 1608 to other processors. In at least one embodiment, one or more access counters can help ensure that memory pages are moved to the physical memory of the processor that accesses the pages most frequently, thereby improving the efficiency of shared memory ranges between processors.

[0199] In at least one embodiment, one or more SoCs 1604 may include any number of caches 1612, including those described herein. For example, in at least one embodiment, one or more caches 1612 may include a Level 3 (“L3”) cache available for one or more CPUs 1606 and one or more GPUs 1608 (e.g., connected to CPUs 1606 and GPUs 1608). In at least one embodiment, one or more caches 1612 may include a write-back cache that can, for example, track the state of a line using a cache coherence protocol (e.g., MEI, MESI, MSI, etc.). In at least one embodiment, although a smaller cache size may be used, the L3 cache may include 4 MB or more, depending on the embodiment.

[0200] In at least one embodiment, one or more SoCs 1604 may include one or more accelerators 1614 (e.g., hardware accelerators, software accelerators, or combinations thereof). In at least one embodiment, one or more SoCs 1604 may include a hardware acceleration cluster, which may include optimized hardware accelerators and / or large on-chip memory. In at least one embodiment, large on-chip memory (e.g., 4MB of SRAM) enables the hardware acceleration cluster to accelerate neural networks and other computations. In at least one embodiment, the hardware acceleration cluster may be used to supplement one or more GPUs 1608 and offload some tasks from one or more GPUs 1608 (e.g., freeing up more cycles from one or more GPUs 1608 to perform other tasks). In at least one embodiment, one or more accelerators 1614 may be used for a target workload (e.g., perceptual, convolutional neural network (“CNN”), recurrent neural network (“RNN”), etc.) that is sufficiently stable to withstand acceleration testing. In at least one embodiment, the CNN may include region-based or region convolutional neural networks (“RCNN”) and fast RCNN (e.g., for object detection) or other types of CNNs.

[0201] In at least one embodiment, one or more accelerators 1614 (e.g., a hardware acceleration cluster) may include one or more deep learning accelerators (“DLAs”). One or more DLAs may include, but are not limited to, one or more Tensor Processing Units (“TPUs”), which may be configured to provide an additional 10 trillion operations per second for deep learning applications and inference. In at least one embodiment, the TPU may be an accelerator configured and optimized for performing image processing functions (e.g., for CNNs, RCNNs, etc.). One or more DLAs may be further optimized for specific sets of neural network types and floating-point operations and inference. In at least one embodiment, one or more DLAs are designed to provide higher performance per millimeter than typical general-purpose GPUs and typically significantly outperform CPUs. In at least one embodiment, one or more TPUs may perform several functions, including single-instance convolution functions supporting, for example, INT8, INT16, and FP16 data types for features and weights, as well as post-processor functions. In at least one embodiment, one or more DLAs can execute neural networks, particularly CNNs, quickly and efficiently on processed or unprocessed data for any of the various functions, including, but not limited to: CNNs for object recognition and detection using data from camera sensors; CNNs for distance estimation using data from camera sensors; CNNs for emergency vehicle detection, recognition, and identification using data from microphone 1696; CNNs for face recognition and vehicle owner recognition using data from camera sensors; and / or CNNs for safety and / or safety-related events.

[0202] In at least one embodiment, the DLA can perform any function of one or more GPUs 1608, and by using inference accelerators, for example, the designer can target one or more DLAs or one or more GPUs 1608 for any function. For example, in at least one embodiment, the designer can concentrate the CNN processing and floating-point operations on one or more DLAs, leaving other functions to one or more GPUs 1608 and / or one or more accelerators 1614.

[0203] In at least one embodiment, one or more accelerators 1614 (e.g., hardware acceleration clusters) may include one or more programmable vision accelerators (“PVAs”), which may alternatively be referred to herein as computer vision accelerators. In at least one embodiment, one or more PVAs may be designed and configured to accelerate computer vision algorithms for advanced driver assistance systems (“ADAS”) 1638, autonomous driving, augmented reality (“AR”) applications, and / or virtual reality (“VR”) applications. One or more PVAs may strike a balance between performance and flexibility. For example, in at least one embodiment, each of one or more PVAs may include, for example, but not limited to, any number of reduced instruction set computer (“RISC”) cores, direct memory access (“DMA”), and / or any number of vector processors.

[0204] In at least one embodiment, the RISC core can interact with an image sensor (e.g., the image sensor of any camera described herein), an image signal processor, and / or other processors. In at least one embodiment, each RISC core may include any number of memories. In at least one embodiment, the RISC core may use any of a variety of protocols, depending on the embodiment. In at least one embodiment, the RISC core may execute a real-time operating system (“RTOS”). In at least one embodiment, the RISC core may be implemented using one or more integrated circuit devices, application-specific integrated circuits (“ASICs”), and / or memory devices. For example, in at least one embodiment, the RISC core may include an instruction cache and / or tightly coupled RAM.

[0205] In at least one embodiment, DMA enables components of (one or more) PVAs to access system memory independently of one or more CPUs 1606. In at least one embodiment, DMA can support any number of features for providing optimization to the PVA, including but not limited to, support for multidimensional addressing and / or circular addressing. In at least one embodiment, DMA can support up to six or more addressing dimensions, which may include, but are not limited to, block width, block height, block depth, horizontal block step, vertical block step, and / or depth step.

[0206] In at least one embodiment, the vector processor may be a programmable processor designed to efficiently and flexibly execute programming for computer vision algorithms and provide signal processing capabilities. In at least one embodiment, the PVA may include a PVA core and two vector processing subsystem partitions. In at least one embodiment, the PVA core may include a processor subsystem, a DMA engine (e.g., two DMA engines), and / or other peripherals. In at least one embodiment, the vector processing subsystem may serve as the main processing engine of the PVA and may include a vector processing unit (“VPU”), an instruction cache, and / or a vector memory (e.g., “VMEM”). In at least one embodiment, the VPU core may include a digital signal processor, such as a Single Instruction Multiple Data (“SIMD”) or Very Long Instruction Word (“VLIW”) digital signal processor. In at least one embodiment, the combination of SIMD and VLIW can improve throughput and speed.

[0207] In at least one embodiment, each vector processor may include an instruction cache and may be coupled to dedicated memory. As a result, in at least one embodiment, each vector processor may be configured to execute independently of other vector processors. In at least one embodiment, vector processors included in a particular PVA may be configured to employ data parallelism. For example, in at least one embodiment, multiple vector processors included in a single PVA may execute the same computer vision algorithm, except on different regions of an image. In at least one embodiment, vector processors included in a particular PVA may execute different computer vision algorithms simultaneously on the same image, or even execute different algorithms on a sequence of images or portions of images. In at least one embodiment, among others, any number of PVAs may be included in the hardware-accelerated cluster, and any number of vector processors may be included in each PVA. In at least one embodiment, one or more PVAs may include additional error-correcting code (“ECC”) memory to enhance overall system security.

[0208] In at least one embodiment, one or more accelerators 1614 (e.g., hardware acceleration clusters) may include an on-chip computer vision network and static random access memory (“SRAM”) for providing high-bandwidth, low-latency SRAM to one or more accelerators 1614. In at least one embodiment, the on-chip memory may include at least 4 MB of SRAM, comprising, for example, but not limited to, eight field-configurable memory blocks accessible to both the PVA and DLA. In at least one embodiment, each pair of memory blocks may include an Advanced Peripheral Bus (“APB”) interface, configuration circuitry, a controller, and a multiplexer. In at least one embodiment, any type of memory may be used. In at least one embodiment, the PVA and DLA may access the memory via a backbone providing high-speed access to the memory for both the PVA and DLA. In at least one embodiment, the backbone may include an on-chip computer vision network that interconnects the PVA and DLA to the memory (e.g., using an APB).

[0209] In at least one embodiment, 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. In at least one embodiment, the interface may provide separate phases and separate channels for transmitting control signals / addresses / data, as well as bursty communication for continuous data transmission. In at least one embodiment, although other standards and protocols may be used, the interface may conform to the International Organization for Standardization (“ISO”) 26262 or the International Electrotechnical Commission (“IEC”) 61508 standard.

