DETERMINING THE OPERATIONAL CAPABILITY OF HUMAN-OPERATED SYSTEMS AND CONTROL APPLICATIONS

Abstract

The approaches presented here involve the automated determination of a person's level of impairment, which may be relevant to their ability to perform a task. A light- and camera-based system can be used to determine factors such as gaze nystagmus, which may indicate intoxication or impairment. A testing system can simulate the movement of a light using a specific pattern and capture image data from at least the eye region of a person attempting to track the movement. The captured image data can be analyzed using a neural network to infer at least one user behavior, and this behavioral assessment can be used to determine a user's capacity or impairment.Appropriate measures may be taken, such as allowing a person with full capacity to operate a vehicle or perform a task, or blocking access to such an operation or performance if it is determined that the person is impaired beyond a permissible level.

Description

BACKGROUNDIn various applications -- such as for vehicle navigation and human operation of robotic assemblies -- it may be useful, if not necessary, to ensure that a human operator is not impaired or otherwise in a state, thereby rendering such operation dangerous or at least unthinkable. This may include, for example, determining that a human operator is not fatigued or under the influence of alcohol or other controlled substance, which could impair functionality or judgment. Traditionally, manual tests have been performed to detect nystagmus or involuntary eye movements caused by the ingestion of drugs or alcohol. Such a test is often performed by a police attendant moving a pen in front of a person's eyes. The operator observes the behavior of the user's eyes as he attempts to track the motion, such as whether there is a lack of smooth tracking or involuntary eye motions in certain areas of motion. Such a manual test approach is not practical in all situations and the results of such a test may be inaccurate and incoherent, at least in part, due to the relative experience levels and training of the students or other persons who are performing the tests.SUMMARYThe invention is defined by the claims. To illustrate the invention, aspects and embodiments that may or may not be within the scope of the claims are described herein.Approaches presented herein provide for the automated determination of a level of impairment of a person, which may be relevant to performing a task. A light and camera-based system may be used to determine factors such as gaze nystagmus that are indicative of a truncation or impairment. A test system may simulate the movement of a light using a particular pattern and capture image data from at least the eye region of a person attempting to track the movement. The captured image data may be analyzed using a neural network to infer at least one behavior of the user, and the behavior determination(s) may / may be used to determine a capacity or impairment of a user. An appropriate action may be taken, such as allowing a full capacity person to operate a vehicle or perform a task, or to block access to such operation or performance if it is determined that the person is impaired beyond an allowable amount.The disclosure extends to any novel aspects or features described and / or illustrated herein.Further features of the disclosure are characterized by the independent and dependent claims.Each feature in one aspect of the disclosure may be applied to other aspects of the disclosure in any suitable combination. In particular, method aspects may be applied to device or system aspects and vice versa.Furthermore, features implemented in hardware may also be implemented in software and vice versa. Accordingly, any reference to software and hardware features in the present description is to be construed as such.Any system or device feature as described herein may also be provided as a method feature, and vice versa. Functionally described system and / or device aspects (including means plus features) may alternatively be expressed in terms of their corresponding structure, such as a suitably programmed processor and associated memory.It should also be appreciated that certain combinations of the various features described and defined in any aspects of the disclosure may be implemented and / or provided and / or used independently of one another.This disclosure also provides computer programs and computer program products comprising software code adapted, when executed on a data processing device, to perform any of the methods described herein and to embody any of the device and system features described herein, including any or all of the substeps of a method.The disclosure also provides a computer or computing system (including networked or distributed systems) having an operating system that supports a computer program to perform any of the methods and / or embody any of the device or system features described herein.The disclosure also provides a computer readable medium having stored thereon one or more of the aforementioned computer programs.The disclosure also provides a signal carrying one or more of the aforementioned computer programs.The disclosure extends to methods and / or apparatuses and / or systems as described herein with reference to the accompanying drawings.Aspects and embodiments of the disclosure will now be described, by way of example only, with reference to the accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGSVarious embodiments in accordance with the present disclosure will be described with reference to the drawings, wherein: FIG. 1 illustrates an example deployment of a system, according to at least one embodiment, that may be used to test a person's eye or gaze behavior; FIGS. 2A, 2B, and 2C illustrate example patterns that may be presented using a test system according to at least one embodiment; FIG. 3 illustrates components of an example test and analysis system, according to at least one embodiment; FIGS. 4A, 4B, and 4C illustrate example processing pipelines for making impairment decisions, according to at least one embodiment; FIG. 5 illustrates an example process for determining a capacity of a subject, according to at least one embodiment; FIG. 6 illustrates components of a distributed system that may be used to test a capacity of a person, according to at least one embodiment; FIG. 7A illustrates inference and / or training logic, in accordance with at least one embodiment; FIG. 7B illustrates inference and / or training logic, in accordance with at least one embodiment; FIG. 8 illustrates an example data center system according to at least one embodiment; FIG. 9 illustrates a computing system according to at least one embodiment; FIG. 10 illustrates a computing system according to at least one embodiment; FIG. 11 illustrates at least portions of a graphics processor according to one or more embodiments; FIG. 12 illustrates at least portions of a graphics processor according to one or more embodiments; FIG. 13 is an example dataflow diagram for an advanced compute pipeline, according to at least one embodiment; FIG. 14 is a system diagram for an example system for training, adapting, instantiating, and employing machine learning models in an advanced computing pipeline, in accordance with at least one embodiment; FIGS. 15A and 15B illustrate a dataflow diagram for a process for training a machine learning model as well as client-server architecture for enhancing annotation tools with pre-trained annotation models, in accordance with at least one embodiment; FIG. 16A illustrates an example of an autonomous vehicle, according to at least one embodiment; FIG. 16B illustrates an example of camera locations and fields of view for the autonomous vehicle of FIG. 16A, according to at least one embodiment; FIG. 16C is a block diagram illustrating an example system architecture for the autonomous vehicle of FIG. 16A, according to at least one embodiment; FIG. 16D is a diagram illustrating a system for communication between one / more cloud-based server(s) and the autonomous vehicle of FIG. 16A, according to at least one embodiment.DETAILED DESCRIPTIONIn the following description, various embodiments will be described. For purposes of explanation, specific configurations and details are set forth in order to provide a thorough understanding of the embodiments. However, one skilled in the art will also recognize that the embodiments may be practiced without the specific details. Furthermore, well-known features may be omitted or simplified in order not to obscure the understanding of the described embodiments.The systems and methods described herein may be used by, among other things, non-autonomous vehicles, semi-autonomous vehicles (e.g., in one or more advanced driver assistance systems (ADAS)), guided and non-guided robots or robot platforms, storage vehicles, off-road vehicles, vehicles coupled to one or more trailers, flying vehicles, boats, shuttle vehicles, emergency vehicles, motor bicycles, electric or motorized bicycles, aircraft, construction vehicles, trains, underwater vehicles, remote controlled vehicles such as drones, and / or other types of vehicles. Furthermore, the systems and methods described herein may be used for a number of purposes, for example, but not limited to, machine control, machine motion, machine propulsion, synthetic data generation, model training or update, perception, augmented reality, virtual reality, mixed reality, robotics, safety and monitoring, simulation and digital twinning, autonomous or semi-autonomous machine applications, deep learning, environmental simulation, object or actuator simulation, and / or digital twinning, data center processing, conversational AI, light transport simulation (e.g., ray tracing, path tracing, etc.), collaborative content creation for 3D assets, Cloud computing and / or other suitable applications.Disclosed embodiments may be included in a number of different systems, such as automotive systems (e.g., an autonomous or semi-autonomous machine control system, an autonomous or semi-autonomous machine perception system), systems implemented using a robot, antenna systems, medial systems, boot systems, smart area monitoring systems, systems for performing deep learning operations, systems for performing simulation operations, systems for performing digital twining operations, systems implemented using an edge device, systems involving 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 simulation, systems for performing collaborative content creation for 3D assets, systems implemented at least in part using cloud computing resources, and / or other types of systems.Approaches in accordance with various illustrative embodiments provide for the automated determination of a level of capacity, capability, or impairment of a person, which may be relevant to performing a task or operation. In at least one embodiment, a system may automatically determine when a person in a vehicle or other such location exhibits a blinded behavior, which may be used to prevent operation of that vehicle by that person in that state. A light and camera based system can be used to determine factors, such as gaze nystagmus--involuntary eye movements caused by the ingestion of drugs or alcohol--indicative of truncation or impairment. An automated test system may use a set, bar, array, or other arrangement or configuration of lights (or movable light source such as a head-up display) to simulate the movement of a light in a particular pattern and capture image data (e.g., a sequence of images or a video) from at least the eye regions of a person, for example, in the driver or operator seat. The lights may be located in any suitable location, such as on a sun visor, or arranged to be projected onto a windshield. The captured eye image data may be analyzed, for example, using a trained neural network to infer a level or indication of truncation (or confidence or probability of truncation, impairment, or other such value) based on factors such as smoothness of eye movement, presence of nystagmus, pupil size response, and other such factors. A combination of computer vision and deep learning may allow for automatizing nystagmus tests and performing them in a vehicle, for example, prior to launch, which may help prevent fatigued, stunk, or otherwise impaired driving by preventing the car from launching (or taking some other such action) if it is inferred that the person is impaired or at least limited in functionality by more than a legally permitted (or some other fixed) amount.Variations of this and others of such functionality may also be used within the scope of the various embodiments, as one of ordinary skill in the art will recognize in light of the teachings and recommendations herein.FIG. 1 illustrates an example situation 100 where a human user 104 or operator sits in the driver's seat of a vehicle 102. Such a position is typical of a person attempting to operate this vehicle. As illustrated, this example vehicle includes a test system that can automatically perform a test for potential impairment of the subject to determine whether the subject can safely operate the vehicle, at least in terms of the type(s) of impairment that can be detected by the test system. For example, for a test system, there may be fixed criteria used to objectively determine whether a person has an impairment level determined to correlate with the ability to safely operate the vehicle. These criteria may vary based on the type of vehicle or the operation or type of test to be performed or the sensitivity of the test system, among other such factors.In this example, the test system attempts to observe the behavior of one or more eyes of the human user 104 when one or more patterns are presented to the user. The example test system includes a light bar 108 that includes a series of lights, such as at least 8-10 light emitting diodes (LEDs), that may be activated in a particular order, sequence, or pattern to cause a displayed light to travel back and forth 110 across the light bar, such as approximately in the form of a sine wave frequency, linear frequency, or other such periodic pattern. Other options for displaying a pattern of light that the user / eyes can track with their or her eyes may also be used, as discussed in more detail elsewhere herein. For example, when a test is performed on nystagmus, the test is typically performed with the light just above the user's eyes at a distance from the ground, such as 1 to 2 inches above the user's eyes. Accordingly, in this example, the light bar 108 is positioned on a driver side visor 106 of the vehicle. The light bar could also be placed in other similar positions, so it could be positioned on a windshield or window frame in front of the user's eyes. The light bar could also be placed in other locations, such as on a steering wheel, a rearview mirror, a dashboard, or other such location. The location should be selected based on factors such as user friendliness and lack of obstructions, among other such factors.In this example, the lights of the light bar 108 may be illuminated in sequence, such as one in a particular sequence at a particular rate of change, to cause the user to appear as if a light is moving in at least one direction across the light bar, although other patterns are also possible, such as repeated back and forth patterns or partial traversals across the light bar. For other types of lighting systems, more complex patterns may be used, such as patterns that appear to move in two or three dimensions, thus enabling other behavior of the human subject's eyes to be tested. During the test, the lights may be illuminated according to the particular pattern and the human may attempt to track 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, such as an infrared (IR) sensor, may be positioned in the vehicle, such as mounted on the rearview mirror 116 or as part of the light bar 108 on the visor 106 to attempt to capture image data representing at least the eye region of the user. The camera may be positioned and calibrated to capture image data of a person in the driver's seat, regardless of (at least a typical range of) the height of the body or other aspect of the person. The camera may be adjustable to be directed at a user's eyes in some embodiments, or may be configured to capture a sufficiently large field of view for the person's eye regions to become visible in the captured image data. In at least one embodiment, captured image data may also capture a sufficiently large field of view to capture additional information about user's face or head to either ensure that head has not moved more than an allowable amount during test or so that eye positioning operation may take into account head movement and its effect on eye position. In at least one embodiment, if it is determined that the user's head has moved more than the allowable amount during test, it may be possible that test must be re-applied or must be applied for a longer time until reliable data can be acquired. A notification (audible, visual, haptic, or otherwise) may be provided to the user if the user / user moves his or her head too much, moves his or her eyes with sufficient frequency or duration, or otherwise in a manner that prevents accurate determination by the test system.In an example test, a user may be notified that the test will begin the same and may be provided with a count-down or other notification such that the user knows that he needs to sit in the correct position, behave relatively still, and need to look at the light bar 108 (or a lit light of the light bar that will function as a starting point, etc.). In some embodiments, during this preparation period, a camera 114 may capture image data provided to an analytical component 112 of the vehicle, which may include at least a processor and memory for analyzing the data, although data analyses may be performed off-device or on a user device (e.g., a smartphone) owned by the user or another passenger in the vehicle, among other such options. For example, the analytical component 112 may analyze the image data of a series of images or video frames to determine whether the user is correctly positioned, behaves sufficiently quietly, and looks approximately in the correct direction. If not, the start of the test may be delayed until the behavior of the user matches it, and in at least some embodiments, a notification -- such as an audible, visual, or haptic notification -- may be provided to prompt the user to correct the behavior and prepare for the test.Once the user (or test subject, if different from a user of a test system or application) meets these or other such criteria for the test to be performed, the test in this example may start by causing the light bar 108 to activate the individual LEDs in a timed sequence so that it looks as if an individual light moves across the light bar at a particular speed. The speed and / or intensity may be set for a given test or may be configurable 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 depending on geography or jurisdiction, due to local laws and regulations. The camera 114 may capture image data including at least the eyes of the user during display of the pattern. The camera can be synchronized with the light bar, such as using a single master clock for timing purposes, so that the captured image data at a given time stamp can be correlated with the location in the pattern displayed by the light bar at that time, such as which light LED was illuminated at that time. In some embodiments, the relative positions in 3D space may be determined, such as relative to a particular starting point or coordinate system, to provide accurate gaze determinations, while in other embodiments, the behavior of the eyes in terms of time lapse and general motion may suffice without accurate distance or position information. The fixed positions of the camera and the light bar as well as the limited positions of a person who sits in the passenger seat and looks at the light bar within a camera view may be sufficient for different test sequences.A sequence of images or video frames captured by the camera during the test process may be analyzed by the analytical component 112 using various techniques, including various algorithms and / or machine learning models. In at least one embodiment, captured image data may be tailored to eye regions or at least bounding boxes determined such that only data near an eye region is analyzed to reduce amount of data to be processed, thereby saving computational and storage resources and also increasing speed for generating test results. In this example, the location of at least one pupil (or other trackable point or feature of an eye) may be determined for each image or video frame and compared to the location of the light displayed in the pattern at that time. This may be performed for all frames in a sequence (or subset of frames to further save processing requirements, such as a selection of 10 or 20 frames per second, even if the camera is capable of 90 or 120 frames per second). A pattern of the movement, or behavior, of the eye / s during the test may be compared to the test pattern over this time period. In some embodiments, movement of a single eye may be sufficient, while in other embodiments, data for both eyes may be used to determine a more accurate gaze direction, as well as data for both eyes may be used to check the gaze convergence for a certain LED position, etc. Comparing the behavior of at least one eye to the test pattern behavior may provide various types of data that may be used to determine a potential impairment. For example, comparative data may be used to determine a person's response time, such as the length of time an eye of a user takes to move in response to movement of a light pattern, as well as the time the eye takes to equalise. For example, if it is simulated that a light is moving across the light bar from left to right, at least one or three time periods may be calculated. This may include the time (or delay) the eye needs to move to the right to track the pattern, the time (or delay) the eye needs to arrive at the end of the light pattern, and the total length of time the eye needs to track the light from left to right compared to the time the actual light pattern needs to traverse the light bar. The image data may also be analyzed to determine the behavior of the eye in attempting to track the pattern. This may include, for example, smoothness of movement of the eye from left to right (or right to left in captured image data - as mirroring may be taken into account in the analysis), any apparently involuntary movements outside the patterns (such as a discontinuity, particularly when the pattern is stationary at an end of the light bar), any continued movement and recovery of the eye beyond the displayed pattern, or any other movement or behavior that does not match or is not expected to match a given light pattern. In some embodiments, there may be a minimum number of traversals of the light bar in one or more directions before a test result can be determined, such as at least one traversal in each direction. While a single traversal may be sufficient for someone who is apparently impaired, at least a second traversal may be used to ensure that a user's strange behavior during a single traversal is not due to an unexpected or external factor, such as an external light, conversation, obstruction, or distraction. In some embodiments, the length of time or number of traversals may depend at least in part on the certainty of the determination, as one who is unable to determine with a minimum level of confidence whether it is "safe" or "not safe" to operate the vehicle (or similar categorizations or classification), additional traversals may be performed, or different patterns may be displayed to attempt to improve this level of confidence. In some embodiments, in such a situation, the test may be performed up to a maximum time or number of traversals or displayed patterns, which in turn is configurable based on local laws or regulations, the risk regarding the type of operation or vehicle, etc. In some embodiments, the test will always have a fixed number of iterations, or features of the pattern, while in other embodiments, the test may be ended if a determination can be made with at least a minimum of confidence (which also meets local or applicable requirements, such as those of a manufacturer of the vehicle or test system).In a second test example, the positions of both eyes may be tracked in image data captured by a camera 114 over time. In some embodiments, the relative positions of the light bar 108 and the camera 114 in the vehicle can be used with a range of positions of the user in a passenger seat to determine an approximate position of the user for determining approximate changes in the gaze direction. In some embodiments, the positions of the light bar 108 and the camera 114 are calibrated with respect to a vehicle coordinate system, and the gaze direction of the user in the vehicle coordinate may be determined by transforming the user gaze in a coordinate system of the camera 114 into a coordinate system of the light bar 108. In other embodiments, a distance determination technique may be used to determine a more accurate position of the user, which may include stereoscopic imaging, laser or lidar scanning, or ultrasonic or radar sampling, among other such options. If the gaze of the user can be determined, more accurate determinations of at least some behaviors can be provided, such as true smoothness of movement or a capability of the user to focus on a precise location of light in a presented pattern. While a somewhat more complicated system may be required for gaze, the ability to accurately determine gaze, rather than relative eye position, may allow, in at least some embodiments, determinations to be made more quickly, which are also more accurate and / or more trusted. In at least some embodiments, additional data may also be captured that may relate to different types of tests, such as as asking a user to track a pattern by pointing with a finger, repeating or speaking a sentence, or performing a specified task, among other such options.Once a determination is made or a test result has been generated, at least one action can be taken. If the user "passes" the test or if it is determined that the user is in a safe or authorized state to perform a certain task or operation, an appropriate notification may be provided to the user, such as an audible approval, a green light, a message, a vehicle launch, or other such action. In some embodiments, information about the result may be logged for future analyses or uses, at least as long as permitted legally, according to regulations and / or user-granted approval. In some embodiments, different test results may be required for different operations, and the user may be allowed to perform these operations based on the test result. For example, a user may be able to perform various operations in a stationary vehicle if it is slightly impaired, but not able to navigate that vehicle or perform other operations determined to represent a greater risk. If a user fails a test, or if it is determined that the user is impaired (or otherwise is not in a state required to perform one or more operations), then the user may be prevented from performing certain operations, such as where an ignition system 114 of a vehicle is disabled so that the vehicle cannot be started until an instruction from the analytic component 112 indicates that the vehicle may be started if the user subsequently passed a test. In other embodiments, the user may not be inhibited from performing an operation, but a notification may be provided indicating that the user should be superior to not performing the operation, among other such options. In at least some embodiments, a user that failed a test may need to wait a minimum amount of time before repeating the test. The test system may also be capable of determining whether a person sitting in an operation site has changed by himself or via communication with another system to prevent a person passing the test for a second person and then the second person sitting in the seat and performing an operation or task. This determination may be made using captured image data, weight sensors