Determining operational capabilities of manual operating systems and control applications

Abstract

The invention discloses determining an operational capability of a manual operating system and control application. The methods presented herein may automatically determine a level of injury to a person, which may relate to execution of a task. A light and camera-based system may be used to determine a factor indicative of drunkenness or injury, such as gaze tremor. The test system may simulate motion of the light using the determined pattern and capture image data of at least an eye region of the person attempting to follow the motion. The captured image data may be analyzed using a neural network to infer at least one behavior of the user, and behavior determination may be used to determine a level of competence or impairment of the user. Appropriate measures may be taken, such as allowing a person with full ability to operate the vehicle or perform a task, or preventing access to such operation or execution if it is determined that the degree of damage to the person exceeds the allowable degree.

Description

Background Art

[0001] In various applications (such as, for example, for vehicle navigation and manual operation of robotic components), it is beneficial (if not necessary) to ensure that the human operator is not impaired or otherwise in a state that would make such operation dangerous or at least unwise. This can include, for example, determining that the human operator is not fatigued, or is not under the influence of alcohol or other controlled substances that could impair function or judgment. Traditionally, manual tests are performed to attempt to detect nystagmus, or involuntary eye movements caused by the ingestion of drugs or alcohol. Such tests are often performed by police officers, who move a pen in front of a person's eyes. The officer observes the behavior of the user's eyes as they attempt to follow the movement, such as whether there is a lack of smooth tracking or whether there are involuntary eye movements within certain ranges of motion. This manual testing method is not practical in all situations, and the results of such tests may be imprecise and inconsistent, due at least in part to the relative experience levels and training of the police or other personnel performing the test. BRIEF DESCRIPTION OF THE DRAWINGS

[0002] Various embodiments according to the present disclosure will be described with reference to the accompanying drawings, in which:

[0003] Figure 1 shows an example system deployment that can be used to test a person's eye or gaze behavior in accordance with at least one embodiment;

[0004] Figure 2A , Figure 2B and Figure 2C illustrates example modes that may be presented using a test system in accordance with at least one embodiment;

[0005] Figure 3 Components of an example testing and analysis system are shown in accordance with at least one embodiment;

[0006] Figure 4A , Figure 4B and Figure 4C An example processing pipeline for making impairment determinations according to at least one embodiment is shown;

[0007] Figure 5 An example process for determining a test subject's capabilities according to at least one embodiment is shown;

[0008] Figure 6 Components of a distributed system that may be used to test a person's capabilities are shown according to at least one embodiment;

[0009] Fig. 7A Inference and / or training logic according to at least one embodiment is shown;

[0010] Figure 7BInference and / or training logic according to at least one embodiment is shown;

[0011] Figure 8 An example data center system is shown in accordance with at least one embodiment;

[0012] Fig. 9 A computer system according to at least one embodiment is shown;

[0013] Fig.10 A computer system according to at least one embodiment is shown;

[0014] Fig.11 illustrates at least a portion of a graphics processor according to one or more embodiments;

[0015] Fig.12 illustrates at least a portion of a graphics processor according to one or more embodiments;

[0016] Fig.13 is an example data flow diagram of a high-level computing pipeline according to at least one embodiment;

[0017] Fig.14 is a system diagram of an example system for training, adapting, instantiating, and deploying machine learning models in an advanced computing pipeline in accordance with at least one embodiment;

[0018] Fig.15A and Fig. 15B A data flow diagram illustrating a process for training a machine learning model, and a client-server architecture for augmenting an annotation tool with a pre-trained annotation model, according to at least one embodiment;

[0019] Fig.16A An example of an autonomous vehicle according to at least one embodiment is shown;

[0020] Fig. 16B According to at least one embodiment, Fig.16A Examples of camera positions and fields of view for autonomous vehicles in ;

[0021] Fig. 16C is a diagram showing, according to at least one embodiment Fig.16A A block diagram of an example system architecture for an autonomous vehicle; and

[0022] Fig.16D According to at least one embodiment, a method for Fig.16A Diagram of a system for communicating between autonomous vehicles in a vehicle. DETAILED DESCRIPTION

[0023] In the following description, various embodiments will be described. For the purpose of explanation, specific configurations and details are set forth to provide a thorough understanding of the embodiments. However, it will also be appreciated by those skilled in the art that the embodiments may be practiced without the specific details. In addition, well-known features may be omitted or simplified to avoid obscuring the described embodiments.

[0024] The systems and methods described herein may be used by, but are not limited to, non-autonomous vehicles, semi-autonomous vehicles (e.g., in one or more advanced driver assistance systems (ADAS)), manned and unmanned robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers, spacecraft, boats, space shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, airplanes, construction vehicles, trains, underwater vehicles, remotely controlled vehicles (e.g., drones), and / or other vehicle types. In addition, the systems and methods described herein may be used for a variety of purposes, such as, but not limited to, machine control, machine motion, machine driving, synthetic data generation, model training or updating, perception, augmented reality, virtual reality, mixed reality, robotics, safety and surveillance, simulation and digital twins, autonomous or semi-autonomous machine applications, deep learning, environmental simulation, object or participant simulation and / or digital twins, data center processing, conversational AI, light transport simulation (e.g., ray tracing, path tracing, etc.), collaborative content creation of 3D assets, cloud computing, and / or any other suitable application.

[0025] The disclosed embodiments may be included in a variety of different systems, such as automotive systems (e.g., control systems for autonomous or semi-autonomous machines, perception systems for autonomous or semi-autonomous machines), systems implemented using robots, aviation systems, medical systems, boating systems, smart area monitoring systems, systems for performing deep learning operations, systems for performing simulation operations, systems for performing digital twin operations, systems implemented using edge devices, systems including one or more virtual machines (VMs), systems for performing synthetic data generation operations, systems implemented at least in part in a data center, systems for performing conversational AI operations, systems for performing light transport simulations, systems for performing collaborative content creation of 3D assets, systems implemented at least in part using cloud computing resources, and / or other types of systems.

[0026] The method according to various illustrative embodiments provides automatic determination of personnel's talent, ability or impairment level, which may be related to the execution of tasks or operations. In at least one embodiment, the system can automatically determine when personnel show drunken behavior in a vehicle or other similar positions, which can be used to prevent the personnel from operating the vehicle in this state. Light and camera-based systems can be used to determine factors such as fixation nystagmus (involuntary eye movement caused by taking drugs or alcohol), which indicate drunkenness or injury. The automatic test system can use groups, bars, arrays or any other arranged or configured lights (or movable light sources, such as heads-up displays) to generate or simulate the movement of light in a determined pattern, and capture image data (such as a series of images or videos) of the eye area of ​​the person at least located in the driver or operator's seat. Light can be located in any appropriate position, such as on a sun visor or arranged to be projected on a windshield. For example, a trained neural network can be used to analyze captured eye image data to infer the degree or sign of drunkenness (or the confidence or probability of drunkenness, injury or other such values) based on the smoothness of eye movement, the presence of nystagmus, pupil size reaction and other such factors. For example, the combination of computer vision and deep learning could enable nystagmus testing to be automated and performed before a vehicle is started, which could help prevent drowsy, drunk, or otherwise impaired driving by preventing the car from starting (or taking other such actions) if it is inferred that the person is impaired or at least functionally limited to a degree permitted by law (or otherwise established).

[0027] It will be apparent to those skilled in the art based on the teachings and suggestions contained herein that variations of this and other such functionality may also be used within the scope of the various embodiments.

[0028] Figure 1 An example situation 100 is shown in which a human user 104 or operator is seated in the driver's seat of a vehicle 102. This position is typical for a person who intends to operate the vehicle. As shown, the example vehicle includes a test system that can automatically perform a person potential impairment test to determine whether the person can safely operate the vehicle, at least with respect to the types of impairments that the test system is capable of detecting. For the example test system, there may be fixed criteria for objectively determining whether a person is at a level of impairment that has been determined to be relevant to the ability to safely operate the vehicle. These criteria may vary depending on the type of vehicle or operation, the type of test to be performed, or the sensitivity of the test system, and other such factors.

[0029] In this example, the test system attempts to monitor the behavior of one or more eyes of a human user 104 when one or more patterns are presented to the user. The example test system includes a light bar 108, which includes a plurality of lights, such as at least 8-10 light emitting diodes (LEDs), which can be activated in a determined order, sequence, or pattern so that the displayed lights appear to move back and forth 110 on the light bar, such as can approximate a sine wave frequency, a linear frequency, or other such periodic pattern. Other options can also be used to display light patterns for the user to follow with his or her eyes, as discussed in more detail elsewhere herein. For example, when performing a nystagmus test, the test is typically performed by placing a light directly above the user's eyes at a distance from the ground, such as 1 to 2 inches above the user's eyes. Therefore, in this example, the light bar 108 is located on the driver's side visor 106 of the vehicle. The light bar can also be placed in other similar locations, such as can be mounted on the windshield or on the window trim located in front of the user's eyes. The light bar can also be placed in other locations, such as a steering wheel, a rearview mirror, a dashboard, or other such locations. The location should be chosen based on factors such as ease of use and lack of obstruction, among others.

[0030] In this example, the lights of the light bar 108 may be illuminated in sequence, e.g., one at a time in a determined sequence and at a determined speed or rate of change, so that it appears to the user that the lights are moving across the light bar in at least one direction, although other patterns are possible, e.g., a repeating back-and-forth pattern or a partial traversal of the light bar. For other types of lighting systems, more complex patterns may be used, e.g., patterns that appear to move in two or three dimensions, which may test other behaviors of the eyes of a human test subject. During the test, the lights may be illuminated according to a determined pattern, and the human may attempt to follow the light pattern with his or her eyes (or gaze) without substantial movement of the user's head. At least one camera 114 or other image capture sensor (e.g., an infrared (IR) sensor) may be positioned in the vehicle, e.g., attached to a rearview mirror 116 or as part of the light bar 108 on the driver's side visor 106, to attempt to capture image data representing at least the user's eye region. The camera may be positioned and calibrated so as to be able to capture image data of a person in the driver's seat, regardless of the person's height or other aspects (at least their typical range). In some embodiments, the camera can be adjusted to face the user's eyes, or can be configured to capture a large enough field of view so that the person's eye area will be visible in the captured image data. In at least one embodiment, the captured image data can also capture a large enough field of view so that other information about the user's face or head can be captured to ensure that the head does not move more than the allowed amount during the test, or to allow the eye position determination operation to take into account the movement of the head and its effect on the eye position. In at least one embodiment, if it is determined that the user's head moves more than the allowed amount during the test, it may be necessary to test again or for a longer period of time until reliable data can be captured. If the user's head moves too much, the user's eye moves frequently or for a long enough period of time, or otherwise behaves in a way that prevents the test system from making an accurate determination, a notification (auditory, visual, tactile, or other means) can be provided to the user.

[0031] In one example test, the user may be notified that the test is about to begin, and a countdown or other notification may be provided to the user so that the user knows to sit in the correct position, sit relatively still, and look at the light bar 108 (or the glowing light of the light bar that will serve as the starting point, etc.). In some embodiments, the camera 114 may capture image data during this preparation period to provide to an analysis component 112 of the vehicle, which may include at least one processor and memory for analyzing the data, although the data analysis may be performed remotely from the device, or on a user device (e.g., a smartphone) owned by the user or another passenger in the vehicle, among other such options. The analysis component 112 may analyze the image data over multiple images or video frames, for example, to determine whether the user is correctly positioned, sufficiently still, and is looking in the general correct direction. If not, the start of the test may be delayed until the user exhibits such behavior, and in at least some embodiments, a notification (e.g., an auditory, visual, or tactile notification) may be provided to prompt the user to correct the behavior and prepare for the test.

[0032] Once the user (or test subject, if different from the user of the test system or application) meets these or other such criteria for the test to be performed, the test in this example can be started by causing the light bar 108 to activate the individual LEDs in a timed sequence so that the individual lights appear to move on the light bar at a determined speed. The speed and / or intensity of the light can be fixed for a given test, or can be configured based on various factors (such as the amount of ambient light or the distance of the user from the light bar, among other such factors). The speed or type of pattern used may also vary by geographic location or jurisdiction due to local laws or regulations. The camera 114 can capture image data including at least the user's eyes during the display of the pattern. The camera can be synchronized with the light bar, such as by using a single master clock for timing purposes, so that the image data captured at a given timestamp can be associated with the position in the pattern displayed by the light bar at that time, such as which LED was illuminated at that time. In some embodiments, the relative position in 3D space can be determined, such as can be determined relative to a determined origin or coordinate system, to provide accurate gaze determination, while in other embodiments, the behavior of the eyes in timing and general movement may be sufficient without the need for accurate distance or position information. Fixed locations for the camera and light bar and a limited location of a person sitting in the passenger seat looking at the light bar within the camera's field of view may be sufficient for various testing procedures.

[0033] The analysis component 112 can use a variety of different techniques (including different algorithms and / or machine learning models) to analyze a series of images or video frames captured by the camera during the test. In at least one embodiment, the captured image data can be cropped to the eye area, or at least a bounding box can be determined so that only the data near the eye area is analyzed, thereby reducing the amount of data to be processed, which can save computing and memory resources and can also increase the speed of generating test results. In this example, the position of at least one pupil (or other trackable point or feature of the eye) can be determined for each image or video frame and compared with the position of the light displayed in the pattern at that point in time. This can be performed for all frames in the sequence, or for a subset of frames to further save processing requirements, such as selecting 10 or 20 frames per second, even if the camera is capable of 90 or 120 frames per second. The movement or behavior pattern of the eye during the test can be compared with the test pattern during the time period. In some embodiments, the movement of a single eye is sufficient, while in other embodiments, data from both eyes is used to determine a more accurate gaze direction, and data from both eyes is used to check whether the gaze converges to a certain LED position, etc. Comparing the behavior of at least one eye with the test pattern behavior can provide various types of data that can be used to determine potential damage. For example, the comparison data can be used to determine a person's reaction time, such as the length of time it takes for a user's eyes to move in response to the movement of the light pattern, and the time it takes for the eyes to catch up. For example, if the simulated light moves from left to right across the light bar, at least one of the three time periods can be calculated. This may include the time (or delay) it takes for the eye to begin moving to the right to follow the pattern, the time (or delay) it takes for the eye to reach the end of the light pattern, and the total time it takes for the eye to follow the light from left to right compared to the time it takes for the actual light pattern to cross the light bar. The image data can also be analyzed to determine the behavior of the eye when trying to follow the pattern. This can include, for example, the smoothness of the movement of the eye moving from left to right (or from right to left in the captured image data - because any mirror images can be taken into account in the analysis), any seemingly involuntary movement outside the pattern (such as jerking, especially when the pattern is still at one end of the light bar), any continued movement and recovery of the eye outside the displayed pattern, or other movements or behaviors that are inconsistent or expected for a given light pattern. In some embodiments, the number of traversals of the light bar in one or more directions can be minimized before determining the test results, such as at least one traversal in each direction. While a single pass may be sufficient for a visibly impaired person, at least a second pass can be used to ensure that the user's unusual behavior during the single pass was not due to accidental or external factors (e.g., external light, conversations, obstructions, or distractions).In some embodiments, the length of time or number of iterations may depend at least in part on the confidence level of the determination, as someone who cannot be determined to be "safe" or "unsafe" to operate a vehicle (or similar classification or categorization) with at least a minimum confidence level may perform additional iterations, or display different patterns, to try to increase that confidence level. In some embodiments, the test in this case may be performed up to a maximum amount of time or maximum number of iterations or patterns displayed, which in turn may be configurable based on local laws or regulations, risk of operation or vehicle type, etc. In some embodiments, the test may always go through a fixed number of iterations or features of a pattern, while in other embodiments, the test may stop when a determination can be made with at least a minimum confidence level (also consistent with local or applicable requirements, such as from a vehicle or test system manufacturer).

[0034] In a second test example, the position of both eyes can be tracked in the image data captured by the camera 114 over time. In some embodiments, the known relative position of the light bar 108 and the camera 114 in the vehicle can be used together with a series of positions of the user in the passenger seat to determine the approximate position of the user, thereby determining the approximate change in the gaze direction. In some embodiments, the positions of the light bar 108 and the camera 114 are calibrated relative to the vehicle coordinate system, and the user's gaze direction in the vehicle coordinates can be determined by transforming the user's gaze in the camera 114 coordinate system into the coordinate system of the light bar 108. In other embodiments, a more accurate position of the user can be determined using distance determination techniques, which may include stereo imaging, laser or lidar scanning, or ultrasonic or radar sampling, and other such options. Being able to determine the user's gaze can more accurately determine at least some behaviors, such as true motion smoothness or the user's ability to focus on the precise position of the light in the presented pattern. Although gaze may require a slightly complex system, the ability to accurately determine gaze rather than relative eye position can enable faster determinations, and in at least some embodiments, can also be more accurate and / or more confident. In at least some embodiments, other data may also be captured, which may be related to different types of tests, such as requiring the user to follow a pattern by pointing a finger, repeat or say a phrase, or perform an instructed task, among other such options.

[0035] Once a determination is made or a test result is generated, at least one action may be taken. If the user "passes" the test, or if it is determined that the user is in a safe or approved state to perform a task or operation, appropriate notifications may be provided to the user, such as audible approval, a green light, a message, starting the vehicle, or other such actions. In some embodiments, the result information may be recorded for future analysis or use, at least to the extent permitted by law, regulations, and / or the permissions granted by the user. In some embodiments, different operations may require different test results, and the user may be allowed to perform these operations based on the test results. For example, if the user is slightly impaired, the user may be able to perform various operations in a stationary vehicle, but cannot drive the vehicle or perform other operations that are determined to represent a greater risk. If the user "fails" the test, or if the user is determined to be impaired (or otherwise not in a state required to perform one or more operations), the user may be prevented from performing certain operations, such as the vehicle's ignition system 114 being locked and unable to start the vehicle, until an instruction from the analysis component 112 indicates that the vehicle can be started because the user subsequently passed the test. In other embodiments, the user may not be prevented from performing the operation, but a notification may be provided indicating that the user should consider not performing the operation, as well as other such options. In at least some embodiments, a user who fails the test may have to wait at least a minimum amount of time before retaking the test. The test system may also be able to determine, by itself or through communication with another system, whether the person sitting in the operating position has changed to prevent a person from passing the test on behalf of a second person who then sits in the seat and performs the operation or task. This determination may be made using captured image data, a weight sensor in the seat, or other such components or features. In some embodiments, if the system detects that the user in the vehicle is the same as the user in the database, the pre-stored or pre-calculated optimal test settings may be applied directly to the user without recalculating these settings, such as the optimal seat position for testing based on the user's height or the position of the LED light bar 108, etc. In some embodiments, if the user leaves the driver's seat, the vehicle may be shut down, or certain functions (such as the transmission) may be locked to prevent the vehicle from performing the relevant operation until the new driver or person passes the test (at least where such operations are required).

[0036] In some embodiments, the test system can be calibrated for a specific user (e.g., the owner of the vehicle). This is useful for accurate testing because different people may have different reaction times, smoothness of action, and normal behavior of other levels or types based on different physiology (or other such factors) between different people. The test system can determine the identity of the user by, for example, computer vision or biometrics, and can determine the appropriate baseline characteristics of the specific user (which can be obtained by testing or observing and learning for a period of time), and then make a determination based on the current behavior relative to the baseline during the test. Such a system can also leave some room for people with potential limited or atypical behavior, otherwise if these behaviors are not properly considered, it may cause the user test to fail. The ability to determine the identity of a person can also enable the vehicle to set different standards for different users according to factors such as roles or training, and can also enable the vehicle to activate or lock certain features or operations based only on the identity. In some embodiments, only authorized and certified personnel who have passed the ability test can operate the vehicle or perform specific operations.

[0037] As described above, in some cases, a user may represent a "borderline" situation, where the behavior exhibited by the user is not obviously within the safe range or unsafe range, and therefore cannot be classified using any classification method with at least a minimum confidence level. The (one or more) actions to be taken in this case may depend on various factors, such as the type of operation to be performed. In some cases, the function may be available unless the user is obviously in an impaired state, while in other cases or embodiments, the function may be locked unless the user is obviously in a non-impaired state. In some embodiments, these features can be locked and the user can be required to take the test again after a few minutes. In other embodiments, these features can be made available, but the user can be monitored over time to see if the degree of impairment increases, for example, the user may have just drunk another glass of wine and the alcohol has not yet fully entered the blood, so the blood alcohol content may increase in the future. In some embodiments, the user may be tested again after a few minutes and must pass the test before continuing to operate.

[0038] Whether such a test is required or passed may depend on various factors. In some cases, there may be legal requirements or job-related requirements. In other cases, this may be a requirement of the manufacturer or owner of the equipment to be operated. In other cases, the test may not be required, but may be optional. For example, such a system may be deployed in a private vehicle, and the potential driver may choose whether to take the test. For optional tests, the potential driver may also have the option of choosing whether to operate the vehicle based on the test results, where certain functions may be locked or may not be locked for certain faults or determined states. In some embodiments, it may be necessary to transmit the failed results to an external agency, while in other cases, the data may not be allowed to be stored or transmitted, or used for various purposes. In some cases, the test may be required only for certain users, such as parents may require their children to pass the test before they can operate the vehicle, or the owner of the vehicle may require unknown or unapproved people (such as valet parking attendants or mechanics) to pass the test before they can operate the vehicle. When multiple tests or modes are available, the owner, manufacturer, employer or other authorized entity may have the option of selecting the test or mode to be used, as well as the use case and the action to be performed for the pass result, the fail result and / or the uncertain result.