[0210] In at least one embodiment, one or more SoCs 1604 may include a real-time eye-tracking hardware accelerator. In at least one embodiment, the real-time eye-tracking hardware accelerator may be used to quickly and efficiently determine the location and extent of an object (e.g., within a world model) to generate real-time visualization simulations for RADAR signal interpretation, for sound propagation synthesis and / or analysis, for simulation of SONAR systems, for general wave propagation simulation, for comparison with LIDAR data for localization and / or other functions, and / or for other purposes.

[0211] In at least one embodiment, one or more accelerators 1614 (e.g., hardware acceleration clusters) have broad applications for autonomous driving. In at least one embodiment, the PVA can be a programmable vision accelerator, which can be used in critical processing stages in ADAS and autonomous vehicles. In at least one embodiment, the capabilities of the PVA at low power and low latency are well-matched to algorithmic domains requiring predictable processing. In other words, the PVA performs well in semi-intensive or intensive conventional computations, even on small datasets that may require predictable runtimes with low latency and low power consumption. In at least one embodiment, for autonomous vehicles, such as vehicle 1600, the PVA is designed to run classical computer vision algorithms because they are efficient in object detection and integer mathematical operations.

[0212] For example, according to at least one embodiment of the technology, PVA is used to perform computer stereo vision. In at least one embodiment, a semi-global matching-based algorithm may be used in some examples, although this is not intended to be limiting. In at least one embodiment, applications for Level 3-5 autonomous driving use dynamic estimation / stereo matching during operation (e.g., structure recovery from motion, pedestrian recognition, lane detection, etc.). In at least one embodiment, PVA can perform computer stereo vision functions on input from two monocular cameras.

[0213] In at least one embodiment, the PVA can be used to perform intensive optical flow. For example, in at least one embodiment, the PVA can process raw RADAR data (e.g., using 4D Fast Fourier Transform) to provide processed RADAR data. In at least one embodiment, the 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.

[0214] In at least one embodiment, the DLA can be used to run any type of network to enhance control and driving safety, including, but not limited to, neural networks whose output is used for a confidence score for each object detection. In at least one embodiment, the confidence score can be represented or interpreted as a probability, or as providing a relative “weight” for each detection relative to other detections. In at least one embodiment, the confidence score enables the system to make further decisions about which detections should be considered true positives rather than false positives. For example, in at least one embodiment, the system can set a threshold for the confidence score and only consider detections exceeding the threshold as true positives. In embodiments using an Automatic Emergency Braking (“AEB”) system, false positives would cause the vehicle to automatically perform emergency braking, which is obviously undesirable. In at least one embodiment, a highly confident detection can be considered a trigger for AEB. In at least one embodiment, the DLA can run a neural network for regressing the confidence score value. In at least one embodiment, the neural network may take at least a subset of parameters as its input, such as bounding box size, obtained ground plane estimate (e.g., from another subsystem), and outputs of one or more IMU sensors 1666 related to the vehicle 1600 orientation, distance, and 3D position estimate of the object obtained from the neural network and / or other sensors (e.g., one or more LiDAR sensors 1664 or one or more RADAR sensors 1660).

[0215] In at least one embodiment, one or more SoCs 1604 may include one or more data storage devices 1616 (e.g., memory). In at least one embodiment, one or more data storage devices 1616 may be on-chip memory of one or more SoCs 1604, which may store neural networks to be executed on one or more GPUs 1608 and / or DLAs. In at least one embodiment, one or more data storage devices 1616 may have a sufficiently large capacity to store multiple instances of the neural network for redundancy and security. In at least one embodiment, one or more data storage devices 1616 may include L2 or L3 caches.

[0216] In at least one embodiment, one or more SoCs 1604 may include any number of processors 1610 (e.g., embedded processors). In at least one embodiment, one or more processors 1610 may include a boot and power management processor, which may be a dedicated processor and subsystem for handling boot power and management functions, as well as associated security implementations. In at least one embodiment, the boot and power management processor may be part of a boot sequence of one or more SoCs 1604 and may provide runtime power management services. In at least one embodiment, the boot power and management processor may provide clock and voltage programming, assist system low-power state transitions, thermal and temperature sensor management of one or more SoCs 1604s, and / or power state management of one or more SoCs 1604s. In at least one embodiment, each temperature sensor may be implemented with its output frequency proportional to temperature, and one or more SoCs 1604s may use the ring oscillator to detect the temperature of one or more CPUs 1606s, one or more GPUs 1608s, and / or one or more accelerators 1614s. In at least one embodiment, if it is determined that the temperature exceeds a threshold, the startup and power management processor may enter a temperature fault routine and place one or more SoCs 1604s into a lower power state and / or place the vehicle 1600 into a safe stopping pattern for the driver (e.g., bring the vehicle 1600 to a safe stop).

[0217] In at least one embodiment, one or more processors 1610 may further include a set of embedded processors that can be used as an audio processing engine. In at least one embodiment, the audio processing engine may be an audio subsystem capable of providing full hardware support for multi-channel audio through multiple interfaces and a wide and flexible range of audio I / O interfaces. In at least one embodiment, the audio processing engine is a dedicated processor core with a digital signal processor with dedicated RAM.

[0218] In at least one embodiment, one or more processors 1610 may also include an always-on processor engine that can provide the necessary hardware features to support low-power sensor management and wake-up use cases. In at least one embodiment, the processor on the always-on processor engine may include, but is not limited to, a processor core, tightly coupled RAM, peripheral support (e.g., timers and interrupt controllers), various I / O controller peripherals, and routing logic.

[0219] In at least one embodiment, one or more processors 1610 may further include a secure clustering engine, which includes, but is not limited to, a dedicated processor subsystem for handling security management of automotive applications. In at least one embodiment, the secure clustering engine may include, but is not limited to, two or more processor cores, tightly coupled RAM, supporting peripheral devices (e.g., timers, interrupt controllers, etc.) and / or routing logic. In secure mode, in at least one embodiment, the two or more cores may operate in lockstep mode and may be used as a single core with comparison logic for detecting any differences between their operations. In at least one embodiment, one or more processors 1610 may further include a real-time camera engine, which may include, but is not limited to, a dedicated processor subsystem for handling real-time camera management. In at least one embodiment, one or more processors 1610 may further include a high dynamic range signal processor, which may include, but is not limited to, an image signal processor, which is a hardware engine as part of the camera processing pipeline.

[0220] In at least one embodiment, one or more processors 1610 may include a video image synthesizer, which may be a processing block (e.g., implemented on a microprocessor) that implements video post-processing functions required by the video playback application to produce the final image for the player window. In at least one embodiment, the video image synthesizer may perform lens distortion correction on one or more wide-angle cameras 1670, one or more surround cameras 1674, and / or one or more cabin monitoring camera sensors. In at least one embodiment, preferably, the cabin monitoring camera sensors are monitored by a neural network running on another instance of the SoC 1604, the neural network being configured to recognize cabin events and respond accordingly. In at least one embodiment, the cabin system may perform, but is not limited to, lip reading to activate cellular service and make phone calls, instruct emails, change the vehicle's destination, activate or change the vehicle's infotainment system and settings, or provide voice-activated web browsing. In at least one embodiment, certain functions are available to the driver when the vehicle is operating in autonomous mode, and are otherwise disabled.

[0221] In at least one embodiment, the video image synthesizer may include enhanced temporal denoising for simultaneous spatial and temporal denoising. For example, in at least one embodiment, when motion occurs in the video, denoising appropriately weights spatial information, thereby reducing the weight of information provided by adjacent frames. In at least one embodiment, when the image or a portion of the image does not contain motion, temporal denoising performed by the video image synthesizer may use information from previous images to reduce noise in the current image.

[0222] In at least one embodiment, the video image compositor can also be configured to perform stereoscopic correction on the input stereo lens frames. In at least one embodiment, when using an operating system desktop, the video image compositor can also be used for user interface compositing and does not require one or more GPUs 1608 to continuously render new surfaces. In at least one embodiment, when one or more GPUs 1608 are powered and actively performing 3D rendering, the video image compositor can be used to offload one or more GPUs 1608 to improve performance and responsiveness.

[0223] In at least one embodiment, one or more SoCs of SoC 1604 may also include a Mobile Industrial Processor Interface (“MIPI”) camera serial interface, a high-speed interface, and / or a video input block that can be used for receiving video and input from a camera and associated pixel input functions. In at least one embodiment, one or more SoCs of SoC 1604 may also include an input / output controller that can be software controlled and can be used to receive I / O signals not assigned to a specific role.