in a seat, or other such components or features. In some embodiments, if the system determines the same user in the vehicle as in the database, the prestored or precomputed optimal test settings may be applied to the user directly without recomputing these settings, such as the optimal seating position or position of an LED light bar 108 for performing the test based on the user's height, etc. In some embodiments, a vehicle may turn off if a user leaves the driver's seat, or may lock certain features (such as the transmission) to prevent the vehicle from performing operations associated therewith until the new driver or person passes the test (at least where such operation is required).In some embodiments, the test system may be calibrated for specific users, such as owners of a vehicle. This may be useful for accurate testing because different people may have different reaction times, smoothness of motion, and other levels or types of normal behavior based on different physiological (or other such factors) of different people. A test system may determine a user's identity via, for example, computer vision or biometrics, and may determine the appropriate baseline characteristics for that particular user (obtained by testing or observing and learning over time) and then make determinations based on current behavior during testing relative to that baseline. Such a system may also make certain adjustments to persons with potentially limited or atypical behavior that would otherwise cause a user to fail a test if that behavior is not taken into account correctly. The ability to determine the identity of a person may also allow a vehicle to set different parameters for different users based on factors such as role or training, and may also allow the vehicle to enable or disable only certain features or operations based on that identity. In some embodiments, only authorized or authenticated persons who passed a capability test may operate a vehicle or perform some operation.As mentioned, in some situations, the user may present a "boundary case" in which the user has a behavior that is not unambiguously within the safe area or insecure area, and therefore cannot be classified with a classification with at least a minimum amount of confidence. The action(s) to be taken in such a situation may depend on various factors, such as the type of operation to be performed. In some cases, features may be available unless a user is clearly in an impaired state, while in other cases or embodiments, features may be disabled unless a user is clearly in an impaired state. In some embodiments, features may be disabled and the user asked to pass the test in a few minutes. In some embodiments, the features may be made available but the user may be observed for a period of time to see if the level of impairment increases, for example if the user has just drown alcohol and the alcohol has not yet fully entered the circulation so that the alcohol content in the blood will increase in the following period. In some embodiments, the user may be retested after a certain number of minutes and must pass that test as well to continue the operation.The need for such a test or the passing of such a test may depend on various factors. In some cases, it may be a legal requirement or a professional requirement. In other cases, it 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 could be deployed in a personal vehicle and the potential driver may have an option to make the test or not. For an optional test, the potential driver may also have the option of selecting whether or not to operate the vehicle based on the test results, whether or not features may be disabled for certain failures or certain conditions. In some embodiments, failed results may be required to be transmitted to an external authority, while in other cases, it may not be permitted to store or transmit or use for other purposes. In some cases, the test may only be required for certain users, for example, when a parent is required that a child pass a test to operate a vehicle, or when an owner of a vehicle is required that an unknown or unauthorized person -- such as a wallet or mechanic -- pass the test before it can operate the vehicle. If multiple tests or patterns are available, an owner, manufacturer, employer, or other authorized entity may have the option of selecting the tests or patterns to be used, as well as the situations of use and the actions to be taken for passed, failed, and / or uncertain results.In at least one embodiment, a horizontal motion pattern may be used to attempt to automatically identify presence of a horizontal gaze nystagmus (HGN) in a person. Nystagmus is the presence of involuntary eye movements which may be caused by the ingestion of drugs or alcohol. The test system can automate a test for HGN, typically performed manually by police officers, in which a beamer moves a pen in front of a driver's eyes from -45 degrees to +45 degrees, about 2-4 inches above the driver's eye level, and about 10-15 inches from the front of the driver's face. In a manual test, the operator counts the number of characters -- such as lack of smooth tracking, nystagmus at maximum deviation, entry of nystagmus at a 45 degree viewing angle, lack of eye coverage, pupil size response to light changes -- indicative of impairment caused by drug and alcohol ingestion. A test system as presented herein can automate such a test and can provide constant and verifier results that eliminate the need for a person to be present (which in many cases takes at least additional time) and can eliminate inconsistencies or inaccuracies in results determined by persons with different training or experience levels, among other potential factors.In the in-vehicle system illustrated in FIG. 1, the pattern may be presented using a light bar 108 or an array of LEDs mounted at or at least partially within the sun visor protection location. The LEDs may be activated sequentially, in at least one generally horizontal direction, at a known frequency (with a typical LED sweep speed of currently about 2 seconds), and then stopped at a maximum right or left location for at least about 4 seconds. Other patterns and speeds may also be used within the scope of the various embodiments. The driver gaze may follow the LEDs without substantial head pose movement and one or more driver observation cameras 114 may capture image data that may be used to observe the driver gaze direction when the driver / driver is attempting to follow the activated LEDs with his or her eyes. In some embodiments, if the driver head pose moves substantially more than a specified threshold amount, a head pose normalization or frontalization algorithm may be applied to compensate for this head pose movement. In at least one embodiment, system may adjust one or more LED positions or operating aspects -- such as a distance to a person in driver's seat, a height, brightness, color, or LED illumination speed -- to achieve optimal settings or results, at least under current or specific conditions. Automatic adjustment may be performed based in part on detecting the driver head position over image data captured using the driver observation camera as well as the environmental data such as brightness in the vehicle cabin (where the LEDs should be brighter during the day than overnight). From the head pose estimate, a head pose position may be determined with respect to the camera position, and the camera may be calibrated with respect to coordinates in a space associated with the cabin positions such that the head pose may be determined with respect to a cabin and / or LED coordinates.As mentioned, various other types of components may be included in such a system that present a pattern to the user to follow and then observe the responsive behavior of that user. For example, FIG. 2A illustrates a view 202 of an example system 200 in which a head-up display 204 component in a vehicle may be used to project a pattern 206 onto a windshield (or other suitable location) of the vehicle. The HUD may be calibrated to display a given pattern with a given size at a particular location, such as a horizontal pattern used to determine nystagmus, which is a particular height above user eyes. An HUD system has the advantage of being adjustable so that the position can be adjusted based on factors such as height and physical characteristics of a person under test. An LED or physical light based system may be less flexible, but may provide better performance in certain circumstances, such as bright daylight. The HUD may project the pattern and a camera 208 or image sensor (or biometric sensor) may capture data that may be used to determine the responsive indicia of the user. In some embodiments, the ability to vary the type of pattern presented, rather than just the brightness or speed of the pattern, may also provide more flexibility in testing. For example, in some embodiments, the test patterns may vary over time, as long as legally allowed, etc., such that a user does not exercise the pattern and gets better to fam the pattern by muscle memory, etc.In some embodiments, patterns may be presented to test other capabilities instead of those determined via a standardized nystagmus type test. Systems such as that illustrated in FIG. 2A are not limited in shape and may help allow variation in patterns. In the example pattern 230 displayed in FIG. 2B, there is a window 232 or screen having a specific size and shape that the operator will use for a given task, and the pattern may be selected or designed accordingly. The pattern 230 may still include a horizontal back and forth portion 234 in this case to test for nystagmus and associated eye behavior, but may also include a second portion 236 that runs around a periphery of the window 232 to ensure that the operator may follow the pattern over a full range of view, which is necessary to perform a given task. In such a test, the user may pass the nystagmus test, but may not pass the overall test if the user / user is unable to view all regions of the window 232 without moving his or her head, indicating lack of full peripheral vision, which may affect the ability to perform the task. More complex patterns, particularly those that include unexpected paths, may also potentially help more accurately test response time and behavior than expected patterns with known presentation frequency.In some embodiments, a capability test may not include a part to test for nystagmus, at least not using a traditional or standardized test. For example, a human operator may receive the task of instructing a robotic device to perform a particular task that requires a specific range or pattern of motion. Accordingly, a test pattern 260 as illustrated in FIG. 2C may be employed that mimics this required motion. In this manner, the test can determine the speed of response, smoothness of motion, and other aspects that apply to the owner when performing specific tasks. A lighted point 262 may be projected onto a window or surface to mimic the task during performance, and the user's response may be observed using approaches such as those discussed elsewhere herein. The pattern can thus be modified or configured as appropriate for the given task. An employer or other authorised entity may then determine the appropriate level of capacity or impairment to allow or not believe operation or the actions to be taken. In some embodiments, no pattern projection or simulation may be necessary because the user / s can see how the actual task is performed at a specific location and be asked to track the operation with his or her eyes without head movement and acquired image or sensor data may be used to determine the user's response behavior. Various other approaches to simulating or presenting a pattern of motion and observing response may also be used within the scope of the various embodiments.FIG. 3 illustrates components of an example system 300 that may be used to perform behavior tests, observation, and / or analysis, according to at least one embodiment. In this example, a test system 308 may perform testing of a subject 302 such as, but not limited to, a human user or potential operator of a vehicle or machine. In this example, the test system 308 may initiate a test in response to an instruction, request, or trigger, whether received automatically or in response to input from the test subject 302, among other such options. In this example, the test system 308 may execute a test application 310 (using, for example, at least one processor and memory) that may determine a type of test to be performed, as well as aspects or data to be used for the performance. This may include, for example, analyzing data about the subject / user or the operation to be performed and determining at least one appropriate pattern to be presented to the subject 302. Once this is determined, a user notification system 320 may notify the user that the test will start immediately. In some embodiments, the user notification system 320 may be the same system as the pattern display device 304, wherein instructions may be communicated using the display device. In at least one embodiment, camera system 306 may capture image data representative of a condition of subject 302 to ensure proper orientation and position for starting test. The user notification system 320 may notify the user if an adjustment is to be made prior to starting the test.Once the test is ready to start, the test system 308 may 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 the particular pattern for viewing by the subject 302. A camera system 306 (or other such image or biometric sensor device) may capture image data (or other data) of the person during presentation of the test pattern. This image data may be provided as input to the test application 310, which may utilize one or more algorithms, machine learning models, or other such techniques to determine a test result for the subject, such as whether the subject has at least a minimum determined capacity or more than an allowable level of capacity impairment, among other such options or determinations. As mentioned, the types of determinations between embodiments may vary based on factors such as the type of the subject, the type of operation performed, or other such factors. Once a condition, capacity, or other such metric has been determined for the subject, one or more actions to take may be determined by the test application. This may include, for example, providing a result to the user using the user notification system 320. This may also include providing data to a control system 312 for a vehicle, machine, robotic system, or other such device for which testing was performed. For example, data may be passed to the control system 312 indicating that the user passed the test and at least demonstrated the required capacity so that the control system may unlock or activate one or more operational mechanisms 314 or for use by the test person. For a vehicle, this may include unlocking the starter and / or transmission or unlocking a brake system so that the test person may operate the vehicle. For a robotic device, this may include unlocking the controllers for the device, etc. In at least one embodiment, results of test may optionally be provided to an observation system 316, if allowed, that data may use to take specific actions, if appropriate, and / or store data in a log 318 for later review or reporting, among other such options. In some embodiments or situations, the subject may have the option of approving or denying the collection of such data.A test application 310, such as that illustrated in FIG. 3, may use a combination of computer vision and deep learning to make capacity decisions for a subject, according to 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 may be used to make inferences based on these changes in position or direction, which may be indicative of a presence of some behavior exhibited by a subject, for example. FIGS. 4A-4C illustrate example processing pipelines that may be used to provide such functionality. FIG. 4A illustrates a first pipeline wherein a sensor arrangement 400 may be used to acquire user images, which may include, for example, at least an eye region of a user or a test person during a test procedure. Software 402, which may include computer vision functionality, may attempt to determine frame-to-frame differences between eye-cutout regions of the image data, such as differences at pupil locations in adjacent frames in a sequence. A trained neural network, such as a convolutional neural network (CNN) 404, may use this difference data to infer a change in gaze direction between a given pair (or sequence) of captured image frames (or video frames) or portions of a video stream. A fast Fourier transform (FFT) module or wavelet module 406 may then infer a user's behavior based in part on the change in gaze direction relative to the pattern that was presented, such as using a Fourier analysis, to compare the eye gate frequency to the corresponding LED frequency, thereby allowing analysis in both a time domain and / or a frequency domain. For example, frequency graphs for the projected pattern may be taken (such as where the projected pattern is a sinusoidal waveform with a certain frequency across the LED), and the corresponding response and differences between peaks or features (such as a signal-to-noise ratio) in these curves may be measured and used to determine or infer a level of capacitance. A software module 408 may determine a level or type of impairment (or capacity or other such metric) using the inferred behavior. Here, the inferencing behavior may be a presence or measure of nystagmus, a response time, smoothness of motion, or other such behavior. The determination may be made using a particular algorithm or formula, such as a weighted combination of values for the various behaviors.FIG. 4B illustrates another example pipeline where a sensor assembly 420 is used to capture images of at least one eye region of a user or subject and these images are passed to a software module 422 that may use computer vision techniques to determine the eye regions of the captured images or image data. The image data for these eye regions may be passed as input to a three-dimensional CNN (3D CNN) 424 to infer the behavior based at least on the location of one or more features (e.g., a pupil centroid point) of the subject's eyes. The behavior determination(s) may be provided as input to a software module 426 to determine a level or type of impairment, etc.FIG. 4C illustrates a third pipeline where a sensor assembly 440 is used to acquire images of at least one eye region of a user or subject and these images are forwarded to a deep neural network (DNN) 442 for gaze to determine a gaze direction (or gaze location) of the user. The gaze information may be passed to a one-dimensional CNN (1D-CNN) CNN 444 to infer at least one behavior of the subject based on, for example, a series of gaze vectors in the time domain. The behavior determination(s) may be provided as input to a software module 446 to determine a level or type of impairment, etc. As mentioned, the level of impairment or other such determination may thereafter be used by the same or a different software module to determine one or more actions to take in response to the level determination.The neural networks, machine learning models, or artificial intelligence used in such pipelines must be at least partially trained, which typically involves the use of a large amount of different trained data. For example, there may not be extensive collected and available data for a horizontal gaze nystagmus of blinded drivers, such that a measure of synthetic training data may be utilized. In at least one embodiment, a generative network (e.g., a generative adversarial network (GAN)) may be used to generate image sequences or video to expand a training set. This may include, for example, generating facial images or eye-patch videos to present persons with various appearances, eye sizes, and other such aspects, with pupils moving in a given pattern, with and without some behavior for which these models are to be trained.FIG. 5 illustrates an example process 500 for determining a capacity of a person, which may be performed according to at least one embodiment. It should be understood that, within the scope of the various embodiments, unless specifically stated otherwise, there may be additional, fewer, or alternative steps performed in similar or alternative orders, or at least partially in parallel, for these and other processes presented herein. Further, although this example will be discussed in terms of nystagmus and associated behavior corresponding to eye movement, there may be other types of behavior, capacity, or ability of a subject that may be determined to be used within the scope of various embodiments as well. In this example, a degradation or capacity test is initiated 502, either manually upon a user's desire, or automatically in response to a trigger or instruction, such as when a person is detected in a driver seat or places a key in an ignition. A determination may be made 504 that the subject is in the correct position and / or orientation, such as using cameras and / or other sensors, to acquire position and orientation data to compare to an acceptable range of positions and orientations for a given test, or type of test. Once the position and orientation have been verified, the test in this example may begin by presenting 506 a simulated pattern of the motion using one or more light sources, such as a light bar or projectable light pattern, as a light stimulus (separate from other subject illumination necessary to capture image data). Image data for at least one eye region of the subject may be captured 508 during presenting the test pattern that should represent the motion and behavior of the subject's eyes. At least a portion of the image data corresponding to the eye region may be provided 510 as input to at least one neural network, as described with reference to FIGS. 4A-4C. At least one behavior determination, such as a presence of nystagmus with associated confidence value, may be received 512 from the at least one neural network corresponding to the test subject during presentation of the test pattern. An impairment, capacity state, or other such metric or classification may be determined 514 for the subject based in part on the behavior determination(s). At least one responsive action may be performed 516 based on the impairment or capacity state ranging from providing a notification to unlocking or locking a specific functionality, among other such options discussed and recommended elsewhere herein. For example, a determination may be made that a person is likely to be fatigued but not concerned, for example when the user follows a pattern slowly but has smooth movement without nystagmus, and such a determination may allow certain tasks or operations to be available but other operations are disabled, which is based on the risk of an incorrect execution based on fatigued as compared to concerned or otherwise incapable.In some embodiments, a continuous observation of the user may be performed during the operation. For example, there may be certain events that the user should notice and / or track, such as a car entering a road or a robotic assembly that grasps a target object or moves that object to a specific location under control of the operator. A camera can capture image data of the user and the user behavior can be observed with respect to these specific events, which in turn can relate to response time, smoothness of movement, presence of nystagmus, and so forth. Determinations made in such situations may not be as precise, but may be sufficient to cause the operation to be interrupted or ended while the user is making an authorized or accurate test while not performing other operations or being subject to any distractions. In some embodiments, failing to pass an observation test by more than a threshold or an allowable amount may cause at least some functionality to be disabled until that person can pass a matching and contiguous test.Aspects of various approaches presented herein may be light enough to execute in real-time at various locations, such as on a client device, such as a personal computer, smart vehicle, or control console. Such processing may be performed on or for content generated at or received from the client device or received from an external source, such as streaming data or other content received / received over at least one network from a server 620 or third party service 660, among other such options. In some cases, at least a portion of the processing, generation, composition, analysis, and / or determination of this content may be performed by / one of these other devices, systems, or entities, which is then provided to the client device (or receiver) for presentation or other such use.By way of example, FIG. 6 illustrates an example network configuration 600 that may be used to provide, generate, modify, encode, process, and / or transmit image data and analytical data or other such content. In at least one embodiment, a client device 602 may generate or receive data for a session using components of a control application 604 on client device 602 and data locally stored on that client device. In at least one embodiment, a content application 624 executing on a server 620 (e.g., a cloud server or edge server) may initiate a session associated with at least one client device 602, for which a session manager and user data stored in a user database 636 are used. A content manager 626 may operate 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 may include, for example, determining a pattern from a pattern database 634 to be presented to a user of the client device through a display 606. A camera 638 or sensor may capture image data that may be provided to the test module 628, which may operate with an analysis module 630 to determine a level of user capacity, capability, or impairment. A control module 632 may then take one or more actions based on this determination. At least a portion of the processed image content, behavioral decisions, or particular control actions may be transmitted to the client device 602 using a suitable transmission manager 622 for transmission through download, streaming, or other such transmission channel. An encoder may be used to encode and / or compress at least some of these data before being transmitted to the client device 602. In at least one embodiment, a client device 602 receiving such content may take appropriate action, such as notifying user using a display 606 or audio component 608. A 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 use in presenting a display pattern, analyzing captured image data, making behavioral determinations or determining actions to take in response to behavioral determinations such that any or all of these actions may be taken locally, rather than via a network, by a server 620, another client device 650, or a third party service 660. A decoder may also be used to decode data received over the network 640 for presentation via the client device 602, such as image or video content through a display 606, and audio, such as sounds and verbal instructions, through at least one audio playback device 608, such as speakers or earphones. In at least one embodiment, at least a portion of this content may already be stored, rendered, or accessible on client device 602, such that transmission over network 640 is not required for at least this portion of content, for example, if this content may have been previously downloaded or locally stored on a hard disk or optical disk. In at least one embodiment, a transmission mechanism, such as streaming data, may be used to transfer this content from server 620, or user database 636, to client device 602. In at least one embodiment, a portion of this content may be obtained, enhanced, and / or streamed from another source, such as a third party service 660 or other client device 650, which may also include a content application 662 for generating, enhancing, analyzing, or providing content. In at least one embodiment, portions of this functionality may be performed using multiple computing devices, or multiple processors within one or more computing devices, which may include, for example, a combination of CPUs and GPUs.In this example, these client devices may include any suitable computing device, which may include, for example, a desktop computer, a notebook computer, a set-top box, a streaming device, a game console, a smartphone, a tablet computer, a VR headset, AR glasses, a wearable computer, or a smart television. Each client device may send a request over at least one wired or wireless network, which may include, for example, the Internet, an Ethernet, a local area network (LAN), or a cellular network, among other such options. In this example, these requests may be sent to an address associated with a cloud provider, which may operate or control one or more electronic resources in a cloud provider environment, which may include, for example, a data center or a server farm. In at least one embodiment, request may be received or processed by at least one edge server residing on a network edge and residing outside of at least one security layer associated with cloud provider environment. In this way, latency may be reduced by allowing the client devices to interact with servers that are in close proximity while simultaneously improving the security of resources in the cloud provider environment.In at least one embodiment, such a system may be used to perform graphical rendering operations. In other embodiments, such a system may be used for other purposes, such as providing image or video content for testing or validating 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 involve one or more virtual machines (VMs). In at least one embodiment, such a system may be implemented at least partially in a data center or at least partially using cloud computing resources.INFERENCE AND TRAINING LOGICFIG. 