[0039] In at least one embodiment, horizontal movement patterns can be used to attempt to automatically identify the presence of horizontal gaze nystagmus (HGN) in a person. Nystagmus refers to the presence of involuntary eye movements that may be caused by the ingestion of drugs or alcohol. The testing system can automatically perform the HGN test that is usually performed manually by a police officer, in which the police officer moves a pen from -45 degrees to +45 degrees in front of the driver's eyes, about 2-4 inches above the driver's eye level and about 10-15 inches in front of the driver's face. In the manual test, the police officer counts the number of signs - such as lack of smooth pursuit, nystagmus at maximum deviation, nystagmus starting before 45 degrees of gaze angle, lack of eye coverage or slow reaction to changes in pupil size light - that indicate impairment that may be caused by drug or alcohol ingestion. The testing system described herein can automatically perform such tests and can provide consistent and verifiable results, thereby eliminating the need for human presence (which in many cases requires at least additional time), and can also avoid inconsistent or inaccurate results determined by people with different training or experience levels, as well as other potential factors.

[0040] exist Figure 1In the in-vehicle system shown, a light bar 108 or an LED array mounted on or at least partially in the sun visor position can be used to present a pattern. The LEDs can be sequentially activated in at least the main horizontal direction at a known frequency (currently a typical LED passing speed is about 2 seconds), and then maintained at the maximum right or left position for at least about 4 seconds. Other modes and speeds can also be used within the scope of various embodiments. The driver's gaze can follow the LED without significant head posture movement, and one or more driver monitoring cameras 114 can capture image data, which can be used to monitor the driver's gaze direction when the driver attempts to follow the activated LED with his or her eyes. In some embodiments, if the driver's head posture movement significantly exceeds a specified threshold amount, a head posture standardization or frontalization algorithm can be applied to compensate for the head posture movement. In at least one embodiment, the system can automatically adjust one or more LED positions or operating aspects (such as the distance, height, brightness, color, or LED lightning speed between the person in the driver's seat) to achieve the best settings or results at least under current or specific conditions. Automatic adjustments may be performed based in part on detecting the driver's head position using image data captured by the driver monitoring camera, as well as environmental data such as the brightness within the cabin, where LEDs should be brighter during the day than at night. Based on the head pose estimation, the head pose position relative to the camera position may be determined, and the camera may be calibrated relative to coordinates in space mapped to locations within the cabin, so that the head pose relative to the cabin and / or LED coordinates may also be determined.

[0041] As described above, such a system may include various other types of components that may present a pattern to a user to follow and then monitor the user's responsive behavior. Figure 2AA view 202 of an example system 200 is shown, in which a head-up display 204 component in a vehicle can be used to project a pattern 206 onto the windshield (or other appropriate location) of the vehicle. The HUD can be calibrated to display a given pattern of a given size at a determined location, such as a horizontal pattern for determining nystagmus, which is located at a determined height above the user's eyes. The HUD system has the advantage of being adjustable, so the position can be adjusted based on factors such as the height and physical characteristics of the person being tested. Systems based on LEDs or physical light may be less flexible, but may perform better in certain situations (such as in broad daylight). The HUD can project a pattern, and a camera 208 or imaging sensor (or biosensor) can capture data that can be used to determine the user's response characteristics. In some embodiments, the ability to change the type of pattern presented (rather than just the brightness or speed of the pattern) can also provide greater flexibility in testing. For example, in some embodiments, the test pattern can change over time (where permitted by law, etc.) so that the user does not practice the pattern and better fakes the pattern through muscle memory, etc.

[0042] In some embodiments, patterns may be presented to test other talents or abilities than those determined by standardized nystagmus-type tests. Figure 2A As shown, a system with no form constraints can help allow for pattern variations. Figure 2B In the example pattern 230 shown in , there is a window 232 or viewing screen of a particular size and shape that the operator will use for a given task, and the pattern can be selected or designed accordingly. In this case, the pattern 230 can still include a back-and-forth horizontal portion 234 to test nystagmus and related eye behavior, but can also include a second portion 236 around the periphery of the window 232 to ensure that the operator can follow the pattern to obtain the full visual range required to perform the given task. In such a test, if the user is unable to view all areas of the window 232 without moving his or her head, the user may pass the nystagmus test but may not pass the overall test, indicating a lack of complete peripheral vision, which may affect the ability to perform the task. More complex patterns, especially those that may include unexpected paths, can also help to potentially test reaction times and behaviors more accurately than expected patterns with a known presentation frequency.

[0043] In some embodiments, the competency test may not include a component for testing nystagmus, at least using conventional or standardized tests. For example, a human operator may be responsible for directing a robotic component to perform a certain task, which requires a specific range of motion or pattern. Thus, a test such as Figure 2CThe test pattern 260 shown is used to simulate this required motion. In this way, the test can determine the reaction speed, smoothness of motion and other aspects that the user may show when performing a specific task. The dotted light 262 can be projected onto a window or surface to simulate the task during execution, and the method discussed elsewhere in this article can be used to monitor the user's reaction behavior. Therefore, the pattern can be appropriately modified or configured according to a given task. Then, the employer or other authorized entity can determine the appropriate ability or damage level to allow or prohibit operation, as well as the action to be taken. In some embodiments, pattern projection or simulation may not be needed, because the user can watch the actual task performed at a specific location, and is required to follow the operation with his or her eyes without head movement, and the captured image or sensor data can be used to determine the user's response behavior. Within the scope of various embodiments, various other methods for simulating or presenting motion patterns and monitoring reaction behavior can also be used.

[0044] Figure 3 Components of an example system 300 according to at least one embodiment are shown, which can be used to perform behavioral testing, monitoring and / or analysis. In this example, a test system 308 can test a test object 302, such as (but not limited to) a human user or potential operator of a vehicle or machinery. In this example, the test system 308 can start the test in response to an instruction, request or trigger, whether automatically receiving or responding to input from the test object 302, and other such options. In this example, the test system 308 can execute (for example, using at least one processor and memory) a test application 310, which can determine the type of test to be performed and the aspects or data to be used for execution. This can include, for example, analyzing data about the test object / user or the operation to be performed, and determining at least one appropriate pattern to be presented to the test object 302. Once determined, a user notification system 320 can notify the user that the test is about to begin. In some embodiments, the user notification system 320 can be the same system as the pattern display device 304, for example, a display device can be used to convey instructions. In at least one embodiment, camera system 306 can capture image data representing the state of test object 302 to ensure the correct orientation and position to begin testing. User notification system 320 can notify the user if adjustments are made prior to beginning testing.

[0045] Once the test is ready to begin, the test system 308 can instruct the pattern display device (e.g., a light bar, an LED array, a head-up display, a holographic projector, or other presentation mechanism) to present a determined pattern for the test object 302 to view. The camera system 306 (or other such image or biosensor device) can capture the image (or other) data of the object during the presentation of the test pattern. The image data can be provided as an input to the test application 310, which can use one or more algorithms, machine learning models, or other such technologies to determine the test results of the object, such as whether the test object has at least a determined minimum ability or whether it exhibits a degree of impairment exceeding the allowable ability, as well as other such options or determinations. As described above, the determination type can vary between embodiments according to factors such as the type of test object, the type of operation to be performed, or other such factors. Once the state, ability, or other such indicators of the test object are determined, the test application can determine one or more actions to be taken. This can include, for example, providing the result to the user using the user notification system 320. This can also include providing data to the control system 312 of the vehicle, machinery, robotic system, or other such device on which the test was performed. For example, data may be transmitted to the control system 312 indicating that the user passed the test and demonstrated at least the required competence, so that the control system may unlock or activate one or more operating mechanisms 314 for use by the test subject. For a vehicle, this may include unlocking the starter and / or transmission, or unlocking the braking system so that the test subject can operate the vehicle. For a robotic component, this may include unlocking controls for the component, etc. In at least one embodiment, where permitted, the test results may optionally be provided to a monitoring system 316, which may use the data to take specific actions where appropriate, and / or the data may be stored in a log 318 for subsequent audit or reporting, among other such options. In at least some embodiments or circumstances, the test subject may opt-in or opt-out of the collection of such data.

[0046] Such as Figure 3 The illustrated test application 310 can use a combination of computer vision and deep learning to make capability determinations on a test subject in accordance with at least one embodiment. Computer vision techniques can be used to determine aspects such as eye position or gaze direction, as well as changes in position or direction. Deep learning can be used to make inferences based on those changes in position or direction, such as may be related to the presence of certain behaviors exhibited by the test subject. FIG. 4A to FIG. 4C An example processing pipeline that may be used to provide such functionality is shown. Figure 4AA first pipeline is shown, where a sensor assembly 400 may be used to capture images of a user, such as during a test procedure, which may include at least the eye region of a user or test subject. Software 402 (which may include computer vision functionality) may attempt to determine inter-frame differences between eye cropped regions of the image data, such as differences in pupil position between adjacent frames in a sequence. A trained neural network (e.g., a convolutional neural network (CNN) 404) may use the difference data to infer changes in gaze direction between a given pair (or sequence) of captured image (or video) frames or portions of a video stream. A fast Fourier transform (FFT) or wavelet module 406 may then infer the user's behavior based in part on changes in gaze direction relative to a presented pattern, such as by using Fourier analysis to compare eye gate frequencies to corresponding LED frequencies, thereby allowing analysis in the time and / or frequency domains. For example, a frequency graph of a projection pattern (e.g., a projection pattern being a sinusoidal sweep of a frequency on an LED) and a corresponding response may be plotted, and differences between peaks or features (e.g., signal-to-noise ratio) in these graphs may be measured and used to determine or infer a level of ability. The software module 408 can use the inferred behavior to determine the level or type of impairment (or ability or other such indicator). Here, the inferred behavior may be the presence or amount of nystagmus, reaction time, smoothness of movement, or other such behavior. The determination can be made using a determined algorithm or formula (e.g., a weighted combination of values ​​of various behaviors).

[0047] Figure 4B Another example pipeline is shown, where a sensor assembly 420 is used to capture images of at least eye regions of a user or test subject, and these images are passed to a software module 422, which can use computer vision techniques to determine eye regions of the captured images or image data. These image data of eye regions can be passed as input to a three-dimensional (3D) CNN 424 to infer behavior based at least in part on the location of one or more features of the test subject's eyes (e.g., pupil centroid point). The behavior determination can be provided as input to a software module 426 to determine the level or type of impairment, etc.

[0048] Figure 4CA third example is shown in which a sensor assembly 440 is used to capture images of at least an eye region of a user or test subject, and these images are passed to a gaze deep neural network (DNN) 442 to determine the gaze direction (or position) of the user. The gaze information can be passed to a one-dimensional (1D) CNN 444 to infer at least one behavior of the test subject based on, for example, a series of gaze vectors in the time domain. The behavior determination can be provided as an input to a software module 446 to determine, for example, the level or type of impairment. As previously described, the impairment level or other such determination can then be used by the same or different software module to determine one or more actions to be taken in response to the level determination.

[0049] The neural networks, machine learning models, or artificial intelligence used in such pipelines will need to be trained, at least in part, which typically involves the use of a large amount of different training data. For example, there may not be large-scale drunk driver horizontal gaze nystagmus data collected and available, so a certain amount of synthetic training data can be used. In at least one embodiment, a generative network (e.g., a generative adversarial network (GAN)) can be used to generate image sequences or videos to enhance the training set. For example, this may include generating facial images or eye cropped videos to present objects with various appearances, eye sizes, and other such aspects, where the pupils move in a given pattern, with or without certain behaviors that these models are to be trained on.

[0050] Figure 5An example process 500 for determining a person's ability that can be performed according to at least one embodiment is shown. It should be understood that for this process and other processes described herein, more steps, fewer steps, or alternative steps may be performed, or performed in a similar or alternative order, or at least partially in parallel, within the scope of various embodiments, unless otherwise explicitly stated. In addition, although this example will be discussed with respect to nystagmus and related behaviors corresponding to eye movements, other types of behaviors, abilities, or talents of the test subject may also be determined within the scope of various embodiments. In this example, an impairment or ability test 502 is initiated, whether manually at the request of a user or automatically in response to a trigger or instruction, such as detecting a person in the driver's seat or inserting a key into the ignition. A determination can be made 504 that the test subject is in the correct position and / or orientation, such as by using a camera and / or other sensor to capture position and orientation data for comparison with an acceptable position and orientation range for a given test or test type. Once the position and orientation are verified, testing can begin, in this example, by presenting 506 a simulated motion pattern using one or more light sources (e.g., light bars or projectable light patterns) as light stimuli (separate from any illumination of the test subject required to capture image data). Image data of at least the eye region of the test subject can be captured 508 during presentation of the test pattern, which image data should represent the movement and behavior of the subject's eye. At least a portion of the image data corresponding to the eye region can be provided 510 as an input to at least one neural network, such as a reference Figures 4A-4C Described. During presentation of the test pattern, at least one behavioral determination, such as the presence of nystagmus and an associated confidence value, may be received 512 from at least one neural network corresponding to the test subject. An impairment, capability state, or other such indicator or classification of the test subject may be determined 514 based in part on (one or more) behavioral determinations. At least one responsive action may be performed 516 based on the determined impairment or capability state, ranging from providing a notification, unlocking or locking a specific function, and other such options discussed and suggested elsewhere herein. For example, it may be determined that a person may be sleepy but not intoxicated, such as a user following a pattern slowly but following a smooth motion without nystagmus, and such a determination may allow certain tasks or operations to be available but other operations to be locked, which may be based on the risk of improper execution caused by drowsiness and intoxication or other incapacity states.

[0051] In some embodiments, the user may be continuously monitored during operation. For example, there may be certain events that the user should be aware of and / or concerned about, such as a car entering a roadway or a robotic assembly grabbing a target object or moving that object to a specific location under operator control. A camera may capture image data of the user, and the user's behavior may be monitored for these specific events, which again may be related to reaction time, smoothness of movement, presence of nystagmus, etc. The determination made in this case may not be as precise, but may be sufficient to cause the operation to be paused or stopped while the user conducts an approved or accurate test without performing any other actions or having any interference. In some embodiments, failure of a monitoring test exceeding a threshold or allowed amount may cause at least certain functions to be locked out until the person is able to pass the appropriate related tests.

[0052] Aspects of the various methods described herein may be lightweight enough to be performed in a variety of locations, such as in real time on a client device such as a personal computer, smart vehicle, or console. Such processing may be performed for or with respect to content generated on or received by the client device or received from an external source, such as streaming data or other content received from a server 620 or a third-party service 660 over at least one network, among other such options. In some cases, at least a portion of the processing, generation, synthesis, analysis, and / or determination of the content may be performed by one of these other devices, systems, or entities and then provided to the client device (or another such recipient) for presentation or other such use.

[0053] As an example, Figure 6An example network configuration 600 is shown that can be used to provide, generate, modify, encode, process and / or transmit image and analysis data or other such content. In at least one embodiment, a client device 602 can generate or receive data for a session using components of a control application 604 on the client device 602 and data stored locally on the client device. In at least one embodiment, a content application 624 executed on a server 620 (e.g., a cloud server or an edge server) can initiate a session associated with at least one client device 602, which can utilize a session manager and user data stored in a user database 636. The content manager 626 can cooperate with a test module 628 to determine one or more types of tests to be performed for a user of the client device 602. This can include, for example, determining a pattern presented to a user of the client device by a display 606 from a pattern database 634. A camera 638 or similar sensor can capture image data, which can be provided to a test module 628, which can cooperate with an analysis module 630 to determine the user's ability, talent, or impairment level. The control module 632 can then take one or more actions based on the determination. At least a portion of the processed image content, behavioral determinations, or determined control actions may be transmitted to the client device 602 using an appropriate transmission manager 622 for transmission via download, streaming, or other such transmission channels. An encoder may be used to encode and / or compress at least a portion of such data before transmitting it to the client device 602. In at least one embodiment, the client device 602 receiving such content may take appropriate actions, such as notifying a user using a display 606 or an audio component 608. The control application on the client device may also or alternatively include a graphical user interface 610, a content manager 612, and a test module 614 for presenting display patterns, analyzing captured image data, making behavioral determinations, or determining actions to be taken in response to behavioral determinations, so that any or all of these actions may be completed locally rather than being completed by a server 620, other client devices 650, or a third-party service 660 over a network. The decoder may also be used to decode data received via the network 640 for presentation via the client device 602, such as images or video content presented via the display 606 and audio (e.g., sounds and verbal commands) presented via at least one audio playback device 608 (e.g., speakers or headphones). In at least one embodiment, at least a portion of the content may already be stored on the client device 602, rendered on the client device 602, or accessible to the client device 602, so at least the portion of the content does not need to be transmitted via the network 640, for example, the content may have been previously downloaded or stored locally on a hard disk or optical disk.In at least one embodiment, the content may be transmitted from the server 620 or user database 636 to the client device 602 using a transmission mechanism (e.g., data streaming). In at least one embodiment, at least a portion of the content may be obtained, enhanced, and / or streamed from another source (e.g., a third-party service 660 or other client device 650), which may also include a content application 662 for generating, enhancing, analyzing, or providing the content. In at least one embodiment, multiple computing devices or multiple processors within one or more computing devices (e.g., which may include a combination of CPUs and GPUs) may be used to perform portions of this functionality.

[0054] In this example, these client devices may include any appropriate computing devices, such as desktop computers, laptops, set-top boxes, streaming devices, game consoles, smartphones, tablet computers, VR headsets, AR goggles, wearable computers, or smart TVs. Each client device may submit a request across at least one wired or wireless network, which may include the Internet, Ethernet, a local area network (LAN), or a cellular network, as well as other such options. In this example, these requests may be submitted to an address associated with a cloud provider, which may operate or control one or more electronic resources in a cloud provider environment, such as a data center or a server farm. In at least one embodiment, the request may be received or processed by at least one edge server located at the edge of the network and outside of 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 that are closer, while also improving the security of resources in the cloud provider environment.

[0055] In at least one embodiment, such a system may be used to perform graphics rendering operations. In other embodiments, such a system may be used for other purposes, such as for providing image or video content to test or verify autonomous machine applications, or for performing deep learning operations. In at least one embodiment, such a system may be implemented using an edge device, or may be combined with one or more virtual machines (VMs). In at least one embodiment, such a system may be implemented at least in part in a data center or at least in part using cloud computing resources.

[0056] Reasoning and training logic

[0057] Fig. 7A Inference and / or training logic 715 is shown for performing reasoning and / or training operations associated with one or more embodiments. Fig. 7A and / or Figure 7B Details regarding the inference and / or training logic 715 are provided.

[0058] In at least one embodiment, the 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 for configuring neurons or layers of a neural network trained and / or used for inference in aspects of one or more embodiments. In at least one embodiment, the 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, where weights and / or other parameter information is loaded to configure logic, including integer and / or floating point units (collectively referred to as arithmetic logic units (ALUs)). In at least one embodiment, the code (such as graph code) loads weights or other parameter information into a processor ALU based on the architecture of the neural network to which the code corresponds. In at least one embodiment, the code and / or data storage 701 stores input / output data during training and / or inference using aspects of one or more embodiments and / or weight parameters during forward propagation of each layer of a neural network trained or used in conjunction with one or more embodiments. In at least one embodiment, any portion of code and / or data storage 701 may be included within other on-chip or off-chip data storage, including a processor's L1, L2, or L3 cache or system memory.

[0059] In at least one embodiment, any portion of 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, code and / or data storage 701 may be cache memory, dynamic random access memory ("DRAM"), static random access memory ("SRAM"), non-volatile memory (e.g., flash memory), or other storage. In at least one embodiment, the choice of whether code and / or data storage 701 is internal or external to a processor, for example, or consists of DRAM, SRAM, flash memory, or some other storage type, may depend on the available storage space on-chip or off-chip, the latency requirements of the training and / or inference functions being performed, the batch size of the data used in inference and / or training of the neural network, or some combination of these factors.

[0060] 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 for storing reverse and / or output weights and / or input / output data corresponding to neurons or layers of a neural network trained and / or used for inference in aspects of one or more embodiments. In at least one embodiment, during training and / or inference using aspects of 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 conjunction with one or more embodiments during back propagation 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 weights and / or other parameter information is loaded to configure logic including integer and / or floating point units (collectively referred to as arithmetic logic units (ALUs)). In at least one embodiment, code (such as graph code) loads weights or other parameter information into a processor ALU based on the architecture of the neural network to which the code corresponds. In at least one embodiment, any portion of 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. In at least one embodiment, any portion of 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, 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 of whether code and / or data storage 705 is internal or external to the processor, for example, whether it is composed of DRAM, SRAM, flash memory, or some other storage type, 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.

[0061] 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 be partially the same storage structure 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.

[0062] 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 based at least in part on or as directed by training and / or inference code (e.g., graph code), the results of which may produce activations (e.g., output values ​​from a layer or neuron within a neural network) stored in activation storage 720, which is a function 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, activations are performed in response to executing instructions or other code, linear algebra and / or matrix-based mathematics performed by ALU 710 to generate activations stored in activation storage 720, where weight values ​​stored in code and / or data storage 705 and / or in 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, any or all of which may be stored in code and / or data storage 705 or code and / or data storage 701 or other on-chip or off-chip storage.

[0063] In at least one embodiment, one or more ALUs 710 are included in one or more processors or other hardware logic devices or circuits, while in another embodiment, one or more ALUs 710 may be outside a processor or other hardware logic device or circuit that uses 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 an execution unit of a processor, which may be within the same processor or distributed between 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 on the same processor or other hardware logic device or circuit, while in another embodiment, they may be in different processors or other hardware logic devices or circuits or some combination of the same and different processors or other hardware logic devices or circuits. In at least one embodiment, any portion of activation storage 720 may be included with other on-chip or off-chip data storage, including the processor's L1, L2, or L3 cache or system memory. Additionally, inference and / or training code may be stored with other code accessible to a 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.

[0064] In at least one embodiment, activation storage 720 may be cache memory, DRAM, SRAM, non-volatile memory (e.g., flash memory), or other storage. In at least one embodiment, activation storage 720 may be completely or partially internal or external to one or more processors or other logic circuits. In at least one embodiment, activation storage 720 may be selected to be internal or external to a processor, for example, or include DRAM, SRAM, flash memory, or other storage types, depending on the storage available on-chip or off-chip, the latency requirements for performing training and / or inference functions, the batch size of data used in inferencing and / or training neural networks, or some combination of these factors. In at least one embodiment, Fig. 7A The inference and / or training logic 715 shown in FIG. 7 may be used in conjunction with an application specific integrated circuit (“ASIC”), such as the ASIC from Google. Processing unit from Graphcore TM Inference Processing Unit (IPU) from Intel Corp (e.g., "LakeCrest") processor. In at least one embodiment, Fig. 7A The illustrated inference and / or training logic 715 may be used in conjunction with central processing unit (“CPU”) hardware, graphics processing unit (“GPU”) hardware, or other hardware such as a field programmable gate array (“FPGA”).