[0224] In at least one embodiment, one or more SoCs of SoC 1604 may also include extensive peripheral interfaces to enable communication with peripheral devices, audio encoders / decoders (“codecs”), power management and / or other devices. One or more SoCs of SoC 1604 may be used to process data from (e.g., connected via gigabit multimedia serial links and Ethernet channels) cameras, sensors (e.g., one or more LiDAR sensors 1664, one or more RADAR sensors 1660, etc., which may be connected via Ethernet), data from bus 1602 (e.g., vehicle 1600 speed, steering wheel position, etc.), data from one or more GNSS sensors 1658 (e.g., connected via Ethernet bus or CAN bus), etc. In at least one embodiment, one or more SoCs of SoC 1604 may also include a dedicated high-performance mass storage controller, which may include its own DMA engine and may be used to free one or more CPUs 1606 from routine data management tasks.

[0225] In at least one embodiment, one or more SoCs 1604 can be an end-to-end platform with a flexible architecture spanning automation levels 3-5, providing a comprehensive functional safety architecture that leverages and effectively utilizes computer vision and ADAS technologies to achieve diversity and redundancy. This provides a platform offering a flexible and reliable driving software stack as well as deep learning tools. In at least one embodiment, one or more SoCs 1604 can be faster, more reliable, and even more energy and space efficient than conventional systems. For example, in at least one embodiment, one or more accelerators 1614, when combined with one or more CPUs 1606, one or more GPUs 1608, and one or more data storage devices 1616, can provide a fast and efficient platform for Level 3-5 autonomous vehicles.

[0226] In at least one embodiment, the computer vision algorithm can be executed on a CPU, which can be configured using a high-level programming language (e.g., C) to execute multiple processing algorithms on a variety of visual data. However, in at least one embodiment, the CPU typically cannot meet the performance requirements of many computer vision applications, such as performance requirements related to execution time and power consumption. In at least one embodiment, many CPUs cannot execute complex object detection algorithms in real time, algorithms used in automotive ADAS applications and practical Level 3-5 autonomous vehicles.

[0227] The embodiments described herein allow multiple neural networks to be executed simultaneously and / or sequentially, and allow the results to be combined to achieve Level 3-5 autonomous driving capabilities. For example, in at least one embodiment, a CNN executed on a DLA or discrete GPU (e.g., one or more GPU 1620s) may include text and word recognition, thereby allowing a supercomputer to read and understand traffic signs, including signs for which the neural network has not yet been specifically trained. In at least one embodiment, the DLA may also include a neural network capable of recognizing, interpreting, and providing semantic understanding of symbols, and passing this semantic understanding to a path planning module running on a CPU Complex.

[0228] In at least one embodiment, for drives of levels 3, 4, or 5, multiple neural networks can run simultaneously. For example, in at least one embodiment, a warning sign consisting of a light bulb accompanied by the warning sign “Caution: flashing lights indicate icy conditions” can be interpreted independently or jointly by multiple neural networks. In at least one embodiment, the warning sign itself can be recognized as a traffic sign by a first deployed neural network (e.g., a trained neural network), and the text “flashing lights indicate icy conditions” can be interpreted by a second deployed neural network, which informs the vehicle’s path planning software (preferably executed on the CPU Complex) that icing conditions exist when flashing lights are detected. In at least one embodiment, flashing lights can be identified by operating a third deployed neural network across multiple frames, informing the vehicle’s path planning software of the presence (or absence) of flashing lights. In at least one embodiment, all three neural networks can run simultaneously, for example within the DLA and / or on one or more GPUs 1608.

[0229] In at least one embodiment, the CNN for facial recognition and vehicle owner identification can use data from camera sensors to identify the presence of an authorized driver and / or the owner of vehicle 1600. In at least one embodiment, a normally open sensor processor engine can be used to unlock the vehicle when the owner approaches the driver's door and turns on the lights, and, in security mode, can be used to disable the vehicle when the owner leaves it. In this way, one or more SoCs 1604 provide protection against theft and / or carjacking.

[0230] In at least one embodiment, the CNN for emergency vehicle detection and identification can use data from microphone 1696 to detect and identify emergency vehicle sirens. In at least one embodiment, one or more SoCs 1604 use the CNN to classify environmental and urban sounds, as well as visual data. In at least one embodiment, the CNN running on DLA is trained to identify the relative approach speed of emergency vehicles (e.g., by using the Doppler effect). In at least one embodiment, the CNN can also be trained to identify emergency vehicles in the area where the vehicle is operating, as identified by one or more GNSS sensors 1658. In at least one embodiment, when operating in Europe, the CNN will seek to detect European sirens, while in the United States the CNN will seek to identify only North American sirens. In at least one embodiment, once an emergency vehicle is detected, a control program can be used, with the assistance of one or more ultrasonic sensors 1662, to execute emergency vehicle safety routines, slow down the vehicle, pull the vehicle to the side of the road, stop, and / or leave the vehicle idle until (one or more) emergency vehicles have passed.

[0231] In at least one embodiment, vehicle 1600 may include one or more CPUs 1618 (e.g., one or more discrete CPUs or one or more dCPUs) that may be coupled to one or more SoCs 1604 via high-speed interconnects (e.g., PCIe). In at least one embodiment, one or more CPUs 1618 may include x86 processors. For example, one or more CPUs 1618 may be used to perform any of the various functions, such as arbitrating the results of potential inconsistencies between ADAS sensors and one or more SoCs 1604, and / or monitoring the status and health of one or more monitoring controllers 1636 and / or on-chip information systems (“information SoCs”) 1630.

[0232] In at least one embodiment, vehicle 1600 may include one or more GPUs 1620 (e.g., one or more discrete GPUs or one or more dGPUs) that may be coupled to one or more SoCs 1604 via a high-speed interconnect (e.g., NVIDIA's NVLINK). In at least one embodiment, one or more GPUs 1620 may provide additional artificial intelligence capabilities, such as by executing redundant and / or different neural networks, and may be used to train and / or update the neural networks based at least in part on inputs from sensors of vehicle 1600 (e.g., sensor data).

[0233] In at least one embodiment, vehicle 1600 may also include a network interface 1624, which may include, but is not limited to, one or more wireless antennas 1626 (e.g., one or more wireless antennas for different communication protocols, such as cellular antennas, Bluetooth antennas, etc.). In at least one embodiment, network interface 1624 may be used to enable wireless connectivity with other vehicles and / or computing devices (e.g., passenger client devices) via an internet cloud (e.g., employing servers and / or other network devices). In at least one embodiment, for communication with other vehicles, a direct link and / or an indirect link (e.g., via a network and the internet) may be established between vehicle 1600 and other vehicles. In at least one embodiment, a vehicle-to-vehicle communication link may be used to provide a direct link. The vehicle-to-vehicle communication link may provide vehicle 1600 with information about vehicles near vehicle 1600 (e.g., vehicles in front, to the side, and / or behind vehicle 1600). In at least one embodiment, the foregoing functionality may be part of a cooperative adaptive cruise control function of vehicle 1600.

[0234] In at least one embodiment, network interface 1624 may include a System-on-Chip (SoC) that provides modulation and demodulation functions and enables one or more controllers 1636 to communicate over a wireless network. In at least one embodiment, network interface 1624 may include a radio frequency (RF) front-end for up-conversion from baseband to RF and down-conversion from RF to baseband. In at least one embodiment, frequency conversion may be performed in any technically feasible manner. For example, frequency conversion may be performed using known processes and / or using a superheterodyne process. In at least one embodiment, the RF front-end functionality may be provided by a separate chip. In at least one embodiment, the network interface may include wireless functions for communication via LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and / or other wireless protocols.

[0235] In at least one embodiment, the vehicle 1600 may also include one or more data storage units 1628, which may include, but are not limited to, off-chip (e.g., one or more SoC 1604) storage. In at least one embodiment, the one or more data storage units 1628 may include, but are not limited to, one or more storage elements, including RAM, SRAM, dynamic random access memory (“DRAM”), video random access memory (“VRAM”), flash memory, hard disk and / or other components and / or devices capable of storing at least one bit of data.