7A illustrates inference and / or training logic 715 used to perform inferencing and / or training operations associated with one or more embodiments. Details regarding inference and / or training logic 715 are provided below in connection with FIGS. 7A and / or 7B.In at least one embodiment, inference and / or training logic 715 may include, among other things, code and / or data storage 701 to store forward and / or output weight and / or input / output data and / or other parameters for configuring neurons or layers of a neural network trained and / or used for inferencing in aspects of one or more embodiments. In at least one embodiment, training logic 715 may include or be coupled to code and / or data storage 701 to store graph code or other software for controlling timing and / or ordering in which weight and / or other parameter information is loaded for configuring logic, including integer and / or floating point units (together arithmetic logic units (ALUs). In at least one embodiment, code, such as graph code, loads weight or other parameter information into processor ALUs based on a neural network architecture to which code corresponds. In at least one embodiment, code and / or data storage 701 stores weight parameters and / or input / output data from each layer of a neural network trained or used in connection with one or more embodiments during forward propagation of input / output data and / or weight parameters during training and / or inferencing using aspects of one or more embodiments. In at least one embodiment, any portion of code and / or data storage 701 may be included with other on-chip or off-chip data storage including processor L1, L2, or L3 cache or system memory.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-addressable memory ("DRAM"), static random-addressable memory ("SRAM"), nonvolatile memory (e.g., flash memory), or other storage. In at least one embodiment, selection of whether code and / or data storage 701 is internal or external to a processor, or is of a DRAM, SRAM, flash, or other storage type, for example, may depend on available on-chip versus off-chip storage, latency requirements of training and / or inferencing functions performed, batch size of data used in inferencing and / or training a neural network, or a combination of these factors.In at least one embodiment, inference and / or training logic 715 may include, among other things, code and / or data storage 705 to store reverse and / or output weight and / or input / output data corresponding to neurons or layers of a neural network trained and / or used for inferencing in aspects of one or more embodiments. In at least one embodiment, code and / or data storage 705 stores weight parameters and / or input / output data from each layer of a neural network trained or used in connection with one or more embodiments during backward propagation of input / output data and / or weight parameters during training and / or inferencing using aspects of one or more embodiments. In at least one embodiment, training logic 715 may include or be coupled to code and / or data storage 705 to store graph code or other software for controlling timing and / or ordering in which weight and / or other parameter information is loaded for configuring logic, including integer and / or floating point units (together arithmetic logic units (ALUs). In at least one embodiment, code, such as graph code, loads weight or other parameter information into processor ALUs based on a neural network architecture to which 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 processor 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, nonvolatile memory (e.g., flash memory), or other storage. In at least one embodiment, selection of whether code and / or data storage 705 is internal or external to a processor, or is of a DRAM, SRAM, flash, or other storage type, for example, may depend on available on-chip versus off-chip storage, latency requirements of training and / or inferencing functions performed, batch size of data used in inferencing and / or training a neural network, or a combination of these factors.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 processor L1, L2, or L3 cache or system memory.In at least one embodiment, inference and / or training logic 715 may include, among other things, one or more arithmetic logic unit(s) ("ALU(s)") 710, including integer and / or floating point units, for performing logical and / or mathematical operations based at least in part on or indicated by training and / or inference code (e.g., graph code), a result of which may produce activations (e.g., output values of layers or neurons within a neural network) stored in activation storage 720, which are functions of input / output and / or weight parameter data stored in code and / or data storage 701 and / or code and / or data storage 705. In at least one embodiment, activations stored in activation storage 720 are generated according to linear algebraic and / or matrix-based math performed by ALU(s) 710 in response to performing instructions or other code, wherein weight values stored in code and / or data storage 705 and / or code and / or data storage 701 are used as operands along 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- or off-chip storage.In at least one embodiment, ALU(s) 710 are included within one or more processors or other hardware logic devices or circuits, while in another embodiment, ALU(s) 710 may be external to a processor or other hardware logic device or circuit using them (e.g., a coprocessor). In at least one embodiment, ALU(s) 710 may be included within execution units of a processor or otherwise within a series of ALUs accessible by execution units of a processor, either in same processor or distributed between different processors of different types (e.g., central processing units, graphics processing units, fixed function units, 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 same processor or other hardware logic device or circuit, while in another embodiment they may be on different processors or other hardware logic devices or circuits, or a combination of 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 processor L1, L2, or L3 cache or system memory. Further, inferencing and / or training code may be stored with other code accessible to a processor or other hardware logic or circuitry and fetched and / or processed using fetch, decode, scheduling, execution, sleep, and / or other logic circuits of a processor.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 reside, completely or partially, within or external to one or more processors or other logic circuitry. In at least one embodiment, selection of whether activation storage 720 is internal or external to a processor, or is of a DRAM, SRAM, flash, or other storage type, for example, may depend on available on-chip versus off-chip storage, latency requirements of training and / or inferencing functions performed, batch size of data used in inferencing and / or training a neural network, or a combination of these factors. In at least one embodiment, inference and / or training logic 715 illustrated in FIG. 7A may be used in conjunction with an application-specific integrated circuit ("ASIC"), such as a Tensorflow® processing unit from Google, an inference processing unit (IPU) from Graphcore™ or a Nervana® processor (e.g., a lake-crest processor) from Intel Corp. In at least one embodiment, inference and / or training logic 715 illustrated in FIG. 7A may be used in conjunction with central processing unit ("CPU") hardware, graphics processing unit ("GPU") hardware, or other hardware, such as field programmable gate arrays ("FPGAs").FIG. 7B illustrates inference and / or training logic 715, in accordance with at least one or more embodiments. In at least one embodiment, inference and / or training logic 715 may include, among other things, hardware logic in which computing resources are dedicated or otherwise used exclusively 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, inference and / or training logic 715 illustrated in FIG. 7B may be used in conjunction with an application-specific integrated circuit (ASIC) such as a Tensorflow® processing unit from Google, an inference processing unit (IPU) from Graphcore™ or a Nervana® processor (e.g., a lake-crest processor) from Intel Corp. In at least one embodiment, inference and / or training logic 715 illustrated in FIG. 7B may be used in conjunction with central processing unit (CPU) hardware, graphics processing unit (GPU) hardware, or other hardware, such as field programmable gate arrays (FPGAs). In at least one embodiment, inference and / or training logic 715 includes, among other things, code and / or data storage 701 and code and / or data storage 705 that may 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. In at least one embodiment illustrated in FIG. 7B, code and / or data storage 701 and code and / or data storage 705 are each associated with a dedicated computing resource, such as computing hardware 702 and computing hardware 706, respectively. In at least one embodiment, computing hardware 702 and computing hardware 706 each include one or more ALUs that perform mathematical functions, such as linear algebraic functions, only on information stored in code and / or data storage 701 and code and / or data storage 705, respectively, the results of which are stored in activation storage 720.In at least one embodiment, code and / or data storage 701 and 705 and corresponding computer hardware 702 and 706, respectively, correspond to different layers of a neural network, such that a resulting activation from a "storage / computer pair 701 / 702" of code and / or data storage 701 and computer hardware 702 is provided as input to "storage / computer pair 705 / 706" of code and / or data storage 705 and computer hardware 706 to mirror conceptual organization of a neural network. In at least one embodiment, each of storage / compute pairs 701 / 702 and 705 / 706 may correspond to more than one neural network layer. In at least one embodiment, additional storage / compute pairs (not shown) may be included in inference and / or training logic 715 after or in parallel with storage / compute pairs 701 / 702 and 705 / 706.DATACENTERFIG. 8 illustrates an example data center 800 in which at least one embodiment may be used. In at least one embodiment, data center 800 includes a data center infrastructure layer 810, a framework layer 820, a software layer 830, and an application layer 840.In at least one embodiment, as shown in FIG. 8, data center infrastructure layer 810 may include resource orchestrator 812, grouped compute resources 814, and node compute resources ("node-C.R.s") 816( 1)- 816(N), where "N" represents a positive, integer. In at least one embodiment, node C.R.s 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.), storage devices (e.g., dynamic read-only memories), storage devices (e.g., solid state or disk drives), network input / output (NW I / O) devices, network switches, virtual machines (VMs), power modules, and cooling modules, etc. In at least one embodiment, one or more node C.R.s of node C.R.s 816( 1)- 816(N) may be a server having one or more of above-mentioned computing resources.In at least one embodiment, grouped computing resources 814 may include separate groupings of node C.R. s residing in one or more racks (not shown) or many racks residing in data centers at different geographic locations (also not shown). Separate groupings of node C.R.s within grouped computing resources 814 may include grouped computing, network, storage, or storage resources configured or allocated to support one or more workloads. In at least one embodiment, multiple node C.R.s, including CPUs or processors, may be grouped into 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.In at least one embodiment, resource orchestrator 812 may configure or otherwise control one or more node C.R.s 816( 1)- 816(N) and / or grouped computing resources 814. In at least one embodiment, resource orchestrator 812 may include a software design infrastructure (SDI) management entity for data center 800. In at least one embodiment, resource orchestrator 812 may include hardware, software, or a combination thereof.In at least one embodiment, as shown in FIG. 8, a framework layer 820 includes a job scheduler 822, a configuration manager 824, a resource manager 826, and a distributed file system 828. In at least one embodiment, framework layer 820 may include a framework to support software 832 of software layer 830 and / or one or more application(s) 842 of application layer 840. In at least one embodiment, software 832 or application(s) 842 may each include web-based service software or applications, such as those provided by Amazon Web Services, Google Cloud, and Microsoft Azure. In at least one embodiment, framework layer 820 may be, among other things, a type of free and open source software web application framework, such as Apache Spark™ (hereafter "Spark"), which may use distributed file system 828 for large volume data processing (e.g., "Big Data"). In at least one embodiment, job scheduler 822 may include a spark driver to facilitate scheduling workloads supported by different layers of data center 800. In at least one embodiment, configuration manager 824 may be capable of configuring various layers, such as software layer 830 and framework layer 820, including spark and distributed file system 828, to support large volume computing. In at least one embodiment, resource manager 826 may be capable of managing clustered or grouped computing resources allocated or allocated to support distributed file system 828 and job scheduler 822. In at least one embodiment, clustered or grouped computing resources may include a grouped computing resource 814 at data center infrastructure layer 810. In at least one embodiment, resource manager 826 may coordinate with resource orchestrator 812 to manage these allocated or allocated computing resources.In at least one embodiment, software 832 included in software layer 830 may include software used by at least portions of node C.R.s 816( 1)- 816(N), grouped computing resources 814, and / or distributed file system 828 of framework layer 820. The one or more types of software may include, but are not limited to, Internet web page search software, email virus scan software, database software, and streaming video content software.In at least one embodiment, application(s)n 842 included in application layer 840 may include one or more types of applications used by at least portions of node C.R.s 816(1)-816(N), grouped computing resources 814, and / or distributed file system 828 of framework layer 820. One or more types of applications may include, but are not limited to, any number of a genomics application, a cognitive computing and a machine learning application, including training or inferencing software, machine learning framework software (e.g., PyTor, TensorFlow, Coffee, etc.), or other machine learning applications used in connection with one or more embodiments.In at least one embodiment, any of configuration manager 824, resource manager 826, and resource orchestrator 812 may implement any number and type of self-modifying operations based on any amount and type of data captured in any technically feasible manner. In at least one embodiment, self-modifying operations may offload a data center operator of data center 800 so that it may not make bad configuration decisions and may not avoid fully-offloaded and / or poorly-running portions of a data center.In at least one embodiment, data center 800 may include tools, services, software, and other resources to train one or more machine learning models or predict or derive information using one or more machine learning models according to one or more embodiments described herein. For example, in at least one embodiment, a machine learning model may be trained by computing weight parameters according to a neural network architecture using software and computing resources as described above with respect to data center 800. In at least one embodiment, trained machine learning models corresponding to one or more neural networks may be used to derive and predict information using resources described above with respect to data center 800 using weight parameters calculated via one or more training techniques described herein.In at least one embodiment, data center may use CPUs, application specific integrated circuits (ASICs), GPUs, FPGAs, or other hardware to perform training and / or inferencing using resources described above. Additionally, one or more software and / or hardware resources described above may be configured as a service to allow users to train or perform inferencing information, such as image recognition, speech recognition, or other artificial intelligence services.Inference and / or training logic 715 is used to perform inferencing and / or training operations associated with one or more embodiments. Details regarding inference and / or training logic 715 are provided below in connection with FIGS. 7A and / or 7B. In at least one embodiment, inference and / or training logic 715 in system of FIG. 8 may be used to inferencing or predict operations based at least in part on weight parameters calculated using neural network training operations, neural network functions and / or architectures, or neural network usage cases as described herein.Such components may be used to automatically determine irregular eye or gaze behavior of a person, which may be indicative of impairment due to drug or alcohol consumption, and may take one or more actions based on the determination.COMPUTER SYSTEMSFIG. 9 is a block diagram illustrating an example computing system, which may be a system with interconnected devices and components, a system-on-a-chip (SOC), or other combination thereof 900 formed with a processor, which may include execution units for executing an instruction, in accordance with at least one embodiment. In at least one embodiment, computing system 900 may include, among other things, a component, such as processor 902, to employ execution units, including logic to perform algorithms for process data, in accordance with present disclosure, such as in embodiment described herein. In at least one embodiment, computing system 900 may include processors such as PENTIUM® family of processors, Xeon™ Itanium® XScale™ and / or StrongARM™ Intel® Core™ or Intel® Nervana™ microprocessors available from Intel Corporation of Santa Clara, California, although other systems (including PCs with other microprocessors, engineering workstations, set-top boxes, and the like) may also be used. In at least one embodiment, computing system 900 may execute a version of a WINDOWS operating system available from Microsoft Corporation, Redmond, Wash., although other operating systems (e.g., UNIX and Linux), embedded software, and / or graphical user interfaces may also be used.Embodiments may be used in other devices, such as handheld devices and embedded applications. Some examples of handheld devices include cellular phones, Internet Protocol devices, digital cameras, personal digital assistants ("PDAs"), and handheld PCs. In at least one embodiment, embedded applications may include a microcontroller, a digital signal processor ("DSP"), a system-on-a-chip, network computers ("NetPCs"), set-top boxes, network hubs, wide area network ("WAN") switches, or any other system capable of performing one or more instructions according to at least one embodiment.In at least one embodiment, computing system 900 may include, but is not limited to, processor 902, which may include, but is not limited to, one or more execution unit(s) 908 for performing machine learning model training and / or inferencing according to techniques described herein. In at least one embodiment, computing system 900 is a single processor epitope or server system, but in another embodiment, computing system 900 may be a multiprocessor system. In at least one embodiment, 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 implementing a combination of instruction sets, or another processor device such as a digital signal processor. In at least one embodiment, processor 902 may be coupled to a processor bus 910 that transmits data signals between processor 902 and other components in computing system 900.In at least one embodiment, processor 902 may include, among other things, a level 1 ("L1") internal cache memory, ("cache") 904. In at least one embodiment, processor 902 may include a single internal cache or multiple levels of internal cache. In at least one embodiment, cache memory may be external to processor 902. Other embodiments may also include a combination of both internal and external caches depending on the particular implementation and needs. In at least one embodiment, a register file 906 may store different types of data in different registers, including, but not limited to, integer registers, floating point registers, status registers, and instruction pointer registers.In at least one embodiment, execution unit 908, including, but not limited to, logic for performing integer and floating point operations, is also present in processor 902. In at least one embodiment, processor 902 may also include microcode ("ucode") read only memory ("ROM") that stores microcode for certain microinstruction. In at least one embodiment, execution unit 908 may include logic to handle a packaged instruction set 909. In one embodiment, by including a packaged instruction set 909 in an instruction set of a general purpose processor 902, along with associated circuitry for executing instructions, operations used by many multimedia applications may be performed using packaged data in a general purpose processor 902. In one or more embodiments, many multimedia applications may be accelerated and more efficiently executed by using the full width of a processor data bus to perform operations on packaged data, thereby eliminating the need to transfer smaller units of data across the processor data bus to perform one or more operations with one data element each.In at least one embodiment, execution unit 908 may also be used in microcontrollers, embedded processors, graphics devices, DSPs, and other types of logic circuitry. In at least one embodiment, computing system 900 may include, among other things, memory 920. In at least one embodiment, memory 920 may be implemented as a dynamic random access memory ("DRAM") device, static random access memory ("SRAM") device, flash memory device, or other memory device. In at least one embodiment, memory 920 may store instruction(s) 919 and / or data 921 represented by data signals executable by processor 902.In at least one embodiment, system logic chip may be coupled to processor bus 910 and memory 920. In at least one embodiment, system logic chip may include, but is not limited to, memory controller hub ("MCH") 916, and processor 902 may communicate with MCH 916 via processor bus 910. In at least one embodiment, MCH 916 may provide a high bandwidth storage path 918 to memory 920 for instruction and data storage and for storage of graphics commands, data, and textures. In at least one embodiment, MCH 916 may route data signals between processor 902, memory 920, and other components in computing system 900 and to bridge data signals between processor bus 910, memory 920, and a system I / O 922. In at least one embodiment, system logic chip may provide a graphics port for coupling to a graphics controller. In at least one embodiment, MCH 916 may be coupled to memory 920 via a high bandwidth memory path 918 and graphics / video card 912 may be coupled to MCH 916 via an accelerated graphics port ("AGP") interconnect 914.In at least one embodiment, computing system 900 may use system I / O 922, which is a proprietary hub interface bus, to couple MCH 916 to I / O controller hub ("ICH") 930. In at least one embodiment, ICH 930 may provide direct connections to some I / O devices via a local I / O bus. In at least one embodiment, local I / O bus may include, but is not limited to, a high speed I / O bus for connecting peripherals to memory 920, chipset, and 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 storage 924, a legacy I / O controller 923 containing user input and keyboard interface(s) 925, a serial expansion port 927 such as a universal serial bus ("USB"), and a network controller 934. The data storage 924 may include a hard disk drive, a floppy disk drive, a CD-ROM device, a flash memory device, or other mass storage device.In at least one embodiment, FIG. 9 illustrates a system including interconnected hardware devices or "chips", while in other embodiments, FIG. 9 may illustrate an example system-on-a-chip ("SoC"). In at least one embodiment, devices with proprietary interconnects, standardized interconnects (e.g., PCIe), or a combination thereof may be interconnected. In at least one embodiment, one or more components of computing system 900 are interconnected using compute express link (CXL) interconnects.Inference and / or training logic 715 is used to perform inferencing and / or training operations associated with one or more embodiments. Details regarding inference and / or training logic 715 are provided below in connection with FIGS. 7A and / or 7B. In at least one embodiment, inference and / or training logic 715 in system of FIG. 9 may be used to inferencing or predict operations based at least in part on weight parameters calculated using neural network training operations, neural network functions and / or architectures, or neural network usage cases as described herein.Such components may be used to automatically determine irregular eye or gaze behavior of a person, which may be indicative of impairment due to drug or alcohol consumption, and may take one or more actions based on the determination.FIG. 10 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, electronic device 1000 may be, for example, but is not limited to, a notebook, a tower server, a rack server, a blade server, a laptop, a desktop, a tablet, a mobile device, a phone, an embedded computer, or any other suitable electronic device.In at least one embodiment, electronic device 1000 may include, among other things, processor 1010 communicatively coupled to any suitable number or type of components, peripherals, modules, or devices. In at least one embodiment, processor 1010 is coupled using a bus or interface, such as a 1°C 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 advance 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 illustrates a system including interconnected hardware devices or "chips", while in other embodiments, FIG. 