[0065] Figure 7B Inference and / or training logic 715 is shown in accordance with at least one or more embodiments. In at least one embodiment, the reasoning and / or training logic 715 may include, but is not limited to, hardware logic in which computing 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 in FIG. 7 may be used in conjunction with an application specific integrated circuit (ASIC), such as the ASIC from Google. Processing unit from Graphcore TM Inference Processing Unit (IPU) from Intel Corp (e.g., "LakeCrest") processor. In at least one embodiment, Figure 7BThe inference and / or training logic 715 shown in 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 in , each of code and / or data storage 701 and code and / or data storage 705 is associated with a dedicated computing resource (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) solely on the information stored in code and / or data storage 701 and code and / or data storage 705, respectively, and the results of performing the functions are stored in activation storage 720.

[0066] In at least one embodiment, each of the code and / or data stores 701 and 705 and the corresponding computing hardware 702 and 706 corresponds to a different layer of the neural network, such that activations from one "storage / compute pair 701 / 702" of the code and / or data store 701 and computing hardware 702 are provided as inputs to the next "storage / compute pair 705 / 706" of the code and / or data store 705 and computing hardware 706, so as to reflect the conceptual organization of the neural network. In at least one embodiment, each storage / compute pair 701 / 702 and 705 / 706 can correspond to more than one neural network layer. In at least one embodiment, additional storage / compute pairs (not shown) can be included in the inference and / or training logic 715 after or in parallel with the storage / compute pairs 701 / 702 and 705 / 706.

[0067] Data Center

[0068] Figure 8 An example data center 800 is shown in which at least one embodiment may be used. 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.

[0069] In at least one embodiment, Figure 8As shown, the data center infrastructure layer 810 may include a resource coordinator 812, grouped computing resources 814, and node computing resources ("node CRs") 816(1)-816(N), where "N" represents any positive integer. In at least one embodiment, the node CRs 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 memories), storage devices (e.g., solid-state drives or disk drives), network input / output ("NWI / O") devices, network switches, virtual machines ("VMs"), power modules and cooling modules, etc. In at least one embodiment, one or more of the node CRs 816(1)-816(N) may be a server having one or more of the above computing resources.

[0070] In at least one embodiment, the grouped computing resources 814 may include a separate grouping (not shown) of node CRs housed in one or more racks, or many racks (also not shown) housed in data centers at various geographic locations. The separate grouping of node CRs within the grouped computing resources 814 may include computing, networks, memory, or storage resources that can be configured or allocated to support groupings of one or more workloads. In at least one embodiment, several node CRs including a CPU or processor may be grouped in one or more racks to provide computing resources to support one or more workloads. In at least one embodiment, one or more racks may also include any number of power modules, cooling modules, and network switches in any combination.

[0071] In at least one embodiment, resource coordinator 812 may configure or otherwise control one or more node CRs 816(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 812 may include hardware, software, or some combination thereof.

[0072] In at least one embodiment, Figure 8As shown, the 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, the framework layer 820 may include a framework that supports software 832 of the software layer 830 and / or one or more applications 842 of the application layer 840. In at least one embodiment, the software 832 or the application 842 may include a web-based service software or application, such as a service or application provided by Amazon Web Services, Google Cloud, and Microsoft Azure. In at least one embodiment, the framework layer 820 may be, but is not limited to, a free and open source software network application framework, such as Apache SparkTM (hereinafter referred to as "Spark") that can utilize the distributed file system 828 for large-scale data processing (e.g., "big data"). In at least one embodiment, the job scheduler 832 may include a Spark driver to facilitate 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 including Spark and a distributed file system 828 for supporting large-scale data processing. In at least one embodiment, the resource manager 826 can 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 can include group computing resources 814 on the data center infrastructure layer 810. In at least one embodiment, the resource manager 826 can coordinate with the resource coordinator 812 to manage these mapped or allocated computing resources.

[0073] 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 node CRs 816(1)-816(N), the grouped computing resources 814, and / or the distributed file system 828 of the framework layer 820. The 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.

[0074] In at least one embodiment, one or more applications 842 included in the application layer 840 may include one or more types of applications used by at least a portion of the node CRs 816(1)-816(N), the grouped computing resources 814, and / or the distributed file system 828 of the framework layer 820. The 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.

[0075] In at least one embodiment, any of the configuration manager 824, resource manager 826, and resource coordinator 812 can implement any number and type of self-modification actions based on any number and type of data acquired in any technically feasible manner. In at least one embodiment, the self-modification actions can relieve a data center operator of the data center 800 from making potentially bad configuration decisions and can avoid underutilized and / or poorly performing portions of the data center.

[0076] In at least one embodiment, the data center 800 may include tools, services, software, or other resources to train one or more machine learning models or 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 may be trained by calculating weight parameters according to a neural network architecture using the software and computing resources described above with respect to the data center 800. In at least one embodiment, by using weight parameters calculated by one or more training techniques described herein, information may be inferred or predicted using trained machine learning models corresponding to one or more neural networks using the resources described above with respect to the data center 800.

[0077] In at least one embodiment, the data center can use a CPU, an application-specific integrated circuit (ASIC), a GPU, an FPGA, or other hardware to use the above resources to perform training and / or reasoning. In addition, one or more of the above software and / or hardware resources can be configured as a service to allow users to train or perform information reasoning, such as image recognition, speech recognition, or other artificial intelligence services.

[0078] Reasoning and / or training logic 715 is used to perform reasoning and / or training operations associated with one or more embodiments. Fig. 7A and / or Figure 7BProvides details about the reasoning and / or training logic 715. In at least one embodiment, the reasoning and / or training logic 715 may be implemented in the system Figure 8 for use in a system for performing 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.

[0079] Such a component may be used to automatically determine irregular eye or gaze behavior of a person, which may indicate impairment due to the use of drugs or alcohol, and may take one or more actions based on that determination.

[0080] Computer Systems

[0081] Fig. 9 900 is a block diagram illustrating an exemplary computer system according to at least one embodiment, which 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 the present disclosure, such as the embodiments described herein, the computer system 900 may include, but is not limited to, components, such as a processor 902, whose execution unit includes logic to execute algorithms for processing data. In at least one embodiment, the computer system 900 may include a processor, such as a processor available from Intel Corporation of Santa Clara, California. Processor family, Xeon TM , XScale TM and / or StrongARM TM , Core TM or Nervana TM microprocessor, although other systems (including PCs with other microprocessors, engineering workstations, set-top boxes, etc.) 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, Wash., although other operating systems (e.g., UNIX and Linux), embedded software, and / or graphical user interfaces may also be used.

[0082] Embodiments may be used in other devices, such as handheld devices and embedded applications. Some examples of handheld devices include cellular phones, Internet Protocol (Internet Protocol) 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, 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.

[0083] 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 reasoning 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 multi-processor system. In at least one embodiment, the processor 902 may include, but is not limited to, a complex instruction set computing ("CISC") microprocessor, a reduced instruction set computing ("RISC") microprocessor, a very long instruction word ("VLIW") computing microprocessor, a processor that implements an 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 may transmit data signals between the processor 902 and other components in the computer system 900.

[0084] 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. Other embodiments may also include a combination of internal and external caches, depending on the particular implementation and needs. 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.

[0085] In at least one embodiment, logic execution unit 908 that performs integer and floating point operations is also located in processor 902, including, but not limited to. In at least one embodiment, processor 902 may also include a microcode ("ucode") read-only memory ("ROM") for storing microcode for certain macroinstructions. In at least one embodiment, execution unit 908 may include logic for processing a packed instruction set 909. In at least one embodiment, by including a packed instruction set 909 in the instruction set of a general purpose processor, and associated circuitry to execute the instructions, operations used by many multimedia applications may be performed using packed data in processor 902. In one or more embodiments, many multimedia applications may be executed faster and more efficiently by using the full width of the processor's data bus to perform operations on packed data, which may not require the transfer of smaller units of data on the processor's data bus to perform one or more operations one data element at a time.

[0086] In at least one embodiment, execution unit 908 may also be used in microcontrollers, embedded processors, graphics devices, DSPs, and other types of logic circuits. 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 other storage device. In at least one embodiment, memory 920 may store instructions 919 and / or data 921 represented by data signals that may be executed by processor 902.

[0087] In at least one embodiment, the system logic chip can be coupled to the processor bus 910 and the memory 920. In at least one embodiment, the system logic chip can include, but is not limited to, a memory controller hub ("MCH") 916, and the processor 902 can communicate with the MCH 916 via the processor bus 910. In at least one embodiment, the MCH 916 can provide a high-bandwidth memory path 918 to the memory 920 for instruction and data storage and for storage of graphics commands, data, and textures. In at least one embodiment, the MCH 916 can 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 922. In at least one embodiment, the system logic chip can provide a graphics port for coupling to a graphics controller. In at least one embodiment, the MCH 916 can be coupled to the memory 920 via a high-bandwidth memory path 918, and the graphics / video card 912 can be coupled to the MCH 916 via an Accelerated Graphics Port ("AGP") interconnect 914.

[0088] In at least one embodiment, the computer system 900 may use a system I / O 922, which is a proprietary hub interface bus to couple the MCH 916 to an I / O controller hub ("ICH") 930. In at least one embodiment, the ICH 930 may provide direct connection to certain I / O devices through 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 used to connect peripheral devices to the memory 920, the chipset, and the processor 902. Examples may include, but are not limited to, an audio controller 929, a firmware hub ("Flash BIOS") 928, a wireless transceiver 926, a data store 924, a traditional I / O controller 923 including a user input and keyboard interface 925, a serial expansion port 927 (e.g., a universal serial bus (USB) port), and a network controller 934. The data store 924 may include a hard drive, a floppy drive, a CD-ROM device, a flash memory device, or other mass storage device.

[0089] In at least one embodiment, Fig. 9 The system is shown as comprising interconnected hardware devices or "chips", while in other embodiments, Fig. 9An exemplary system on chip (SoC) may be shown. In at least one embodiment, the devices may be interconnected with a proprietary interconnect, a standardized interconnect (e.g., PCIe), or some combination thereof. In at least one embodiment, one or more components of computer system 900 are interconnected using a compute express link (CXL) interconnect.

[0090] The reasoning and / or training logic 715 is used to perform reasoning and / or training operations related to one or more embodiments. Fig. 7A and / or Figure 7B Provide details about the reasoning and / or training logic 715. In at least one embodiment, the reasoning and / or training logic 715 may be Fig. 9 for use in a system for performing 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.

[0091] Such a component may be used to automatically determine irregular eye or gaze behavior of a person, which may indicate impairment due to the use of drugs or alcohol, and may take one or more actions based on that determination.

[0092] Fig.10 1 is a block diagram illustrating an electronic device 1000 for 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 notebook computer, a tower server, a rack server, a blade server, a laptop computer, a desktop computer, a tablet computer, a mobile device, a phone, an embedded computer, or any other suitable electronic device.

[0093] In at least one embodiment, the electronic device 1000 may include, but is not limited to, a processor 1010 communicatively coupled to any suitable number or kind of components, peripherals, modules, or devices. In at least one embodiment, the processor 1010 is coupled using a bus or interface, such as an Ic bus, a system management bus ("SMBus"), a low pin count (LPC) bus, a serial peripheral interface ("SPI"), a high-definition audio ("HDA") bus, a serial advanced technology attachment ("SATA") bus, a universal serial bus ("USB") (versions 1, 2, 3), or a universal asynchronous receiver / transmitter ("UART") bus. In at least one embodiment, Fig.10 A system is shown that includes interconnected hardware devices or "chips", while in other embodiments, Fig.10 An exemplary system on chip (SoC) may be shown. In at least one embodiment, Fig.10 The devices shown in can be interconnected with a proprietary interconnect, a standardized interconnect (e.g., PCIe), or some combination thereof. In at least one embodiment, Fig.10One or more components of the system are interconnected using Compute Express Link (CXL) interconnect lines.

[0094] In at least one embodiment, Fig.10 It may include a display 1024, a touch screen 1025, a touch pad 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 (e.g., a solid state disk (“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 with, for example, the LPDDR3 standard. These components may each be implemented in any suitable manner.

[0095] In at least one embodiment, other components may be communicatively coupled to the processor 1010 through the components described above. In at least one embodiment, the accelerometer 1041, ambient light sensor (“ALS”) 1042, compass 1043, and gyroscope 1044 may be communicatively coupled to the sensor hub 1040. In at least one embodiment, the thermal sensor 1039, fan 1037, keyboard 1036, and touchpad 1030 may be communicatively coupled to the EC 1035. In at least one embodiment, the speaker 1063, earphone 1064, and microphone (“mic”) 1065 may be communicatively coupled to the audio unit (“audio codec and class D amplifier”) 1062, which in turn may be communicatively coupled to the DSP 1060. In at least one embodiment, the audio unit 1062 may include, for example, but not limited to, an audio encoder / decoder (“codec”) and a class D amplifier. In at least one embodiment, the SIM card (“SIM”) 1057 may be communicatively coupled to the WWAN unit 1056. In at least one embodiment, components such as the WLAN unit 1050 and the Bluetooth unit 1052 and the WWAN unit 1056 may be implemented as a next generation form factor (NGFF).

[0096] The reasoning and / or training logic 715 is used to perform reasoning and / or training operations associated with one or more embodiments. Fig. 7A and / or Figure 7BProvide details about the reasoning and / or training logic 715. In at least one embodiment, the reasoning and / or training logic 715 may be Fig.10 Systems for performing 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.

[0097] Such a component may be used to automatically determine irregular eye or gaze behavior of a person, which may indicate impairment due to the use of drugs or alcohol, and may take one or more actions based on that determination.

[0098] Fig.11 1100 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 can be a single-processor desktop system, a multi-processor workstation system, or a server system with 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-chip (SoC) integrated circuit for use in a mobile, handheld, or embedded device.

[0099] In at least one embodiment, the system 1100 may include or be incorporated into a server-based gaming platform, including a gaming console, a mobile gaming console, a handheld gaming console, or an online gaming console for gaming and media consoles. In at least one embodiment, the system 1100 is a mobile phone, a smart phone, a tablet computing device, or a mobile Internet device. In at least one embodiment, the processing system 1100 may also include a wearable device coupled to or integrated in a wearable device, such as a smart watch wearable device, a smart glasses device, an augmented reality device, or a virtual reality device. In at least one embodiment, the 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.

[0100] In at least one embodiment, one or more processors 1102 each include one or more processor cores 1107 to process 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 specific instruction group 1109. In at least one embodiment, the instruction group 1109 can facilitate complex instruction set computing (CISC), reduced instruction set computing (RISC), or calculate by very long instruction words (VLIW). In at least one embodiment, one or more processor cores 1107 can each process different instruction groups 1109, which can include instructions that help emulate other instruction groups. In at least one embodiment, one or more processor cores 1107 can also include other processing devices, such as digital signal processors (DSPs).

[0101] In at least one embodiment, one or more processors 1102 include cache memory 1104. In at least one embodiment, one or more processors 1102 may have a single internal cache or multiple levels of internal cache. In at least one embodiment, the cache memory is shared between various components of one or more processors 1102. In at least one embodiment, one or more processors 1102 also use an external cache (e.g., a level 3 (L3) cache or a last level cache (LLC)) (not shown), which may be shared between one or more processor cores 1107 using known cache coherence techniques. In at least one embodiment, one or more processors 1102 additionally include a register file 1106, and the processor 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, the register file 1106 may include general registers or other registers.

[0102] 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 one or more processors 1102 and other components in the system 1100. In at least one embodiment, one or more interface buses 1110 may be a processor bus, such as a version of a direct media interface (DMI) bus, in one embodiment. In at least one embodiment, one or more interface buses 1110 are not limited to DMI buses, 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 processing system 1100, while the platform controller hub (PCH) 1130 provides connections to I / O devices through a local I / O bus.

[0103] In at least one embodiment, the 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 have appropriate performance to be used as a processor memory. In at least one embodiment, the memory device 1120 may be used as a system memory of the processing system 1100 to store data 1122 and instructions 1121 for use when one or more processors 1102 execute applications or processes. In at least one embodiment, the memory controller 1116 is also coupled to an optional external graphics processor 1112, which may communicate with one or more graphics processors 1108 in the one or more processors 1102 to perform graphics and media operations. In at least one embodiment, a display device 1111 may be connected to the processor 1102. In at least one embodiment, the display device 1111 may include one or more of the internal display devices, such as in a mobile electronic device or laptop device or an external display device connected via a display interface (e.g., DisplayPort, etc.). In at least one embodiment, the display device 1111 may include a head mounted display (HMD), such as a stereoscopic display device used in virtual reality (VR) applications or augmented reality (AR) applications.

[0104] In at least one embodiment, the platform controller hub 1130 enables peripheral devices to be connected to the memory device 1120 and the processor 1102 via a high-speed I / O bus. In at least one embodiment, the I / O peripherals 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, a data storage device 1124 (e.g., a hard drive, flash memory, etc.). In at least one embodiment, the data storage device 1124 can be connected via a storage interface (e.g., SATA) or via a peripheral bus, such as a peripheral component interconnect bus (e.g., PCI, PCIe). In at least one embodiment, the touch sensor 1125 can include a touch screen sensor, a pressure sensor, or a fingerprint sensor. In at least one embodiment, the wireless transceiver 1126 can be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile network transceiver, such as a 3G, 4G, or long-term evolution (LTE) transceiver. In at least one embodiment, the firmware interface 1128 enables communication with the system firmware and can be, for example, a unified extensible firmware interface (UEFI). In at least one embodiment, the network controller 1134 can enable a network connection to a wired network. In at least one embodiment, a high-performance network controller (not shown) is coupled to one or more interface buses 1110. In at least one embodiment, the audio controller 1146 is a multi-channel high-definition audio controller. In at least one embodiment, the processing system 1100 includes an optional legacy I / O controller 1140 for coupling legacy (e.g., Personal System 2 (PS / 2)) devices to the system 1100. In at least one embodiment, the platform controller hub 1130 can also be connected to one or more universal serial bus (USB) controllers 1142, which connect input devices such as a keyboard and mouse 1143 combination, a camera 1144, or other USB input devices.

[0105] In at least one embodiment, instances of 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, platform controller hub 1130 and / or memory controller 1116 may be external to one or more processors 1102. For example, in at least one embodiment, system 1100 may include external memory controller 1116 and platform controller hub 1130, which may be configured as a memory controller hub and a peripheral controller hub in a system chipset that communicates with processor 1102.

[0106] The reasoning and / or training logic 715 is used to perform reasoning and / or training operations associated with one or more embodiments. Fig. 7A and / or Figure 7B Detail is provided 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 1100. For example, in at least one embodiment, the training and / or inference techniques described herein may use one or more ALUs that are embodied in the graphics processor. In addition, in at least one embodiment, the inference and / or training operations described herein may use the ALUs other than the ALUs. Fig. 7A and / or Figure 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) that configure the ALU of the graphics processor to execute one or more of the machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

[0107] Such a component may be used to automatically determine irregular eye or gaze behavior of a person, which may indicate impairment due to the use of drugs or alcohol, and may take one or more actions based on that determination.

[0108] Fig.12 is a block diagram of a processor 1200 having one or more processor cores 1202A-1202N, an integrated memory controller 1214, and an integrated graphics processor 1208 in accordance with at least one embodiment. In at least one embodiment, the processor 1200 may include additional cores, up to and including the additional core 1202N represented by the dashed box. In at least one embodiment, each of the one or more processor cores 1202A-1202N includes one or more internal cache units 1204A-1204N. In at least one embodiment, each processor core may also have access to one or more shared cache units 1206.

[0109] In at least one embodiment, one or more internal cache units 1204A-1204N and one or more shared cache units 1206 represent a cache memory hierarchy within the processor 1200. In at least one embodiment, the one or more cache memory units 1204A-1204N may include at least one level of instruction and data cache within each processor core and one or more levels of cache in a shared mid-level cache, such as level 2 (L2), level 3 (L3), level 4 (L4), or other levels of cache, where the highest level of cache before external memory is classified as LLC. In at least one embodiment, cache coherency logic maintains coherency between the various cache units 1206 and 1204A-1204N.

[0110] In at least one embodiment, the processor 1200 may also include a set of one or more bus controller units 1216 and a system agent core 1210. In at least one embodiment, the one or more bus controller units 1216 manage a set of peripheral buses, such as one or more PCI or PCIe buses. In at least one embodiment, the system agent core 1210 provides management functions for various processor components. In at least one embodiment, the system agent core 1210 includes one or more integrated memory controllers 1214 to manage access to various external memory devices (not shown).

[0111] In at least one embodiment, one or more processor cores 1202A-1202N include support for multiple threads simultaneously. In at least one embodiment, system agent core 1210 includes components for coordinating and operating one or more processor cores 1202A-1202N during multithreaded processing. In at least one embodiment, system agent core 1210 may additionally include a power control unit (PCU) that includes logic and components for regulating one or more power states of one or more processor cores 1202A-1202N and graphics processor 1208.

[0112] In at least one embodiment, the processor 1200 also includes a graphics processor 1208 for performing graphics processing operations. In at least one embodiment, the graphics processor 1208 is coupled to one or more shared cache units 1206 and a system agent core 1210 including one or more integrated memory controllers 1214. In at least one embodiment, the system agent core 1210 also includes a display controller 1211 for driving the graphics processor output to one or more coupled displays. In at least one embodiment, the display controller 1211 may also be a separate module coupled to the graphics processor 1208 via at least one interconnect, or may be integrated within the graphics processor 1208.