[0236] In at least one embodiment, the vehicle 1600 may also include one or more GNSS sensors 1658 (e.g., GPS and / or auxiliary GPS sensors) to assist in map creation, perception, occupancy raster generation, and / or path planning functions. In at least one embodiment, any number of GNSS sensors 1658 may be used, including, for example, but not limited to, GPS sensors connected to a serial interface (e.g., RS-232) bridge using a USB connector with Ethernet.

[0237] In at least one embodiment, vehicle 1600 may also include one or more RADAR sensors 1660. One or more RADAR sensors 1660 can be used by vehicle 1600 for remote vehicle detection, even in dark and / or inclement weather conditions. In at least one embodiment, the RADAR functional safety level may be ASIL B. One or more RADAR sensors 1660 may use a CAN bus and / or bus 1602 (e.g., to transmit data generated by one or more RADAR sensors 1660) for control and access to object tracking data, and in some examples, may access Ethernet to access raw data. In at least one embodiment, a wide variety of RADAR sensor types can be used. For example, but not limited to, one or more RADAR sensors 1660 may be suitable for front, rear, and side RADAR use. In at least one embodiment, one or more RADAR sensors 1660 are pulse Doppler RADAR sensors.

[0238] In at least one embodiment, one or more RADAR sensors 1660 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, etc. In at least one embodiment, the long-range RADAR can be used for adaptive cruise control functions. In at least one embodiment, the long-range RADAR system can provide a wide field of view achieved through two or more independent scans (e.g., within a 250m range). In at least one embodiment, one or more RADAR sensors 1660 can help distinguish between stationary and moving objects and can be used by the ADAS system 1638 for emergency braking assistance and forward collision warning. One or more sensors 1660 included in the long-range RADAR system may include, but are not limited to, a monostatic multimode RADAR with multiple (e.g., six or more) fixed RADAR antennas and high-speed CAN and FlexRay interfaces. In at least one embodiment, having six antennas, with the four central antennas, can create a focused beammap designed to record the surrounding environment of the vehicle 1600 at a high speed while minimizing traffic interference from adjacent lanes. In at least one embodiment, the other two antennas can expand the field of view, thereby enabling rapid detection of vehicles entering or leaving the lane 1600.

[0239] In at least one embodiment, as an example, a mid-range RADAR system may include, for example, a range of up to 160m (front) or 80m (rear), and a field of view of up to 42 degrees (front) or 150 degrees (rear). In at least one embodiment, a short-range RADAR system may include, but is not limited to, any number of RADAR sensors 1660 designed to be mounted at both ends of the rear bumper. When mounted at both ends of the rear bumper, in at least one embodiment, the RADAR sensor system may generate two beams that continuously monitor the blind spots at and near the rear of the vehicle. In at least one embodiment, the short-range RADAR system may be used in ADAS system 1638 for blind spot detection and / or lane change assistance.

[0240] In at least one embodiment, the vehicle 1600 may also include one or more ultrasonic sensors 1662. One or more ultrasonic sensors 1662, which may be positioned at the front, rear, and / or sides of the vehicle 1600, can be used for parking assistance and / or creating and updating occupancy detectors. In at least one embodiment, a wide variety of ultrasonic sensors 1662 can be used, and different ultrasonic sensors 1662 can be used for different detection ranges (e.g., 2.5m, 4m). In at least one embodiment, the ultrasonic sensors 1662 can operate at the ASIL B functional safety level.

[0241] In at least one embodiment, vehicle 1600 may include one or more LiDAR sensors 1664. The one or more LiDAR sensors 1664 may be used for object and pedestrian detection, emergency braking, collision avoidance, and / or other functions. In at least one embodiment, the one or more LiDAR sensors 1664 may be of functional safety level ASIL B. In at least one embodiment, vehicle 1600 may include multiple (e.g., two, four, six, etc.) LiDAR sensors 1664 that can use Ethernet (e.g., providing data to a Gigabit Ethernet switch).

[0242] In at least one embodiment, one or more LiDAR sensors 1664 may be able to provide a list of objects and their distances for a 360-degree field of view. In at least one embodiment, one or more commercially available LiDAR sensors 1664 may, for example, have an advertising range of approximately 100m, an accuracy of 2cm-3cm, and support a 100Mbps Ethernet connection. In at least one embodiment, one or more non-protruding LiDAR sensors may be used. In such embodiments, one or more LiDAR sensors 1664 may be implemented as small devices embedded in the front, rear, sides, and / or corners of a vehicle 1600. In at least one embodiment, one or more LiDAR sensors 1664 may provide a horizontal field of view of up to 120 degrees and a vertical field of view of 35 degrees, even for objects with low reflectivity, and have a range of 200m. In at least one embodiment, one or more forward-facing LiDAR sensors 1664 may be configured for a horizontal field of view between 45 degrees and 135 degrees.

[0243] In at least one embodiment, LIDAR technology (such as 3D flash LIDAR) may also be used. 3D flash LIDAR uses a laser flash as a transmission source to illuminate approximately 200m around the vehicle 1600. In at least one embodiment, the flash LIDAR unit includes, but is not limited to, a receiver that records the laser pulse propagation time and reflected light on each pixel, which in turn corresponds to the range from the vehicle 1600 to the object. In at least one embodiment, flash LIDAR can allow the generation of highly accurate and distortion-free images of the surrounding environment using each laser flash. In at least one embodiment, four flash LIDAR sensors may be deployed, one on each side of the vehicle 1600. In at least one embodiment, the 3D flash LIDAR system includes, but is not limited to, a solid-state 3D line-of-sight array LIDAR camera with no moving parts other than a fan (e.g., a non-scanning LIDAR device). In at least one embodiment, the flash LIDAR device can use a 5-nanosecond Class I (eye-safe) laser pulse per frame and can capture reflected laser light in the form of a 3D ranging point cloud and co-registered intensity data.

[0244] In at least one embodiment, vehicle 1600 may further include one or more IMU sensors 1666. In at least one embodiment, one or more IMU sensors 1666 may be located at the center of the rear axle of vehicle 1600. In at least one embodiment, one or more IMU sensors 1666 may include, for example, but not limited to, one or more accelerometers, one or more magnetometers, one or more gyroscopes, a magnetic compass, multiple magnetic compasses, and / or other sensor types. In at least one embodiment, for example in a six-axis application, one or more IMU sensors 1666 may include, but are not limited to, accelerometers and gyroscopes. In at least one embodiment, for example in a nine-axis application, one or more IMU sensors 1666 may include, but are not limited to, accelerometers, gyroscopes, and magnetometers.

[0245] In at least one embodiment, one or more IMU sensors 1666 can be implemented as a miniature, high-performance GPS-assisted inertial navigation system (“GPS / INS”) combining a microelectromechanical system (“MEMS”) inertial sensor, a high-sensitivity GPS receiver, and an advanced Kalman filtering algorithm to provide position, velocity, and attitude estimations; in at least one embodiment, one or more IMU sensors 1666 enable vehicle 1600 to estimate heading without input from a magnetic sensor obtained by directly observing and correlating velocity changes from GPS to one or more IMU sensors 1666. In at least one embodiment, one or more IMU sensors 1666 and one or more GNSS sensors 1658 can be combined in a single integrated unit.

[0246] In at least one embodiment, vehicle 1600 may include one or more microphones 1696 placed inside and / or around vehicle 1600. In at least one embodiment, in addition, one or more microphones 1696 may be used for emergency vehicle detection and identification.

[0247] In at least one embodiment, vehicle 1600 may also include any number of camera types, including one or more stereo cameras 1668, one or more wide-angle cameras 1670, one or more infrared cameras 1672, one or more surround cameras 1674, one or more long-range cameras 1698, one or more mid-range cameras 1676, and / or other camera types. In at least one embodiment, the cameras can be used to capture image data around the entire perimeter of vehicle 1600. In at least one embodiment, the type of camera used depends on vehicle 1600. In at least one embodiment, any combination of camera types can be used to provide the necessary coverage around vehicle 1600. In at least one embodiment, the number of cameras deployed may vary depending on the embodiment. For example, in at least one embodiment, vehicle 1600 may include six cameras, seven cameras, ten cameras, twelve cameras, or other numbers of cameras. The cameras may be examples, but are not limited to, supporting Gigabit Multimedia Serial Link (“GMSL”) and / or Gigabit Ethernet. In at least one embodiment, references previously made herein... Figure 16A and Figure 16B Each camera can be described in more detail.