10 may illustrate an example system-on-a-chip ("SoC"). In at least one embodiment, devices illustrated in FIG. 10 may be interconnected with proprietary interconnects, standardized interconnects (e.g., PCIe), or a combination thereof. In at least one embodiment, one or more components of FIG. 10 are interconnected using compute express link (CXL) interconnects.In at least one embodiment, FIG. 10 may include a display 1024, a touch screen 1025, a touchpad 1030, a near field communication ("NFC") unit 1045, a sensor hub 1040, a thermal sensor 1046, an express chipset ("EC") 1035, a trusted platform module ("TPM") 1038, a BIOS / firmware / flash memory ("BIOS, FW Flash") 1022, a DSP 1060, a drive 1020 such as a solid state drive ("SSD") or a hard disk drive ("HDD"), a wireless local area network ("WLAN") 1050, a Bluetooth unit 1052, a wireless wide area network ("WWAN") unit 1056, a global positioning system (GPS) 1055, a camera ("USB 3.0 camera") 1054, such as a USB 3.0 camera and / or a low power double data rate ("LPDDR") storage unit ("LPDDR3") 1015, implemented in the LPDDR3 standard, for example. These components may each be implemented in any suitable manner.In at least one embodiment, other components may be communicatively coupled to processor 1010 via components discussed above. In at least one embodiment, accelerometer 1041, ambient light sensor ("ALS") 1042, compass 1043, and gyroscope 1044 may be communicatively coupled to sensor hub 1040. In at least one embodiment, a thermal sensor 1039, a fan 1037, a keyboard 1036, and a touchpad 1030 may be communicatively coupled to EC 1035. In at least one embodiment, speaker 1063, earphones 1064, and microphone ("mic") 1065 may be communicatively coupled to audio unit ("audio codec and class-d amplifier") 1062, which in turn may be communicatively coupled to DSP 1060. In at least one embodiment, audio unit 1062 may include, for example, an audio coder / decoder ("codec") and a class-D amplifier, among others. In at least one embodiment, SIM card ("SIM") 1057 may be communicatively coupled to WWAN unit 1056. In at least one embodiment, components such as WLAN unit 1050 and Bluetooth unit 1052, as well as WWAN unit 1056, may be implemented in a next generation form factor ("NGFF").Inference and / or training logic 715 is used to perform inferencing and / or training operations associated with one or more embodiments. Details regarding inference and / or training logic 715 are provided below in connection with FIGS. 7A and / or 7B. In at least one embodiment, inference and / or training logic 715 in system of FIG. 10 may be used to inferencing or predict operations based at least in part on weight parameters calculated using neural network training operations, neural network functions and / or architectures, or neural network usage cases as described herein.Such components may be used to automatically determine irregular eye or gaze behavior of a person, which may be indicative of impairment due to drug or alcohol consumption, and may take one or more actions based on the determination.FIG. 11 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 processor(s) 1102 and one or more graphics processor(s) 1108, and may be a single processor endpoint system, multiprocessor workstation system, or server system having a large number of processors 1102 or processor cores 1107. In at least one embodiment, system 1100 is a processing platform included in a system-on-a-chip (SoC) integrated circuit for use in mobile, handheld, or embedded devices.In at least one embodiment, system 1100 may include or be included in a server-based gaming platform, a gaming console including a gaming and media console, a mobile gaming console, a handheld gaming console, or an online gaming console. In at least one embodiment, system 1100 is a mobile phone, smart phone, tablet computing device, or mobile Internet device. In at least one embodiment, processing system 1100 may also include, be coupled to, or incorporated into a wearable device, such as a smart watch wearable device, smart glasses device, augmented reality device, or virtual reality device. In at least one embodiment, processing system 1100 is a television or set-top box device having one or more processor(s) 1102 and a graphical interface generated by one or more graphics processor(s) 1108.In at least one embodiment, one or more processor(s) 1102 each include one or more processor core(s) 1107 to process instructions that, when executed, perform operations for system and user software. In at least one embodiment, each of one and more processor core(s) 1107 is configured to process a specific instruction set 1109. In at least one embodiment, instruction set 1109 may facilitate complex instruction set computing (CISC), reduced instruction set computing (RISC), or via very long instruction word (VLIW) computing. In at least one embodiment, processor core(s) 1107 may each process a different instruction set 1109, which may include instructions to facilitate emulation of other instruction sets. In at least one embodiment, processor core(s) 1107 may also include other processing devices, such as a digital signal processor (DSP).In at least one embodiment, processor(s) 1102 may include cache 1104. In at least one embodiment, processor(s) 1102 may include a single internal cache or multiple levels of internal cache. In at least one embodiment, cache memory is shared by various components of processor / processors 1102. In at least one embodiment, processor / processors 1102 also use an external cache (e.g., a level 3(L3) cache or last level cache (LLC)) (not shown) that may be shared by processor cores 1107 using known cache coherency techniques. In at least one embodiment, register file 1106 is additionally included in processor(s) 1102, which may include different types of registers for storing different types of data (e.g., integer registers, floating point registers, status registers, and an instruction pointer register). In at least one embodiment, register file 1106 may include general purpose registers or other registers.In at least one embodiment, one or more processor(s) 1102 is / are coupled to one or more interface bus(s) 1110 for transmitting communication signals, such as address, data, or control signals, between processors 1102 and other components in system 1100. In at least one embodiment, interface bus(s) 1110, in one embodiment, may be a processor bus, such as a version of a direct media interface (DMI) bus. In at least one embodiment, interface bus(s) 1110 is / are not limited to a DMI bus and may include one or more peripheral component interconnect buses (e.g., PCI, PCI Express), memory buses, or other types of interface buses. In at least one embodiment, processor(s) 1102 may include an integrated memory controller 1116 and a platform controller hub 1130. In at least one embodiment, memory controller 1116 facilitates communication between a memory device and other components of system 1100 while platform controller hub (PCH) 1130 provides connections to I / O devices via a local I / O bus.In at least one embodiment, memory device 1120 may be a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, a flash memory device, phase change memory device, or other memory device that has appropriate performance to serve as process memory. In at least one embodiment, storage device 1120 may operate as system memory for system 1100 to store data 1122 and instructions 1121, for use when one or more processor(s) 1102 executes / execute an application or process. In at least one embodiment, memory controller 1116 is also coupled to an optional external graphics processor 1112, which may communicate with one or more graphics processor(s) 1108 in processor(s) 1102 to perform graphics and media operations. In at least one embodiment, a display device 1111 may be connected to processor(s) 1102. In at least one embodiment, display device 1111 may include one or more of an internal display device, such as in a mobile electronic device or a laptop device, or an external display device connected via a display interface (e.g., DisplayPort, etc.). In at least one embodiment, display device 1111 may include a head mounted display (HMD) such as a stereoscopic display device for use in virtual reality (VR) applications or augmented reality (AR) applications.In at least one embodiment, platform controller hub 1130 may enable peripherals to connect to memory device 1120 and processor(s) 1102 via a high-speed I / O bus. In at least one embodiment, 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, touch sensors 1125, a data storage device 1124 (e.g., hard disk drive, flash memory, etc.). In at least one embodiment, data storage device 1124 may be connected via a storage interface (e.g., SATA) or via a peripheral bus, such as a peripheral component interconnect bus (e.g., PCI, PCI Express). In at least one embodiment, touch sensors 1125 may include touch screen sensors, pressure sensors, or fingerprint sensors. In at least one embodiment, wireless transceiver 1126 may be a WiFi 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, firmware interface 1128 enables communication with system firmware and may be, for example, a unified extensible firmware interface (UEFI). In at least one embodiment, network controller 1134 enables network connection to a wired network. In at least one embodiment, a high performance network controller (not shown) is coupled to an interface bus(s) 1110. In at least one embodiment, audio controller 1146 is a multi-channel high definition audio controller. In at least one embodiment, system 1100 includes an optional legacy I / O controller 1140 for coupling old devices (e.g., personal system 2 (PS / 2) devices) to system. In at least one embodiment, platform controller hub 1130 may also be connected to one or more Universal Serial Bus (USB) controller 1 142 connection input devices, such as keyboard and mouse 1143 combinations, camera 1144, or other USB input devices.In at least one embodiment, an instance 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 / may be external to one or more processor(s) 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 memory controller hub and peripheral controller hub within a system chipset in communication with processor(s) 1102.Inference and / or training logic 715 is used to perform inferencing and / or training operations associated with one or more embodiments. Details regarding inference and / or training logic 715 are provided below in connection with FIGS. 7A and / or 7B. In at least one embodiment, portions or all of inference and / or training logic 715 may be included in graphics processor 1500. For example, in at least one embodiment, training and / or inferencing techniques described herein may use one or more ALUs included in a graphics processor. Additionally, in at least one embodiment, inferencing and / or training operations described herein may be performed using logic different from logic illustrated in FIGS. 7A and / or 7B. In at least one embodiment, weight parameters may be stored in on-chip or off-chip memories and / or registers (shown or not shown), thus configuring ALUs of a graphics processor to perform one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.Such components may be used to automatically determine irregular eye or gaze behavior of a person, which may be indicative of impairment due to drug or alcohol consumption, and may take one or more actions based on the determination.FIG. 12 is a block diagram of a processor 1200 including one or more processor core(s) 1202A- 1202N, an integrated memory controller 1214, and an integrated graphics processor 1208, according to at least one embodiment. In at least one embodiment, processor 1200 may include additional cores up to and including additional core 1202N, represented by dashed boxes. In at least one embodiment, processor core(s) 1202A- 1202N each include one or more internal cache unit(s) 1204A- 1204N. In at least one embodiment, each processor core also has access to one or more shared cached unit(s) 1206.In at least one embodiment, internal cache unit(s) 1204A- 1204N and shared cache unit(s) 1206 represent a cache memory hierarchy within processor 1200. In at least one embodiment, cache unit(s) 1204A- 1204N may include at least one level of an instruction and data cache within each processor core and one or more levels of a shared mid-level cache, such as level 2 (L2), level 3 (L3), level 4 (L4), or other levels of a cache, where a highest level of a cache before external memory is classified as LLC. In at least one embodiment, cache coherence logic maintains coherence between different cache units 1206 and 1204A- 1204N.In at least one embodiment, processor 1200 may also include a set of one or more bus controller unit(s) 1216 and a system agent core 1210. In at least one embodiment, one or more bus controller unit(s) 1216 manage / manage a set of peripheral buses, such as one or more PCI or PCI Express buses. In at least one embodiment, system agent core 1210 provides management functionality for various processor components. In at least one embodiment, system agent core 1210 includes one or more integrated memory controllers 1214 to manage access to various external storage devices (not shown).In at least one embodiment, one or more processor core(s) 1202A- 1202N include support for simultaneous multi-threading. In at least one embodiment, system agent core 1210 includes components for coordinating and operating processor cores 1202A- 1202N during multi-threading processing. In at least one embodiment, system agent core 1210 may additionally include a power control unit (PCU) that includes logic and components to regulate one or more power states of processor cores 1202A- 1202N and graphics processor 1208.In at least one embodiment, processor 1200 additionally includes graphics processor 1208 to perform graphics processing operations. In at least one embodiment, graphics processor 1208 is coupled to shared cache unit(s) 1206, and system agent core 1210, including one or more integrated memory controllers 1214. In at least one embodiment, system agent core 1210 also includes a display controller 1211 to drive graphics processor output to one or more coupled displays. In at least one embodiment, display controller 1211 may also be a separate module coupled to graphics processor 1208 via at least one interconnect or may be integrated with graphics processor 1208.In at least one embodiment, a ring-based interconnect unit 1212 is used to couple internal components of processor 1200. In at least one embodiment, an alternative interconnect unit may be used, such as a point-to-point interconnect, a switched interconnect, or other techniques. In at least one embodiment, graphics processor 1208 is coupled to ring-based interconnect unit 1212 via an I / O link 1213.In at least one embodiment, I / O link 1213 represents at least one of several types of I / O interconnects, including an on-packet I / O interconnect that facilitates communication between various processor components and a high-performance embedded memory module 1218, such as an eSIM module. In at least one embodiment, processor core(s) 1202A- 1202N and graphics processor 1208 each use embedded memory modules 1218 as shared last level cache.In at least one embodiment, processor core(s) 1202A- 1202N is / are homogeneous cores executing a common instruction set architecture. In at least one embodiment, processor core(s) 1202A- 1202N is / are heterogeneous in instruction set architecture (ISA), where one or more of processor core(s) 1202A- 1202N execute a common instruction set, while one or more of other cores of processor core(s) 1202A- 1202N execute a subset of a common instruction set or a different instruction set. In at least one embodiment, processor core(s) 1202A- 1202N is / are heterogeneous in terms of microarchitecture, wherein one or more cores having relatively higher power consumption are coupled to one or more power cores having lower power consumption. In at least one embodiment, processor 1200 may be implemented on one or more chips or as an SoC integrated circuit.Inference and / or training logic 715 is used to perform inferencing and / or training operations associated with one or more embodiments. Details regarding inference and / or training logic 715 are provided below in connection with FIGS. 7A and / or 7B. In at least one embodiment, portions of or all inference and / or training logic 715 may be incorporated into processor 1200. For example, in at least one embodiment, training and / or inferencing techniques described herein may use one or more ALUs embodied in a graphics processor 1208, processor core(s) 1202A- 1202N, or other components in FIG. 12. Additionally, in at least one embodiment, inferencing and / or training operations described herein may be performed using logic different from logic illustrated in FIGS. 7A and / or 7B. In at least one embodiment, weight parameters may be stored in on-chip or off-chip memories and / or registers (shown or not shown), thus configuring ALUs of graphics processor 1200 to perform one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.Such components may be used to automatically determine irregular eye or gaze behavior of a person, which may be indicative of impairment due to drug or alcohol consumption, and may take one or more actions based on the determination.VIRTUALIZED COMPUTING PLATFORMFIG. 13 is an example dataflow diagram for a process 1300 of generating and deploying an image processing and inferencing pipeline, in accordance with at least one embodiment. In at least one embodiment, process 1300 may be deployed for use with imaging devices, processing devices, and / or other device types at one or more facilities 1302. The process 1300 may be performed in a training system 1304 and / or a deployment system 1306. In at least one embodiment, training system 1304 may 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 in deployment system 1306. In at least one embodiment, deployment system 1306 may be configured to offload processing and computing resources in a distributed computing environment to reduce infrastructure requirements at device 1302. In at least one embodiment, one or more applications in a pipeline may use or invoke services (e.g., inference, visualization, compute, AI, etc.) of deployment system 1306 during execution of applications.In at least one embodiment, some applications used in advanced processing and inferencing pipelines may use machine learning models or other AI to perform one or more processing steps. In at least one embodiment, machine learning models at device 1302 may be trained using data 1308 (such as imaging data) generated at device 1302 (and stored at one or more Picture Archiving and Communication System (PACS) servers at device 1302), may be trained using imaging or sequencing data 1308 from another device(s), or a combination thereof. In at least one embodiment, training system 1304 may be used to provide applications, services, and / or other resources for generating functioning, usable machine learning models to deployment system 1306.In at least one embodiment, model register 1324 may be backed up by object storage, which may support version and object metadata. In at least one embodiment, object storage may be accessible, for example, via cloud storage in a cloud platform compatible with an application programming interface (API). In at least one embodiment, machine learning models within model register 1324 may be uploaded, listed, modified, or deleted by developers or partners of a system interacting with an API. In at least one embodiment, an API may provide access to methods that allow users with corresponding permissions to associate models with applications such that models may be executed as part of execution of containerized instantiations of applications.In at least one embodiment, training system 1304 (FIG. 13 ) may include a scenario where a device 1302 trains its own machine learning model or has / have 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(s), sequencing devices, and / or other device types may be received. In at least one embodiment, after imaging data 1308 is received, AI-based annotation 1310 may be used to assist in generating annotations corresponding to imaging data 1308 to be used as ground truth data for a machine learning model. In at least one embodiment, AI-based annotation 1310 may include one or more machine learning models (e.g., convolutional neural networks (CNNs)) 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, AI-based annotation 1310 may then be used directly, or adjusted or fine tuned using an annotation tool to generate ground truth data. In at least one embodiment, AI-based annotation 1310, 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, a trained machine learning model may be referred to as output model(s) 1316 and used by deployment system 1306 as described herein.In at least one embodiment, a training pipeline may include a scenario where a device 1302 needs a machine learning model for use in performing one or more processing tasks for one or more applications in deployment system 1306, but device 1302 may not currently have such a machine learning model (or may not have a model that is optimized, efficient, or effective for such purposes). In at least one embodiment, an existing machine learning model may be selected from a model register 1324. In at least one embodiment, model register 1324 may include machine learning models trained to perform a series of different inference tasks on imaging data. In at least one embodiment, machine learning models in model register 1324 may have been trained on imaging data from devices other than device 1302 (e.g., remote devices). In at least one embodiment, machine learning models may have been trained on imaging data from one location, two locations, or any number of locations. In at least one embodiment, when training on imaging data from a specific location, training may occur at that location, or at least occur in a manner that protects confidentiality of imaging data or limits transfer of imaging data out of offices. In at least one embodiment, after a model has been trained - or partially trained - at a location, a machine learning model may be added to model register 1324. In at least one embodiment, a machine learning model may then be retrained or updated at any number of other devices, and a retrained or updated model may be made available in model register 1324. In at least one embodiment, a machine learning model may then be selected from model register 1324, referred to as output model(s) 1316, and may then be used in deployment system 1306 to perform one or more processing tasks for one or more applications of a deployment system.In at least one embodiment, a scenario may include a device 1302 requiring a machine learning model for use in performing one or more processing tasks for one or more applications in deployment system 1306, however, device 1302 may not currently have such a machine learning model (or may not have a model that is optimized, efficient, or effective for such purposes). In at least one embodiment, a machine learning model selected from model register 1324 may not be fine tuned or optimized for imaging data 1308 generated at device 1302 due to differences in populations, robustness of training data used to train a machine learning model, diversity of irregularities of training data, and / or other problems with training data. In at least one embodiment, AI-based annotation 1310 may be used to assist in generating annotations corresponding to imaging data 1308 to be used as ground truth data for retraining or updating a machine learning model. In at least one embodiment, labeled data 1312 may be used as ground truth data for training a machine learning model. In at least one embodiment, retraining or updating a machine learning model may be referred to as model training 1314. In at least one embodiment, model training 1314-e.g., AI-based annotation 1310, labeled data 1312, or a combination thereof-may be used as ground truth data for retraining or updating a machine learning model. In at least one embodiment, a trained machine learning model may be referred to as output model(s) 1316 and used by deployment system 1306 as described herein.In at least one embodiment, deployment system 1306 may include software 1318, services 1320, hardware 1322, and / or other components, features, and functionality. In at least one embodiment, deployment system 1306 may include a software "stack" such that software 1318 may be built on services 1320 and may use services 1320 to perform some or all processing tasks, and services 1320 and software 1318 may be built on hardware 1322 and may use hardware 1322 to perform processing, storage, and / or other computing tasks of deployment system 1306. In at least one embodiment, software 1318 may include any number of different containers, where each container may execute instantiation of an application. In at least one embodiment, each application may perform one or more processing tasks in an advanced processing and inferencing pipeline (e.g., inferencing, object detection, feature detection, segmentation, image enhancement, calibration, etc.). In at least one embodiment, an advanced processing and inferencing pipeline may be defined based on selections of different containers desired or required for processing imaging data 1308, in addition to containers receiving and configuring imaging data for use by each container and / or for use by a device 1302 after processing by a pipeline (e.g., for reconverting outputs to a usable data type). In at least one embodiment, a combination of containers within software 1318 (e.g., comprising a pipeline) may be referred to as a virtual instrument (as described in more detail herein), and a virtual instrument may utilize services 1320 and hardware 1322 to perform some or all processing tasks of applications instantiated in containers.In at least one embodiment, a data processing pipeline may receive input data (e.g., imaging data 1308) in a specific format in response to an inference request (e.g., a request from a user of deployment system 1306). In at least one embodiment, input data may represent one or more images, video, and / or other data representations generated by one or more imaging devices. In at least one embodiment, data may be subjected to pre-processing as part of data processing pipeline to prepare data for processing by one or more applications. In at least one embodiment, post-processing may be performed on an output of one or more inferencing tasks or other processing tasks of a pipeline to prepare output data for a next application and / or to prepare output data for transmission and / or use by a user (e.g., in response to an inferencing request). In at least one embodiment, inferencing tasks may be performed by one or more machine learning models, such as trained or deployed neural networks, which may include output model(s) 1316 of training system 1304.In at least one embodiment, tasks of data processing pipeline may be encapsulated in container(s), each representing a discrete, fully functional instantiation of an application and virtualized computing environment capable of referencing machine learning models. In at least one embodiment, containers or applications in a private domain (e.g., limited access domain) may be published by a container registry (described in more detail herein), and trained or deployed models may be stored in model registry 1324 and associated with one or more applications. In at least one embodiment, images of applications (e.g., container images) may be available in a container register, and after being selected by a user from a container register for deployment in a pipeline, an image may be used to generate a container for instantiation of an application for use by a user system.In at least one embodiment, developers (e.g., software developers, clinicians, clinicians, etc.) Applications (e.g., as containers) for performing image processing and / or inferencing on supplied data may develop, publish, and store. In at least one embodiment, development, publication, and / or storage may be performed using a software development kit (SDK) associated with a system (e.g., to ensure that a developed application and / or container conforms or is / are compatible with a system). In