[0113] In at least one embodiment, a ring-based interconnect unit 1212 is used to couple the internal components of the processor 1200. In at least one embodiment, alternative interconnect units may be used, such as point-to-point interconnects, switched interconnects, or other technologies. In at least one embodiment, the graphics processor 1208 is coupled to the ring-based interconnect unit 1212 via an I / O link 1213.

[0114] In at least one embodiment, I / O link 1213 represents at least one of a variety of I / O interconnects, including packaged I / O interconnects that facilitate communication between various processor components and high-performance embedded memory modules 1218 (e.g., eDRAM modules). In at least one embodiment, each of one or more processor cores 1202A-1202N and graphics processor 1208 uses embedded memory module 1218 as a shared last level cache.

[0115] In at least one embodiment, one or more processor cores 1202A-1202N are homogeneous cores that execute a common instruction set architecture. In at least one embodiment, one or more 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 in one or more processor cores 1202A-1202N execute a subset or a different instruction set of the common instruction set. In at least one embodiment, one or more processor cores 1202A-1202N are heterogeneous in terms of microarchitecture, wherein one or more cores with relatively high power consumption are coupled with 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 implemented as a SoC integrated circuit.

[0116] The reasoning and / or training logic 715 is used to perform reasoning and / or training operations associated with one or more embodiments. Fig. 7A and / or Figure 7B Detail is provided 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 processor 1200. For example, in at least one embodiment, the training and / or inference techniques described herein may use one or more ALUs embodied in Fig.12 In addition, in at least one embodiment, the inference and / or training operations described herein may use the inference and / or training operations described herein. Fig. 7A and / or Figure 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) that configure the ALU of the graphics processor 1200 to execute one or more of the machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

[0117] Such a component may be used to automatically determine irregular eye or gaze behavior of a person, which may indicate impairment due to the use of drugs or alcohol, and may take one or more actions based on that determination.

[0118] Virtualized computing platform

[0119] Fig.13 is an example data flow diagram of a process 1300 for generating and deploying an image processing and inference pipeline according to at least one embodiment. In at least one embodiment, the process 1300 can be deployed for use with imaging devices, processing devices, and / or other device types at one or more facilities 1302. The process 1300 can be executed within a training system 1304 and / or a deployment system 1306. In at least one embodiment, the training system 1304 can be used to perform training, deployment, and implementation of machine learning models (e.g., neural networks, object detection algorithms, computer vision algorithms, etc.) for use with the deployment system 1306. In at least one embodiment, the deployment system 1306 can be configured to offload processing and computing resources in a distributed computing environment to reduce infrastructure requirements of the facility 1302. In at least one embodiment, one or more applications in the pipeline can use or call services (e.g., reasoning, visualization, computation, AI, etc.) of the deployment system 1306 during application execution.

[0120] In at least one embodiment, some applications used in the high-level processing and reasoning 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 the facility 1302 using data 1308 (e.g., imaging data) generated at the facility 1302 (and stored on one or more picture archiving and communication system (PACS) servers at the facility 1302), the machine learning model may be trained using imaging or sequencing data 1308 from another one or more facilities, or a combination thereof. In at least one embodiment, the training system 1304 may be used to provide applications, services, and / or other resources to generate a working, deployable machine learning model for the deployment system 1306.

[0121] In at least one embodiment, the model registry 1324 can be backed by an object store, which can support versioning and object metadata. In at least one embodiment, the object store can be accessed from within the cloud platform through, for example, a cloud storage compatible application programming interface (API). In at least one embodiment, the machine learning models within the model registry 1324 can be uploaded, listed, modified, or deleted by developers or partners of the system interacting with the API. In at least one embodiment, the API can provide access to methods that allow users with appropriate credentials to associate a model with an application so that the model can be executed as part of the execution of a containerized instantiation of the application.

[0122] In at least one embodiment, the training system 1304 ( Fig.13 ) may include situations where the facility 1302 is training their 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 an imaging device, a sequencing device, and / or other type of device may be received. In at least one embodiment, once the imaging data 1308 is received, the AI-assisted annotation 1310 may be used to help generate annotations corresponding to the imaging data 1308 to be used as ground truth data for the machine learning model. In at least one embodiment, the AI-assisted annotation 1310 may include one or more machine learning models (e.g., a convolutional neural network (CNN)) that may be trained to generate annotations corresponding to certain types of imaging data 1308 (e.g., from certain devices). In at least one embodiment, the AI-assisted annotation 1310 may then be used directly, or may be adjusted or fine-tuned using an annotation tool to generate ground truth data. In at least one embodiment, the AI-assisted annotation 1310, the labeled data 1312, or a combination thereof may be used as ground truth data for training a machine learning model. In at least one embodiment, the trained machine learning model may be referred to as output model(s) 1316 and may be used by the deployment system 1306 as described herein.

[0123] In at least one embodiment, the training pipeline may include a scenario where the facility 1302 requires a machine learning model for performing one or more processing tasks for one or more applications in the deployment system 1306, but the facility 1302 may not currently have such a machine learning model (or may not have a model that is optimized, efficient, or effective for this purpose). In at least one embodiment, an existing machine learning model may be selected from the model registry 1324. In at least one embodiment, the model registry 1324 may include machine learning models that are trained to perform a variety of different reasoning tasks on imaging data. In at least one embodiment, the machine learning models in the model registry 1324 may be trained on imaging data from a different facility (e.g., a facility located far away) rather than the facility 1302. In at least one embodiment, the machine learning model may have been trained on imaging data from one location, two locations, or any number of locations. In at least one embodiment, when training is performed on imaging data from a specific location, the training may be performed at that location, or at least in a manner that protects the confidentiality of the imaging data or restricts the transfer of the imaging data from off-site. In at least one embodiment, once a model is trained or partially trained at one location, the machine learning model can be added to the 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 used in the model registry 1324. In at least one embodiment, the machine learning model can then be selected from the model registry 1324 (and referred to as the output model 1316) and can be deployed in the deployment system 1306 to perform one or more processing tasks for one or more applications of the deployment system.

[0124] In at least one embodiment, the scenario may include a facility 1302 that requires a machine learning model for performing one or more processing tasks for one or more applications in the deployment system 1306, but the facility 1302 may not currently have such a machine learning model (or may not have an optimized, efficient, or effective model). In at least one embodiment, the machine learning model selected from the model registry 1324 may not be fine-tuned or optimized for the imaging data 1308 generated at the facility 1302 due to population differences, robustness of the training data used to train the machine learning model, diversity of training data anomalies, and / or other issues with the training data. In at least one embodiment, AI-assisted annotations 1310 can be used to help generate annotations corresponding to the imaging data 1308 for use as ground truth data for training or updating the machine learning model. In at least one embodiment, labeled clinical data 1312 can be used as ground truth data for training the machine learning model. In at least one embodiment, retraining or updating the machine learning model can be referred to as model training 1314. In at least one embodiment, model training 1314 (e.g., AI-assisted annotation 1310, labeled data 1312, or a combination thereof) can be used as ground truth data to retrain or update a machine learning model. In at least one embodiment, the trained machine learning model can be referred to as an output model 1316 and can be used by the deployment system 1306, as described herein.

[0125] In at least one embodiment, the deployment system 1306 may include software 1318, services 1320, hardware 1322, and / or other components, features, and functions. In at least one embodiment, the deployment system 1306 may include a software "stack" such that the software 1318 may be built on top of the services 1320 and may use the services 1320 to perform some or all of the processing tasks, and the services 1320 and software 1318 may be built on top of the hardware 1322 and use the hardware 1322 to perform the processing, storage, and / or other computing tasks of the deployment system. In at least one embodiment, the software 1318 may include any number of different containers, each of which may perform an instantiation of an application. In at least one embodiment, each application may perform one or more processing tasks (e.g., reasoning, object detection, feature detection, segmentation, image enhancement, calibration, etc.) in a high-level processing and reasoning pipeline. In at least one embodiment, in addition to receiving and configuring imaging data for use by each container and / or containers used by facility 1302 after processing through the pipeline, a high-level processing and reasoning pipeline can also be defined based on the selection of different containers desired or required to process imaging data 1308 (e.g., to convert the output back into a usable data type. In at least one embodiment, the combination of containers within software 1318 (e.g., which constitute a pipeline) can be referred to as a virtual instrument (as described in more detail herein), and the virtual instrument can utilize services 1320 and hardware 1322 to perform some or all of the processing tasks of an application instantiated in a container.

[0126] In at least one embodiment, the data processing pipeline can 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 the deployment system 1306). In at least one embodiment, the input data can 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 can be pre-processed as part of the data processing pipeline to prepare the data for processing by one or more applications. In at least one embodiment, post-processing can 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 prepare the output data for transmission and / or use by the user (e.g., as a response to the inference request). In at least one embodiment, the inference task can be performed by one or more machine learning models, such as a trained or deployed neural network, which can include the output model 1316 of the training system 1304.

[0127] In at least one embodiment, tasks of a data processing pipeline may be encapsulated in containers, each container representing a discrete, fully functional instantiation of an application and a virtualized computing environment capable of referencing a machine learning model. In at least one embodiment, containers or applications may be published to a private (e.g., limited access) area of ​​a container registry (described in more detail herein), and trained or deployed models may be stored in the model registry 1324 and associated with one or more applications. In at least one embodiment, an image of an application (e.g., a container image) may be available in a container registry, and once a user selects an image from a container registry for deployment in a pipeline, the image may be used to generate an instantiated container for the application for use by the user's system.

[0128] In at least one embodiment, a developer (e.g., a software developer, a clinician, a physician, etc.) can develop, publish, and store applications (e.g., as containers) for performing image processing and / or reasoning on provided data. In at least one embodiment, the development, publishing, and / or storage can be performed using a software development kit (SDK) associated with the system (e.g., to ensure that the developed applications and / or containers conform to or are compatible with the system). In at least one embodiment, the developed applications can be tested locally (e.g., at a first facility, on data from the first facility) using the SDK, which is installed as part of the system (e.g., Fig.12 Processor 1200 in the process 1300) may support at least some services 1320. In at least one embodiment, because a DICOM object may contain from one to hundreds of images or other data types, and because the data varies, the developer may be responsible for managing (e.g., setting up constructs for building pre-processing into the application, etc.) the extraction and preparation of the incoming data. In at least one embodiment, once validated by process 1300 (e.g., for accuracy), the application is made available in the container registry for selection and / or implementation by the user to perform one or more processing tasks on the data at the user's facility (e.g., a second facility).

[0129] In at least one embodiment, the developer can then share the application or container over a network for use by a system (e.g., Fig.131300). In at least one embodiment, the completed and validated application or container can be stored in the container registry, and the associated machine learning model can be stored in the model registry 1324. In at least one embodiment, the requesting entity (which provides the reasoning or image processing request) can browse the container registry and / or the model registry 1324 to obtain applications, containers, data sets, machine learning models, etc., select the desired combination of elements to be included in the data processing pipeline, and submit the image processing request. In at least one embodiment, the request may include the input data necessary to execute the request (and in some examples, patient-related data), and / or may include a selection of applications and / or machine learning models to be executed when processing the request. In at least one embodiment, the request can then be passed to one or more components (e.g., a cloud) of the deployment system 1306 to perform processing of the data processing pipeline. In at least one embodiment, the processing performed by the deployment system 1306 may include referencing elements (e.g., applications, containers, models, etc.) selected from the container registry and / or the model registry 1324. In at least one embodiment, once the results are generated by the pipeline, the results may be returned to the user for reference (eg, for viewing in a viewing application suite executing locally, on a local workstation or terminal).

[0130] In at least one embodiment, to assist in processing or executing applications or containers in the pipeline, services 1320 may be utilized. In at least one embodiment, services 1320 may include computing services, artificial intelligence (AI) services, visualization services, and / or other service types. In at least one embodiment, services 1320 may provide functionality common to one or more applications in software 1318, and thus may abstract functionality into services that may be called or utilized by applications. In at least one embodiment, the functionality provided by services 1320 may run dynamically and more efficiently, while also allowing applications to process data in parallel (e.g., using parallel computing platform 1230 ( Fig.12)) to scale well. In at least one embodiment, rather than requiring that each application that shares the same functionality provided by the service 1320 must have a corresponding instance of the service 1320, the service 1320 can be shared between and among various applications. 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 that can provide machine learning model training and / or retraining capabilities. In at least one embodiment, a data enhancement service may be further included that can provide GPU-accelerated data (e.g., DICOM, RIS, CIS, REST-compliant, RPC, raw, etc.) extraction, resizing, scaling, and / or other enhancements. In at least one embodiment, a visualization service may be used that can add image rendering effects (e.g., 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, reasoning, imaging, and / or support for other applications within the pipeline of the virtual instrument.

[0131] In at least one embodiment, where the service 1320 includes an AI service (e.g., an inference service), as part of the execution of an application, one or more machine learning models can be executed by calling (e.g., as an API call) an inference service (e.g., an inference server) to execute one or more machine learning models or processing thereof. In at least one embodiment, where another application includes one or more machine learning models for a segmentation task, the application can call the inference service to execute the machine learning model for performing one or more processing operations associated with the segmentation task. In at least one embodiment, the software 1318 implementing the high-level processing and inference pipeline, which includes a segmentation application and anomaly detection application, can be pipelined because each application can call the same inference service to perform one or more inference tasks.

[0132] In at least one embodiment, the hardware 1322 may include a GPU, a CPU, a graphics card, an AI / deep learning system (e.g., an AI supercomputer such as NVIDIA's DGX), a cloud platform, or a combination thereof. In at least one embodiment, different types of hardware 1322 may be used to provide efficient, purpose-built support for the software 1318 and services 1320 in the deployment system 1306. In at least one embodiment, the use of GPU processing may be implemented to perform local processing (e.g., at the facility 1302) within the AI / deep learning system, in the cloud system, and / or other processing components of the deployment system 1306 to improve the efficiency, accuracy, and effectiveness of image processing and generation. In at least one embodiment, as a non-limiting example, with respect to deep learning, machine learning, and / or high-performance computing, the software 1318 and / or services 1320 may be optimized for GPU processing. In at least one embodiment, at least some of the computing environments of the deployment system 1306 and / or training system 1304 may be executed in a data center, one or more supercomputers, or high-performance computer systems with GPU-optimized software (e.g., a combination of hardware and software of an NVIDIA DGX system). In at least one embodiment, as described herein, hardware 1322 may include any number of GPUs that may be called to perform data processing in parallel. In at least one embodiment, the cloud platform may also include GPU optimized execution for deep learning tasks, GPU processing for machine learning tasks or other computing tasks. In at least one embodiment, an AI / deep learning supercomputer and / or GPU optimized software (e.g., as provided on NVIDIA's DGX system) may be used as a hardware abstraction and scaling platform to execute a cloud platform (e.g., NVIDIA's NGC). In at least one embodiment, the cloud platform may integrate an application container cluster system or coordination system (e.g., KUBERNETES) on multiple GPUs to achieve seamless scaling and load balancing.

[0133] Fig.14 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, the system 1400 can be used to implement Fig.13 The process 1300 and / or other processes of the system 1400 may include a high-level processing and reasoning pipeline. In at least one embodiment, the system 1400 may include a training system 1304 and a deployment system 1306. In at least one embodiment, the training system 1304 and the deployment system 1306 may be implemented using software 1318, services 1320, and / or hardware 1322, as described herein.

[0134] In at least one embodiment, system 1400 (e.g., training system 1304 and / or deployment system 1306) can be implemented in a cloud computing environment (e.g., using cloud 1426). In at least one embodiment, system 1400 can be implemented locally (with respect to a healthcare service facility) or as a combination of cloud computing resources and local computing resources. In at least one embodiment, access to APIs in cloud 1426 can be restricted to authorized users by establishing security measures or protocols. In at least one embodiment, the security protocol can include a network token, which can be signed by an authentication (e.g., AuthN, AuthZ, Gluecon, etc.) service and can carry appropriate authorization. In at least one embodiment, the API of the virtual instrument (described herein) or other instances of system 1400 can be restricted to a set of public IPs that have been audited or authorized for interaction.

[0135] In at least one embodiment, the various components of system 1400 can communicate 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, communications between facilities and components of system 1400 (e.g., for sending inference requests, for receiving results of inference requests, etc.) can be transmitted via one or more data buses, wireless data protocols (Wi-Fi), wired data protocols (e.g., Ethernet), etc.

[0136] In at least one embodiment, similar to the present disclosure regarding Fig.13 As described, the training system 1304 can execute one or more training pipelines 1404. In at least one embodiment, where the deployment system 1306 will use one or more machine learning models in one or more deployment pipelines 1410, the one or more training pipelines 1404 can 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 the one or more training pipelines 1404, one or more output models 1316 can be generated. In at least one embodiment, the one or more training pipelines 1404 can include any number of processing steps, such as, but not limited to, conversion or adaptation of imaging data (or other input data). In at least one embodiment, different training pipelines 1404 can be used for different machine learning models used by the deployment system 1306. In at least one embodiment, similar to the description regarding Fig.13 The one or more training pipelines 1404 of the first example described may be used for a first machine learning model, similar to the one described with respect to Fig.13The one or more training pipelines 1404 of the second example described may be used for a second machine learning model, similar to the one or more training pipelines 1404 of the second example described. Fig.13 The one or more training pipelines 1404 of the third example described may be used for a third machine learning model. In at least one embodiment, any combination of tasks within the training system 1304 may be used according to the requirements of each corresponding machine learning model. In at least one embodiment, one or more machine learning models may have been trained and are ready for deployment, so the training system 1304 may not perform any processing on the machine learning model, and one or more machine learning models may be implemented by the deployment system 1306.

[0137] In at least one embodiment, the output model 1316 and / or the pre-trained model 1406 may include any type of machine learning model, depending on the implementation or embodiment. In at least one embodiment and without limitation, the machine learning model used by the system 1400 may include using linear regression, logistic regression, decision tree, support vector machine (SVM), naive Bayes, k-nearest neighbor (Knn), k-means clustering, random forest, dimensionality reduction algorithm, gradient boosting algorithm, neural network (e.g., autoencoder, convolution, recursion, perceptron, long / short term memory (LSTM), Hopfield, Boltzmann, deep belief, deconvolution, generative adversarial, liquid state machine, etc.), and / or other types of machine learning models.

[0138] In at least one embodiment, one or more training pipelines 1404 may include AI-assisted annotation, as described herein with respect to at least Fig.14In more detail. In at least one embodiment, the labeled clinical data 1312 (e.g., traditional annotations) can be generated by any number of techniques. In at least one embodiment, in some examples, labels or other annotations can be generated in a drawing program (e.g., an annotation program), a computer-aided design (CAD) program, a labeling program, another type of application suitable for generating ground-truth annotations or labels, and / or can be hand-drawn. In at least one embodiment, the ground-truth data can be synthetically generated (e.g., generated from a computer model or rendering), truly generated (e.g., designed and generated from real-world data), machine-generated (e.g., using feature analysis and learning to extract features from data and then generate labels), manually annotated (e.g., a labeler or annotation expert, defining the location of the label), and / or a combination thereof. In at least one embodiment, for each instance of imaging data 1308 (or other data types used by machine learning models), there can be corresponding ground-truth data generated by the training system 1304. In at least one embodiment, AI-assisted annotations can be performed as part of the deployment pipeline 1410; supplementing or replacing the AI-assisted annotations included in the training pipeline 1404. In at least one embodiment, the system 1400 may include a multi-layer platform that may include a software layer (e.g., software 1318) of a diagnostic application (or other application type) that may perform one or more medical imaging and diagnostic functions. In at least one embodiment, the system 1400 may be communicatively coupled (e.g., via an encrypted link) to a PACS server network of one or more facilities. In at least one embodiment, the system 1400 may be configured to access and reference data from a PACS server to perform operations such as training a machine learning model, deploying a machine learning model, image processing, reasoning, and / or other operations.

[0139] In at least one embodiment, the software layer may be implemented as a secure, encrypted, and / or authenticated API through which an application or container may be invoked (e.g., called) from an external environment (e.g., facility 1302). In at least one embodiment, the application may then call 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 services 1320 may utilize hardware 1322 to perform processing tasks in an effective and efficient manner. In at least one embodiment, a pair of DICOM adapters 1402A, 1402B may be used to send communications to or receive communications by the training system 1304 and the deployment system 1306.

[0140] In at least one embodiment, the deployment system 1306 may execute one or more deployment pipelines 1410. In at least one embodiment, the one or more deployment pipelines 1410 may include any number of applications, which may be sequential, non-sequential, or otherwise applied to imaging data (and / or other data types) - including AI-assisted annotations, generated by imaging devices, sequencing devices, genomics devices, etc., as described above. In at least one embodiment, as described herein, the 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 scanning instrument, a virtual sequencing instrument, etc.). In at least one embodiment, there may be more than one deployment pipeline 1410 for a single device, depending on the information desired from the data generated by the device. In at least one embodiment, in the case where an abnormality is desired to be detected from an MRI machine, there may be one or more first deployment pipelines 1410, and in the case where image enhancement is desired from the output of the MRI machine, there may be one or more second deployment pipelines 1410.

[0141] In at least one embodiment, the image generation application may include a processing task that includes the use of a machine learning model. In at least one embodiment, a user may wish 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 be included in an application that performs a processing task. In at least one embodiment, the application may be selectable 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 the system 1400 (e.g., services 1320 and hardware 1322), one or more deployment pipelines 1410 may be more user-friendly, provide easier integration, and produce more accurate, efficient, and timely results.

[0142] In at least one embodiment, deployment system 1306 may include a user interface ("UI") 1414 (e.g., a graphical user interface, a web interface, etc.) that may be used to select applications to be included in deployment pipeline 1410, to arrange applications, to modify or change applications or their parameters or configuration, to use and interact with deployment pipeline 1410 during setup and / or deployment, and / or to otherwise interact with deployment system 1306. In at least one embodiment, although not shown with respect to training system 1304, UI 1414 (or a different user interface) may be used to select models to use in deployment system 1306, to select models for training or retraining in training system 1304, and / or to otherwise interact with training system 1304.