[0248] In at least one embodiment, the vehicle 1600 may also include one or more vibration sensors 1642. In at least one embodiment, the one or more vibration sensors 1642 can measure vibrations of components of the vehicle 1600 (e.g., axles). For example, in at least one embodiment, changes in vibration can indicate changes in road surface conditions. In at least one embodiment, when two or more vibration sensors 1642 are used, differences between vibrations can be used to determine road surface friction or slippage (e.g., when there is a vibration difference between a power drive axle and a free-rotating axle).

[0249] In at least one embodiment, vehicle 1600 may include ADAS system 1638. ADAS system 1638 may include, but is not limited to, SoC. In at least one embodiment, ADAS system 1638 may include, but is not limited to, any number of autonomous / adaptive / automatic cruise control (“ACC”) systems, cooperative adaptive cruise control (“CACC”) systems, forward collision warning (“FCW”) systems, automatic emergency braking (“AEB”) systems, lane departure warning (“LDW”) systems, lane keeping assist (“LKA”) systems, blind spot warning (“BSW”) systems, rear cross traffic warning (“RCTW”) systems, collision warning (“CW”) systems, lane centering (“LC”) systems, and / or other systems, features, and / or functions, and combinations thereof.

[0250] In at least one embodiment, the ACC system may use one or more RADAR sensors 1660, one or more LIDAR sensors 1664, and / or any number of cameras. In at least one embodiment, the ACC system may include a longitudinal ACC system and / or a lateral ACC system. In at least one embodiment, the longitudinal ACC system monitors and controls the distance to vehicles adjacent to vehicle 1600 and automatically adjusts the speed of vehicle 1600 to maintain a safe distance from the vehicle ahead. In at least one embodiment, the lateral ACC system performs distance holding and suggests that vehicle 1600 change lanes when necessary. In at least one embodiment, lateral ACC is associated with other ADAS applications, such as LC and CW.

[0251] In at least one embodiment, the CACC system uses information from other vehicles, which may be received from other vehicles via network interface 1624 and / or one or more wireless antennas 1626 via a wireless link or indirectly via a network connection (e.g., via the Internet). In at least one embodiment, the direct link may be provided by a vehicle-to-vehicle (“V2V”) communication link, while the indirect link may be provided by an infrastructure-to-vehicle (“I2V”) communication link. Typically, the V2V communication concept provides information about the vehicle immediately preceding it (e.g., a vehicle immediately in front of vehicle 1600 and in the same lane as it), while the I2V communication concept provides information about traffic further ahead. In at least one embodiment, the CACC system may include one or both of the I2V and V2V information sources. In at least one embodiment, given information about vehicles preceding vehicle 1600, the CACC system can be more reliable and has the potential to improve traffic flow smoothness and reduce road congestion.

[0252] In at least one embodiment, the FCW system is designed to warn the driver of danger so that the driver can take corrective action. In at least one embodiment, the FCW system uses a forward-facing camera and / or one or more RADAR sensors 1660, coupled to a dedicated processor, DSP, FPGA, and / or ASIC, electrically coupled to driver feedback, such as a display, speaker, and / or vibration components. In at least one embodiment, the FCW system can provide warnings, for example, in the form of audible, visual warnings, vibrations, and / or rapid braking pulses.

[0253] In at least one embodiment, the AEB system detects an impending forward collision with another vehicle or other object and can automatically apply brakes if the driver does not take corrective action within a specified time or distance parameter. In at least one embodiment, the AEB system may use one or more forward-facing cameras and / or one or more RADAR sensors 1660 coupled to a dedicated processor, DSP, FPGA, and / or ASIC. In at least one embodiment, when the AEB system detects a hazard, it typically first warns the driver to take corrective action to avoid a collision, and if the driver does not take corrective action, the AEB system may automatically apply brakes to attempt to prevent or at least mitigate the effects of the predicted collision. In at least one embodiment, the AEB system may include techniques such as dynamic braking to support and / or brakes for impending collisions.

[0254] In at least one embodiment, when vehicle 1600 crosses lane markings, the LDW system provides visual, auditory, and / or tactile warnings, such as steering wheel or seat vibrations, to alert the driver. In at least one embodiment, the LDW system is inactive when the driver indicates intentional lane departure, such as by activating turn signals. In at least one embodiment, the LDW system may use a front-facing camera coupled to a dedicated processor, DSP, FPGA, and / or ASIC, which is electrically coupled to provide driver feedback such as a display, speaker, and / or vibration components. The LKA system is a variant of the LDW system. In at least one embodiment, if vehicle 1600 begins to leave the lane, the LKA system provides steering input or braking to correct vehicle 1600.

[0255] In at least one embodiment, the BSW system detects and warns the driver of a vehicle in the blind spot. In at least one embodiment, the BSW system can provide visual, auditory, and / or tactile alerts to indicate that merging or changing lanes is unsafe. In at least one embodiment, the BSW system can provide additional warnings when the driver uses the turn signal. In at least one embodiment, the BSW system can use one or more rear-facing cameras and / or one or more RADAR sensors 1660 coupled to a dedicated processor, DSP, FPGA, and / or ASIC, electrically coupled to driver feedback, such as a display, speaker, and / or vibration assembly.

[0256] In at least one embodiment, the RCTW system can provide visual, auditory, and / or tactile notifications when an object is detected outside the range of the rear camera while the vehicle 1600 is reversing. In at least one embodiment, the RCTW system includes an AEB system to ensure the application of the vehicle brakes to avoid a collision. In at least one embodiment, the RCTW system may use one or more rear-facing RADAR sensors 1660 coupled to a dedicated processor, DSP, FPGA, and / or ASIC, which are electrically coupled to driver feedback such as a display, speaker, and / or vibration assembly.

[0257] In at least one embodiment, conventional ADAS systems may be prone to generating false alarms, which can be annoying and distracting to the driver, but are generally not catastrophic because conventional ADAS systems warn the driver and allow the driver to determine whether a safe situation truly exists and take appropriate action. In at least one embodiment, in the event of conflicting results, the vehicle 1600 itself decides whether to follow the result of the primary computer or the secondary computer (e.g., the first controller 1636 or the second controller 1636). For example, in at least one embodiment, ADAS system 1638 may be a backup and / or auxiliary computer for providing perception information to a backup computer rationality module. In at least one embodiment, the backup computer rationality monitor may run redundant software on hardware components to detect faults in perception and dynamic driving tasks. In at least one embodiment, the output from ADAS system 1638 may be provided to a monitoring MCU. In at least one embodiment, if the outputs from the primary computer and the auxiliary computer conflict, the monitoring MCU decides how to reconcile the conflict to ensure safe operation.

[0258] In at least one embodiment, the master computer may be configured to provide a confidence score to the supervisory MCU to indicate the master computer's confidence in the selected result. In at least one embodiment, if the confidence score exceeds a threshold, the supervisory MCU may follow the master computer's instructions regardless of whether the auxiliary computer provides conflicting or inconsistent results. In at least one embodiment, if the confidence score does not meet the threshold, and if the master computer and the auxiliary computer indicate different results (e.g., conflicting), the supervisory MCU may arbitrate between the computers to determine the appropriate result.

[0259] In at least one embodiment, the supervisory MCU may be configured to run a neural network trained and configured to determine, at least in part, the conditions under which the auxiliary computer provides a false alarm based on outputs from both the host computer and the auxiliary computer. In at least one embodiment, the neural network in the supervisory MCU may learn when the output of the auxiliary computer can be trusted and when it cannot. For example, in at least one embodiment, when the auxiliary computer is a RADAR-based FCW system, the neural network in the supervisory MCU may learn when the FCW system recognizes a metallic object that is not actually dangerous, such as a drain grat or manhole cover that would trigger an alarm. In at least one embodiment, when the auxiliary computer is a camera-based LDW system, the neural network in the supervisory MCU may learn to override the LDW when a cyclist or pedestrian is present and lane departure is actually the safest operation. In at least one embodiment, the supervisory MCU may include at least one of a DLA or GPU suitable for running a neural network with associated memory. In at least one embodiment, the supervisory MCU may include and / or be included as a component of one or more SoC 1604s.