at least one embodiment, an application being developed with an SDK that may support at least some services 1320 may be tested locally (e.g., at a first device, data from a first device) as a system (e.g., processor 1200 of FIG. 12 ). In at least one embodiment, since DICOM objects may contain between one and hundreds of images or other types of data, and due to variation of data, a developer may be responsible for managing (e.g., setting constructs for, incorporating pre-processing into an application, etc.) extraction and preparation of incoming data. In at least one embodiment, an application, after being validated (e.g., in terms of accuracy) by process 1300, may be available in a container register for selection and / or implementation by a user to perform one or more processing tasks with respect to data at a device (e.g., a second device) of a user.In at least one embodiment, developers may then share applications or containers over a network for access and use by users of a system (e.g., process 1300 of FIG. 13 ). In at least one embodiment, completed and validated applications or containers may be stored in a container register and associated machine learning models may be stored in model register 1324. In at least one embodiment, a requesting entity - providing an inference or image processing request - may search a container register and / or model register 1324 for an application, container, dataset, machine learning model, etc., select a desired combination of items for inclusion in a data processing pipeline, and send an image processing request. In at least one embodiment, a request may include input data (and in some examples, associated patient data) necessary to perform a request, and / or may include selection of applications and / or machine learning models to be executed in processing a request. In at least one embodiment, a request may be forwarded to one or more components of deployment system 1306 (e.g., a cloud) to perform processing of data processing pipeline. In at least one embodiment, processing by deployment system 1306 may include referencing selected elements (e.g., applications, containers, models, etc.) from a container register and / or model register 1324. In at least one embodiment, after results are generated by a pipeline, results may be returned to a user for reference (e.g., for viewing in a viewing application suite executing on a / a local on-premium workstation or terminal).In at least one embodiment, services 1320 may be used to support processing or execution of applications or containers in pipelines. In at least one embodiment, services 1320 may include compute services, artificial intelligence (AI) services, visualization services, and / or other types of services. In at least one embodiment, services 1320 may provide functionality common to one or more applications in software 1318, such that functionality may be abstracted for a service that may be invoked or utilized by applications. In at least one embodiment, functionality provided by services 1320 may run dynamically and more efficiently while also well scaling by allowing applications to process data in parallel (e.g., using a parallel computing platform 1230 (FIG. 12 ). In at least one embodiment, services 1320 may be shared between and from different applications, rather than each application sharing a same functionality offered by services 1320 requiring a respective instance of services 1320. In at least one embodiment, services may include an inference server or inference engine, which may be used as non-limiting examples to perform detection or segmentation tasks. In at least one embodiment, a model training service may be included that may provide capabilities for machine learning model training and / or retraining. In at least one embodiment, a data enhancement service may further be included that may provide GPU-accelerated data (e.g., DICOM, RIS, CIS, REST compliant, RPC, raw, etc.) extraction, resizing, scaling, and / or other enhancement. In at least one embodiment, a visualization service may be used that may add image rendering effects, such as ray tracing, rasterization, denoising, sharpening, etc., to provide realism to two-dimensional (2D) and / or three-dimensional (3D) models. In at least one embodiment, virtual instrument services may be included that provide beamforming, segmentation, inferencing, imaging, and / or support for other applications within pipelines of virtual instruments.In at least one embodiment, where services 1320 include an AI service (e.g., an inference service), one or more machine learning models may be executed by invoking an inference service (e.g., an inference server) (e.g., as an API call) to execute a machine learning model(s), or processing thereof, as part of application execution. In at least one embodiment, when another application includes one or more machine learning models for segmentation tasks, an application may invoke an inference service to execute machine learning models to perform one or more processing operations associated with segmentation tasks. In at least one embodiment, software 1318 implementing an advanced processing and inferencing pipeline that includes a segmentation application and an abnormality detection application may be simplified because each application may invoke a same inference service to perform one or more inference tasks.In at least one embodiment, hardware 1322 may include GPUs, CPUs, graphics cards, an AI / deep learning system (e.g., an AI supercomputer such as NVIDIAs DGX), a cloud platform, or a combination thereof. In at least one embodiment, different types of hardware 1322 may be used to provide efficient, dedicated support for software 1318 and services 1320 in deployment system 1306. In at least one embodiment, use of GPU processing for local processing (e.g., at device 1302) may be implemented within an AI / deep learning system, in a cloud system, and / or in other processing components of deployment system 1306 to improve efficiency, accuracy, and effectiveness of image processing and generation. In at least one embodiment, software 1318 and / or services 1320 for GPU processing may be optimized with respect to deep learning, machine learning, and / or high-performance computing, as non-limiting examples. In at least one embodiment, at least a portion of computing environment of deployment system 1306 and / or training system 1304 may be executed in a data center, one or more super computers or high performance computing systems, with GPU-optimized software (e.g., hardware and software combination of NVIDIAs DGX system). In at least one embodiment, hardware 1322 may include any number of GPUs that may be invoked to perform processing of data as described herein in parallel. In at least one embodiment, a cloud platform may further include GPU processing for GPU-optimized execution of deep learning tasks, machine learning tasks, or other computing tasks. In at least one embodiment, cloud platform (e.g., NVIDIAs NGC) may be executed using / AI / deep learning supercomputer / n and / or GPU-optimized software (e.g., as provided on NVIDIAs DGX systems) as a hardware abstraction and scaling platform. In at least one embodiment, cloud platform may integrate an application container clustering system or orchestration system (e.g., KUBERNETES) on multiple GPUs to enable seamless scaling and load balancing.FIG. 14 is a system diagram for an example system 1400 for generating and deploying an imaging pipeline, according to at least one embodiment. In at least one embodiment, system 1400 may be used to implement process 1300 of FIG. 13 and / or other processes including advanced processing and inferencing pipelines. In at least one embodiment, system 1400 may include training system 1304 and deployment system 1306. In at least one embodiment, training system 1304 and deployment system 1306 may be implemented using software 1318, services 1320, and / or hardware 1322 as described herein.In at least one embodiment, system 1400 (e.g., training system 1304 and / or deployment system 1306) may be implemented in a cloud computing environment (e.g., using cloud 1426). In at least one embodiment, system 1400 may be implemented locally with respect to a healthcare facility or as a combination of cloud and local computing resources. In at least one embodiment, access to APIs in cloud 1426 may be restricted to authorized users through proprietary security measures or protocols. In at least one embodiment, a security protocol may include web tokens that may be signed by an authentication service (e.g., AuthN, AuthZ, Gluecon, etc.) and may include corresponding authorization. In at least one embodiment, virtual instrument APIs (described herein), or other instantiations of system 1400, may be constrained to a set of public IPs that have been checked or authorized for interaction.In at least one embodiment, various components of system 1400 may communicate between them and among each other using a number of different types of networks, 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 devices and components of system 1400 (e.g., for transmitting inference requests, for receiving results of inference requests, etc.) may be communicated via a data bus(s), wireless data protocols (WiFi), wired data protocols (e.g., Ethernet), etc.In at least one embodiment, training system 1304 may execute training pipeline(s) 1404 similar to those described herein with respect to FIG. 13. In at least one embodiment, training pipeline(s) 1404 may be used when one or more machine learning models in deployment pipeline(s) 1410 are to be used by deployment system 1306 to train or retrain one or more (e.g., pretrained) models and / or implement one or more of pretraining models 1406 (e.g., without need for retraining or updating). In at least one embodiment, output model(s) 1316 may be generated as a result of training pipeline(s) 1404. In at least one embodiment, training pipeline(s) 1404 may 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, a different training pipeline(s) 1404 may be used for different machine learning models used by deployment system 1306. In at least one embodiment, training pipeline(s) 1404, similar to a first example described with respect to FIG. 13, may be used for a first machine learning model, training pipeline(s) 1404, similar to a second example described with respect to FIG. 13, may be used for a second machine learning model, and training pipeline(s) 1404, similar to a third example described with respect to FIG. 13, may be used for a third machine learning model. In at least one embodiment, any combination of tasks within a training system 1304 may be used depending on what is required for each machine learning model. In at least one embodiment, one or more machine learning models may be already trained and ready for deployment, such that machine learning models may not be subjected to processing by training system 1304 and may be implemented by deployment system 1306.In at least one embodiment, output model(s) 1316 and / or pre-trained model(s) 1406 may include any types of machine learning models depending on implementation or embodiment. In at least one embodiment, machine learning models used by system 1400 may include, but are not limited to, machine learning model(s), linear regression, logistic regression, decision trees, support vector machines (SVM), naive bayes, k nearest neighbors (Knn), k means clustering, random forest, dimensionality reduction algorithms, gradient boosting algorithms, neural networks (e.g., auto-encoders, convolutional, recurrent, perceptrons, long / short term / memory (LSTM), hopfield, Boltzmann, deep belief, deconvolutional, generative opposing, supplying, supplying, supplying, supplying, supplying, supplying, supplying, supplying, supplying, supplying, and supplying, and the like, and supplying, and supplying, and supplying, and supplying, to the like, and supplying, and supplying, to the like, and supplying, to the like, and supplying, and supplying, to the machine learning, and the like, and the like, and the like, and the like, to the machine learning, and the like, and the like, respectively, and the like, and the like, and the like, and the like, and the like, and the like, and, Liquid state machine, etc.) uses / use, and / or includes other types of machine learning models.In at least one embodiment, training pipeline(s) 1404 may include AI-based annotation as described herein with respect to at least FIG. 14. In at least one embodiment, labeled data 1312 (e.g., traditional annotation) may be generated by a number of techniques. In at least one embodiment, labels or other annotations may be generated within a drawing program (e.g., an annotation program), a computerized construction (CAD) program, a labeling program, another type of program suitable for generating annotations or labels for ground truth, and / or may be drawn manually, in some examples. In at least one embodiment, ground truth data may be produced as follows: synthetic (e.g., generated from computer models or renderings), real-world (e.g., designed and produced from real-world data), machine automated (e.g., using feature analysis and learning to extract features from data and thereafter generate labels), annotated by a human (e.g., labeler, or annotation expert, defines location of labels), and / or a combination thereof. In at least one embodiment, for each instance of imaging data 1308 (or a type of data used by machine learning models), there may be corresponding ground truth data generated by training system 1304. In at least one embodiment, AI-based annotation may be performed as part of deployment pipeline(s) 1410; either in addition to or instead of AI-based annotation included in training pipelines 1404. In at least one embodiment, system 1400 may include a multi-layer platform including a software layer (e.g., software 1318) for diagnostic applications (or other types of applications) that may perform one or more medical imaging and diagnostic functions. In at least one embodiment, system 1400 may be communicatively coupled (e.g., via encrypted links) to PACS server networks of one or more devices. In at least one embodiment, system 1400 may be configured to access and reference data from PACS servers to perform operations such as training machine learning models, employing machine learning models, image processing, inferencing, and / or other operations.In at least one embodiment, a software layer may be implemented as a secure, encrypted, and / or authenticated API, via which applications or containers may be evoked (e.g., invoked) by external environments (e.g., device 1302). In at least one embodiment, applications may thereafter invoke or execute one or more services 1320 to perform computational, AI, or visualization tasks associated with respective applications, and 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, communications sent to or received from training system 1304 and deployment system 1306 may occur using a pair of DICOM adapters 1402A, 1402B.In at least one embodiment, deployment system 1306 may execute deployment pipeline(s) 1410. In at least one embodiment, deployment pipeline(s) 1410 may include any number of applications that may be sequentially, non-sequentially, or otherwise applied to imaging data (and / or other types of data) generated by imaging devices, sequencing devices, genomics devices, etc., including AI-based annotation as described above. In at least one embodiment, deployment pipeline(s) 1410 as described herein may be referred to for an individual device as a virtual instrument for a device (e.g., a virtual ultrasound instrument, a virtual CT scan instrument, a virtual sequencing instrument, etc.). In at least one embodiment, for a single device, there may be more than one deployment pipeline 1410 depending on information desired from data generated by a device. In at least one embodiment, if detections of irregularities from an MRI machine are desired, there may be a first deployment pipeline 1410, and if image enhancement is desired for output of an MRI machine, there may be a second deployment pipeline 1410.In at least one embodiment, an image generation application may include a processing task that includes use of a machine learning model. In at least one embodiment, a user may wish to use his own machine learning model or select a machine learning model from a model register 1324. In at least one embodiment, a user may implement his own machine learning model or select a machine learning model for inclusion in an application for performing a processing task. In at least one embodiment, applications may be selectable and customizable, and by defining constructs of applications, deployment and implementation of applications are presented to a particular user as a more seamless user experience. In at least one embodiment, by utilizing other features of system 1400, such as services 1320 and hardware 1322, deployment pipelines 1410 may be even more user friendly, provide easier integration, and produce more accurate, more efficient, and timely results.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 for inclusion in deployment pipelines 1410, arrange applications, modify or modify applications or parameters or constructs thereof, use and interact with deployment pipelines 1410 during deployment and / or deployment, and / or otherwise interact with deployment system 1306. In at least one embodiment, although not illustrated with respect to training system 1304, UI 1414 (or other user interface) may be used to select models for use in deployment system 1306, select models for training, or retraining, in training system 1304, and / or otherwise interact with training system 1304.In at least one embodiment, a pipeline manager 1412 can be used, in addition to an application orchestration system 1428 to manage interaction between applications or containers of deployment pipelines 1410 and services 1320 and / or hardware 1322. In at least one embodiment, pipeline manager 1412 can be configured to facilitate application-to-application, application-to-services 1320, and / or application or service-to-hardware 1322 interactions. In at least one embodiment, pipeline manager 1412, although illustrated as included in software 1318, which is not to be interpreted as limiting, may be included in services 1320 in some examples. In at least one embodiment, application orchestration system 1428 (e.g., Kubernetes, DOCKER, etc.) may include a container orchestration system that may group applications into containers as logical units for coordination, management, scaling, and deployment. In at least one embodiment, each application may be executed by associating applications of deployment pipelines 1410 (e.g., a reconstruction application, a segmentation application, etc.) with individual containers in a self-contained environment (e.g., at a kernel level) to increase speed and efficiency.In at least one embodiment, each application and / or container (or image thereof) may be individually developed, modified, and deployed (e.g., a first user or developer may develop, modify, and deploy a first application and a second user or developer may develop, modify, and deploy a second application separate from a first user or developer), which may allow focus and attention to be directed to a task of a single application and / or container without being obstructed by tasks of other applications or containers. In at least one embodiment, communication and cooperation between different containers and applications may be supported by pipeline manager 1412 and application orchestration system 1428. In at least one embodiment, application orchestration system 1428 and / or pipeline manager 1412, as long as expected input and / or output from each container or application is / is known to a system (e.g., based on constructs of applications or containers), can facilitate communication between and between and sharing of resources between and between each / each of applications or containers. In at least one embodiment, since one or more of applications or containers in deployment pipelines 1410 may share same services and resources, application orchestration system 1428 may orchestration sharing services or resources between and among different applications or containers, balance and determine load therefor. In at least one embodiment, a scheduler may be used to track resource requirements of applications or containers, current use or scheduled use of those resources, and resource availability. Thus, in at least one embodiment, a scheduler may allocate resources to different applications and distribute resources between and among applications in view of requirements and availability of a system. In some examples, a scheduler (and / or other component of the application orchestration system 1428) may determine resource availability and distribution based on constraints imposed on a system (e.g., user constraints), such as quality of service (QoS), urgency of the need for data outputs (e.g., to determine whether to perform real-time processing or delayed processing), etc.In at least one embodiment, services 1320 utilized and shared by applications or containers in deployment system 1306 may include compute services 1416, AI services 1418, visualization services 1420, and / or other types of services. In at least one embodiment, applications may invoke (e.g., execute) one or more services 1320 to perform processing operations for an application. In at least one embodiment, computing services 1416 may be utilized by applications to perform super computing or other high-performance computing (HPC) tasks. In at least one embodiment, computing service(s) 1416 may be utilized to perform parallel processing (e.g., using a parallel computing platform 1430) to process data about one or more applications and / or one or more tasks of a single application, substantially simultaneously. In at least one embodiment, parallel computing platform 1430 (e.g., NVIDIAs CUDA) may enable general purpose computing on GPUs (GPGPUs) (e.g., GPUs / Graphics 1422). In at least one embodiment, a software layer of parallel computing platform 1430 may provide access to virtual instruction sets and parallel computing elements of GPUs for execution of compute kernels. In at least one embodiment, 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, interprocess communication (IPC) calls for multiple containers and / or for multiple processes within a container may be generated to use same data from a shared segment of parallel computing platform 1430 memory (e.g., when multiple different stages of an application or multiple applications process same information). In at least one embodiment, rather than making a copy of data and moving data to different locations in memory (e.g., a read / write operation), same data at same location of 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, when data is used to generate new data as a result of processing, this information may be stored over a new location and shared between different applications. In at least one embodiment, location of data and a location of updated or modified data may be part of a definition of how payload within containers is understood.In at least one embodiment, AI services 1418 may be utilized to perform inferencing services for executing machine learning models associated with applications (e.g., having task to perform one or more processing tasks of an application). In at least one embodiment, AI services 1418 may utilize AI system 1424 to execute machine learning models (e.g., neural networks such as CNNs) for segmentation, reconstruction, object detection, feature detection, classification, and / or other inferencing tasks. In at least one embodiment, applications of deployment pipelines 1410 may use one or more output models 1316 from training system 1304 and / or other models of applications to perform inference on imaging data. In at least one embodiment, two or more examples may be available for inferencing using application orchestration system 1428 (e.g., a scheduler). In at least one embodiment, a first category may include a high priority / low latency path that may achieve higher quality of service agreements, such as performing inference on urgent requests in incidents or for a radiologist during diagnosis. In at least one embodiment, a second category may include a default priority path that may be used for requests that may not be urgent or when analysis may be performed at a later time. In at least one embodiment, application orchestration system 1428 may distribute resources (e.g., services 1320 and / or hardware 1322) based on priority paths for different inferencing tasks of AI services 1418.In at least one embodiment, shared storage may be connected to AI services 1418 within system 1400. In at least one embodiment, shared storage may be operated as a cache (or other storage device type) and used to process application inference requests. In at least one embodiment, when an inference request is sent, a request may be received from a set of API instances of deployment system 1306, and one or more instances may be selected (e.g., for best match, for load balancing, etc.) to process a request. In at least one embodiment, to process a request, a request may be input to a database, a machine learning model may be found from a model register 1324, if not yet in a cache, a validation step may ensure that matching machine learning model is loaded into a cache (e.g., shared storage), and / or a copy of a model may be stored in a cache. In at least one embodiment, a scheduler (e.g., pipeline manager 1412) may be used to launch an application referenced in a request if no application is yet running or if there are not enough instances of an application. In at least one embodiment, an inference server may be launched if an inference server has not yet been launched to execute a model. A series of inference servers per model may be launched. In at least one embodiment, in a pull model in which inference servers are clustered, models may be cached when load balancing is advantageous. In at least one embodiment, inference servers may be statically loaded in corresponding distributed servers.In at least one embodiment, inferencing may be performed using an inference server running in a container. In at least one embodiment, an instance of an inference server may be associated with a model (and optionally a plurality of versions of a model). In at least one embodiment, if an instance of an inference server does not exist, when a request to perform an inference on a model is received, a new instance may be loaded. In at least one embodiment, upon launching an inference server, a model may be communicated to an inference server, such that a same container may be used to serve different models as long as an inference server is running as a different instance.In at least one embodiment, during application execution, an inference request for a given application may be received and a container (e.g., hosting an instance of an inference server) may be loaded (if not yet) and a boot may be invoked. In at least one embodiment, pre-processing logic in a container may perform loading, decoding, and / or other additional pre-processing on incoming data (e.g., using a CPU(s) and / or GPU(s)). In at least one embodiment, after data is prepared for inference, a container may perform inference on data as needed. In at least one embodiment, this may include a single inference call to an image (e.g., a hand-held x-ray image) or may require inference on hundreds of images (e.g., a breast CT). In at least one embodiment, an application may summarize results prior to completion, which may include, but is not limited to, a single confidence value, pixel level segmentation, voxel level segmentation, generating visualization, or generating text to summarize findings. 