[0143] In at least one embodiment, in addition to the application coordination system 1428, a pipeline manager 1412 may be used to manage the interaction between the application or container of the deployment pipeline 1410 and the service 1320 and / or the hardware 1322. In at least one embodiment, the pipeline manager 1412 may be configured to facilitate the interaction 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 the software 1318, this is not intended to be limiting, and in some examples, the pipeline manager 1412 may be included in the service 1320. In at least one embodiment, the application coordination system 1428 (e.g., Kubernetes, DOCKER, etc.) may include a container coordination system that can group applications into containers as a logical unit for coordination, management, scaling, and deployment. In at least one embodiment, by associating applications from the deployment pipeline 1410 (e.g., rebuilding applications, splitting applications, etc.) with individual containers, each application can be executed in a self-contained environment (e.g., at the kernel level) to improve speed and efficiency.

[0144] In at least one embodiment, each application and / or container (or its image) can be developed, modified, and deployed separately (e.g., a first user or developer can develop, modify, and deploy a first application, and a second user or developer can develop, modify, and deploy a second application separate from the first user or developer), which can allow for focus and attention on the tasks of a single application and / or container without being hindered by the tasks of another application or container. In at least one embodiment, the pipeline manager 1412 and the application coordination system 1428 can assist in communication and collaboration between different containers or applications. In at least one embodiment, as long as the expected inputs and / or outputs of each container or application are known to the system (e.g., based on the construction of the application or container), the application coordination system 1428 and / or the pipeline manager 1412 can facilitate communication and sharing of resources between and among each application or container. In at least one embodiment, since one or more applications or containers in the deployment pipeline 1410 can share the same services and resources, the application coordination system 1428 can coordinate, load balance, and determine the sharing of services or resources between and among the various applications or containers. In at least one embodiment, the scheduler can be used to track resource requirements of applications or containers, current or planned use of those resources, and resource availability. Thus, in at least one embodiment, the scheduler can allocate resources to different applications and allocate resources between and among applications, taking into account the needs and availability of the system. In some examples, the scheduler (and / or other components of the application coordination 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 (e.g., to determine whether to perform real-time processing or delayed processing), etc.

[0145] In at least one embodiment, the services 1320 utilized and shared by the applications or containers in the 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 call (e.g., execute) one or more services 1320 to perform processing operations for the application. In at least one embodiment, an application may utilize computing services 1416 to perform supercomputing or other high performance computing (HPC) tasks. In at least one embodiment, one or more computing services 1416 may be utilized to perform parallel processing (e.g., using a parallel computing platform 1430) to process data substantially simultaneously by one or more applications and / or one or more tasks of a single application. In at least one embodiment, a parallel computing platform 1430 (e.g., NVIDIA's CUDA) may implement general computing on a GPU (GPGPU) (e.g., GPU / graphics 1422). In at least one embodiment, the software layer of the parallel computing platform 1430 may provide access to a virtual instruction set and parallel computing elements of a GPU 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 among multiple containers, and / or between and among 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 multiple processes within a container to enable the same data (e.g., where multiple different stages of an application or multiple applications are processing the same information) to be used for a shared memory segment from the parallel computing platform 1430. In at least one embodiment, instead of copying data and moving the data to different locations in memory (e.g., read / write operations), the same data in the same location of the memory may be used for any number of processing tasks (e.g., at the same time, at different times, etc.). In at least one embodiment, since the data as a result of processing is used to generate new data, this information of the new location of the data may be stored and shared between various applications. In at least one embodiment, the location of the data and the location of the updated or modified data may be part of the definition of how to understand the payload in the container.

[0146] In at least one embodiment, one or more AI services 1418 may be utilized to perform reasoning services for executing machine learning models associated with an application (e.g., the task is to execute one or more processing tasks of the application). In at least one embodiment, one or more AI services 1418 may utilize an AI system 1424 to execute machine learning models (e.g., neural networks such as CNN) for segmentation, reconstruction, object detection, feature detection, classification, and / or other reasoning tasks. In at least one embodiment, an application of the deployment pipeline 1410 may use one or more output models 1316 and / or other models of the application for the self-training system 1304 to perform reasoning on imaging data. In at least one embodiment, two or more examples of reasoning using an application coordination system 1428 (e.g., a scheduler) may be available. In at least one embodiment, a first category may include a high priority / low latency path that can achieve a higher service level agreement, such as for performing reasoning on an urgent request in an emergency, or for a radiologist during a diagnostic process. In at least one embodiment, a second category may include a standard priority path that may be used for requests that may not be urgent or for situations where analysis can be performed at a later time. In at least one embodiment, application coordination system 1428 can allocate resources (e.g., services 1320 and / or hardware 1322) for different reasoning tasks of AI service 1418 based on priority paths.

[0147] In at least one embodiment, the shared memory may be mounted to one or more AI services 1418 in the system 1400. In at least one embodiment, the shared memory 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, a set of API instances of the deployment system 1306 may receive the request and may select one or more instances (e.g., for best fit, for load balancing, etc.) to process the request. In at least one embodiment, in order to process the request, the request may be entered into a database, and if it is not already in the cache, the machine learning model may be located from the model registry 1324, a verification step may ensure that the 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, if the application is not yet running or there are not enough instances of the application, a scheduler (e.g., a scheduler of the pipeline manager 1412) may be used to start the application referenced in the request. In at least one embodiment, if the inference server has not yet been started to execute the model, the inference server may be started. Any number of inference servers may be started for each model. In at least one embodiment, in a pull model that clusters inference servers, the model can be cached whenever load balancing is beneficial. In at least one embodiment, the inference servers can be statically loaded into the corresponding distributed servers.

[0148] In at least one embodiment, inference can be performed using an inference server running in a container. In at least one embodiment, an instance of an inference server can be associated with a model (and optionally with multiple versions of a model). In at least one embodiment, if an instance of an inference server does not exist when a request is received to perform inference on a model, a new instance can be loaded. In at least one embodiment, when the inference server is started, the 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 is running as a different instance.

[0149] In at least one embodiment, during application execution, a request for reasoning for a given application may be received, and a container (e.g., an instance of a hosting reasoning server) may be loaded (if not already loaded), and a launcher may be called. In at least one embodiment, preprocessing logic in the container may load, decode, 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 reasoning, the container may perform reasoning on the data as needed. In at least one embodiment, this may include a single reasoning call for one image (e.g., a hand X-ray), or may require reasoning for hundreds of images (e.g., a chest CT). 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 results. In at least one embodiment, different priorities may be assigned to different models or applications. For example, some models may have a real-time (TAT less than 1 minute) priority, while other models may have a lower priority (e.g., TAT less than 10 minutes). In at least one embodiment, model execution time may be measured from a requesting mechanism or entity and may include collaborative network traversal time as well as execution time of an inference service.

[0150] In at least one embodiment, the transmission of requests between the service 1320 and the reasoning application can be hidden behind a software development kit (SDK), and robust transmission can be provided through queues. In at least one embodiment, requests will be placed in queues through an API for individual application / tenant ID combinations, and the SDK will pull requests from the queue and provide the request 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 through queues may be useful because it can allow any instance of the application to pick up work when it is available. Results can be transmitted back through the queue to ensure that no data is lost. In at least one embodiment, the queue can also provide the ability to split the work, because the highest priority work can enter the queue connected to most instances of the application, and the lowest priority work can enter the queue connected to a single instance, which processes the tasks in the order received. In at least one embodiment, the application can run on a GPU-accelerated instance generated in the cloud 1426, and the reasoning service can perform reasoning on the GPU.

[0151] In at least one embodiment, visualization services 1420 may be utilized to generate visualizations for viewing application and / or deployment pipeline 1410 outputs. In at least one embodiment, visualization services 1420 may utilize GPU / graphics 1422 to generate visualizations. In at least one embodiment, visualization services 1420 may implement rendering effects such as ray tracing to generate higher quality visualizations. In at least one embodiment, visualizations may include, but are not limited to, 2D image rendering, 3D volume rendering, 3D volume reconstruction, 2D tomographic slices, virtual reality displays, augmented reality displays, and the like. 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 system users (e.g., doctors, nurses, radiologists, etc.) to interact with. In at least one embodiment, visualization services 1420 may include internal visualizers, movies, and / or other rendering or image processing capabilities or functions (e.g., ray tracing, rasterization, internal optics, etc.).

[0152] In at least one embodiment, hardware 1322 may include GPU / graphics 1422, AI system 1424, cloud 1426, and / or any other hardware for executing training system 1304 and / or deployment system 1306. In at least one embodiment, GPU / graphics 1422 (e.g., NVIDIA's TESLA and / or QUADRO GPUs) may include any number of GPUs that may be used to perform processing tasks for any feature or functionality of compute services 1416, AI services 1418, visualization services 1420, other services, and / or software 1318. For example, for AI services 1418, GPU / graphics 1422 may be used to perform pre-processing on imaging data (or other data types used by machine learning models), post-processing on the output of machine learning models, and / or perform inference (e.g., to execute machine learning models). In at least one embodiment, cloud 1426, AI system 1424, and / or other components of system 1400 may use GPU / graphics 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 cloud 1426 (or at least part of a task being deep learning or reasoning) may be executed using one or more AI systems 1424. Likewise, while 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.

[0153] In at least one embodiment, AI system 1424 may include a purpose-built computing system (e.g., a supercomputer or HPC) configured for reasoning, deep learning, machine learning, and / or other artificial intelligence tasks. In at least one embodiment, in addition to CPU, RAM, storage, and / or other components, features, or functions, AI system 1424 (e.g., NVIDIA's DGX) may also include software (e.g., a software stack) that can use multiple GPUs / graphics 1422 to perform GPU-optimized tasks. 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.

[0154] In at least one embodiment, cloud 1426 may include a GPU-accelerated infrastructure (e.g., NVIDIA's NGC) that may provide a GPU-optimized platform for executing processing tasks of system 1400. In at least one embodiment, cloud 1426 may include an AI system 1424 for executing 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 an application coordination system 1428 that utilizes multiple GPUs to enable seamless scaling and load balancing between and among applications and services 1320. In at least one embodiment, cloud 1426 may be responsible for executing at least some services 1320 of system 1400, including one or more compute services 1416, one or more AI services 1418, and / or one or more visualization services 1420, as described herein. In at least one embodiment, cloud 1426 can perform large and small batch inference (e.g., executing NVIDIA's TENSORRT), provide accelerated parallel computing APIs and platforms 1430 (e.g., NVIDIA's CUDA), execute application coordination system 1428 (e.g., Kubernetes), provide graphics rendering APIs and platforms (e.g., for ray tracing, 2D graphics, 3D graphics, and / or other rendering techniques to produce higher quality movie effects), and / or can provide other functionality for system 1400.

[0155] Fig.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, a non-limiting example may be used. Fig.14The process 1500 may be performed by the system 1400 of FIG. 1400. In at least one embodiment, the process 1500 may utilize services and / or hardware as described herein. In at least one embodiment, the refined model 1512 generated by the process 1500 may be executed by the deployment system for one or more containerized applications in the deployment pipeline.

[0156] In at least one embodiment, model training 1514 may include retraining or updating an initial model 1504 (e.g., a pre-trained model) using new training data (e.g., new input data (such as customer data set 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 loss layer of the initial model 1504 may be reset or deleted, and / or replaced with an updated or new output or loss layer. In at least one embodiment, the initial model 1504 may have previously fine-tuned parameters (e.g., weights and / or biases) retained from previous training, so training or retraining 1514 may not take as long or require as much processing as training the model from scratch. In at least one embodiment, during model training 1514, by resetting or replacing the output or loss layer of the initial model 1504, when generating predictions on the new customer data set 1506, the parameters for the new data set may be updated and re-adjusted based on the loss calculation associated with the accuracy of the output or loss layer.

[0157] In at least one embodiment, the pre-trained model 1506 may be stored in a data store or registry. In at least one embodiment, the pre-trained model 1506 may have been trained at least in part at one or more facilities other than the facility performing process 1500. In at least one embodiment, in order to protect the privacy and rights of patients, subjects, or customers of different facilities, the pre-trained model 1506 may have been trained locally using locally generated customer or patient data. In at least one embodiment, the cloud and / or other hardware may be used to train the pre-trained model 1306, but confidential, privacy-protected patient data may not be transmitted to, used by, or accessed by any component of the cloud (or other non-local hardware). In at least one embodiment, if the pre-trained model 1506 is trained using patient data from more than one facility, the pre-trained model 1506 may have been trained separately for each facility before training on patient or customer data from another facility. In at least one embodiment, for example where customer or patient data has been issued privacy issues (e.g., by being relinquished, used for experimental purposes, etc.), or where the 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 model 1506 locally and / or externally, such as in a data center or other cloud computing infrastructure.

[0158] In at least one embodiment, when selecting an application to use in a deployment pipeline, a user may also select a machine learning model to use for a particular application. In at least one embodiment, a user may not have a model to use, so the user may select a pre-trained model to use with the application. In at least one embodiment, the pre-trained model may not be optimized to generate accurate results on the customer data set 1506 of the user facility (e.g., based on patient diversity, demographics, type of medical imaging equipment used, etc.). In at least one embodiment, the pre-trained model may be updated, retrained, and / or fine-tuned for use at various facilities before deploying the pre-trained model into a deployment pipeline for use with one or more applications.

[0159] In at least one embodiment, a user may select a pre-trained model to update, retrain, and / or fine-tune, and the pre-trained model may be referred to as the initial model 1504 for the training system in process 1500. In at least one embodiment, a customer data set 1506 (e.g., imaging data, genomic data, sequencing data, or other data types generated by equipment at a facility) may be used to perform model training (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 truth data corresponding to the customer data set 1506 may be generated by the training system 1304. In at least one embodiment, the ground truth data may be generated, at least in part, at the facility by a clinician, scientist, physician, practitioner.

[0160] In at least one embodiment, AI-assisted annotation can be used in some examples to generate ground truth data. In at least one embodiment, AI-assisted annotation (e.g., implemented using an AI-assisted annotation SDK) can utilize a machine learning model (e.g., a neural network) to generate ground truth data for suggestions or predictions for a customer data set. In at least one embodiment, a user can use the annotation tool within a user interface (graphical user interface (GUI)) on a computing device.

[0161] In at least one embodiment, user 1510 can interact with the GUI via computing device 1508 to edit or fine-tune annotations or automatic annotations. In at least one embodiment, a polygon editing feature can be used to move vertices of a polygon to a more precise or fine-tuned position.

[0162] In at least one embodiment, once the customer dataset 1506 has associated ground truth data, the ground truth data (e.g., from AI-assisted annotations, manual labeling, etc.) can be used during model training to generate a refined model 1512. In at least one embodiment, the customer dataset 1506 can be applied to the initial model 1504 any number of times, and the ground truth 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, the refined model 1512 can be deployed within one or more deployment pipelines at a facility for use in performing one or more processing tasks with respect to medical imaging data.

[0163] In at least one embodiment, the refined model 1512 can be uploaded to the pre-trained models in the model registry for selection by another facility. In at least one embodiment, this process can be completed at any number of facilities, so that the refined model 1512 can be further refined any number of times on new data sets to generate a more general model.

[0164] Fig. 15B is an example illustration of a client-server architecture 1532 for enhancing an annotation tool with 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 AI-assisted annotation tool 1536 in an imaging application can assist a radiologist, for example, in identifying organs and abnormalities. In at least one embodiment, the imaging application can include a software tool that, as a non-limiting example, helps a user 1510 identify 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 for 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 as, for example, but not limited to, ground truth data for training. In at least one embodiment, when the computing device 1508 sends extreme points for AI-assisted annotation, for example, a deep learning model can receive the data as input and return an inference result for segmenting an organ or anomaly. In at least one embodiment, the pre-instantiated annotation tool (e.g., Fig. 15B The AI-assisted annotation tools 1536 in the annotation assistant server 1540 can be enhanced by making an API call (e.g., API call 1544) to a server (such as an annotation assistant server 1540), which may include a set of pre-trained models 1542 stored in, for example, an annotation model registry. In at least one embodiment, the annotation model registry can store pre-trained models 1542 (e.g., machine learning models, such as deep learning models) that are pre-trained to perform AI-assisted annotation for specific organs or abnormalities. In at least one embodiment, these models can be further updated by using a training pipeline. In at least one embodiment, the pre-installed annotation tools can be improved over time as new annotated data is added.

[0165] Autonomous Vehicles

[0166] Fig.16A An example of autonomous vehicle 1600 is shown in accordance with at least one embodiment. In at least one embodiment, autonomous vehicle 1600 (alternatively referred to herein as "vehicle 1600") may be, but is not limited to, a passenger vehicle such as a car, truck, bus, and / or another type of vehicle that may accommodate one or more passengers. In at least one embodiment, vehicle 1600 may be a semi-tractor-trailer for hauling cargo. In at least one embodiment, vehicle 1600 may be an aircraft, a robotic vehicle, or another type of vehicle.

[0167] An autonomous vehicle may be described according to the automation levels defined by the National Highway Traffic Safety Administration (“NHTSA”) of the U.S. Department of Transportation and the Society of Automotive Engineers (“SAE”) in the “Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles” (e.g., Standard No. J3016-201806, dated June 15, 2018, Standard No. J3016-201609, dated September 30, 2016, and previous and future versions of this standard). In at least one embodiment, the vehicle 1600 may be capable of functioning according to one or more of the levels 1 to 5 of the autonomous driving levels. For example, in at least one embodiment, depending on the embodiment, the vehicle 1600 may be capable of conditional automation (level 3), high automation (level 4), and / or full automation (level 5).

[0168] In at least one embodiment, the vehicle 1600 may include, but is not limited to, components such as a chassis, a body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other components of the vehicle. In at least one embodiment, the vehicle 1600 may include, but is not limited to, a propulsion system 1650, such as an internal combustion engine, a hybrid power plant, an all-electric engine, and / or another type of propulsion system. In at least one embodiment, the propulsion system 1650 may be connected to a drive train of the vehicle 1600, which may include, but is not limited to, a transmission, so that the vehicle 1600 can be propelled. In at least one embodiment, the propulsion system 1650 may be controlled in response to receiving a signal from a throttle / accelerator 1652.

[0169] In at least one embodiment, when propulsion system 1650 is operating (e.g., when the vehicle is traveling), steering system 1654 (which may include, but is not limited to, a steering wheel) is used to steer vehicle 1600 (e.g., along a desired path or route). In at least one embodiment, steering system 1654 may receive signals from steering actuator 1656. In at least one embodiment, the steering wheel may be optional for full automation (Level 5) functionality. In at least one embodiment, brake sensor system 1646 may be used to operate vehicle brakes in response to signals received from brake actuator 1648 and / or brake sensors.

[0170] In at least one embodiment, controller 1636 may include, but is not limited to, one or more system on a chip (“SoC”) ( Fig.16A1600) and / or a graphics processing unit ("GPU") to provide signals (e.g., representing commands) to one or more components and / or systems of the vehicle 1600. For example, in at least one embodiment, the controller 1636 may send signals to operate vehicle brakes through the brake actuator 1648, operate the steering system 1654 through one or more steering actuators 1656, and operate the propulsion system 1650 through one or more throttles / accelerators 1652. In at least one embodiment, the one or more controllers 1636 may include one or more onboard (e.g., integrated) computing devices that process sensor signals and output operating commands (e.g., signals representing commands) to achieve autonomous driving and / or assist the driver in driving the vehicle 1600. In at least one embodiment, the one or more controllers 1636 may include a first controller 1636 for autonomous driving functions, a second controller 1636 for functional safety functions, a third controller 1636 for artificial intelligence functions (e.g., computer vision), a fourth controller 1636 for infotainment functions, a fifth controller 1636 for redundancy in emergency situations, and / or other controllers. In at least one embodiment, a single controller 1636 may handle two or more of the above functions, two or more controllers 1636 may handle a single function, and / or any combination thereof.

[0171] In at least one embodiment, the one or more controllers 1636 provide signals for controlling one or more components and / or systems of the vehicle 1600 in response to sensor data received from one or more sensors (e.g., sensor inputs). In at least one embodiment, the sensor data can be received from sensors such as, but not limited to, one or more global navigation satellite system ("GNSS") sensors 1658 (e.g., one or more global positioning system sensors), one or more RADAR sensors 1660, one or more ultrasonic sensors 1662, one or more LIDAR sensors 1664, one or more inertial measurement unit (IMU) sensors 1666 (e.g., one or more accelerometers, one or more gyroscopes, one or more magnetic compasses, one or more magnetometers, etc.), one or more microphones 1696, one or more stereo cameras 1668, one or more wide angle cameras 1670 (e.g., fisheye cameras), one or more infrared cameras 1672, one or more surround cameras 1674 (e.g., 360 degree cameras), telephoto cameras (e.g., infrared cameras), and / or a plurality of other sensors. Fig.16A Not shown), mid-range camera ( Fig.16A1500), one or more speed sensors 1644 (e.g., for measuring the speed of the vehicle 1500), one or more vibration sensors 1642, one or more steering sensors 1640, one or more brake sensors (e.g., as part of a brake sensor system 1646), and / or other sensor types are received.

[0172] In at least one embodiment, one or more controllers 1636 may receive input (e.g., represented by input data) from a dashboard 1632 of the vehicle 1600 and provide output (e.g., represented by output data, display data, etc.) via a human machine interface (“HMI”) display 1634, an audible annunciator, a speaker, and / or other components of the vehicle 1600. In at least one embodiment, the output may include information such as vehicle speed, velocity, time, map data (e.g., high definition map ( Fig.16A 1600 ), location data (e.g., the location of the vehicle 1600 , such as on a map), directions, locations of other vehicles (e.g., occupancy barriers), information about objects and states of objects sensed by one or more controllers 1636 , etc. For example, in at least one embodiment, HMI display 1634 may display information about the presence of one or more objects (e.g., road signs, warning signs, traffic light changes, etc.) and / or information about driving maneuvers the vehicle has, is, or will make (e.g., changing lanes now, taking exit 34B in two miles, etc.).