[0260] In at least one embodiment, the ADAS system 1638 may include an auxiliary computer that performs ADAS functions using conventional computer vision rules. In at least one embodiment, the auxiliary computer may use classic computer vision rules (if-then), and the presence of a neural network in the supervisory MCU can improve reliability, security, and performance. For example, in at least one embodiment, diverse implementations and intentional non-identity make the entire system more fault-tolerant, especially for failures caused by software (or software-hardware interface) functionality. For example, in at least one embodiment, if a software vulnerability or bug exists in the software running on the host computer, and different software code running on the auxiliary computer provides the same overall result, the supervisory MCU can more confidently assume that the overall result is correct and that the vulnerability in the software or hardware on the host computer will not lead to a significant error.

[0261] In at least one embodiment, the output of the ADAS system 1638 can be input to the perception module and / or the dynamic driving task module of the host computer. For example, in at least one embodiment, if the ADAS system 1638 indicates a forward collision warning due to an object directly ahead, the perception block can use this information when identifying the object. In at least one embodiment, as described herein, the assistance computer can have its own neural network trained to reduce the risk of false alarms.

[0262] In at least one embodiment, vehicle 1600 may also include an infotainment SoC 1630 (e.g., an in-vehicle infotainment system (IVI)). Although shown and described as an SoC, in at least one embodiment, the infotainment system SoC 1630 may not be an SoC and may include, but is not limited to, two or more discrete components. In at least one embodiment, the infotainment SoC 1630 may include, but is not limited to, a combination of hardware and software that can be used to provide audio (e.g., music, personal digital assistant, navigation instructions, news, radio, etc.), video (e.g., television, movies, streaming media, etc.), telephone (e.g., hands-free calling), network connectivity (e.g., LTE, WiFi, etc.) and / or information services (e.g., navigation system, rear parking assist, radio data system, vehicle-related information such as fuel level, total coverage distance, brake fuel level, fuel level, door opening / closing, air filter information, etc.) to vehicle 1600. For example, the infotainment SoC 1630 may include a radio, disk player, navigation system, video player, USB and Bluetooth connectivity, automobile, in-vehicle entertainment system, WiFi, steering wheel audio controls, hands-free voice control, head-up display (“HUD”), HMI display 1634, telematics device, control panel (e.g., for controlling and / or interacting with various components, features and / or systems) and / or other components. In at least one embodiment, the infotainment SoC 1630 may further be used to provide information (e.g., visual and / or auditory) to a user of vehicle 1600, such as information from ADAS system 1638, autonomous driving information (such as planned vehicle maneuvers), trajectory, surrounding environment information (e.g., intersection information, vehicle information, road information, etc.) and / or other information.

[0263] In at least one embodiment, the infotainment SoC 1630 may include any number and type of GPU functionality. In at least one embodiment, the infotainment SoC 1630 may communicate with other devices, systems, and / or components of the vehicle 1600 via a bus 1602 (e.g., CAN bus, Ethernet, etc.). In at least one embodiment, the infotainment SoC 1630 may be coupled to a monitoring MCU, enabling the GPU of the infotainment system to perform some autonomous driving functions in the event of a failure of the main controller 1636 (e.g., the main computer and / or backup computer of the vehicle 1600). In at least one embodiment, the infotainment SoC 1630 may cause the vehicle 1600 to enter a driver-to-safe-stop mode, as described herein.

[0264] In at least one embodiment, vehicle 1600 may also include instrument panel 1632 (e.g., digital instrument panel, electronic instrument panel, digital instrument control panel, etc.). In at least one embodiment, instrument panel 1632 may include, but is not limited to, controllers and / or supercomputers (e.g., discrete controllers or supercomputers). In at least one embodiment, instrument panel 1632 may include, but is not limited to, any number and combination of a set of instruments, such as speedometer, fuel level, oil pressure, tachometer, odometer, turn indicator, shift position indicator, one or more seatbelt warning lights, one or more parking brake warning lights, one or more engine malfunction lights, auxiliary restraint system (e.g., airbag) information, lighting controls, safety system controls, navigation information, etc. In some examples, information may be displayed and / or shared between infotainment SoC 1630 and instrument panel 1632. In at least one embodiment, instrument panel 1632 may be included as part of infotainment SoC 1630, or vice versa.

[0265] The inference and / or training logic 715 is used to perform inference and / or training operations associated with one or more embodiments. The following is in conjunction with... Figure 7A and / or Figure 7B Details are provided regarding the inference and / or training logic 715. In at least one embodiment, the inference and / or training logic 715 can be implemented in the system. Figure 16A The operation is used to infer or predict the operation based at least in part on weight parameters calculated using neural network training operations, neural network functions and / or architectures or neural network use cases described herein.

[0266] Figure 16B It is based on at least one embodiment in a cloud-based server and Figure 16AA diagram of a system 1676 for communication between autonomous vehicles 1600. In at least one embodiment, system 1676 may include, but is not limited to, one or more servers 1678, one or more networks 1690, and any number and type of vehicles, including vehicle 1600. In at least one embodiment, one or more servers 1678 may include, but is not limited to, multiple GPUs 1684(A)-1684(H) (collectively referred to herein as GPU 1684), PCIe switches 1682(A)-1682(D) (collectively referred to herein as PCIe switch 1682), and / or CPUs 1680(A)-1680(B) (collectively referred to herein as CPU 1680). GPU 1684, CPU 1680, and PCIe switch 1682 may be interconnected with high-speed interconnects, such as, but not limited to, NVLink interface 1688 developed by NVIDIA and / or PCIe connection 1686. The GPU 1684 is connected via NVLink and / or NVSwitchSoC, and the GPU 1684 and PCIe switch 1682 are connected via PCIe interconnect. In at least one embodiment, although eight GPUs 1684, two CPUs 1680, and four PCIe switches 1682 are shown, this is not intended to be limiting. In at least one embodiment, each of one or more servers 1678 may include, but is not limited to, any combination of any number of GPUs 1684, CPUs 1680, and / or PCIe switches 1682. For example, in at least one embodiment, one or more servers 1678 may each include eight, sixteen, thirty-two, and / or more GPUs 1684.

[0267] In at least one embodiment, one or more servers 1678 may receive image data representing an image from a vehicle via one or more networks 1690, the image showing unexpected or changed road conditions, such as recently commenced roadworks. In at least one embodiment, one or more servers 1678 may transmit an updated neural network 1692 and / or map information 1694, including but not limited to information about traffic and road conditions, to the vehicle via one or more networks 1690. In at least one embodiment, updating the map information 1694 may include, but is not limited to, updating the HD map 1622, such as information about construction sites, potholes, sidewalks, floods, and / or other obstacles. In at least one embodiment, the neural network 1692, the updated neural network 1692, and / or the map information 1694 may be generated from new training and / or experience represented by data received from any number of vehicles in the environment, and / or at least based on training performed in a data center (e.g., using one or more servers 1678 and / or other servers).

[0268] In at least one embodiment, one or more servers 1678 can be used to train a machine learning model (e.g., a neural network) at least in part based on training data. In at least one embodiment, the training data can be generated by the vehicle, and / or can be generated in a simulation (e.g., using a game engine). In at least one embodiment, any amount of training data is labeled (e.g., where the associated neural network benefits from supervised learning) and / or undergoes other preprocessing. In at least one embodiment, no amount of training data is labeled and / or preprocessed (e.g., where the associated neural network does not require supervised learning). In at least one embodiment, once the machine learning model is trained, the machine learning model can be used by the vehicle (e.g., transmitted to the vehicle via one or more networks 1690), and / or the machine learning model can be used by one or more servers 1678 to remotely monitor the vehicle.

[0269] In at least one embodiment, one or more servers 1678 may receive data from the vehicle and apply the data to a state-of-the-art real-time neural network for real-time intelligent inference. In at least one embodiment, one or more servers 1678 may include a deep learning supercomputer and / or a dedicated AI computer powered by one or more GPUs 1684, such as the DGX and DGX Station machines developed by NVIDIA. However, in at least one embodiment, one or more servers 1678 may include a deep learning infrastructure in a data center using CPU power.

[0270] In at least one embodiment, the deep learning infrastructure of one or more servers 1678 may be capable of fast, real-time inference and can use this capability to assess and verify the health of the processor, software, and / or associated hardware in vehicle 1600. For example, in at least one embodiment, the deep learning infrastructure may receive periodic updates from vehicle 1600, such as image sequences and / or objects located by vehicle 1600 in the image sequence (e.g., via computer vision and / or other machine learning object classification techniques). In at least one embodiment, the deep learning infrastructure may run its own neural network to identify objects and compare them with objects identified by vehicle 1600, and if the results do not match and the deep learning infrastructure determines that the AI ​​in vehicle 1600 is malfunctioning, one or more servers 1678 may signal to vehicle 1600 to instruct the fail-safe computer of vehicle 1600 to take control, notify passengers, and complete a safe stopping operation.