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<1 min) priority, while others may have a lower priority (e.g., TAT<10 min). In at least one embodiment, model execution times may be measured by a requesting institution or entity, and may include partner network traversal as well as execution on an inference service.In at least one embodiment, transfer of requests between services 1320 and inferencing applications may be concealed behind a software development kit (SDK) and robust transport may be provided through a queue. In at least one embodiment, a request is queued via an API for a single application / tenant ID combination, and an SDK will retrieve a request from a queue and pass a request to an application. In at least one embodiment, a name of a queue may be provided in an environment from which an SDK will pick it up. In at least one embodiment, asynchronous communication through a queue may be useful as this may allow each instance of an application to catch up work once it becomes available. Results can be transferred back via a queue to ensure that no data is lost. In at least one embodiment, queues may also provide an ability to segment work, where highest priority work may go to a queue with most associated instances of an application, while lowest priority work may go to a queue with a single associated instance that processes tasks in received order. In at least one embodiment, an application may run on a GPU-accelerated instance generated in cloud 1426, and an inference service may perform inferencing on a GPU.In at least one embodiment, visualization services 1420 may be utilized to generate visualizations for viewing outputs of applications and / or deployment pipelines 1410. In at least one embodiment, GPUs / graphics 1422 may be utilized by visualization services 1420 to generate visualizations. In at least one embodiment, rendering effects, such as ray tracing, may be implemented by visualization services 1420 to generate higher quality visualizations. In at least one embodiment, visualizations may include, but are not limited to, 2D image rendering rings, 3D volume rendering rings, 3D volume reconstruction, 2D tomography slices, virtual reality displays, augmented reality displays, etc. In at least one embodiment, virtualized environments may be used to generate a virtual interactive display or environment (e.g., a virtual environment) for interaction by users of a system (e.g., clinicians, clinicians, radiologists, etc.). In at least one embodiment, visualization services 1420 may include an internal visualizer, kinematography, and / or other rendering or image processing capabilities or functionality (e.g., ray tracing, rasterization, internal optics, etc.).In at least one embodiment, hardware 1322 may include GPUs / graphics 1422, AI system 1424, cloud 1426, and / or other hardware used to execute training system 1304 and / or deployment system 1306. In at least one embodiment, GPUs / graphics 1422 (e.g., NVIDIAs TESLA and / or QUADRO GPUs) may include any number of GPUs used to perform processing tasks of compute services 1416, AI services 1418, visualization services 1420, other services, and / or any features or functionality of software 1318. For example, with respect to AI services 1418, GPUs / graphics 1422 may be used to perform preprocessing on imaging data (or other types of data used by machine learning models), post-processing on outputs of machine learning models, and / or to perform inferencing (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 GPUs / 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 GPUs, and cloud 1426, or at least a portion intended for deep learning or inferencing, may be executed using one or more AI systems 1424. As such, although hardware 1322 is illustrated as discrete components, this is not to be interpreted as limiting, and any components of hardware 1322 may be combined with or utilized by other components of hardware 1322.In at least one embodiment, AI system 1424 may include a dedicated computing system (e.g., a supercomputer or an HPC) configured for inferencing, deep learning, machine learning, and / or other artificial intelligence tasks. In at least one embodiment, AI system 1424 (e.g., NVIDIAs DGX) may include GPU optimized software (e.g., a software stack) that may be executed using a plurality of GPUs / graphics 1422, in addition to CPUs, RAM, storage, and / or other components, features, or functionality. In at least one embodiment, one or more AI systems 1424 may be implemented in cloud 1426 (e.g., in a data center) for performing some or all AI-based processing tasks of system 1400.In at least one embodiment, cloud 1426 may include a GPU-accelerated infrastructure (e.g., NVIDIAs NGC) that may provide a GPU-optimized platform for performing processing tasks of system 1400. In at least one embodiment, cloud 1426 may include an AI system(s) 1424 for performing one or more AI-based tasks of system 1400 (e.g., as a hardware abstraction and scaling platform). In at least one embodiment, cloud 1426 may be incorporated into an application orchestration system 1428 using 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 provided for executing at least some services 1320 of system 1400, including compute services 1416, AI services 1418, and / or visualization services 1420 as described herein. In at least one embodiment, cloud 1426 may perform small and large batch inference (e.g., executing NVIDIAs TENSOR RT), provide an accelerated parallel computing API and platform 1430 (e.g., NVIDIAs CUDA), execute an application orchestration system 1428 (e.g., KUBERNETES), provide a graphics rendering API and platform (e.g., for ray tracing, 2D graphics, 3D graphics, and / or other rendering techniques for producing higher quality kinematography), and / or provide other functionality for system 1400.FIG. 15A illustrates a dataflow diagram for a process 1500 for training, retraining, or updating a machine learning model, according to at least one embodiment. In at least one embodiment, process 1500 may be performed using a non-limiting example system 1400 of FIG. 14. In at least one embodiment, process 1500 may utilize services and / or hardware as described herein. In at least one embodiment, refined models 1512 generated by process 1500 may be executed by a deployment system for one or more containerized applications in deployment pipelines.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 dataset 1506, and / or new ground truth data associated with input data. In at least one embodiment, to retraining or update initial model 1504, output or loss layer(s) of initial model 1504 may be reset or deleted and / or replaced with an updated or new output or loss layer(s). In at least one embodiment, an initial model 1504 may previously have finely tuned parameters (e.g., weights and / or biases) remaining from previous training so that training or retraining 1514 will not last as long or require as much processing as training a model of reason. In at least one embodiment, during model training 1514, by resetting or replacing output or loss layers of initial model 1504, parameters for a new dataset may be updated and retuned based on loss calculations associated with accuracy of output or loss layers in generating predictions to a new, customer dataset 1506.In at least one embodiment, pre-trained models 1506 may be stored in a data store or register. In at least one embodiment, pre-trained models 1506 may have been trained at least in part on one or more devices that are different from a device executing process 1500. In at least one embodiment, pre-trained models 1506 to protect privacy and rights of patients, subjects, or clients at different facilities may have been trained on-premium using customer or patient data generated on-premium. In at least one embodiment, pre-trained models 1306 may be trained using cloud and / or other hardware, but confidential, privacy protected patient data may not be transferred to, used by, or accessible by other components of a cloud (or other off-premises hardware). In at least one embodiment, when pre-trained model 1506 is trained using patient data from more than one device, a pre-trained model 1506 may have been individually trained for each device before being trained on patient or customer data from another device. In at least one embodiment, for example, if customer or patient data has been freed from privacy concerns (e.g., by disambiguation, for experimental use, etc.), or if customer or patient data is included in a public dataset, customer or patient data from any number of facilities may be used to train pre-trained model 1506 on-premium and / or off-premium, such as in a data center or other cloud computing infrastructure.In at least one embodiment, in selecting applications for use in deployment pipelines, a user may also select machine learning models to be used for specific applications. In at least one embodiment, a user may not have a model for use, such that a user may select a pre-trained model for use with an application. In at least one embodiment, pre-trained model may not be optimized for generating accurate results at a customer dataset 1506 of a user's facility (e.g., based on patient diversity, demographics, types of medical imaging devices used, etc.). In at least one embodiment, prior to inserting a pre-trained model into an insertion pipeline for use with an application(s), pre-trained models may be updated, retrained, and / or fine tuned for use at a respective device.In at least one embodiment, a user may select a pre-trained model that needs to be updated, retrained, and / or fine tuned, and this pre-trained model may be referred to as an initial model 1504 for a training system within process 1500. In at least one embodiment, a customer dataset 1506 (e.g., imaging data, genomics data, sequencing data, or other types of data generated by devices at a facility) may be used to perform model training (which may include, but is not limited to, transfer learning) on initial model 1504 to generate refined model 1512. In at least one embodiment, ground truth data corresponding to customer dataset 1506 may be generated by training system 1304. In at least one embodiment, ground truth data may be generated at least in part by clinicians, researchers, physicians, clinicians, at a facility.In at least one embodiment, AI-based annotation may be used in some examples to generate ground truth data. In at least one embodiment, AI-based annotation (e.g., implemented using an AI-based annotation SDK) may utilize machine learning models (e.g., neural networks) to generate proposed or predicted ground truth data for a customer dataset. In at least one embodiment, a user may use annotation tools within a user interface (a graphical user interface (GUI)) on a computing device.In at least one embodiment, user 1510 may interact with a GUI via computing device 1508 to edit or fine tune (auto) annotations. In at least one embodiment, a polygon editing feature may be used to move vertices of a polygon to more accurate or fine tuned locations.In at least one embodiment, once customer dataset 1506 has associated ground truth data, ground truth data (e.g., from AI-based annotation, manual labeling, etc.) may be used during model training to generate refined model 1512. In at least one embodiment, customer dataset 1506 may be applied to initial model 1504 any number of times, and ground truth data may be used to update parameters of initial model 1504 until an acceptable accuracy for refined model 1512 is achieved. In at least one embodiment, once refined model 1512 has been generated, refined model 1512 can be deployed within one or more deployment pipelines at a device for performing one or more processing tasks with respect to medical imaging data.In at least one embodiment, refined model 1512 can be uploaded to pre-trained models in a model register to be selected by another device. In at least one embodiment, this process may be performed on any number of facilities, such that refined model 1512 can be further refined any number of times on new data sets to generate a more universal model.FIG. 15B is an example illustration of a client-server architecture 1532 for enhancing annotation tools with pre-trained annotation models, according to at least one embodiment. In at least one embodiment, AI-based annotation tool 1536 may be instantiated based on a client-server architecture 1532. In at least one embodiment, in imaging applications, AI-based annotation tool 1536 may assist radiologists, for example, to identify organs and irregularities. In at least one embodiment, imaging applications may include tools that help user 1510, as a non-limiting example, identify a few extreme points on a particular organ of interest in raw images 1534 (e.g., in a 3D MRI or CT scan) and automatically receive annotated results for all 2D slices of a particular organ. In at least one embodiment, results may be stored in a data store as training data 1538, and used (for example and without limitation) as ground truth data for training. In at least one embodiment, for example, if computing device 1508 sends extreme points for AI-based annotation, a deep learning model may receive this data as input and return inference results for a segmented organ or abnormality. In at least one embodiment, pre-instantiated annotation tools, such as AI-based annotation tool 1536 in FIG. 15B, may be enhanced by making API calls (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, for example, in an annotation model register. In at least one embodiment, an annotation model register may store pre-trained / pre-trained model(s) 1542 (e.g., machine learning models, such as deep learning models), which are pre-trained to perform AI-based annotation on a particular organ or abnormality. These models may be further updated using training pipelines. In at least one embodiment, pre-installed annotation tools may be enhanced over time as new labeled data is added.AUTONOMOUS VEHICLEFIG. 16A illustrates an example of an autonomous vehicle 1600, according to 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 other type of vehicle that receives one or more passengers. In at least one embodiment, vehicle 1600 may be a semi-tractor-trailer truck used for transporting cargo. In at least one embodiment, vehicle 1600 may be an aircraft, robotic vehicle, or other type of vehicle.Autonomous vehicles may be described in terms of automation levels defined by National Highway Traffic Safety Administration ("NHTSA"), a division of the U.S. Department of Transportation, and Society of Automotive Engineers ("SAE") "Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles" (e.g., Standard No. J3016-201806 published June 15, 2018, Standard No. J3016-20169 published September 30, 2016 and earlier and future versions of this standard). In at least one embodiment, vehicle 1600 may be capable of functionality in accordance with one or more of level 1 to level 5 of autonomous driving levels. For example, in at least one embodiment, vehicle 1600 may be capable of conditional automation (level 3), high automation (level 4), and / or full automation (level 5), depending on embodiment.In at least one embodiment, vehicle 1600 may include components such as a chassis, a vehicle body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other components of a vehicle, among others. In at least one embodiment, vehicle 1600 may include, but is not limited to, a propulsion system 1650 such as an internal combustion engine, a hybrid electric power plant, a full electric motor, and / or another type of propulsion system. In at least one embodiment, propulsion system 1650 may be connected to a powertrain of vehicle 1600 including, among other things, a transmission to enable propulsion of vehicle 1600. In at least one embodiment, propulsion system 1650 may be controlled in response to receiving signals from one / more throttle plate(s) / accelerator(s) 1652.In at least one embodiment, a steering system 1654 that may include, but is not limited to, a steering wheel is used to steer (e.g., along a desired route or route) vehicle 1600 when propulsion system 1650 is operating (e.g., when vehicle 1600 is in motion). In at least one embodiment, steering system 1654 may receive signals from one / more steering actuator(s) 1656. In at least one embodiment, a steering wheel for full automation functionality (level 5) may be optional. In at least one embodiment, a brake sensor system 1646 may be used to operate vehicle brakes in response to receiving signals from one / more brake actuator(s) 1648 and / or brake sensors.In at least one embodiment, one / more controllers 1636 including, but not limited to, one or more system-on-chips ("SoCs") (not shown in FIG. 16A ) and / or one / more graphics processing unit(s) ("GPU(s)") provide signals (e.g., representative of commands) to one or more components and / or one or more systems of vehicle 1600. For example, in at least one embodiment, one / more controllers 1636 may send signals to operate vehicle brakes via one / more brake actuator(s) 1648, to operate steering system 1654 via one / more steering actuator(s) 1656, to operate propulsion system 1650 via one / more throttle plate(s) / accelerator(s) 1652. In at least one embodiment, one / more controllers 1636 may include one or more (e.g., integrated) on-board computing devices that process sensor signals and issue operation commands (e.g., signals representing commands) to enable autonomous driving and / or assist a human driver in driving vehicle 1600. In at least one embodiment, one / more controllers 1636 may include a first controller for autonomous driving functions, a second controller for functional safety functions, a third controller for artificial intelligence functionality (e.g., computer vision), a fourth controller for infotainment functionality, a fifth controller for emergency redundancy, and / or other controllers. In at least one embodiment, a single controller may handle two or more of above functionalities, two or more controllers may handle a single functionality, and / or any combination thereof.In at least one embodiment, one / more controllers 1636 provide signals for controlling one or more components and / or systems of vehicle 1600 in response to sensor data received from one or more sensors (e.g., sensor inputs). In at least one embodiment, sensor data may be received from, for example, but is not limited to, global navigation satellite system ("GNSS") sensors 1658 (e.g., global positioning system sensors), RADAR sensors 1660, ultrasonic sensors 1662, LIDAR sensors 1664, inertial measurement unit ("IMU") sensors 1666 (e.g., accelerometers, gyroscopes, magnetic compass or magnetic compass, magnetometer, etc.), microphones 1696, stereo cameras 1668 wide angle cameras 1670 (e.g., fish eye cameras), infrared cameras 1672, environmental cameras 1674 (e.g., 360 degree cameras), among others, Remote cameras (not shown in FIG. 16A ), mid-range cameras (not shown in FIG. 16A ), speed sensors 1644 (e.g., for measuring the speed of the vehicle 1600), vibration sensors 1642, steering sensors 1640, brake sensors (e.g., as part of a brake sensor system 1646), and / or other types of sensors.In at least one embodiment, one / more controllers 1636 may receive input (e.g., represented by input data) from an instrument cluster 1632 of vehicle 1600 and provide output (e.g., represented by output data, display data, etc.) via a human-machine interface ("HMI") display 1634, an acoustic detector, a speaker, and / or via other components of vehicle 1600. In at least one embodiment, outputs may include information such as vehicle speed, tempo, time, map data (e.g., a highly defined map (not shown in FIG. 16A ), location data (e.g., location of vehicle 1600 such as on a map), direction, location of other vehicles (e.g., an occupancy grid), information about objects, and status of objects as perceived by controller(s) 1636, etc. For example, in at least one embodiment, HMI display 1634 may display information about presence of one or more objects (e.g., a road sign, sign of caution, traffic light circuit, etc.) and / or information about driving maneuvers that vehicle has made, or will make (e.g., changing lanes now, departure 34B to 2 miles, etc.).In at least one embodiment, vehicle 1600 may further include a network interface 1624, which may use one / more wireless antenna(s) 1626 and / or one / more modem(s) to communicate over one or more networks. For example, in at least one embodiment, network interface 1624 may be capable of communication via long term evolution ("LTE"), wideband code division multiple access ("WCDMA"), universal mobile telecommunications system ("UMTS"), global system for mobile communication ("GSM"), IMT CDMA multi-carrier ("CDMA2000") networks, etc. In at least one embodiment, wireless antenna(s) 1626 may also enable communication between objects in an environment (e.g., vehicles, mobile devices, etc.) using protocols for a local area network(s) such as Bluetooth, Bluetooth Low Energy ("LE"), Z-Wave, ZigBee, etc., and / or for a low power wide area network(s) ("LPWANs") such as LoRaWAN, SigOx, etc.Inference and / or training logic 715 is used to perform inferencing and / or training operations associated with one or more embodiments. Details regarding inference and / or training logic 715 are provided herein in connection with FIGS. 7A and / or 7B. In at least one embodiment, inference and / or training logic 715 in system of FIG. 16A may be used to inferencing or predict operations based at least in part on weight parameters calculated using neural network training operations, neural network functions and / or architectures, or neural network usage cases as described herein.Such components may be used to automatically determine irregular eye or gaze behavior of a person, which may be indicative of impairment due to drug or alcohol consumption, and may take one or more actions based on the determination.FIG. 16B illustrates an example of camera locations and fields of view for the autonomous vehicle 1600 of FIG. 16A, according to at least one embodiment. In at least one embodiment, 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 cameras may be located at different locations on vehicle 1600.In at least one embodiment, camera types for cameras may include, but are not limited to, digital cameras that may be adapted for use with components and / or systems of vehicle 1600. In at least one embodiment, camera(s) may operate at Automotive Safety Integrity Level ("ASIL") B and / or another ASIL. In at least one embodiment, camera types may be capable of any image capture rate, such as 60 frames per second (fps), 1220 fps, 240 fps, etc., depending on embodiment. In at least one embodiment, cameras may be capable of using rolling shutters (rolling shutters), global shutters (global shutters), another type of shutter, or a combination thereof. In at least one embodiment, a color filter array may include a red clear clear clear ("RCCC") color filter array, a red clear clear blue ("RCCB") color filter array, a red blue green clear ("RBGC") color filter array, a foveon X3 color filter array, a Bayer sensor ("RGGB") color filter array, a monochrome sensor color filter array, and / or another type of color filter array. In at least one embodiment, clear pixel cameras, such as cameras with an RCCC, an RCCB, and / or an RBGC color filter array, may be used in an effort to increase photosensitivity.In at least one embodiment, one / more camera(s) may be used to perform advanced functions for advanced driver assistance systems ("ADAS") (e.g., as part of a redundant or fail-safe system). For example, in at least one embodiment, a multi-function mono camera may be installed to provide functions such as lane departure warning, traffic sign assist, and smart headlight control. In at least one embodiment, one / more camera(s) (e.g., all cameras) may simultaneously record and provide image data (e.g., video).In at least one embodiment, one or more cameras may be mounted in a mounting arrangement, such as a custom (three-dimensional ("3D") printed) arrangement, to eliminate stray light and reflections from within vehicle 1600 (e.g., instrument panel reflected reflections in windshield mirrors) that may interfere with image data acquisition capabilities of camera. With respect to the side mirror mounting assembly, in at least one embodiment, side mirror assemblies may be custom 3D printed such that a camera mounting plate conforms to a shape of a side mirror. In at least one embodiment, camera(s) may be integrated into side mirrors. In at least one embodiment, for side view cameras, camera(s) may also be integrated within four pillars at each corner of a cabin.In at least one embodiment, cameras having a field of view that includes portions of an environment in front of vehicle 1600 (e.g., forward facing cameras) may be used for environmental viewing to identify forward driving routes and obstacles, as well as to assist in providing information critical to generating an occupancy grid and / or determining preferred vehicle driving routes using one or more controller(s) 1636 and / or control SoCs. In at least one embodiment, forward facing cameras may be used to perform many similar ADAS functions, such as LIDAR, including but not limited to emergency braking, pedestrian detection, and collision avoidance. In at least one embodiment, forward-facing cameras may also be used for ADAS functions and systems, including, but not limited to, lane departure warnings ("LDW"), autonomous cruise control ("ACC"), and / or other functions such as traffic sign detection.In at least one embodiment, various cameras may be used in a forward-facing configuration, for example including a monocular camera platform that includes a CMOS (Complementary Metal Oxide Semiconductor) color imager. In at least one embodiment, a wide-angle camera 1670 may be used to view objects coming into view from a periphery (e.g., pedestrians, crossing traffic, or bicycles). Although only one wide-angle camera 1670 is illustrated in FIG. 16B, 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 long-view 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, remote camera(s) 1698 may also be used for object detection and classification as well as simple object tracking.In at least one embodiment, any number of stereo cameras 1668 may also be included in a forward-facing configuration. In at least one embodiment, stereo camera(s) 1668 may include an integrated control unit comprising a scalable processing unit that may provide programmable logic ("FPGA") and a multi-core microprocessor with a controller area network ("CAN") or Ethernet integrated interface on a single chip. In at least one embodiment, such a unit may be used to generate a 3D map of an environment of vehicle 1600 including distance estimation for all points in an image. In at least one embodiment, one / more stereo camera(s) 1668 may include, among other things, a compact stereo vision sensor(s) that may include, among other things, two camera lenses (one left and right each) and an image processing chip, whereby distance from vehicle 1600 to a target object may be measured and generated information (e.g., metadata) may be used to enable 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 or alternatively to those described herein.In at least one embodiment, cameras having a field of view that includes portions of an environment lateral to vehicle 1600 (e.g., side view cameras) may be used for environment view, thus providing information used to generate and update an occupancy grid as well as generate side impact collision warnings. For example, in at least one embodiment, surround cameras 1674 (e.g., four surround cameras as illustrated in FIG. 16B ) may be positioned on vehicle 1600. In at least one embodiment, environmental cameras 1674 may include, but are not limited to, any number and combination of wide-angle cameras, fish-eye cameras, 360-degree cameras, and / or similar cameras. For example, in at least one embodiment, four fish-eye cameras may be positioned at a front, a rear, and sides of vehicle 1600. In at least one embodiment, vehicle 1600 may use three surround cameras 1674 (e.g., left, right, and rear), and may use one or more other cameras (e.g., a forward facing camera) as a fourth surround view camera.In at least one embodiment, cameras having a field of view including portions of an environment behind vehicle 1600 (e.g., rear view cameras) may be used for parking assist, environmental view, rear collision warnings, and generating and updating an occupancy grid. In at least one embodiment, a wide range of cameras may be used, including, but not limited to, cameras also suitable as forward facing cameras (e.g., remote cameras 1698 and / or mid-range cameras 1676 stereo cameras 1668 infrared cameras 1672, etc.) as described herein.Inference and / or training logic 715 is used to perform inferencing and / or training operations associated with one or more embodiments. Details regarding inference and / or training logic 715 are provided herein in connection with FIGS. 7A and / or 7B. In at least one embodiment, inference and / or training logic 715 in system of FIG. 16B may be used to inferencing or predict operations based at least in part on weight parameters calculated using neural network training operations, neural network functions and / or architectures, or neural network usage cases as described herein.Such components may be used to automatically determine irregular eye or gaze behavior of a person, which may be indicative of impairment due to drug or alcohol consumption, and may take one or more actions based on the determination.FIG. 16C is a block diagram illustrating an example system architecture for the autonomous vehicle 1600 of FIG. 16A, according to at least one embodiment. In at least one embodiment, vehicle 1600 components, features, and systems are each illustrated in FIG. 16C as being connected via a bus 1602. In at least one embodiment, bus 1602 may include, but is not limited to, a CAN data interface (alternatively referred to herein as a "CAN bus"). In at least one embodiment, a CAN may be a network within vehicle 1600 used to assist in control of various features and functionality of vehicle 1600, such as actuation of brakes, acceleration, brakes, 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., a CAN ID). In at least one embodiment, bus 1602 may be read to find steering wheel angle, ground speed, engine revolutions per minute ("RPMs"), button positions, and / or other vehicle status indicators. In at least one embodiment, bus 1602 may be a CAN bus that is ASIL-B compliant.In at least one embodiment, CAN, FlexRay, and / or Ethernet protocols may be used additionally or alternatively. 