[0173] In at least one embodiment, the vehicle 1600 further includes a network interface 1624, which can use one or more wireless antennas 1626 and / or one or more modems to communicate through one or more networks. For example, in at least one embodiment, the network interface 1624 may be able to communicate through long-term evolution ("LTE"), wideband code division multiple access ("WCDMA"), universal mobile telecommunications system ("UMTS"), global system for mobile communications ("GSM"), IMT-CDMA multi-carrier ("CDMA2000") networks, etc. In at least one embodiment, one or more wireless antennas 1626 can also use one or more local area networks (e.g., Bluetooth, Bluetooth Low Energy (LE), Z-Wave, ZigBee, etc.) and / or one or more low power wide area networks (hereinafter referred to as "LPWAN") (e.g., LoRaWAN, SigFox, etc. protocols) to enable communication between objects in the environment (e.g., vehicles, mobile devices).

[0174] Reasoning and / or training logic 715 is used to perform reasoning and / or training operations associated with one or more embodiments. Fig. 7Aand / or Figure 7B Provides details about the reasoning and / or training logic 715. In at least one embodiment, the reasoning and / or training logic 715 may be implemented in the system Fig.16A for inferring or predicting 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.

[0175] Such a component may be used to automatically determine irregular eye or gaze behavior of a person, which may indicate impairment due to the use of drugs or alcohol, and may take one or more actions based on that determination.

[0176] Fig. 16B According to at least one embodiment, Fig.16A 1600. In at least one embodiment, the cameras and respective fields of view are an example embodiment 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 locations on the vehicle 1600.

[0177] In at least one embodiment, the camera type used for the camera may include, but is not limited to, a digital camera that may be suitable for use with components and / or systems of the vehicle 1600. In at least one embodiment, the camera may operate at Automotive Safety Integrity Level ("ASIL") B and / or other ASILs. In at least one embodiment, the camera type may have any image capture rate, such as 60 frames per second (fps), 1220fps, 240fps, etc., depending on the embodiment. 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-clear-clear ("RCCC") filter array, a red-clear-clear-blue ("RCCB") filter array, a red-blue-green-transparent ("RCC") filter array, a red-clear-clear-blue ("RCCB") filter array, a red-blue-green-transparent ("RCC") filter array, a red-clear-clear-blue ("RCC") filter array, a red-clear-clear-blue ("RCC") filter array, a red-clear-clear-green ...green ("RCC") filter array, a red-clear-

[0178] ("RBGC") filter arrays, Foveon X3 filter arrays, Bayer sensor ("RGGB") filter arrays, monochrome sensor filter arrays, and / or other types of filter arrays. In at least one embodiment, a transparent pixel camera, such as a camera with an RCCC, RCCB, and / or RBGC filter array, may be used in an effort to improve photosensitivity.

[0179] In at least one embodiment, one or more cameras can be used to perform advanced driver assistance system ("ADAS") functions (e.g., as part of a redundant or fail-safe design). For example, in at least one embodiment, a multi-function mono camera can be installed to provide functions including lane departure warning, traffic sign assistance, and intelligent headlight control. In at least one embodiment, one or more cameras (e.g., all cameras) can simultaneously record and provide image data (e.g., video).

[0180] In at least one embodiment, one or more cameras can be mounted in a mounting assembly, such as a custom designed (three-dimensional ("3D") printed) assembly, so as to cut out stray light and reflections from within the vehicle 1600 (e.g., reflections of the dashboard reflecting in the windshield mirror) that may interfere with the camera's image data capture capabilities. With respect to the rearview mirror mounting assembly, in at least one embodiment, the rearview mirror assembly can be 3D printed custom so that the camera mounting plate matches the shape of the rearview mirror. In at least one embodiment, one or more cameras can be integrated into the rearview mirror. In at least one embodiment, for side view cameras, one or more cameras can also be integrated within four pillars at each corner of the cabin.

[0181] In at least one embodiment, a camera (e.g., a forward-facing camera) having a field of view that includes a portion of the environment in front of the vehicle 1600 can be used for surround vision, as well as to help identify the forward path and obstacles with the assistance of one or more controllers 1636 and / or control SoCs, thereby providing information critical to generating an occupancy grid and / or determining a preferred vehicle path. In at least one embodiment, the forward-facing camera can be used to perform many ADAS functions similar to LIDAR, including but not limited to emergency braking, pedestrian detection, and collision avoidance. In at least one embodiment, the forward-facing camera can also be used for ADAS functions and systems, including but not limited to lane departure warning ("LDW"), automatic cruise control ("ACC"), and / or other functions (e.g., traffic sign recognition).

[0182] In at least one embodiment, a variety of cameras can be used in a forward-facing 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 1670 can be used to sense objects entering from the periphery (e.g., pedestrians, people crossing the street, or bicycles). Although in Fig. 16BOnly one wide-angle camera 1670 is shown, however, in other embodiments, there may be any number (including zero) of wide-angle cameras on the vehicle 1600. In at least one embodiment, any number of remote cameras 1698 (e.g., a remote stereo camera pair) may be used for depth-based object detection, particularly for objects for which a neural network has not yet been trained. In at least one embodiment, the remote cameras 1698 may also be used for object detection and classification and basic object tracking.

[0183] In at least one embodiment, any number of stereo cameras 1668 may also be included in the forward configuration. In at least one embodiment, one or more stereo cameras 1668 may include an integrated control unit including a scalable processing unit that may provide programmable logic ("FPGA") and a multi-core microprocessor with a controller area network ("CAN") or Ethernet interface integrated on a single chip. In at least one embodiment, such a unit may be used to generate a 3D map of the environment of the vehicle 1600, including distance estimates for all points in the image. In at least one embodiment, one or more stereo cameras 1668 may include, but are not limited to, a compact stereo vision sensor, which may include, but are not limited to, two camera lenses (one on each side) and an image processing chip that may measure the distance from the vehicle 1600 to the 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 1668 may be used in addition to those described herein.

[0184] In at least one embodiment, a camera having a field of view of a portion of the environment including the sides of the vehicle 1600 (e.g., a side view camera) can be used for surround viewing to provide information for creating and updating occupancy grids and generating side collision warnings. For example, in at least one embodiment, one or more surround cameras 1674 (e.g., such as Fig. 16B Four surround cameras 1674 (shown) may be positioned on the vehicle 1600. In at least one embodiment, the one or more surround cameras 1674 may include, but are not limited to, any number and combination of one or more wide angle cameras, one or more fisheye lenses, one or more 360 ​​degree cameras, and / or the like. For example, in at least one embodiment, four fisheye lens cameras may be located at the front, rear, and sides of the vehicle 1600. In at least one embodiment, the vehicle 1600 may use three surround cameras 1674 (e.g., left, right, and rear), and may utilize one or more other cameras (e.g., a forward-facing camera) as a fourth surround camera.

[0185] In at least one embodiment, a camera having a field of view that includes a portion of the environment behind the vehicle 1600 (e.g., a rear view camera) can be used for parking assistance, surround view, rear collision warning, and creating and updating occupancy grids. In at least one embodiment, a variety of cameras can be used, including but not limited to cameras that are also suitable as one or more forward-facing cameras (e.g., long-range camera 1698 and / or one or more mid-range cameras 1676, one or more stereo cameras 1668, one or more infrared cameras 1672, etc.), as described herein.

[0186] The reasoning and / or training logic 715 is used to perform reasoning and / or training operations related to one or more embodiments. Fig. 7A and / or Figure 7B Provides details about the reasoning and / or training logic 715. In at least one embodiment, the reasoning and / or training logic 715 can be used to Fig. 16B In a system of the present invention, an operation can be performed based at least in part on weight parameters calculated using the neural network training operations, neural network functions and / or architectures, or neural network use cases described herein.

[0187] Such a component may be used to automatically determine irregular eye or gaze behavior of a person, which may indicate impairment due to the use of drugs or alcohol, and may take one or more actions based on that determination.

[0188] Fig. 16C is a diagram showing a method for Fig.16A A block diagram of an example system architecture of an autonomous vehicle 1600. In at least one embodiment, Fig. 16C Each of the components, features, and systems of the vehicle 1600 in the embodiment includes a connection via bus 1602. In at least one embodiment, bus 1602 may include, but is not limited to, a CAN data interface (also referred to herein as a "CAN bus"). In at least one embodiment, CAN may be a network inside the vehicle 1600 that helps control various features and functions of the vehicle 1600, such as actuation of brakes, acceleration, braking, steering, windshield wipers, etc. In at least one embodiment, bus 1602 may be configured to have dozens or even hundreds of nodes, each with its own unique identifier (e.g., CAN ID). In at least one embodiment, bus 1602 may be read to find steering wheel angle, ground speed, engine revolutions per minute ("RPM"), button position, and / or other vehicle status indicators. In at least one embodiment, bus 1602 may be a CAN bus that complies with ASIL B.

[0189] In at least one embodiment, in addition to or in place of CAN, FlexRay and / or Ethernet protocols may be used. In at least one embodiment, there may be any number of buses forming bus 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 different 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 collision avoidance functions, while a second bus may be used for actuation control. In at least one embodiment, each bus in bus 1602 may communicate with any component of vehicle 1600, and two or more buses in bus 1602 may communicate with corresponding components. In at least one embodiment, any number of systems on chip ("SoC") 1604 (such as SoC 1604 (A) and SoC 1604 (B)), each controller 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, the vehicle 1600 may include one or more controllers 1636, such as those described herein. Fig.16A Those described. In at least one embodiment, 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 for control of the vehicle 1600, artificial intelligence of the vehicle 1600, infotainment of the vehicle 1600, and / or other functions.

[0191] In at least one embodiment, the vehicle 1600 may include any number of SoCs 1604. In at least one embodiment, each SoC 1604 may include, but is not limited to, a central processing unit ("CPU") 1606, a graphics processing unit ("GPU") 1608, a processor 1610, a cache 1612, an accelerator 1614, a data store 1616, and / or other components and features not shown. In at least one embodiment, the SoC 1604 may be used to control the vehicle 1600 in a variety of platforms and systems. For example, in at least one embodiment, the SoC 1604 may communicate with an interface 1624 (which may be received from one or more servers via a network) Fig. 16C A high definition (“HD”) map 1622 (not shown) for obtaining map refreshes and / or updates is combined in a system (e.g., a system of the vehicle 1600).

[0192] In at least one embodiment, CPU 1606 may include a CPU cluster or CPU complex (hereinafter alternatively referred to as "CCPLEX"). In at least one embodiment, CPU 1606 may include multiple cores and / or a second level ("L2") cache. For example, in at least one embodiment, CPU 1606 may include eight cores in a consistent multiprocessor configuration. In at least one embodiment, CPU 1606 may include four dual-core clusters, each with a dedicated L2 cache (e.g., 2 megabytes (MB) L2 cache). In at least one embodiment, CPU 1606 (e.g., CCPLEX) may be configured to support simultaneous cluster operations, such that any combination of CPU 1606 clusters can be active at any given time.

[0193] In at least one embodiment, one or more CPUs 1606 may implement power management capabilities, including, but not limited to, one or more of the following features: individual hardware blocks may be automatically clock gated when idle to conserve dynamic power; each core clock may be gated when such core is not actively executing instructions due to executing a wait for interrupt ("WFI") / wait for event ("WFE") instruction; each core may be independently power gated; each core cluster may be independently clock gated when all cores are clock gated or power gated; and / or each core cluster may be independently power gated when all cores are power gated. In at least one embodiment, CPU 1606 may further implement an enhanced algorithm for managing power states, wherein allowed power states and expected wakeup times are specified, and hardware / microcode determines which optimal power state to enter for the core, cluster, and CCPLEX. In at least one embodiment, the processing core may support a simplified power state entry sequence in software, wherein work is offloaded to microcode.

[0194] In at least one embodiment, GPU 1608 may include an integrated GPU (also referred to herein as an "iGPU"). In at least one embodiment, GPU 1608 may be programmable and may be efficient for parallel workloads. In at least one embodiment, GPU 1608 may use an enhanced tensor instruction set. In at least one embodiment, GPU 1608 may include one or more streaming microprocessors, each of which 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, GPU 1608 may include at least eight streaming microprocessors. In at least one embodiment, GPU 1608 may use a computing application programming interface (API). In at least one embodiment, GPU 1608 may use one or more parallel computing platforms and / or programming models (e.g., NVIDIA's CUDA model).

[0195] In at least one embodiment, one or more of the GPUs 1608 may be power optimized for optimal performance in automotive and embedded use cases. For example, in at least one embodiment, the GPUs 1608 may be fabricated on fin field effect transistor (“FinFET”) circuits. In at least one embodiment, each streaming microprocessor may incorporate multiple mixed precision processing cores that are partitioned into multiple, for example, but not limited to, 64 PF32 cores and 32 PF64 cores that may be partitioned 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 scheduler (e.g., a warp scheduler) or sequencer, 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 through a mix of computation and addressing calculations. 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 memory bandwidth of approximately 900 GB / sec in some examples. In at least one embodiment, synchronous graphics random access memory ("SGRAM"), such as graphics double data rate type five synchronous random access memory ("GDDR5"), may be used in addition to or in lieu of HBM memory.

[0197] In at least one embodiment, the GPU 1608 may include unified memory technology. In at least one embodiment, address translation service (“ATS”) support may be used to allow the GPU 1608 to directly access the CPU 1606 page table. In at least one embodiment, when the GPU 1608 memory management unit (“MMU”) of the GPU experiences a miss, an address translation request may be sent to the CPU 1606. In response, in at least one embodiment, the 2CPU of the CPU 1606 may look up the virtual to physical mapping for the address in the page table and transmit the translation back to the GPU 1608. In at least one embodiment, the unified memory technology may allow a single unified virtual address space to be used for both the memory CPU 1606 and the GPU 1608, thereby simplifying GPU 1608 programming and porting applications to the GPU 1608.

[0198] In at least one embodiment, GPU 1608 may include any number of access counters that can track the frequency of accesses to other processors' memory by GPU 1608. In at least one embodiment, the access counters can help ensure that memory pages are moved to the physical memory of the processor that accesses the page most frequently, thereby improving the efficiency of memory ranges shared 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, cache 1612 may include a level 3 ("L3") cache available to both CPU 1606 and GPU 1608 (e.g., it is connected to both CPU 1606 and GPU 1608). In at least one embodiment, cache 1612 may include a write-back cache that may track the state of a line, for example, by using a cache coherence protocol (e.g., MEI, MESI, MSI, etc.). In at least one embodiment, the L3 cache may include 4MB of memory or more, depending on the embodiment, although smaller cache sizes may be used.

[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 a combination thereof). In at least one embodiment, SoC 1604 may include a hardware acceleration cluster that may include optimized hardware accelerators and / or large on-chip memory. In at least one embodiment, large on-chip memory (e.g., 4MB SRAM) may enable the hardware acceleration cluster to accelerate neural networks and other calculations. In at least one embodiment, the hardware acceleration cluster may be used to supplement GPU 1608 and offload some tasks of GPU 1608 (e.g., freeing up more cycles of GPU 1608 for performing other tasks). In at least one embodiment, accelerator 1614 may be used for target workloads (e.g., perception, convolutional neural networks ("CNN"), recurrent neural networks ("RNN"), etc.), which are stable enough to be changed to acceleration. In at least one embodiment, CNN may include region-based or regional convolutional neural networks ("RCNN") and fast RCNN (e.g., as used for object detection) or other types of CNN.

[0201] In at least one embodiment, the accelerator 1614 (e.g., a hardware acceleration cluster) may include one or more deep learning accelerators ("DLA"). In at least one embodiment, the DLA may include, but is 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 reasoning. In at least one embodiment, the TPU may be an accelerator configured to perform and optimize image processing functions (e.g., for CNN, RCNN, etc.). In at least one embodiment, the DLA may be further optimized for a specific set of neural network types and floating point operations and reasoning. In at least one embodiment, the design of the DLA can provide higher performance per millimeter than a typical general-purpose GPU, and generally greatly exceeds the performance of a CPU. In at least one embodiment, the TPU can perform a variety of functions, including single-instance convolution functions, such as supporting INT8, INT16, and FP16 data types for features and weights, and post-processor functions. In at least one embodiment, the DLA can quickly and efficiently execute neural networks, particularly CNNs, on processed or unprocessed data for any of a variety of functions, including but not limited to: a CNN for object recognition and detection using data from a camera sensor; a CNN for distance estimation using data from a camera sensor; a CNN for emergency vehicle detection and recognition and detection using data from a microphone; a CNN for facial recognition and vehicle owner recognition using data from a camera sensor; and / or a CNN for safety and / or security-related events.

[0202] In at least one embodiment, the DLA can perform any function of the GPU 1608, and by using an inference accelerator, for example, the designer can target either the DLA or the GPU 1608 for any function. For example, in at least one embodiment, the designer can focus the processing of CNNs and floating point operations on the DLA and leave other functions to the GPU 1608 and / or accelerator 1614.

[0203] In at least one embodiment, the accelerator 1614 may include a programmable vision accelerator ("PVA"), which may alternatively be referred to herein as a computer vision accelerator. In at least one embodiment, the PVA 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. In at least one embodiment, the PVA may provide a balance between performance and flexibility. For example, in at least one embodiment, each PVA 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., an image sensor of any camera described herein), an image signal processor, and the like. In at least one embodiment, each RISC core can include any number of memories. In at least one embodiment, the RISC core can use any of a variety of protocols, depending on the embodiment. In at least one embodiment, the RISC core can execute a real-time operating system ("RTOS"). In at least one embodiment, the RISC core can 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 can include an instruction cache and / or a tightly coupled RAM.

[0205] In at least one embodiment, the DMA may enable components of the PVA to access system memory independently of the CPU 1606. In at least one embodiment, the DMA may support any number of features for providing optimizations to the PVA, including, but not limited to, support for multi-dimensional addressing and / or circular addressing. In at least one embodiment, the DMA may support up to six or more addressing dimensions, which may include, but are not limited to, block width, block height, block depth, horizontal block stepping, vertical block stepping, and / or depth stepping.

[0206] In at least one embodiment, the vector processor may be a programmable processor that may be designed to efficiently and flexibly execute computer vision algorithms programmed 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 operate 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"), a very long instruction word ("VLIW") digital signal processor. In at least one embodiment, the combination of SIMD and VLIW may increase throughput and speed.

[0207] In at least one embodiment, each vector processor may include an instruction cache and may be coupled to a dedicated memory. As a result, in at least one embodiment, each vector processor may be configured to execute independently of the other vector processors. In at least one embodiment, the vector processors included in a particular PVA may be configured to exploit data parallelism. For example, in at least one embodiment, multiple vector processors included in a single PVA may execute a common computer vision algorithm, but on different areas of an image. In at least one embodiment, the vector processors included in a particular PVA may execute different computer vision algorithms on an image simultaneously, or even different algorithms on consecutive images or portions of an image. In at least one embodiment, any number of PVAs may be included in a hardware acceleration cluster, and any number of vector processors may be included in each PVA, among other things. In at least one embodiment, the PVA may include additional error correction code ("ECC") memory to enhance the security of the overall system.

[0208] In at least one embodiment, the accelerator 1614 may include an on-chip computer vision network and static random access memory ("SRAM") to provide high bandwidth, low latency SRAM for the accelerator 1614. In at least one embodiment, the on-chip memory may include at least 4MB of SRAM, including, for example but not limited to, 8 field-configurable memory blocks that are accessible by both the PVA and the DLA. In at least one embodiment, each pair of memory blocks may include an advanced peripheral bus ("APB") interface, configuration circuits, controllers, and multiplexers. 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 that provides high-speed access to the memory to 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 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 stages and separate channels for transmitting control signals / addresses / data, as well as burst-type communications for continuous data transmission. In at least one embodiment, the interface may conform to the International Organization for Standardization ("ISO") 26262 or the International Electrotechnical Commission ("IEC") 61508 standards, although other standards and protocols may be used.

[0210] In at least one embodiment, one or more SoCs 1604 may include a real-time ray tracing hardware accelerator. In at least one embodiment, the real-time ray tracing hardware accelerator may be used to quickly and efficiently determine the position and range of objects (e.g., within a world model) 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 simulations, for comparison with LIDAR data for positioning and / or other functions, and / or for other purposes.

[0211] In at least one embodiment, accelerator 1614 can have a wide range of autonomous driving uses. In at least one embodiment, PVA can be used in key processing stages in ADAS and autonomous vehicles. In at least one embodiment, the capabilities of PVA are well suited for algorithmic domains that require predictable processing, low power consumption, and low latency. In other words, PVA performs well on semi-intensive or intensive conventional computations, even on small data sets that may require predictable runtimes with low latency and low power consumption. In at least one embodiment, an autonomous vehicle, such as in vehicle 1600, the PVA may be designed to run classic computer vision algorithms because they can be efficient at object detection and integer math operations.

[0212] For example, according to at least one embodiment of the technology, the 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 motion estimation / stereo matching (e.g., structure from motion, pedestrian recognition, lane detection, etc.). In at least one embodiment, the 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 dense optical flow. For example, in at least one embodiment, the PVA can process raw RADAR data (e.g., using a 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, for example, but not limited to, a neural network that outputs a confidence measure for each object detection. In at least one embodiment, the confidence can be expressed or interpreted as a probability, or provide a relative "weight" of each detection compared to other detections. In at least one embodiment, the confidence metric enables the system to make further decisions about which detections should be considered true positive detections rather than false positive detections. In at least one embodiment, the system can set a threshold for the confidence and only consider detections that exceed the threshold as true positive detections. In an embodiment using an automatic emergency braking ("AEB") system, a false positive detection will 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 confidence values. In at least one embodiment, the neural network may take as its input at least some subset of parameters, such as bounding box dimensions, a ground plane estimate obtained (e.g., from another subsystem), output from the MU sensor 1666, which is related to the vehicle 1600 orientation, distance, 3D position estimate of the object obtained from the neural network and / or other sensors (e.g., LIDAR sensor 1664 or RADAR sensor 1660), etc.