[0271] In at least one embodiment, one or more servers 1678 may include one or more GPUs 1684 and one or more programmable inference accelerators (e.g., NVIDIA's TensorRT 3 devices). In at least one embodiment, the combination of GPU-driven servers and inference acceleration enables real-time response. In at least one embodiment, for example, where performance is less critical, servers driven by CPUs, FPGAs, and other processors may be used for inference. In at least one embodiment, inference and / or training logic 715 is used to execute one or more embodiments. Details regarding the inference and / or training logic 715 are incorporated below. Figure 7A Provided with and / or 7B.

[0272] Other variations are within the spirit of this disclosure. Therefore, although the disclosed technology is readily adaptable to various modifications and alternative constructions, certain embodiments thereof are illustrated in the accompanying drawings and have been described in detail above. However, it should be understood that the disclosure is not intended to be limited to one or more specific forms disclosed, but rather, it is intended to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of this disclosure as defined in the appended claims.

[0273] Unless otherwise stated or obviously contradicted by the context, the terms “a,” “an,” and “the,” and similar references, used in the context of describing the disclosed embodiments (particularly in the context of the appended claims), should be interpreted as encompassing both singular and plural forms, rather than as definitions of the terms. Unless otherwise stated, the terms “comprising,” “having,” “including,” and “containing” should be interpreted as open-ended terms (meaning “including, but not limited to”). The term “connection” (referring to a physical connection where not modified) should be interpreted as partially or wholly contained, attached to, or joined together, even with some intervention. Unless otherwise indicated herein, references to numerical ranges herein are intended only as a way of abbreviating each individual value falling within that range, and each individual value is incorporated into the specification as if it were separately described herein. Unless otherwise indicated or contradicted by the context, the use of the terms “set” (e.g., “item set”) or “subset” should be interpreted as a non-empty set comprising one or more members. Furthermore, unless otherwise indicated or contradicted by the context, the term “subset” of the corresponding set does not necessarily mean an appropriate subset of the corresponding set, but rather that the subset and the corresponding set can be equal.

[0274] Unless otherwise explicitly stated or clearly contradicted by the context, connective phrases such as “at least one of A, B, and C” or “at least one of A, B, and C” are understood in the context to generally refer to items, terms, etc., which can be A or B or C, or any non-empty subset of the set A, B, and C. For example, in an illustrative example of a set with three members, the connective phrases “at least one of A, B, and C” and “at least one of A, B, and C” refer to any of the following sets: {A}, {B}, {C}, {A, B}, {A, C}, {B, C}, {A, B, C}. Therefore, such connective language is generally not intended to imply that some embodiments require the presence of at least one of A, at least one of B, and at least one of C. Additionally, unless otherwise stated or contradicted by the context, the term “multiple” indicates a plural state (e.g., “multiple items” means multiple items). Multiple means at least two items, but more may be indicated if explicitly stated or by the context. Furthermore, unless otherwise stated or clearly understood from the context, the phrase “based on” means “at least partially based on” rather than “based on only”.

[0275] Unless otherwise indicated herein or clearly contradicted by the context, the operations of the processes described herein may be performed in any suitable order. In at least one embodiment, processes such as those described herein (or variations thereof and / or combinations thereof) are executed under the control of one or more computer systems configured with executable instructions and are implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed jointly on one or more processors via hardware or a combination thereof. In at least one embodiment, the code is stored on a computer-readable storage medium, for example, in the form of a computer program comprising a plurality of instructions executable by one or more processors. In at least one embodiment, the computer-readable storage medium is a non-transitory computer-readable storage medium that excludes transient signals (e.g., propagating transient electrical or electromagnetic transmissions) but includes non-transitory data storage circuitry (e.g., buffers, caches, and queues). In at least one embodiment, code (e.g., executable code or source code) is stored on one or more non-transitory computer-readable storage media (or other memory for storing executable instructions) on which executable instructions are stored, which, when executed by one or more processors of a computer system (i.e., as a result of execution), cause the computer system to perform the operations described herein. In at least one embodiment, the set of non-transitory computer-readable storage media comprises multiple non-transitory computer-readable storage media, and one or more of the individual non-transitory storage media lack all the code, but the multiple non-transitory computer-readable storage media collectively store all the code. In at least one embodiment, the executable instructions are executed such that different instructions are executed by different processors; for example, the non-transitory computer-readable storage media store the instructions, and the main central processing unit (“CPU”) executes some instructions while the graphics processing unit (“GPU”) executes other instructions. In at least one embodiment, different components of the computer system have separate processors, and the different processors execute different subsets of the instructions.

[0276] Therefore, in at least one embodiment, the computer system is configured to implement one or more services that perform the operations of the processes described herein, either individually or collectively, and such a computer system is configured with suitable hardware and / or software to enable the implementation of the operations. Furthermore, the computer system implementing at least one embodiment of this disclosure is a single device, and in another embodiment it is a distributed computer system comprising multiple devices operating in different ways, such that the distributed computer system performs the operations described herein, and that a single device does not perform all the operations.

[0277] The use of any and all examples or exemplary language (e.g., “such as”) provided herein is intended only to better illustrate embodiments of this disclosure and does not constitute a limitation on the scope of the disclosure unless otherwise required. No language in the specification should be construed as indicating that any unclaimed element is essential to the practice of the disclosure.

[0278] All references cited in this article, including publications, patent applications and patents, are incorporated herein by reference as if each reference were individually and specifically indicated to be incorporated herein by reference and the entire contents of which are described herein.

[0279] The terms “coupled” and “connected”, and their derivatives, may be used in the specification and claims. It should be understood that these terms may not be intended to be synonyms with each other. Rather, in certain examples, “connected” or “coupled” may be used to indicate that two or more elements are in direct or indirect physical or electrical contact with each other. “Coupled” may also mean that two or more elements are not in direct contact with each other, but still cooperate or interact with each other.

[0280] Unless otherwise expressly stated, it will be understood that throughout this specification, terms such as “processing,” “computing,” “determining,” etc., refer to the actions and / or processes of a computer or computing system or similar electronic computing device that process and / or convert data represented as physical quantities (e.g., electrons) in the registers and / or memory of the computing system into other data represented as physical quantities in the memory, registers, or other such information storage, transmission, or display devices of the computing system.

[0281] In a similar manner, the term "processor" can refer to any device or part of memory that processes electronic data from registers and / or memory and converts that electronic data into other electronic data that can be stored in registers and / or memory. As a non-limiting example, a "processor" can be a CPU or a GPU. A "computing platform" can include one or more processors. As used herein, a "software" process can include, for example, software and / or hardware entities that perform work over time, such as tasks, threads, and intelligent agents. Similarly, each process can refer to multiple processes that execute instructions sequentially or intermittently, sequentially, or in parallel. The terms "system" and "method" are used interchangeably herein, provided that a system can embody one or more methods, and a method can be considered a system.

[0282] This document refers to the process of acquiring, obtaining, receiving, or inputting analog or digital data into a subsystem, computer system, or computer-implemented machine. Acquiring, obtaining, receiving, or inputting analog and digital data can be accomplished in various ways, such as by receiving data as a parameter to a function call or an application programming interface (API) call. In some implementations, the process of acquiring, obtaining, receiving, or inputting analog or digital data can be accomplished by transmitting data via a serial or parallel interface. In another implementation, the process of acquiring, obtaining, receiving, or inputting analog or digital data can be accomplished by transmitting data from a providing entity to an acquiring entity via a computer network. Reference can also be made to providing, outputting, transmitting, sending, or presenting analog or digital data. In various examples, the process of providing, outputting, transmitting, sending, or presenting analog or digital data can be implemented by transmitting data as an input or output parameter to a function call, an API call, or an inter-process communication mechanism.

[0283] While the discussion above illustrates example implementations of the described technologies, other architectures can be used to implement the described functionality and are intended to fall within the scope of this disclosure. Furthermore, although specific assignments of responsibilities have been defined above for discussion purposes, various functions and responsibilities can be assigned and divided in different ways depending on the circumstances.