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 used for redundancy. For example, a first bus may be used for the collision avoidance functionality and a second bus may be used for the actuation control. In at least one embodiment, each bus of bus 1602 may communicate with any components of vehicle 1600, and two or more buses of bus 1602 may communicate with corresponding components. In at least one embodiment, any number of system-on-chips ("SoC(s)") 1604 (such as SoC 1604(A) and SoC 1604(B)), each of one / more controller(s) 1636, and / or each computer within a vehicle may each have access to same input data (e.g., inputs from sensors of vehicle 1600), and may be connected to a common bus, such as CAN bus.In at least one embodiment, vehicle 1600 may include one or more controllers 1636, such as those described herein with respect to FIG. 16A. In at least one embodiment, controller / s 1636 may be used for a number of functions. In at least one embodiment, controller 1636 may be coupled to any of various other components and systems of vehicle 1600 and used for control of vehicle 1600, artificial intelligence of vehicle 1600, infotainment for vehicle 1600, and / or other functions.In at least one embodiment, vehicle 1600 may include any number of SoCs 1604. In at least one embodiment, each SoCs 1604 may include, but is not limited to, central processing units ("CPU(s)") 1606, graphics processing units ("GPU(s)") 1608, processors 1610, caches 1612, accelerators 1614, data stores 1616, and / or other components and features not illustrated. In at least one embodiment, SoC(s) 1604 may be used to control vehicle 1600 in a number of platforms and systems. For example, in at least one embodiment, SoC(s) 1604 in a system (e.g., system of vehicle 1600) may be combined with a high definition ("HD") card 1622, whereby card refreshes and / or updates may be obtained via network interface 1624 from one or more servers (not shown in FIG. 16C ).In at least one embodiment, CPU(s) 1606 may include a CPU cluster or complex (alternatively referred to herein as "CCPLEX"). In at least one embodiment, CPU(s) 1606 may include multiple cores and / or level two caches ("L2" caches). For example, in at least one embodiment, CPU(s) 1606 may include eight cores in a coherent multiprocessor configuration. In at least one embodiment, CPU(s) 1606 may include four dual core clusters, each cluster having a dedicated L2 cache (e.g., a 2 megabyte (MB) L2 cache). In at least one embodiment, CPU(s) 1606 (e.g., CCPLEX) may be configured to support concurrent clustering operations, thereby allowing any combination of clusters of CPU(s) 1606 to be active at a particular time.In at least one embodiment, one or more CPU(s) 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 (automatically gated, gated off from clock signal in a gate) to conserve dynamic power; each core clock may be gated off when such a core does not actively execute instructions due to execution of wait-for-interrupt ("WFI") / wait-for-event ("WFE") instructions; each core may be independently power gated ("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(s) 1606 may further implement an improved performance state management algorithm where allowed performance states and expected wake-up times are specified and hardware / microcode for core, cluster, and CCPLEX determines best performance state that they may enter. In at least one embodiment, processing cores may support simplified sequences for entering power states in software, offloading work to microcode.In at least one embodiment, GPU(s) 1608 may include an integrated GPU (alternatively referred to herein as "iG"). In at least one embodiment, GPU(s) 1608 may be programmable and efficient for parallel workloads. In at least one embodiment, GPU(s) 1608 may use an enhanced tensor command set. In at least one embodiment, GPU(s) 1608 may include one or more streaming microprocessors, where each streaming microprocessor may include a level one cache ("L1" cache) (e.g., an L1 cache with at least 96 KB storage capacity) and two or more streaming microprocessors may share an L2 cache (e.g., an L2 cache with a 512 KB storage capacity). In at least one embodiment, GPU(s) 1608 may include at least eight streaming microprocessors. In at least one embodiment, GPU(s) 1608 may use compute application programming interface(s) (API(s)). In at least one embodiment, GPU(s) 1608 may use one or more parallel computing platforms and / or programming models (e.g., NVIDIA's CUDA model).In at least one embodiment, one or more of GPU(s) 1608 may be power optimized to achieve best performance in automotive and embedded use cases. In at least one embodiment, GPU(s) 1608 may be fabricated on, for example, a fin field effect transistor ("FinFET") circuit. In at least one embodiment, each streaming microprocessor may include a number of mixed precision processing cores divided into multiple blocks. For example, 64 PF32 cores and 32 PF64 cores could be divided into four processing blocks, among others. In at least one embodiment, each processing block could be assigned 16 FP32 cores, 8 FP64 cores, 16 INT32 cores, two NVIDIA tensor mixed precision deep learning matrix arithmetic cores, a level zero ("L0") instruction cache, a scheduler (e.g., a warp scheduler), or a sequencer, dispatch unit, and / or a 64 KB register file. In at least one embodiment, streaming microprocessors may include independent parallel integer and floating point data paths to enable efficient execution of workloads with a mixture of computational and addressing computations. In at least one embodiment, streaming microprocessors may include independent thread scheduling functions to enable finer grain synchronization and cooperation between parallel threads. In at least one embodiment, streaming microprocessors may include a combined L1 data cache and a shared memory unit to improve performance while simplifying programming.In at least one embodiment, one or more GPU(s) 1608 may include high bandwidth memory ("HBM") and / or a 16 GB HBM2 memory subsystem to provide a peak memory bandwidth of about 900 GB / second, in some examples. In at least one embodiment, synchronous graphics random access memory ("SGRAM") may be used in addition to or alternatively from HBM memory, e.g., double data rate synchronous graphics random access memory type 5 ("GDDR5").In at least one embodiment, GPU(s) 1608 may include unified memory (unified memory) technology. In at least one embodiment, address translation services ("ATS") support may be used to enable GPU(s) 1608 to directly access page tables of CPU(s) 1606. In at least one embodiment, an address translation request may be sent to CPU(s) 1606 when a GPU of memory management unit ("MMU") of GPU(s) 1608 misses. In response, in at least one embodiment, 2 CPUs of CPU(s) 1606 may search in their page tables for a virtual-to-physical mapping for an address and transmit translation back to GPU(s) 1608. In at least one embodiment, unified memory technology may enable a single unified virtual address space for memory of both CPU(s) 1606 and GPU(s) 1608, thereby facilitating programming of GPU(s) 1608 and porting applications to GPU(s) 1608.In at least one embodiment, GPU(s) 1608 may include any number of access counters that may track frequency of GPU(s) 1608 accessing memory of other processors. In at least one embodiment, access counters may help ensure that memory pages are moved into physical memory of a processor that most frequently accesses pages, thereby improving efficiency of interprocessor shared memory areas.In at least one embodiment, one or more of SoC(s) 1604 may include any number of cache(s) 1612, including those described herein. For example, in at least one embodiment, a cache / cache 1612 may include a level three cache ("L3" cache) available to both CPU(s) 1606 and GPU(s) 1608 (e.g., connected to CPU(s) 1606 and GPU(s) 1608). In at least one embodiment, cache(s) 1612 may include a write back cache that may track states of lines, e.g., using a cache coherency protocol (e.g., MEI, MESI, MSI, etc.) In at least one embodiment, an L3 cache may include 4 MB of memory or more, depending on embodiment, although smaller cache sizes may be used.In at least one embodiment, one or more of SoC(s) 1604 may include one or more accelerators 1614 (e.g., hardware accelerators, software accelerators, or a combination thereof). In at least one embodiment, SoC(s) 1604 may include a hardware accelerator cluster that may include optimized hardware accelerators and / or large on-chip memory. In at least one embodiment, large on-chip memory (e.g., 4 MB SRAM) may allow a hardware accelerator cluster to accelerate neural networks and other computations. In at least one embodiment, a hardware accelerator cluster may be used to supplement GPU(s) 1608 and offload GPU(s) 1608 from some tasks (e.g., to enable more cycles of GPU(s) 1608 to execute other tasks). In at least one embodiment, accelerators 1614 may be used for targeted workloads (e.g., perception, convolutional neural networks ("CNNs"), recurrent neural networks ("RNNs"), etc.) that are stable enough to be accessible to acceleration. In at least one embodiment, a CNN may include region-based or regional convolutional neural networks ("RCNNs") and fast RCNNs (e.g., as used for object detection) or other types of CNNs.In at least one embodiment, accelerators 1614 (e.g., hardware acceleration clusters) may include one or more deep learning accelerators ("DLA"). In at least one embodiment, DLA(s) may include, among other things, one or more tensor processing units ("TPUs"), which may be configured to additionally provide ten billion operations per second for deep learning applications and inferencing. In at least one embodiment, TPUs may be accelerators configured and optimized to perform image processing functions (e.g., for CNNs, RCNNs, etc.). In at least one embodiment, DLA(s) may be further optimized for a specific set of neural network types and floating point operations, as well as inferencing. In at least one embodiment, DLA(s) design can provide more power per millimeter than a typical general purpose GPU and typically far outbreaks performance of a CPU. In at least one embodiment, TPU(s) may perform multiple functions, including a single instance convolution function that supports, for example, INT8, INT16, and FP16 data types for both features and weights and postprocessor functions. In at least one embodiment, DLA(s) may quickly and efficiently execute neural networks, particularly CNNs, on processed or unprocessed data for any of a variety of functions including, for example, but not limited to: a CNN for object identification and detection using data from camera sensors; a CNN for estimating distances using data from camera sensors; a CNN for emergency vehicle detection and identification and detection using data from microphones; a CNN for face detection and vehicle holder identification using data from camera sensors; and / or a CNN for security relevant events.In at least one embodiment, DLA(s) may perform any function of GPU(s) 1608, and using an inference accelerator, for example, a designer may employ either DLA(s) or GPU(s) 1608 for any function. For example, in at least one embodiment, a designer may focus processing of CNNs and floating point operations on DLA(s) and leave other functions GPU(s) 1608 and / or other accelerator(s) 1614.In at least one embodiment, accelerator / s 1614 may include a Vision Accelerator ("PVA"), which may alternatively be referred to herein as a computer vision accelerator. In at least one embodiment, PVA may be configured and configured to accelerate computer vision algorithms for advanced driver assistance system ("ADAS") 1638, autonomous driving, augmented reality ("AR") applications, and / or virtual reality ("VR") applications. In at least one embodiment, a PVA may provide a balance between performance and flexibility. For example, in at least one embodiment, each PVA may include, but is not limited to, any number of reduced instruction set computing cores ("RISC"), direct memory access ("DMA"), and / or any number of vector processors.In at least one embodiment, RISC cores may interact with image sensors (e.g., image sensors of each of cameras described herein), image signal processor(s), etc. In at least one embodiment, each RISC core may include any memory space. In at least one embodiment, RISC cores may use one of multiple protocols depending on embodiment. In at least one embodiment, RISC cores may execute a real-time operating system ("RTOS"). In at least one embodiment, RISC cores may be implemented using one or more integrated circuit devices, application specific integrated circuits ("ASICs"), and / or memory devices. For example, in at least one embodiment, RISC cores may include an instruction cache and / or a close-coupled RAM.In at least one embodiment, DMA may allow components of PVA to access system memory independent of CPU(s) 1606. In at least one embodiment, DMA may support any number of functions provided to optimize PVA, including, but not limited to, support multi-dimensional addressing and / or circular addressing. In at least one embodiment, 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 gradation, vertical block gradation, and / or depth gradation.In at least one embodiment, vector processors may be programmable processors that may be configured to efficiently and flexibly execute programming of computer vision algorithms and provide signal processing capabilities. In at least one embodiment, a PVA may include a PVA core and two vector processing subsystem partitions. In at least one embodiment, a PVA core may include a processor subsystem, a DMA engine(s) (e.g., two DMA engines), and / or other peripheral devices. In at least one embodiment, a vector processing subsystem may operate as a primary processing engine of a 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, VPU core may include a digital signal processor, such as a single instruction multiple dat ("SIMD"), very long instruction word ("VLIW") digital signal processor. In at least one embodiment, a combination of SIMD and VLIW may improve throughput and speed.In at least one embodiment, each of vector processors may include an instruction cache and be coupled to a dedicated memory. As a result, in at least one embodiment, each vector processor may be configured to operate independently of other vector processors. In at least one embodiment, vector processors included in a particular PVA may be configured to use 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 in different regions of an image. In at least one embodiment, vector processors included in a particular PVA may concurrently execute different computer vision algorithms on an image or even execute different algorithms on sequential 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, a PVA may include additional error correction code (ECC) memory to improve overall system security.In at least one embodiment, accelerators 1614 may include a computer vision network on a chip and a static random-access memory ("SRAM") to provide high bandwidth, low latency SRAM to accelerators 1614. In at least one embodiment, on-chip memory may include at least 4 MB SRAM, including, for example, but not limited to, eight field configurable (field configurable) memory blocks accessible by both a PVA and a DLA. In at least one embodiment, each pair of memory blocks may include an advanced peripheral bus ("APB") interface, a configuration circuit, a controller, and a multiplexer. In at least one embodiment, any type of memory may be used. In at least one embodiment, a PVA and a DLA may access memory via a backbone that provides high speed access to memory to a PVA and a DLA. In at least one embodiment, a backbone may include a computer vision network on a chip that connects a PVA and a DLA to memory (e.g., using APB).In at least one embodiment, a computer vision network on a chip may include an interface that determines that both a PVA and a DLA provide ready and valid signals prior to transmission of control signals / addresses / data. In at least one embodiment, an interface may provide separate phases and separate channels for transmission of control signals / addresses / data, as well as burst communications for continuous data transfer. In at least one embodiment, an interface may comply with International Organization for Standardization ("ISO") 26262 or International Electrotechnical Commission ("IEC") 61508 standards, although other standards and protocols may also be used.In at least one embodiment, one or more of SoC(s) 1604 may include a hardware accelerator for real-time ray tracing (ray tracing). In at least one embodiment, a hardware accelerator may be used to quickly and efficiently determine positions and extents of objects (e.g., within a world model), generate real-time visualization simulations, interpret RADAR signals, synthesize and / or analyze sound propagation, simulate SONAR systems, general wave propagation simulation, compare to LIDAR data for purposes of localization and / or other functions, and / or for other purposes.In at least one embodiment, accelerators 1614 may have a wide field of use for autonomous driving. In at least one embodiment, a PVA may be used for important processing levels in ADAS and autonomous vehicles. In at least one embodiment, PVA capabilities are well suited for algorithmic areas requiring predictable processing at low power and low latency. In other words, a PVA is well usable in semi-dense or dense periodic computations even for small data sets that may require predictable low latency, low power runtimes. In at least one embodiment, such as in vehicle 1600, PVAs may be configured to execute classical computer vision algorithms, as they may be efficient in object detection and operate with integer math.For example, according to at least one embodiment of the technology, a PVA is used to perform computer stereo vision. In at least one embodiment, in some examples, a semi-global matching based algorithm may be used, although this is not intended to be limiting. In at least one embodiment, Level 3-5 autonomous driving applications use motion estimation / stereo matching during operation (on-the-fly) (e.g., structure of motion, pedestrian detection, lane detection, etc.) In at least one embodiment, a PVA may perform computer stereo vision functions upon inputs from two monocular cameras.In at least one embodiment, a PVA may be used to perform dense optical flow. For example, in at least one embodiment, a PVA may process raw RADAR data (e.g., using a 4D fast Fourier transform) to provide processed RADAR data. In at least one embodiment, a PVA is used for time-of-flight depth processing by processing raw time-of-flight data to provide processed time-of-flight data, for example.In at least one embodiment, a DLA may be used to operate any type of network to improve control and driving safety, for example, but not limited to, a neural network that outputs a confidence measure for each object detection. In at least one embodiment, confidence may be represented or interpreted as probability or as providing a relative "weight" of each detection as compared to other detections. In at least one embodiment, a measure of confidence allows a system to make further decisions about which detections should be considered true positive detections rather than false positive detections. In at least one embodiment, a system may set a threshold for confidence and consider only such detections as true positive detections exceeding threshold. In an embodiment using an automatic emergency braking ("AEB") system, false positive detections would cause the vehicle to automatically perform emergency braking, which is obviously undesirable. In at least one embodiment, high confidence detections may be considered as triggers for an AEB. In at least one embodiment, a DLA may operate a neural network to regression confidence value. In at least one embodiment, neural network may use as input at least a subset of parameters, such as bounding box dimensions, ground plane estimate obtained (e.g., from another subsystem), output of IMU sensor(s) 1666 that correlates with vehicle 1600 orientation, distance, 3D location estimates of object obtained from neural network and / or other sensors (e.g., LIDAR sensor(s) 1664 or RADAR sensor(s) 1660), and others.In at least one embodiment, one or more of SoC(s) 1604 may include data store / stores 1616 (e.g., memory). In at least one embodiment, data store / stores 1616 may be on-chip memory of SoC(s) 1604, which may store neural networks to execute on GPU(s) 1608 and / or a DLA. In at least one embodiment, data store / stores 1616 may be large enough to store multiple instances of neural networks for redundancy and security purposes. In at least one embodiment, data store / stores 1616 may include L2 or L3 cache(s).In at least one embodiment, one or more of SoC(s) 1604 may include any number of processor(s) 1610 (e.g., embedded processors). In at least one embodiment, processor(s) 1610 may include a boot and power management processor, which may be a dedicated processor and subsystem that perform boot power and management functions and associated security enforcement. In at least one embodiment, a boot and power management processor may be part of a boot sequence of SoC(s) 1604 and provide for runtime power management services. In at least one embodiment, a boot power and management processor may provide clock and voltage programming, support for low power system state transitions, management of thermals and temperature sensors of SoC(s) 1604, and / or management of power supply states of SoC(s) 1604. In at least one embodiment, each temperature sensor may be implemented as a ring oscillator whose output frequency is proportional to temperature, and SoC(s) 1604 may use ring oscillators to detect temperatures of CPU(s) 1606, GPU(s) 1608, and / or accelerator(s) 1614. In at least one embodiment, if temperatures are determined to exceed a threshold, a boot and power management processor may enter a temperature fault routine and place SoC(s) 1604 in a lower power consumption state and / or place vehicle 1600 in a chauffer to safe stop (e.g., place vehicle 1600 in a safe stop) mode.In at least one embodiment, processor(s) 1610 may further include a set of embedded processors that may serve as an audio processing engine, which may be an audio subsystem that enables full hardware support for multi-channel audio over multiple interfaces and a wide and flexible range of audio I / O interfaces. In at least one embodiment, an audio processing engine is a dedicated processor core with a dedicated RAM digital signal processor.In at least one embodiment, processor(s) 1610 may further include a permanently powered-on processor engine that may provide necessary hardware features to aid in managing low-power sensors and wake-up usage cases. In at least one embodiment, a permanently powered-on processor engine may include, but is not limited to, a processor core, a close-coupled RAM memory, supporting peripheral devices (e.g., timers and interrupt controllers), various I / O controller peripheral devices, and routing logic.In at least one embodiment, processor(s) 1610 may further include a security cluster engine that includes, among other things, a dedicated processor subsystem for handling security management for automotive applications. In at least one embodiment, a safety cluster engine may include, among other things, two or more processor cores, close coupled RAM memory, supporting peripherals (e.g., timers, an interrupt controller, etc.), and / or routing logic. In a safety mode, in at least one embodiment, two or more cores may operate in a lockstep mode and function as a single core with compare logic to detect differences between their operations. In at least one embodiment, processor(s) 1610 may further include a real-time camera engine, which may include, among other things, a dedicated processor subsystem for handling real-time camera management. In at least one embodiment, processor(s) 1610 may further include a high dynamic range signal processor, which may include, among other things, an image signal processor that is a hardware engine that is part of a camera processing pipeline.In at least one embodiment, processor(s) 1610 may include a video image combiner, which may be a processing block (e.g., implemented on a microprocessor) that implements video post-processing functions needed by a video playback application to produce a final image for a playback window. In at least one embodiment, a video image combiner may perform lens distortion correction on wide-angle camera(s) 1670, surround camera(s) 1674, and / or in-cabin surveillance camera sensor(s). In at least one embodiment, cabin internal monitoring camera sensor(s) is / are preferably monitored by a neural network running on another instance of SoC 1604, configured to identify and respond accordingly to events in cabin. In at least one embodiment, an in-cabin system may perform lip reads, among other things, to activate cellular service and place a phone call, dictate emails, change destination of a vehicle, activate or change an infotainment system and settings of vehicle, or provide voice-activated web surfing. In at least one embodiment, certain features are available to a driver when a vehicle is operating in an autonomous mode and are otherwise deactivated.In at least one embodiment, a video image combiner may include improved temporal noise reduction for both spatial and temporal noise reduction. For example, in at least one embodiment in which motion occurs in a video, noise reduction may weight spatial information accordingly, thereby reducing weights of information provided by adjacent frames. In at least one embodiment where an image or a portion of an image does not include motion, temporal noise reduction performed by video image combiner may use information from a previous image to reduce noise in a current image.In at least one embodiment, a video image combiner may also be configured to perform stereo equalization on frames of input stereo lenses. In at least one embodiment, a video image combiner may be further used for user interface composition when an operating system desktop is used and GPU(s) 1608 is / are not required to continually render new surfaces. In at least one embodiment, a video image combiner may be used to offload GPU(s) 1608 when GPU(s) 1608 is / are turned on and actively perform / perform 3D rendering to improve performance and responsiveness.In