[0215] In at least one embodiment, one or more SoCs 1604 may include one or more data stores 1616 (e.g., memory). In at least one embodiment, data store 1616 may be on-chip memory of SoC 1604 that may store neural networks to be executed on GPU 1608 and / or DLA. In at least one embodiment, the capacity of data store 1616 may be large enough to store multiple instances of a neural network for redundancy and safety. In at least one embodiment, data store 1616 may include an L2 or L3 cache.

[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, the processor 1610 may include a boot and power management processor, which may be a dedicated processor and subsystem to handle boot power and management functions and related security implementations. In at least one embodiment, the boot and power management processor may be part of the boot sequence of the SoC 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 in system low power state transitions, manage SoC 1604 thermal and temperature sensors, and / or manage SoC 1604 power states. In at least one embodiment, each temperature sensor may be implemented as a ring oscillator whose output frequency is proportional to the temperature, and the SoC 1604 may use the ring oscillator to detect the temperature of the CPU 1606, GPU 1608, and / or accelerator 1614. In at least one embodiment, if the temperature is determined to exceed a threshold, the boot and power management processor may enter a temperature fault routine and place the SoC 1604 into a lower power state and / or place the vehicle 1600 into a driver safety stop mode (e.g., bringing the vehicle 1600 to a safe stop).

[0217] In at least one embodiment, processor 1610 may further include a set of embedded processors that may be used as an audio processing engine, which may be an audio subsystem capable of comprehensive 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 having a digital signal processor with dedicated RAM.

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

[0219] In at least one embodiment, the processor 1610 may also include a safety cluster engine, which may include but is not limited to a dedicated processor subsystem for handling safety management of automotive applications. In at least one embodiment, the safety cluster engine may include but is not limited to two or more processor cores, tightly coupled RAM, supporting peripherals (e.g., timers, interrupt controllers, etc.) and / or routing logic. In safe mode, in at least one embodiment, two or more cores may operate in lockstep mode and act as a single core with comparison logic to detect any differences between their operations. In at least one embodiment, the processor 1610 may also 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, the processor 1610 may also 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, the processor 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 image of the final player window. In at least one embodiment, the video image synthesizer may perform lens distortion correction for the wide-angle camera 1670, the surround camera 1674, and / or the in-cabin monitoring camera sensor. In at least one embodiment, the in-cabin monitoring camera sensor is preferably monitored by a neural network running on another instance of the SoC 1604, configured to recognize in-cabin events and respond accordingly. In at least one embodiment, the in-cabin system may perform, but is not limited to, lip reading to activate cellular service and place calls, dictate emails, change the vehicle's destination, activate or change the vehicle's infotainment system and settings, or provide voice-activated web surfing. 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 noise reduction for both spatial and temporal noise reduction. For example, in at least one embodiment where motion occurs in the video, the noise reduction appropriately weights the spatial information, reducing the weight of information provided by adjacent frames. In at least one embodiment, where an image or portion of an image does not include motion, the temporal noise reduction 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 may also be configured to perform stereoscopic rectification on the input stereoscopic lens frames. In at least one embodiment, the video image compositor may also be used for user interface composition when the operating system desktop is in use and does not require the GPU 1608 to continuously render new surfaces. In at least one embodiment, when the GPU 1608 is powered on and actively performing 3D rendering, the video image compositor may be used to offload the GPU 1608 to improve performance and responsiveness.

[0223] In at least one embodiment, one or more SoCs 1604 may also include a Mobile Industry Processor Interface ("MIPI") camera serial interface for receiving video and input from a camera, a high-speed interface, and / or a video input block that may be used for a camera and associated pixel input functionality. In at least one embodiment, one or more SoCs 1604 may also include one or more input / output controllers that may be software controlled and may be used to receive I / O signals that are not committed to a specific role.

[0224] In at least one embodiment, one or more SoCs 1604 may also include a wide range of peripheral interfaces to enable communication with peripheral devices, audio encoders / decoders ("codecs"), power management, and / or other devices. In at least one embodiment, SoC 1604 may be used to process data from cameras (e.g., connected via Gigabit Multimedia Serial Link and Ethernet Channel), sensors (e.g., LIDAR sensor 1664, RADAR sensor 1660, etc., which may be connected via Ethernet Channel), data from bus 1602 (e.g., speed of vehicle 1600, steering wheel position, etc.), data from GNSS sensor 1658 (e.g., connected via Ethernet bus or CAN bus), etc. In at least one embodiment, one or more of SoCs 1604 may also include dedicated high-performance mass storage controllers, which may include their own DMA engines and may be used to free CPU 1606 from routine data management tasks.

[0225] In at least one embodiment, SoC 1604 can be an end-to-end platform with a flexible architecture spanning automation levels 3-5, thereby providing a comprehensive functional safety architecture that fully utilizes and effectively uses the diversity and redundancy of computer vision and ADAS technologies, and provides a platform for flexible and reliable driving software stacks as well as deep learning tools. In at least one embodiment, SoC 1604 can be faster, more reliable, and even more energy-efficient and space-efficient than traditional systems. For example, in at least one embodiment, accelerator 1614, when combined with CPU 1606, GPU 1608, and data storage 1616, can provide a fast and efficient platform for level 3-5 autonomous vehicles.

[0226] In at least one embodiment, computer vision algorithms can be executed on a CPU, which can be configured using a high-level programming language such as C to perform a variety of processing algorithms across a variety of visual data. However, in at least one embodiment, CPUs often fail to meet the performance requirements of many computer vision applications, such as those related to execution time and power consumption. In at least one embodiment, many CPUs are unable to execute complex object detection algorithms in real time, which are used in in-vehicle ADAS applications and actual Level 3-5 autonomous vehicles.

[0227] The embodiments described herein allow for multiple neural networks to be executed simultaneously and / or sequentially, and for the results to be combined together to achieve Level 3-5 autonomous driving functionality. For example, in at least one embodiment, a CNN executed on a DLA or discrete GPU (e.g., GPU 1620) may include text and word recognition, allowing for the reading and understanding of traffic signs, including signs for which the neural network has not 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 that semantic understanding to a path planning module running on the CPU Complex.

[0228] In at least one embodiment, multiple neural networks may be run simultaneously, such as for Level 3, 4, or 5 driving. For example, in at least one embodiment, a warning sign stating "WARNING: FLASHING LIGHTS INDICATE ICE CONDITIONS" as well as electric lights may be interpreted by multiple neural networks independently or collectively. In at least one embodiment, such a warning sign itself may be recognized as a traffic sign by a first deployed neural network (e.g., an already trained neural network), and the text "FLASHING LIGHTS INDICATE ICE CONDITIONS" may be interpreted by a second deployed neural network, which notifies the vehicle's path planning software (preferably executing on a CPU Complex) that an icing condition exists when flashing lights are detected. In at least one embodiment, flashing lights may be recognized by running a third deployed neural network over multiple frames, notifying the vehicle's path planning software of the presence (or absence) of flashing lights. In at least one embodiment, all three neural networks may be run simultaneously, such as within a DLA and / or on GPU 1608.

[0229] In at least one embodiment, the CNN for facial recognition and owner recognition can use data from the camera sensor to identify the presence of an authorized driver and / or owner of the vehicle 1600. In at least one embodiment, the always-on sensor processing engine can be used to unlock the vehicle when the owner approaches the driver's door and turns on the lights, and in a security mode, can disable such a vehicle when the owner leaves such a vehicle. In this way, the SoC 1604 provides security against theft and / or carjacking.

[0230] In at least one embodiment, a 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, the SoC 1604 uses the CNN to classify environmental and urban sounds, as well as classify visual data. In at least one embodiment, the CNN running on the DLA is trained to identify the relative closing 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 specific to the local area where the vehicle is operating, as identified by the GNSS sensor 1658. In at least one embodiment, when operating in Europe, the CNN will seek to detect European sirens, and when in North America, 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 to execute emergency vehicle safety routines, slow the vehicle, pull to the side of the road, park and / or idle the vehicle with the help of ultrasonic sensors 1662 until the emergency vehicle passes.

[0231] In at least one embodiment, the vehicle 1600 may include a CPU 1618 (e.g., a discrete CPU or dCPU) that may be coupled to the SoC 1604 via a high-speed interconnect (e.g., PCIe). For example, in at least one embodiment, the CPU 1618 may include an X86 processor. The CPU 1618 may be used to perform any of a variety of functions, including arbitrating potentially inconsistent results between ADAS sensors and the SoC 1604, and / or monitoring the status and health of the controller 1636 and / or, for example, an infotainment system on a chip ("infotainment SoC") 1630. In at least one embodiment, the SoC 1604 includes one or more interconnects, and the interconnect may include a peripheral component interconnect express (PCIe).

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

[0233] In at least one embodiment, the vehicle 1600 may further include a network interface 1624, which may include, but is not limited to, a wireless antenna 1626 (e.g., one or more wireless antennas 1626 for different communication protocols, such as a cellular antenna, a Bluetooth antenna, etc.). In at least one embodiment, the network interface 1624 may be used to implement wireless connections with an Internet cloud service (e.g., with a server and / or other network device), with other vehicles, and / or with a computing device (e.g., a passenger's client device). In at least one embodiment, in order to communicate with other vehicles, a direct link may be established between the vehicle 160 and the other vehicle and / or an indirect link may be established (e.g., across a network and through the Internet). In at least one embodiment, a vehicle-to-vehicle communication link may be used to provide a direct link. In at least one embodiment, the inter-vehicle communication link may provide information to the vehicle 1600 about vehicles near the vehicle 1600 (e.g., vehicles in front of, to the side of, and / or behind the vehicle 1600). In at least one embodiment, such aforementioned functionality may be part of the cooperative adaptive cruise control functionality of the vehicle 1600.

[0234] In at least one embodiment, the network interface 1624 may include a SoC that provides modulation and demodulation functions and enables the controller 1636 to communicate over a wireless network. In at least one embodiment, the network interface 1624 may include a radio frequency front end for upconverting from baseband to radio frequency, and downconverting from radio frequency to baseband. In at least one embodiment, frequency conversion may be performed in any technically feasible manner. For example, frequency conversion may be performed by a well-known process and / or using a superheterodyne process. In at least one embodiment, the radio frequency front end function may be provided by a separate chip. In at least one embodiment, the network interface may include a wireless function for communicating via LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and / or other wireless protocols.

[0235] In at least one embodiment, the vehicle 1600 may also include data storage 1628, which may include, but is not limited to, off-chip (e.g., off-chip SoC 1604) storage. In at least one embodiment, the data storage 1628 may include, but is 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 that can store at least one bit of data.

[0236] In at least one embodiment, the vehicle 1600 may further include a GNSS sensor 1658 (e.g., GPS and / or assisted GPS sensor) to assist in mapping, perception, occupancy grid 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 using a USB connector with an Ethernet to serial (e.g., RS-232) bridge.

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

[0238] In at least one embodiment, the RADAR sensor 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 by two or more independent scans, such as within a range of 250m (meters). In at least one embodiment, the RADAR sensor 1660 can help distinguish between static and moving objects and can be used by the ADAS system 1638 for emergency brake assistance and forward collision warning. In at least one embodiment, the sensor 1660 included in the long-range radar system may include, but is not limited to, a single-base multi-mode radar with multiple (e.g., six or more) fixed radar antennas and high-speed CAN and FlexRay interfaces. In at least one embodiment, using six antennas, the central four antennas can create a focused beam pattern designed to record the surroundings of the vehicle 1600 at higher speeds while minimizing interference from traffic in adjacent lanes. In at least one embodiment, the other two antennas can expand the field of view so that vehicles entering or leaving the lane of the vehicle 1600 can be quickly detected.

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

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

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

[0242] In at least one embodiment, the LIDAR sensor 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, for example, a commercially available LIDAR sensor 1664 may have an advertised range of approximately 100m, with an accuracy of 2cm-3cm, and support, for example, a 100Mbps Ethernet connection. In at least one embodiment, one or more non-protruding LIDAR sensors may be used. In such an embodiment, the LIDAR sensor 1664 may include a small device that can be embedded in the front, rear, side, and / or corner positions of the vehicle 1600. In at least one embodiment, the LIDAR sensor 1664, in such an embodiment, may provide a horizontal field of view of up to 120 degrees and a vertical field of view of 35 degrees, with a range of 200m even for low reflectivity objects. In at least one embodiment, the front LIDAR sensor 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. In at least one embodiment, 3D flash LIDAR uses laser flash as a transmission source to illuminate the surroundings of the vehicle 1600, up to about 200m away. In at least one embodiment, the flash LiDAR unit includes, but is not limited to, a receiver that records the laser pulse transmission time and the reflected light on each pixel, which in turn corresponds to the distance from the vehicle 1600 to the object. In at least one embodiment, the flash LIDAR can allow a highly accurate and distortion-free image of the surrounding environment to be generated at each laser flash. In at least one embodiment, four flash LIDAR sensors can 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 staring array LIDAR camera that has 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 Class I (eye-safe) laser pulses of 5 nanoseconds per frame, and can capture the reflected laser as a 3D range point cloud and co-registered intensity data.

[0244] In at least one embodiment, the vehicle 1600 may further include an IMU sensor 1666. In at least one embodiment, the IMU sensor 1666 may be located at the center of the rear axle of the vehicle 1600. In at least one embodiment, the IMU sensor 1666 may include, for example, but not limited to, an accelerometer, a magnetometer, a gyroscope, one or more magnetic compasses, and / or other sensor types. In at least one embodiment, for example, in a six-axis application, the IMU sensor 1666 may include, but is not limited to, an accelerometer and a gyroscope. In at least one embodiment, for example, in a nine-axis application, the IMU sensor 1666 may include, but is not limited to, an accelerometer, a gyroscope, and a magnetometer.

[0245] In at least one embodiment, the IMU sensor 1666 can be implemented as a miniature, high-performance GPS-aided inertial navigation system ("GPS / INS") that combines a micro-electromechanical system ("MEMS") inertial sensor, a high-sensitivity GPS receiver, and an advanced Kalman filter algorithm to provide estimates of position, velocity, and attitude. In at least one embodiment, the IMU sensor 1666 can enable the vehicle 1600 to estimate heading by directly observing and correlating velocity changes from the GPS to the IMU sensor 1666 without the need for input from a magnetic sensor. In at least one embodiment, the IMU sensor 1666 and the GNSS sensor 1658 can be combined in a single integrated unit.

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

[0247] In at least one embodiment, the vehicle 1600 may also include any number of camera types, including stereo cameras 1668, wide-angle cameras 1670, infrared cameras 1672, surround cameras 1674, long-range cameras 1698, mid-range cameras 1676, and / or other camera types. In at least one embodiment, the cameras may be used to capture image data around the entire periphery of the vehicle 1600. In at least one embodiment, which camera type is used depends on the vehicle 1600. In at least one embodiment, any combination of camera types may be used to provide the necessary coverage around the vehicle 1600. In at least one embodiment, the number of deployed cameras may vary depending on the embodiment. For example, in at least one embodiment, the vehicle 1600 may include six cameras, seven cameras, ten cameras, twelve cameras, or another number of cameras. In at least one embodiment, by way of example but not limitation, the cameras may support Gigabit Multimedia Serial Link ("GMSL") and / or Gigabit Ethernet communications. In at least one embodiment, each camera may be as described earlier in this document with respect to Fig.16A and Fig. 16B A more detailed description is given below.

[0248] In at least one embodiment, the vehicle 1600 may also include a vibration sensor 1642. In at least one embodiment, the vibration sensor 1642 may measure vibrations of a component of the vehicle 1600, such as an axle. For example, in at least one embodiment, changes in vibration may indicate changes in the road surface. In at least one embodiment, when two or more vibration sensors 1642 are used, the difference between the vibrations may be used to determine friction or slippage of the road surface (e.g., when the vibration difference is between a powered drive shaft and a free-spinning shaft).

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

[0250] In at least one embodiment, the ACC system may use a RADAR sensor 1660, a LIDAR sensor 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 another vehicle directly in front of the vehicle 1600 and automatically adjusts the speed of the vehicle 1600 to maintain a safe distance from the vehicle in front. In at least one embodiment, the lateral ACC system performs distance keeping and suggests that the vehicle 1600 change lanes when necessary. In at least one embodiment, the lateral ACC is related to other ADAS applications such as LC and CW.

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

[0252] In at least one embodiment, the FCW system is designed to alert the driver to the hazard so that such driver can take corrective action. In at least one embodiment, the FCW system uses a front camera and / or RADAR sensor 1660, which is 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 component. In at least one embodiment, the FCW system can provide warnings, such as in the form of sounds, visual warnings, vibrations and / or rapid brake pulses.

[0253] In at least one embodiment, the AEB system detects an impending forward collision with another vehicle or other object, and if the driver does not take corrective action within specified time or distance parameters, the brakes may be automatically applied. In at least one embodiment, the AEB system may use a forward-facing camera and / or RADAR sensor 1660 coupled to a dedicated processor, DSP, FPGA, and / or ASIC. In at least one embodiment, when the AEB system detects a hazard, the AEB system will typically first alert the driver to take corrective action to avoid the collision, and if the driver does not take corrective action, the AEB system may automatically apply brakes in an effort to prevent or minimize the effects of the predicted collision. In at least one embodiment, the AEB system may include technologies such as dynamic brake support and / or collision approach braking.

[0254] In at least one embodiment, the LDW system provides visual, auditory, and / or tactile warnings, such as steering wheel or seat vibrations, to alert the driver when the vehicle 1600 crosses a lane marking. In at least one embodiment, the LDW system is not activated when the driver indicates an intentional departure from the lane, such as by activating a turn signal. 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 vibrator component. In at least one embodiment, the LKA system is a variation of the LDW system. In at least one embodiment, if the vehicle 1600 begins to leave its lane, the LKA system provides steering input or braking to correct the vehicle 1600.

[0255] In at least one embodiment, the BSW system detects and warns the driver that the vehicle is in the blind spot of the car. In at least one embodiment, the BSW system can provide visual, audible, and / or tactile alerts to indicate that it is unsafe to merge or change lanes. In at least one embodiment, the BSW system can provide additional warnings when the driver uses a turn signal. In at least one embodiment, the BSW system can use a rear side camera and / or radar sensor 1660, which is coupled to a dedicated processor, DSP, FPGA, and / or ASIC, which is electrically coupled to driver feedback, such as a display, speaker, and / or vibration component.

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

[0257] In at least one embodiment, conventional ADAS systems may be prone to false positive results, which may be annoying and distracting to the driver, but are generally not catastrophic because conventional ADAS systems alert the driver and allow the driver to decide whether the safety condition is real and take corresponding actions. In at least one embodiment, in the case of conflicting results, the vehicle 1600 itself decides whether to pay attention to the results from the main computer or the auxiliary computer (e.g., the first controller or the second controller in the controller 1636). For example, in at least one embodiment, the ADAS system 1638 can be a backup and / or auxiliary computer for providing perception information to the backup computer rationality module. In at least one embodiment, the backup computer rationality monitor can run redundant different software on hardware components to detect faults in perception and dynamic driving tasks. In at least one embodiment, the output from the ADAS system 1638 can be provided to the monitoring MCU. In at least one embodiment, if the output from the main computer and the output from the auxiliary computer conflict, the monitoring MCU determines how to coordinate the conflict to ensure safe operation.

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

[0259] In at least one embodiment, the supervisory MCU may be configured to run one or more neural networks that are trained and configured to determine the conditions for a false alarm provided by the auxiliary computer based at least in part on the output from the primary computer and the output of 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, one or more neural networks in the supervisory MCU may learn when the FCW system identifies a metal object that is not actually dangerous, such as a drain grate or manhole cover that triggers 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 a lane departure is actually the safest maneuver. In at least one embodiment, the supervisory MCU may include at least one of a DLA or a 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 the SoC 1604.

[0260] In at least one embodiment, the ADAS system 1638 may include an auxiliary computer that performs ADAS functions using traditional 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 may improve reliability, safety, and performance. For example, in at least one embodiment, the diverse implementations and intentional non-identity make the entire system more fault-tolerant, especially to failures caused by software (or software-hardware interface) functions. For example, in at least one embodiment, if there is a software bug or error in the software running on the main computer, and the different software codes running on the auxiliary computer provide consistent overall results, the monitoring MCU may have greater confidence that the overall results are correct and that errors in the software or hardware on the main computer will not cause significant errors.

[0261] In at least one embodiment, the output of the ADAS system 1638 may be fed to the perception block of the main computer and / or the dynamic driving task block of the main computer. For example, in at least one embodiment, if the ADAS system 1638 indicates that a forward collision warning is issued due to an object immediately ahead, the perception block may use that information in identifying the object. In at least one embodiment, the auxiliary computer may have its own neural network that is trained and therefore reduces the risk of false positives, as described herein.

[0262] In at least one embodiment, the vehicle 1600 may also include an infotainment SoC 1630 (e.g., an in-vehicle infotainment system (IVI)). Although illustrated 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., TV, movies, streaming media, etc.), phone (e.g., hands-free calling), network connectivity (e.g., LTE, WiFi, etc.), and / or information services (e.g., navigation system, rear parking space assistance, radio data system, vehicle-related information (e.g., fuel level, total distance traveled, brake fuel level, oil level, door open / closed, air filter information, etc.)) to the vehicle 1600. For example, the infotainment SoC 1630 may include a radio, a disk player, a navigation system, a video player, USB and Bluetooth connectivity, a car, in-car entertainment, WiFi, steering wheel audio controls, hands-free voice control, a head-up display ("HUD"), an HMI display 1634, a telematics device, a 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 also be used to provide information (e.g., visual and / or auditory) to a user of the vehicle 1600, such as information from an ADAS system 1638, autonomous driving information (e.g., planned vehicle operations, 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 the bus 1602. In at least one embodiment, the infotainment SoC 1630 may be coupled to a supervisory MCU so that the GPU of the infotainment system may perform some autonomous driving functions in the event of a failure of the main controller 1636 (e.g., the main and / or backup computer of the vehicle 1600). In at least one embodiment, the infotainment SoC 1630 may place the vehicle 1600 in a driver-safe parking mode, as described herein.