[0284] Furthermore, although the subject matter has been described in language specific to structural features and / or methodological actions, it should be understood that the subject matter claimed in the appended claims is not necessarily limited to the specific features or actions described. Rather, specific features and actions are disclosed as exemplary forms for implementing the claims.

Claims

1. A method comprising: The location data from at least two sources is used to generate fused location data, which is fused in part based on weights determined using relative confidence values ​​associated with each of the sources. The fused localization is compared with localizations from at least two trusted and independent sources to verify the fused localization; Lane graphics are generated based at least on verification of the fused localization and using environmental perception from at least two sources; The lane pattern is compared with lane pattern data from at least two trusted sources to verify the lane pattern; After receiving two or more votes from at least two comparators, it is determined that the lane pattern is verified, with each comparator evaluating the lane pattern against lane pattern data from the at least two trusted and independent sources. as well as Based at least on the verification of the lane map, the lane map is provided to the control system for navigating objects within the environment.

2. The method of claim 1, wherein, The object is a vehicle, and the lane map is generated using map data from at least one source and environmental perception from at least one source corresponding to the vehicle.

3. The method of claim 1, wherein, The source of the location data includes at least one of the following: a camera, a Global Positioning System (GPS), or a radar system.

4. The method of claim 1, wherein, The sources of environmental perception include at least one of the following: camera, radar system, LIDAR system, ultrasonic system, or high-definition HD map data.

5. The method according to claim 1, further comprising: Before performing a comparison with the localization from the at least two trusted and independent sources, the confidence level of the fused localization is determined; as well as When the confidence level is lower than the confidence level threshold, the fused location is replaced with the most confident location value from the at least two sources.

6. The method according to claim 1, further comprising: Before performing a comparison with lane pattern data from the at least two trusted sources, the confidence level of the lane pattern is determined; as well as When the confidence level is lower than the confidence threshold, the lane graph is replaced with lane graph data from the at least two trusted sources that corresponds to the highest confidence level.

7. The method according to claim 1, further comprising: It has been determined that the location of at least one source is temporarily unavailable or unreliable; as well as Historical location data is used to replace the location of the at least one source.

8. The method according to claim 1, further comprising: The location of the at least two sources or the selection of environmental awareness of the at least two sources is dynamically adjusted based on availability or confidence.

9. The method according to claim 1, further comprising: Use a parallel path generator to generate the second lane graphic; as well as Dynamically determine whether to provide the lane graphic or the second lane graphic for navigation of the object.

10. A vehicle control system, comprising: A location fusion module is used to generate fused location based on independent locations received from at least two trusted and independent sources; A location monitor is used to determine the confidence level of the fused location based at least on the consistency between the fused location and the independent location; A lane graphics fusion module is used to generate a fused lane graphic based at least on the fused localization and independent lane graphics generated using environmental perception from at least two independent sources. A lane graphics monitor is used to determine the confidence level of the fused lane graphics based at least on the consistency between the fused lane graphics and the individual lane graphics. as well as A control system is configured to enable the vehicle to navigate according to the fused lane map in response to the fused lane map having a confidence level that satisfies a minimum confidence level.

11. The vehicle control system according to claim 10, wherein The control system is configured to, in response to the fused lane having a confidence level less than a minimum confidence level, cause the vehicle to navigate according to an alternative lane pattern, the alternative lane pattern including at least one of the independent lane pattern or a lane pattern from an auxiliary lane pattern source.

12. The vehicle control system according to claim 10, further comprising: Multiple sensors of two or more sensor types, configured to capture data for determining at least one of the independent positioning or the independent lane pattern, wherein the sensor types include at least one of camera, radar, ultrasonic, LIDAR, GPS, or IMU data.

13. The vehicle control system according to claim 10, further comprising: Multiple positioning comparators are used to compare the independent positioning with the fused positioning; as well as Multiple lane pattern comparators are used to compare the individual lane patterns with the fused lane pattern, wherein the positioning monitor determines the confidence level of the fused positioning based at least on the results of the positioning comparators, and wherein the lane pattern monitor determines the confidence level of the fused lane pattern based at least on the results of the lane pattern comparators.

14. The vehicle control system according to claim 10, further comprising: An arbitrator is used to determine when location data from at least one source is temporarily unavailable or unreliable, wherein historical location data can be used in place of location data from the at least one source.

15. The vehicle control system of claim 10, wherein, The vehicle control system can dynamically adjust the selection of the at least two independent sources based on availability or confidence level.

16. A system comprising: One or more processing units are used for: The location data from at least two sources is used to generate fused location data, which is based in part on weighted fusion using relative confidence values ​​associated with each of the sources. The fused localization is compared with localizations from at least two trusted and independent sources to verify the fused localization; Lane graphics are generated based at least on verification of the fused localization and using environmental perception from at least two sources; The lane pattern is compared with lane pattern data from at least two trusted sources to verify the lane pattern; After receiving two or more votes from at least two comparators, it is determined that the lane pattern is verified, with each comparator evaluating the lane pattern against lane pattern data from the at least two trusted and independent sources respectively; as well as Based at least on the verification of the lane map, the lane map is provided to the control system for navigating objects within the environment.

17. The system of claim 16, wherein, The one or more processing units are further configured to: The confidence level of the fused location is determined before performing a comparison with the at least two trusted and independent location values; When the confidence level is lower than the confidence level threshold, the fused location is replaced by the most confident location value from the location data of the at least two sources. Before performing a comparison with lane graph data from the at least two trusted and independent sources, the confidence level of the lane graph is determined; as well as When the confidence level is lower than the confidence level threshold, the lane pattern is replaced with the most confident lane pattern from the lane pattern data of the at least two reliable and independent sources.

18. The system of claim 16, wherein, One or more processing units are further used for: Determine that location data from at least one source is temporarily unavailable or unreliable; and Historical location data is used to replace the location data of the at least one source.

19. The system of claim 16, wherein, One or more processing units are further used for: Use a parallel path generator to generate the second lane graphic; and Dynamically determine whether to provide the lane graphic or the second lane graphic for navigation of the object.

20. The system of claim 16, wherein, The system includes at least one of the following: A system used to perform simulation operations; A system used to perform simulated operations for testing or verifying autonomous machine applications; A system used for rendering graphics output; A system used to perform deep learning operations; Systems implemented using edge devices; A system that merges one or more virtual machines (VMs); Systems partially implemented in data centers; or A system that partially utilizes cloud computing resources.

21. A processor, comprising: One or more processing units, for Generate a verified location, which is obtained by fusing location data from at least two sources and comparing it with location values ​​from at least two trusted and independent sources. A lane pattern for environment verification is generated using at least the verified positioning, wherein the lane pattern is verified after receiving two or more votes from at least two comparators, each comparator evaluating the lane pattern against lane pattern data from the at least two trusted and independent sources. as well as The verified lane graph is provided to the control system for navigating objects within the environment.

22. The processor of claim 21, wherein at least one of the one or more processing units is further configured to: The verified location is generated by comparing the unverified location with the location values ​​of the at least two trusted and independent sources; An unverified lane map is generated using environmental perception from at least two sources and the verified localization. as well as The verified lane graph is generated by comparing the unverified lane graph with lane graph data from at least two trusted and independent sources.

23. The processor of claim 22, wherein at least one of the one or more processing units is further configured to: Before performing a comparison of the location values ​​for the at least two trusted and independent sources, determine the confidence level of the unverified location; and When the confidence level is lower than the confidence level threshold, the most confident location value from the location data of the at least two sources is used to replace the unverified location.

24. The processor of claim 22, wherein at least one of the one or more processing units is further configured to: Before performing a comparison of lane pattern data against the at least two trusted sources, the confidence level of the unverified lane pattern is determined; and When the confidence level is lower than the confidence level threshold, the unverified lane graph is replaced with the lane graph data corresponding to the highest confidence level from the lane graph data from the at least two trusted sources.

25. The processor of claim 21, wherein the object is a vehicle, and wherein the verified lane graph is a lane graph generated using map data from at least one source and environmental perception from at least one source corresponding to the vehicle.

26. The processor of claim 22, wherein the source of the positioning data includes at least one of: a camera, a Global Positioning System (GPS), or a radar system; and The sources of environmental perception include at least one of the following: camera, radar system, LIDAR system, ultrasonic system, or high-definition HD map data.

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