[0264] In at least one embodiment, the vehicle 1600 may also include an instrument cluster 1632 (e.g., a digital instrument panel, an electronic instrument panel, a digital instrument panel, etc.). In at least one embodiment, the instrument cluster 1632 may include, but is not limited to, a controller and / or a supercomputer (e.g., a discrete controller or a supercomputer). In at least one embodiment, the instrument cluster 1632 may include, but is not limited to, a set of instruments in any number and combination, such as a speedometer, a fuel level, an oil pressure, a tachometer, an odometer, a turn indicator, a shift position indicator, a seat belt warning light, a parking brake warning light, an engine fault light, information about a supplementary restraint system (e.g., an airbag), lighting control, safety system control, navigation information, etc. In some examples, information may be displayed and / or shared between the infotainment SoC 1630 and the instrument cluster 1632. In at least one embodiment, the instrument cluster 1632 may be included as part of the infotainment SoC 1630, and vice versa.

[0265] Reasoning and / or training logic 715 is used to perform reasoning and / or training operations associated with one or more embodiments. Details about reasoning and / or training logic 715 are provided herein in conjunction with Figure 1 -2 provided. See Fig. 7A In at least one embodiment, the inference and / or training logic 715 may be used in the system Fig. 16C 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.

[0266] Such a component may be used to automatically determine irregular eye or gaze behavior of a person, which may indicate impairment due to drug or alcohol use, and may take one or more actions based on that determination.

[0267] Fig.16D According to at least one embodiment, a method for Fig.16A1600. In at least one embodiment, the system may include, but is not limited to, a server 1678, a network 1690, and any number and type of vehicles, including vehicle 1600. In at least one embodiment, server 1678 may include, but is not limited to, a plurality of GPUs 1684(A)-1684(H) (collectively referred to herein as GPUs 1684), PCIe switches 1682(A)-1682(D) (collectively referred to herein as PCIe switches 1682), and / or CPUs 1680(A)-1680(B) (collectively referred to herein as CPUs 1680). In at least one embodiment, GPUs 1684, CPUs 1680, and PCIe switches 1682 may be interconnected with a high-speed interconnect, such as, but not limited to, NVLink interface 1688 and / or PCIe connection 1686 developed by NVIDIA. In at least one embodiment, the GPU 1684 is connected via NVLink and / or NVSwitch SoC 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 described, this is not intended to be limiting. In at least one embodiment, each server 1678 may include, but is not limited to, any number of GPUs 1684, CPUs 1680, and / or PCIe switches 1682 in any combination. For example, in at least one embodiment, the servers 1678 may each include eight, sixteen, thirty-two, and / or more GPUs 1684.

[0268] In at least one embodiment, server 1678 may receive, via network 1690 and from a vehicle, image data representing an image showing an unexpected or changed road condition, such as recently initiated road construction. In at least one embodiment, server 1678 may transmit, via network 1690, to the vehicle, neural network 1692, updated or otherwise, and / or map information 1694, including, but not limited to, information regarding traffic and road conditions. In at least one embodiment, updates to map information 1694 may include, but are not limited to, updates to high-definition map 1622, such as information regarding construction sites, potholes, detours, flooding, and / or other obstructions. In at least one embodiment, neural network 1692 and / or map information 1694 may have been generated from new training and / or experience represented by data received from any number of vehicles in the environment, and / or based at least in part on training performed at a data center (e.g., using server 1678 and / or other servers).

[0269] In at least one embodiment, server 1678 can be used to train a machine learning model (e.g., a neural network) based at least in part on the training data. In at least one embodiment, the training data can be generated by the vehicle, and / or can be generated in simulation (e.g., using a game engine). In at least one embodiment, any amount of the 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, any amount of the training data is not 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 network 1690), and / or the machine learning model can be used by server 1678 to remotely monitor the vehicle.

[0270] In at least one embodiment, server 1678 can receive data from the vehicle and apply the data to the latest real-time neural network for real-time intelligent reasoning. In at least one embodiment, server 1678 can include a deep learning supercomputer and / or a dedicated AI computer driven by GPU 1684, such as the DGX and DGX Station machines developed by NVIDIA. However, in at least one embodiment, server 1678 can include a deep learning infrastructure using a CPU-driven data center.

[0271] In at least one embodiment, the deep learning infrastructure of server 1678 may be capable of fast, real-time inference, and may use this capability to assess and verify the health of the processor, software, and / or associated hardware. For example, in at least one embodiment, the deep learning infrastructure may receive periodic updates from vehicle 1600, such as an image sequence and / or objects that vehicle 1600 has located 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 to the objects identified by vehicle 1600, and if the results do not match and the deep learning infrastructure concludes that the AI ​​in vehicle 1600 has failed, server 1678 may send a signal to vehicle 1600 instructing the fail-safe computer of vehicle 1600 to assume control, notify passengers, and complete a safe parking maneuver.

[0272] In at least one embodiment, the server 1678 may include a GPU 1684 and one or more programmable reasoning accelerators (e.g., NVIDIA's TensorRT 3 device). In at least one embodiment, the combination of GPU-powered servers and reasoning acceleration can enable real-time responses. In at least one embodiment, for example, in situations where performance is less critical, servers powered by CPUs, FPGAs, and other processors can be used for reasoning.

[0273] Various embodiments may be described by the following terms:

[0274] 1. A method comprising:

[0275] presenting a light motion pattern using an automated lighting device comprising one or more light sources;

[0276] capturing image data of at least an eye region of a person during presentation of the light motion pattern;

[0277] providing at least a subset of the image data as input to a neural network;

[0278] receiving an indication of an impairment of the person computed using the neural network based at least in part on an eye response of the person to the light motion pattern; and

[0279] A response action corresponding to the damage indication is performed.

[0280] 2. A method according to clause 1, wherein the response action includes preventing the person from performing the operation or providing the person with a suggestion not to perform the operation under the current impairment state.

[0281] 3. A method according to clause 1, wherein the automatic lighting device is installed in a vehicle or a machine and is configured to present the light movement pattern at a position above the eye area of ​​the person at least within a determined horizontal angle range.

[0282] 4. The method of clause 1, wherein the automatic lighting device is mounted on a sun visor or positioned to present the light motion pattern on a windshield of the vehicle or machine.

[0283] 5. The method of clause 1, wherein the automated lighting device comprises at least one of a light bar, a two-dimensional light array, or a heads-up display projector for presenting the light motion pattern.

[0284] 6. The method according to clause 1, further comprising:

[0285] The person is instructed to avoid head movements for the duration of the presentation and to follow the light motion pattern using only changes in gaze direction.

[0286] 7. A method according to claim 1, wherein the neural network is trained to infer the impairment indication of the person based in part on the eye responses exhibiting at least one of the following: lack of smooth pursuit, nystagmus at maximum deviation, nystagmus onset before 45 degrees gaze angle, lack of eye coverage, pupil size reaction to light changes slower than a determined reaction speed, or convergence of eye gaze derived based on data from the person's two eyes.

[0287] 8. The method according to clause 1, further comprising:

[0288] The light motion pattern is dynamically adjusted based in part on a reaction or state of the person determined during the presentation.

[0289] 9. The method according to clause 1, further comprising:

[0290] The person's eye movement frequency is compared to the movement frequency of the light movement pattern.

[0291] 10. A processor, comprising:

[0292] One or more circuits for:

[0293] causing automatic lighting equipment to present light motion patterns;

[0294] causing image data of a person to be captured during presentation of the light motion pattern;

[0295] providing a portion of the image data corresponding to an eye region of the person as an input to a neural network;

[0296] receiving an inferred impairment value for the person, the inferred impairment value calculated based on the portion of the image data using the neural network; and

[0297] A response action corresponding to the damage indication is performed.

[0298] 11. A processor according to clause 10, wherein the response action includes preventing the person from performing the operation or providing a suggestion to the person not to perform the operation under the current impairment state.

[0299] 12. A processor according to clause 10, wherein the automatic lighting device is installed to present the light movement pattern at a position above the eye area of ​​the person at least within a determined horizontal angle range.

[0300] 13. The processor of clause 10, wherein the one or more circuits are further configured to:

[0301] During the presentation, presentation instructions are provided to the person to avoid head movements and to follow the light motion pattern using only changes in gaze direction.

[0302] 14. The processor of clause 10, wherein the one or more circuits are further configured to:

[0303] The light motion pattern is dynamically adjusted based in part on a reaction or state of the person determined during the presentation.

[0304] 15. The processor of clause 10, wherein the processor is comprised of at least one of:

[0305] A system for performing simulation operations;

[0306] Systems for performing simulated operations to test or validate autonomous machine applications;

[0307] Systems for performing digital twin operations;

[0308] A system for performing light transport simulations;

[0309] Systems for rendering graphics output;

[0310] Systems for performing deep learning operations;

[0311] Systems implemented using edge devices;

[0312] Systems for generating or presenting virtual reality (VR) content;

[0313] Systems for generating or presenting augmented reality (AR) content;

[0314] Systems for generating or presenting mixed reality (MR) content;

[0315] A system comprising one or more virtual machines (VMs);

[0316] A system implemented at least in part in a data center;

[0317] A system for performing hardware testing using simulation;

[0318] Systems for synthetic data generation;

[0319] A collaborative content creation platform for 3D assets; or

[0320] A system implemented at least in part using cloud computing resources.

[0321] 16. A system comprising:

[0322] One or more processors for capturing image data representing changes in a person's gaze during a light motion pattern presented by an automated lighting device, and inferring an impairment value for the person using a neural network that receives the image data as input.

[0323] 17. A system according to clause 16, wherein the one or more processors are further configured to:

[0324] A response action corresponding to the damage value is performed.

[0325] 18. A system according to clause 16, wherein the gaze change is determined using at least one neural network that takes as input at least a portion of the captured image data corresponding to the eye region of the person.

[0326] 19. The system of clause 16, wherein the impairment value is inferred using at least one neural network trained to infer at least one user behavior from user eye movements.

[0327] 20. The system of clause 15, wherein the system comprises at least one of the following:

[0328] A system for performing simulation operations;

[0329] Systems for performing simulated operations to test or validate autonomous machine applications;

[0330] Systems for performing digital twin operations;

[0331] A system for performing light transport simulations;

[0332] Systems for rendering graphical output;

[0333] Systems for performing deep learning operations;

[0334] Systems implemented using edge devices;

[0335] Systems for generating or presenting virtual reality (VR) content;

[0336] Systems for generating or presenting augmented reality (AR) content;

[0337] Systems for generating or presenting mixed reality (MR) content;

[0338] A system comprising one or more virtual machines (VMs);

[0339] A system implemented at least in part in a data center;

[0340] Systems that perform hardware testing using simulation;

[0341] Systems for synthetic data generation;

[0342] A collaborative content creation platform for 3D assets; or

[0343] A system implemented at least in part using cloud computing resources.

[0344] Other variations are within the spirit of the present disclosure. Thus, while the disclosed technology is susceptible to various modifications and alternative constructions, certain illustrated embodiments thereof are shown in the drawings and have been described in detail above. However, it should be understood that there is no intention to limit the disclosure to one or more specific forms disclosed, but on the contrary, it is intended to cover all modifications, alternative constructions, and equivalents that fall within the spirit and scope of the present disclosure as defined by the appended claims.

[0345] Unless otherwise noted or clearly contradictory to the context, in the context of describing the disclosed embodiments (particularly in the context of the appended claims), the use of the terms "one" and "an" and "the" and similar references should be interpreted as covering the singular and plural, rather than as definitions of terms. Unless otherwise noted, the terms "include", "have", "include" and "contain" should be interpreted as open terms (meaning "including but not limited to") unless otherwise noted. The term "connected" (which refers to a physical connection when unmodified) should be interpreted as partially or completely included, attached to or connected together, even if there are some interventions. Unless otherwise noted herein, references to numerical ranges herein are intended only to be used as a shorthand method of referring to each individual value falling within the range, respectively, and each individual value is incorporated into the specification as if it were individually described herein. Unless otherwise noted or contradictory to the context, the use of the term "set" (e.g., "item set") or "subset" should be interpreted as a non-empty set including one or more members. Furthermore, unless otherwise indicated or contradicted by context, the term "subset" of a corresponding set does not necessarily mean a proper subset of the corresponding set, but rather a subset and a corresponding set may be equivalent.

[0346] Unless expressly indicated otherwise or clearly contradicted by context, conjunctions such as phrases of the form "at least one of A, B, and C" or "at least one of A, B and C" are understood in context to be generally used to refer to an item, clause, or the like that may be A or B or C, or any non-empty subset of the set A and B and C. For example, in the illustrative example of a set having three members, the conjunction 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}. Thus, such conjunction language is not generally intended to imply that certain embodiments require the presence of at least one of A, at least one of B, and at least one of C. In addition, unless expressly indicated otherwise or contradicted by context, the term "plurality" refers to a plural state (e.g., "plurality of items" means a plurality of items). The number of items in a plurality of items is at least two, but may be more if expressly indicated or indicated by context. Further, the phrase "based on" means "based at least in part on" rather than "based solely on" unless otherwise specified or clear from context.

[0347] Unless otherwise indicated herein or clearly contradictory to 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 and / or combinations thereof) are performed 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 are jointly executed on one or more processors by hardware or a combination thereof. In at least one embodiment, the code is stored on a computer-readable storage medium in the form of, for example, a computer program that includes a plurality of instructions that can be executed 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., propagated transient electrical or electromagnetic transmissions), but includes non-transitory data storage circuits (e.g., buffers, caches, and queues). In at least one embodiment, code (e.g., executable code or source code) is stored on a set of 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 being executed), causes the computer system to perform the operations described herein. In at least one embodiment, a set of non-transitory computer-readable storage media includes a plurality of non-transitory computer-readable storage media, and one or more of the individual non-transitory storage media in the plurality of non-transitory computer-readable storage media lacks all the code, but the plurality of non-transitory computer-readable storage media stores all the code together. In at least one embodiment, the executable instructions are executed so that different instructions are executed by different processors, for example, a non-transitory computer-readable storage medium stores instructions, and a main central processing unit ("CPU") executes some instructions, while a graphics processing unit ("GPU") executes other instructions. In at least one embodiment, different components of the computer system have separate processors, and different processors execute different subsets of instructions.

[0348] Thus, in at least one embodiment, a computer system is configured to implement one or more services that individually or collectively perform the operations of the processes described herein, and such a computer system is configured with applicable hardware and / or software that enables the implementation of the operations. In addition, a computer system that implements at least one embodiment of the present disclosure is a single device, and in another embodiment is a distributed computer system that includes multiple devices that operate in different ways, so that the distributed computer system performs the operations described herein, and so that a single device does not perform all operations.

[0349] The use of any and all examples or exemplary language (e.g., "such as") provided herein is intended only to better illustrate embodiments of the present disclosure and does not limit the scope of the disclosure unless otherwise required. No language in the specification should be construed as indicating that any non-claimed element is essential to practicing the disclosure.

[0350] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

[0351] In the specification and claims, the terms "coupled" and "connected," as well as their derivatives, may be used. It should be understood that these terms may not be intended as synonyms for each other. On the contrary, in specific 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.

[0352] Unless explicitly stated otherwise, it is to be understood that throughout the specification, terms such as “processing”, “computing”, “calculating”, “determining” and the like refer to the actions and / or processes of a computer or computing system or similar electronic computing device that processes and / or converts data represented as physical quantities (e.g., electronic) in registers and / or memories of the computing system into other data similarly represented as physical quantities in the memories, registers or other such information storage, transmission or display devices of the computing system.

[0353] In a similar manner, the term "processor" may refer to any device or part of a memory that processes electronic data from registers and / or memory and converts the electronic data into other electronic data that can be stored in registers and / or memory. As a non-limiting example, a "processor" may be a CPU or a GPU. A "computing platform" may include one or more processors. As used herein, a "software" process may include, for example, software and / or hardware entities that perform work over time, such as tasks, threads, and intelligent agents. Similarly, each process may refer to multiple processes to execute instructions sequentially or in parallel, continuously or intermittently. The terms "system" and "method" may be used interchangeably herein, as long as a system may embody one or more methods, and a method may be considered a system.

[0354] In this document, reference may be made to obtaining, acquiring, receiving or inputting analog or digital data into a subsystem, a computer system or a computer-implemented machine. It is possible to obtain, acquire, receive or input analog and digital data in a variety of ways, such as by receiving data as a parameter of a function call or a call to an application programming interface. In some implementations, the process of obtaining, acquiring, 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 obtaining, acquiring, 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. It is also possible to reference 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 accomplished by transmitting data as an input or output parameter of a function call, an application programming interface or an interprocess communication mechanism.

[0355] Although the above discussion sets forth example implementations of the described techniques, other architectures may be used to implement the described functionality and are intended to fall within the scope of the present disclosure. In addition, although specific responsibilities are defined above for discussion purposes, various functions and responsibilities may be allocated and divided in different ways, depending on the circumstances.

[0356] Furthermore, although the subject matter has been described in language specific to structural features and / or methodological acts, it should be understood that the subject matter claimed in the appended claims is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as exemplary forms of implementing the claims.

Claims

1. A method comprising: presenting a light motion pattern using an automated lighting device comprising one or more light sources; capturing image data of at least an eye region of a person during presentation of the light motion pattern; providing at least a subset of the image data as input to a neural network; receiving an indication of an impairment of the person calculated using the neural network based at least in part on an eye response of the person to the light motion pattern; as well as A response action corresponding to the damage indication is performed.

2. The method of claim 1, wherein the response action comprises preventing the person from performing an operation or providing a suggestion to the person not to perform the operation under the current impairment state.

3. The method according to claim 1, wherein the automatic lighting device is installed in a vehicle or a machine and is configured to present the light movement pattern at a position above the eye area of ​​the person at least within a determined horizontal angle range.

4. The method of claim 1, wherein the automatic lighting device is mounted on a sun visor or positioned to present the light motion pattern on a windshield of the vehicle or machine.

5. The method of claim 1, wherein the automated lighting device comprises at least one of a light bar, a two-dimensional light array, or a heads-up display projector for presenting the light motion pattern.

6. The method according to claim 1, further comprising: The person is instructed to avoid head movements for the duration of the presentation and to follow the light motion pattern using only changes in gaze direction.

7. The method of claim 1 , wherein the neural network is trained to infer the impairment indication for the person based in part on the eye responses exhibiting at least one of: lack of smooth pursuit, nystagmus at maximum deviation, nystagmus onset before a 45 degree gaze angle, lack of eye coverage, pupil size response to light changes slower than a determined response speed, or convergence of eye gaze derived based on data for the person's two eyes.

8. The method according to claim 1, further comprising: The light motion pattern is dynamically adjusted based in part on a reaction or state of the person determined during the presentation.

9. The method according to claim 1, further comprising: The person's eye movement frequency is compared to the movement frequency of the light movement pattern.

10. A processor, comprising: One or more circuits for: Enable automatic lighting equipment to present light motion patterns; causing image data of a person to be captured during presentation of the light motion pattern; providing a portion of the image data corresponding to an eye region of the person as an input to a neural network; receiving an inferred impairment value for the person, the inferred impairment value calculated based on the portion of the image data using the neural network; as well as A response action corresponding to the damage indication is performed.

11. The processor of claim 10, wherein the responsive action comprises preventing the person from performing an operation or providing a suggestion to the person not to perform the operation under the current impairment state.

12. The processor of claim 10, wherein the automatic lighting device is installed to present the light movement pattern at a position above the eye area of ​​the person at least within a determined horizontal angle range.

13. The processor of claim 10, wherein the one or more circuits are further configured to: During the presentation, presentation instructions are provided to the person to avoid head movements and to follow the light motion pattern using only changes in gaze direction.

14. The processor of claim 10, wherein the one or more circuits are further configured to: The light motion pattern is dynamically adjusted based in part on a reaction or state of the person determined during the presentation.

15. The processor of claim 10, wherein the processor is included in at least one of: A system for performing simulation operations; Systems for performing simulated operations to test or validate autonomous machine applications; Systems for performing digital twin operations; A system for performing light transport simulations; Systems for rendering graphics output; Systems for performing deep learning operations; Systems implemented using edge devices; Systems for generating or presenting virtual reality (VR) content; Systems for generating or presenting augmented reality (AR) content; Systems for generating or presenting mixed reality (MR) content; A system comprising one or more virtual machines VM; A system implemented at least in part in a data center; A system for performing hardware testing using simulation; Systems for synthetic data generation; A collaborative content creation platform for 3D assets; or A system implemented at least in part using cloud computing resources.

16. A system comprising: One or more processors for capturing image data representing changes in a person's gaze during a light motion pattern presented by an automated lighting device, and inferring an impairment value for the person using a neural network that receives the image data as input.

17. The system of claim 16, wherein the one or more processors are further configured to: A response action corresponding to the damage value is performed.

18. The system of claim 16, wherein the gaze change is determined using at least one neural network that takes as input at least a portion of the captured image data corresponding to an eye region of the person.

19. The system of claim 16, wherein the impairment value is inferred using at least one neural network trained to infer at least one user behavior from user eye movements.

20. The system of claim 15, wherein the system comprises at least one of the following: A system for performing simulation operations; Systems for performing simulated operations to test or validate autonomous machine applications; Systems for performing digital twin operations; A system for performing light transport simulations; Systems for rendering graphics output; Systems for performing deep learning operations; Systems implemented using edge devices; Systems for generating or presenting virtual reality (VR) content; Systems for generating or presenting augmented reality (AR) content; Systems for generating or presenting mixed reality (MR) content; A system comprising one or more virtual machines VM; A system implemented at least in part in a data center; Systems that perform hardware testing using simulation; Systems for synthetic data generation; A collaborative content creation platform for 3D assets; or a system implemented at least in part using cloud computing resources.

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