at least one embodiment, one or more SoCs of SoC(s) 1604 may further include a Mobile Industry Processor Interface ("MIPI") camera serial interface for receiving video and inputs from cameras, a high-speed interface, and / or a video input block that may / may be used for a camera and associated pixel input functions. In at least one embodiment, one or more SoC(s) 1604 may further include one / more input / output controllers that may be / can be controlled by software and used to receive I / O signals that are not intended for a particular role.In at least one embodiment, one or more SoC(s) 1604 may further include a wide range of peripheral interfaces to enable communication with peripherals, audio encoders / decoders ("codecs"), power management, and / or other devices. In at least one embodiment, SoC(s) 1604 may be used to process data from cameras (e.g., connected via gigabit multimedia serial link and Ethernet channels), sensors (e.g., LIDAR sensor(s) 1664, RADAR sensor(s) 1660, etc., which may be connected via Ethernet channels), data from bus 1602 (e.g., speed of vehicle 1600, steering wheel position, etc.), data from GNSS sensor(s) 1658 (e.g., connected via an Ethernet bus or a CAN bus), etc. In at least one embodiment, one or more SoC(s) of SoC(s) 1604 may further include dedicated high-performance mass storage controllers, which may include their own DMA engines and which may be used to offload CPU(s) 1606 from data management routine tasks.In at least one embodiment, SoC(s) 1604 may be an end-to-end platform having a flexible architecture that includes automation levels 3- 5 and thus provides a comprehensive functional safety architecture that utilizes computer vision and ADAS techniques for diversity and redundancy and efficiently employs, and provides a platform for a flexible, reliable drive software stack, along with deep learning tools. In at least one embodiment, SoC(s) 1604 may be faster, more reliable, and even more energy and space saving than conventional systems. For example, in at least one embodiment, accelerator / accelerators 1614, in combination with CPU(s) 1606, GPU(s) 1608, and data store(s) 1616, may provide a fast, efficient Level 3-5 autonomous vehicle platform.In at least one embodiment, computer vision algorithms can be executed on CPUs that can be configured using a high-level programming language, such as C, to execute a wide range of processing algorithms for a wide range of visual data. However, in at least one embodiment, CPUs are often unable to meet 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 real-time object detection algorithms used in in-vehicle ADAS applications and practical level 3-5 autonomous vehicles.The embodiments described herein allow multiple neural networks to be performed simultaneously and / or sequentially and the results combined to enable level 3-5 autonomous driving functionality. For example, in at least one embodiment, a CNN executing on a DLA or a discrete GPU (e.g., GPU(s) 1620) may include text and word recognition that allows traffic signs to be read and understood, including signs for which a neural network has not been specifically trained. In at least one embodiment, a DLA may further include a neural network capable of identifying, interpreting, and providing a semantic understanding of a sign and passing this semantic understanding to route planning modules running on a CPU complex.In at least one embodiment, multiple neural networks may be operated simultaneously, such as when driving at level 3, 4, or 5. For example, in at least one embodiment, a warning sign labeled "Caution: flashing lights indicate glattness" may be interpreted independently or jointly along with an electrical light from multiple neural networks. In at least one embodiment, such a warning sign itself may be identified as a traffic sign by a first deployed neural network (e.g., a trained neural network), text "flashing lights indicate smoothis" may be interpreted by a second deployed neural network informing vehicle route planning software (preferably executing on a CPU complex) that smoothis is present when flashing lights are detected. In at least one embodiment, a flashing light may be identified over multiple frames by operation of a third deployed neural network, informing vehicle routing software of a presence (or absence) of flashing lights. In at least one embodiment, all three neural networks may operate simultaneously, e.g., within a DLA and / or on GPU(s) 1608.In at least one embodiment, a face detection and owner identification CNN may use data from camera sensors to identify presence of an authorized driver and / or owner of vehicle 1600. In at least one embodiment, a continuously powered-on sensor processing engine may be used to unlock a vehicle when an owner approaches a driver door and powers lights on, and to disable such a vehicle in a security mode when an owner exits such a vehicle. In this way, the SoC(s) 1604 provide security against theft and / or forced car development.In at least one embodiment, a emergency vehicle detection and identification CNN may use data from microphones 1696 to detect and identify emergency vehicle scenes. In at least one embodiment, SoC(s) 1604 uses a CNN to classify environmental and urban sounds as well as to classify visual data. In at least one embodiment, a CNN operating on a DLA is trained to identify a relative approach speed of an emergency vehicle (e.g., using a Doppler effect). In at least one embodiment, a CNN may also be trained to identify emergency vehicles specific to a local area in which a vehicle is operating, as identified by GNSS sensor(s) 1658. In at least one embodiment, a CNN will attempt to identify European sirens when operating in Europe, and a CNN in North America will attempt to identify only North American sirens. In at least one embodiment, once a emergency vehicle is detected, a control program with assistance from one or more ultrasonic sensor(s) 1662 may be used to execute a safety routine for emergency vehicle, brake a vehicle, drive aside on a road, park a vehicle, and / or maintain a vehicle idle until emergency vehicles pass.In at least one embodiment, vehicle 1600 may include CPU(s) 1618 (e.g., discrete CPU(s) or dCPU(s)), which may be coupled to SoC(s) 1604 via a high-speed link (e.g., PCIe). In at least one embodiment, CPU(s) 1618 may include, for example, an X86 processor. CPU(s) 1618 may / may be used, for example, to perform any of a number of functions, including arbitration of potentially inconsistent results between ADAS sensors and SoC(s) 1604, and / or monitoring status and state of the controller / controllers 1636 and / or an infotainment system on a chip ("Infotainment-SoC") 1630. In one embodiment, SoC(s) 1604 may include one or more interconnects and an interconnect may include a peripheral component interconnect express (PCIe).In at least one embodiment, vehicle 1600 may include GPU(s) 1620 (e.g., discrete GP(s) or dPU(s)), which may be coupled to SoC(s) 1604 via a high-speed link (e.g., NVIDIAs NVLINK channel). In at least one embodiment, GPU(s) 1620 may provide additional artificial intelligence functionality, e.g., by executing redundant and / or different neural networks, and may / may be used to train and / or update neural networks based at least in part on inputs (e.g., sensor data) from sensors of a vehicle 1600.In at least one embodiment, vehicle 1600 may further include a network interface 1624, which may include, but is not limited to, wireless antenna(s) 1626 (e.g., one or more wireless antennas for various communication protocols, such as a cellular antenna, a Bluetooth antenna, etc.). In at least one embodiment, network interface 1624 may be used to enable wireless connectivity to Internet cloud services (e.g., with servers) and / or other network devices), other vehicles, and / or computing devices (e.g., passenger client devices). In at least one embodiment, a direct connection between vehicle 160 and another vehicle and / or an indirect connection (e.g., via networks and via Internet) may be established for communication with other vehicles. In at least one embodiment, direct connections may be provided using a vehicle-to-vehicle communication connection. In at least one embodiment, a vehicle-to-vehicle communication link may provide information to vehicle 1600 about vehicles proximate to vehicle 1600 (e.g., vehicles in front of, laterally, and / or behind vehicle 1600). In at least one embodiment, such aforementioned functionality may be part of cooperative adaptive distance control functionality of vehicle 1600.In at least one embodiment, network interface 1624 may include a SoC that provides modulation and demodulation functionality and enables / enables controllers 1636 to communicate over wireless networks. In at least one embodiment, network interface 1624 may include a radio frequency front end for up-converting from baseband to radio frequency and down-converting from radio frequency to baseband. In at least one embodiment, frequency conversions may be performed in any technically suitable manner. For example, frequency conversions may be performed by known processes and / or using super heterodyne processes. In at least one embodiment, radio frequency front end functionality may be provided by a separate chip. In at least one embodiment, network interfaces may include wireless functionality for communication over LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWA, and / or other wireless protocols.In at least one embodiment, vehicle 1600 may further include one / more data stores 1628, which may / may include off-chip (e.g., off-SoC(s) 1604) storage, among others. In at least one embodiment, 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 drives, and / or other components and / or devices capable of storing at least one bit of data.In at least one embodiment, vehicle 1600 may further include GNSS sensor(s) 1658 (e.g., GPS and / or assisted GPS sensors) to support mapping, perception, occupancy grid generation, and / or route planning functions. In at least one embodiment, any number of GNSS sensor(s) 1658 may be used, including, for example, but not limited to, GPS using a USB port with an Ethernet-to-Serial (e.g., RS-232) bridge.In at least one embodiment, vehicle 1600 may further include RADAR sensor(s) 1660. In at least one embodiment, RADAR sensor(s) 1660 may be used by vehicle 1600 for long-range vehicle detection, even in darkness and / or bad weather conditions. In at least one embodiment, functional RADAR safety levels may be ASIL B. In at least one embodiment, RADAR sensor(s) 1660 may use a CAN bus and / or bus 1602 (e.g., to transmit data generated by RADAR sensor(s) 1660) to control and access object tracking data, with access to Ethernet channels, to access raw data in some examples. In at least one embodiment, a wide range of RADAR sensor types may be used. For example, RADAR sensor(s) 1660 may be suitable for use of RADAR at the front, back, and side, among others. In at least one embodiment, one or more sensors of RADAR sensor(s) 1660 are a pulse Doppler RADAR sensor.In at least one embodiment, RADAR sensor(s) 1660 may include various configurations, such as long range (long range) with narrow field of view, short range (short range) with wide field of view, side coverage with short range, etc. In at least one embodiment, long range RADAR may be used for adaptive ranging functionality. In at least one embodiment, long-range RADAR systems may provide a wide field of view realized by two or more independent scans, e.g., within a range of 250 m (meters). In at least one embodiment, RADAR sensor(s) 1660 may aid in distinguishing between static and moving objects and may be used by ADAS system 1638 for emergency braking assist and forward collision warning. In at least one embodiment, sensors 1660(s) included in a long-range RADAR system may include, but are not limited to, monostatic multimode RADAR with multiple (e.g., six or more) fixed RADAR antennas and a high-speed CAN and FlexRay interface. In at least one embodiment with six antennas, four central antennas may generate a focused beam pattern configured to record the environment of vehicle 1600 at higher speeds with minimal interference from traffic in adjacent lanes. In at least one embodiment, two other antennas may extend field of view so that vehicles entering or leaving a lane of vehicle 1600 may be quickly detected.In at least one embodiment, mid-range RADAR systems may include, for example, a range up to 160 m (forward) or 80 m (rearward) and a field of view up to 42 degrees (forward) or 150 degrees (rearward). In at least one embodiment, short-range RADAR systems may include, among other things, any number of RADAR sensor(s) 1660 configured for installation at both ends of a rear bumper. When a RADAR sensor system is installed at both ends of a rear bumper in at least one embodiment, it may generate two beams that constantly monitor blind spot in a rearward direction and in the vicinity of a vehicle. In at least one embodiment, short-range RADAR systems in ADAS system 1638 may be used to detect blind spot and / or to aid in lane change.In at least one embodiment, vehicle 1600 may further include one / more ultrasonic sensor(s) 1662. In at least one embodiment, ultrasonic sensors 1662, which may / may be positioned at a location forward, rearward, and / or laterally on vehicle 1600, may be used for park assist and / or for generating and updating an occupancy grid. In at least one embodiment, a wide range of ultrasonic sensor(s) 1662may be used, and different ultrasonic sensor(s) 1662may be used for different detection ranges (e.g., 2.5 m, 4 m). In at least one embodiment, ultrasonic sensor(s) 1662 may operate according to ASIL B functional safety levels.In at least one embodiment, vehicle 1600 may include one / more LIDAR sensor(s) 1664. In at least one embodiment, LIDAR sensor(s) 1664 may be used for object and pedestrian detection, emergency braking, collision avoidance, and / or other functions. In at least one embodiment, LIDAR sensor(s) 1664 may operate according to functional safety level ASIL B. In at least one embodiment, vehicle 1600 may include multiple LIDAR sensors 1664 (e.g., two, four, six, etc.), which may use an Ethernet channel (e.g., to provide data to a gigabit Ethernet switch).In at least one embodiment, LIDAR sensor(s) 1664 may provide a list of objects and their distances for a 360 degree field of view. In at least one embodiment, commercially available LIDAR sensor(s) 1664 may have, for example, a range of approximately 100 m indicated with an accuracy of 2 cm to 3 cm and with support for a 100 Mbps Ethernet connection. In at least one embodiment, one or more non-protruding LIDAR sensors may be used. In such an embodiment, the LIDAR sensor(s) 1664 may include a small device that may be embedded at a location in front, rear, side, and / or corner of the vehicle 1600. In at least one embodiment, in such an embodiment, Mr / Lidar sensor(s) 1664 may provide a horizontal field of view of up to 120 degrees and a vertical field of view of up to 35 degrees, with a range of 200 m, even for low reflectivity objects. In at least one embodiment, one / more front mounted LIDAR sensor(s) 1664 may be configured for a horizontal field of view between 45 degrees and 135 degrees.In at least one embodiment, LIDAR technologies, such as 3D flash LIDAR, may also be used. In at least one embodiment, 3D flash LIDAR uses a laser flash as a transmission source to illuminate environment of vehicle 1600 up to about 200 m. In at least one embodiment, flash LIDAR unit includes, among other things, a receptor that records laser pulse transit time and reflected light on each pixel, which in turn corresponds to the range of vehicle 1600 to objects. In at least one embodiment, flash LIDAR can be used to generate highly accurate and distortion-free images of the environment with each laser flash. In at least one embodiment, four flash LIDAR sensors may be employed, one on each side of vehicle 1600. In at least one embodiment, 3D flash LIDAR systems include, but are not limited to, a solid state, 3D LIDAR camera with a rigid array without moving parts other than a fan (e.g., a non-scanning LIDAR device). In at least one embodiment, a flash LIDAR device may use a 5 nanosecond class I (eye safe) laser pulse per frame and acquire reflected laser light as a 3D range point cloud and commonly registered intensity data.In at least one embodiment, vehicle 1600 may further include one / more IMU sensor(s) 1666. In at least one embodiment, IMU sensor(s) 1666 may be disposed in a center of a rear axle of vehicle 1600. In at least one embodiment, IMU sensor(s) 1666 may include, for example, but are not limited to, accelerometers, magnetometers, gyroscope(s), magnetic compass, magnetic compass, and / or other types of sensors. In at least one embodiment, such as in six-axis applications, IMU sensor(s) 1666 may include accelerometers and gyroscopes, among others. In at least one embodiment, such as in nine-axis applications, IMU sensor(s) 1666 may include, but are not limited to, accelerometers, gyroscopes, and magnetometers.In at least one embodiment, IMU sensor(s) 1666 may be implemented as a miniature high performance GPS-based inertial navigation system ("GPS / INS") that combines micro-electro-mechanical system (MEMS)-based inertial sensors, a high sensitivity GPS receiver, and advanced Kalman filter algorithms to provide estimates of position, velocity, and location. In at least one embodiment, IMU sensor(s) 1666 may enable vehicle 1600 to estimate its heading without requiring input from a magnetic sensor by directly observing and correlating changes in speed from a GPS to IMU sensor(s) 1666. In at least one embodiment, IMU sensor(s) 1666 and GNSS sensor(s) 1658 may be combined into a single integrated unit.In at least one embodiment, vehicle 1600 may include one or more microphones 1696 mounted in and / or around vehicle 1600. In at least one embodiment, microphone(s) 1696 may be used for emergency vehicle detection and identification, among other things.In at least one embodiment, vehicle 1600 may further include any number of camera types, including stereo camera(s) 1668; wide-angle camera(s) 1670; infrared camera(s) 1672; surround camera(s) 1674; remote camera(s) 1698; mid-range camera(s) 1676; and / or other camera types. In at least one embodiment, cameras may be used to capture image data around an entire periphery of vehicle 1600. In at least one embodiment, camera types used depend on vehicle 1600. In at least one embodiment, any combination of camera types may be used to provide necessary coverage around vehicle 1600. In at least one embodiment, a number of cameras deployed may be different depending on embodiment. In at least one embodiment, vehicle 1600 may include, for example, six cameras, seven cameras, ten cameras, twelve cameras, or another number of cameras. For example, in at least one embodiment, cameras may support gigabit multimedia serial link ("GMSL") and / or gigabit Ethernet communication, among others. In at least one embodiment, each camera may be as described in more detail hereinabove with respect to FIGS. 16A and 16B.In at least one embodiment, vehicle 1600 may further include vibration sensors 1642. In at least one embodiment, vibration sensors 1642 may measure vibrations of components of vehicle 1600, such as axles. For example, in at least one embodiment, changes in vibrations may indicate a change in roadway surfaces. In at least one embodiment, when two or more vibration sensors 1642 are used, differences between vibrations may be used to determine friction or lubricity of road surface (e.g., when there is a difference in vibration between a driven axle and a freely rotating axle).In at least one embodiment, vehicle 1600 may include an ADAS system 1638. In at least one embodiment, ADAS system 1638 may include, but is not limited to, a SoC in some examples. In at least one embodiment, ADAS system 1638 may include any number and combination of, but is not limited to, an autonomous / adaptive / automatic distance control ("ACC") system, a cooperative adaptive distance control ("CACC") system, a forward impact 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, Further, the features of a lane centering ("LC") system and / or other systems, features, and / or functions may be included.In at least one embodiment, ACC system may use RADAR sensors 1660; LIDAR sensors 1664, and / or any number of cameras. In at least one embodiment, ACC system may include a longitudinal ACC system and / or a lateral ACC system. In at least one embodiment, longitudinal ACC system monitors and controls distance to another vehicle immediately in front of vehicle 1600 and automatically adjusts speed of vehicle 1600 to maintain a safe distance to forward vehicles. In at least one embodiment, side ACC system maintains distance and notifies vehicle 1600 to lane change as needed. In at least one embodiment, page ACC is associated with other ADAS applications, such as LC and CW.In at least one embodiment, a CACC system uses information from other vehicles that may be received via network interface 1624 and / or wireless antenna(s) 1626 from other vehicles via a wireless connection or indirectly via a network connection (e.g., via Internet). In at least one embodiment, direct connections may be provided through a vehicle-to-vehicle ("V2V") communication connection, while indirect connections may be provided through an infrastructure-to-vehicle ("I2V") communication connection. Generally, in V2V communication, information about immediately forward vehicles (e.g., vehicles immediately forward and in the same lane as vehicle 1600) is provided, while in I2V communication, information about traffic is provided at a longer distance. In at least one embodiment, a CACC system may include one or both of information sources I2V and V2V. In at least one embodiment, a CACC system may be more reliable thanks to information about vehicles in front of vehicle 1600, and has potential to improve traffic flow and reduce road jams.In at least one embodiment, an FCW system is designed to alert a driver of a risk so that such a driver may correctively intervene. In at least one embodiment, an FCW system uses a front-facing camera and / or RADAR sensors 1660 coupled to a dedicated processor, an FPGA, and / or an ASIC that is / are electrically coupled to provide driver feedback such as display, speaker, and / or vibrating component. In at least one embodiment, an FCW system may provide a warning, e.g., in the form of a sound, a visual warning, a vibration, and / or a rapid brake pulse.In at least one embodiment, an AEB system detects an imminent forward collision with another vehicle or object and may automatically apply brakes if a driver does not correctively intervene within a specified time or distance parameter. In at least one embodiment, AEB system may use one or more forward facing cameras and / or one or more RADAR sensors 1660 coupled to a dedicated processor, DSP, FPGA, and / or ASIC. In at least one embodiment, if an AEB system detects a risk, it will typically first warn a driver to correctively intervene to avoid a collision, and if driver does not correctively intervene, AEB system will automatically apply brakes in an effort to avoid or at least mitigate an impact of a predicted collision. In at least one embodiment, AEB system may include techniques such as dynamic brake assist and / or imminent impact braking.In at least one embodiment, an LDW system is equipped with visual, audible, and / or tactile warnings, such as steering wheel or seat vibrations, to warn driver when vehicle 1600 is passing lane markings. In at least one embodiment, an LDW system is not activated when a driver indicates an intended lane departure, such as by activating a turn signaler. In at least one embodiment, an LDW system may use forward facing cameras 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, an LKA system is a variant of an LDW system. In at least one embodiment, an LKA system provides steering input or braking to correct vehicle 1600 when vehicle 1600 begins to exit lane.In at least one embodiment, a BSW system detects and alerts a driver of vehicles in the blind spot of an automobile. In at least one embodiment, a BSW system may provide a visual, audible, and / or tactile alert to indicate that ranking or lane change is uncertain. In at least one embodiment, a BSW system may provide an additional warning when a driver uses a turn signal. In at least one embodiment, a BSW system may use rear facing camera(s) and / or RADAR sensor(s) 1660, which is / are coupled to a dedicated processor, DSP, FPGA, and / or ASIC, which is / are electrically coupled to a driver feedback, such as a display, speaker, and / or vibration component.In at least one embodiment, an RCTW system may provide visual, audible, and / or tactile notification when an object outside of range of rear camera is detected when vehicle 1600 is being driven backwards. In at least one embodiment, an RCTW system includes an AEB system to ensure that vehicle brakes are applied to avoid impact. In at least one embodiment, an RCTW system may use one or more rear-facing RADAR sensor(s) 1660, which is / are coupled to a dedicated processor, DSP, FPGA, and / or ASIC, which is / are electrically coupled to provide driver feedback such as a display, speaker, and / or vibrating component.In at least one embodiment, conventional ADAS systems may be susceptible to false positive results, which may be annoying and distracted to a driver, but are typically not catastrophic, as conventional ADAS systems warn a driver and allow that driver to decide whether a safety condition is actually present and act accordingly. In at least one embodiment, vehicle 1600 itself decides, in case of conflicting results, whether to respect a result from a primary computer or a secondary computer (e.g., a first controller or a second controller of controllers 1636). For example, in at least one embodiment, ADAS system 1638 may be a backup and / or secondary computer that provides perception information to a rationality module of backup computer. In at least one embodiment, redundant various software may run on hardware components on a rationality monitor of backup computer to detect perception errors and dynamic driving tasks. In at least one embodiment, outputs from ADAS system 1638 may be provided to a higher level MCU. In at least one embodiment, when outputs from a host and outputs from a secondary conflict, a parent MCU determines how to balance the conflict to ensure safe operation.In at least one embodiment, a host computer may be configured to provide a parent MCU with a confidence value that indicates that host computer's confidence in a selected result. In at least one embodiment, if this confidence value exceeds a threshold, this parent MCU may follow that host's instruction, regardless of whether that host provides an conflicting or inconsistent result. In at least one embodiment, where a confidence value does not reach a threshold and where primary and secondary computers indicate different results (e.g., conflict), a parent MCU may arbitrate between computers to determine an appropriate result.In at least one embodiment, a parent MCU may be configured to operate a neural network trained and configured to determine, based at least in part on outputs from primary and outputs from secondary, conditions under which that secondary provides false alarms. In at least one embodiment, neural network(s) in a parent MCU may learn when output of a secondary computer may become trusted and when not. For example, in at least one embodiment, if this secondary computer is a RADAR-based FCW system, then neural network(s) in this parent MCU may learn when an FCW system identifies metallic objects that are not actually hazards, such as a drainage grid or a manhole cover that triggers an alarm. In at least one embodiment, if a secondary computer is a camera-based LDW system, a neural network in a parent MCU may learn to override LDWs when cyclists or pedestrians are present and lane departure is actually a safest maneuver. In at least one embodiment, a parent MCU may include at least one DLA or GPU suitable for operating one or more neural networks with associated memory. In at least one embodiment, a parent MCU may include and / or be included as a component of SoC(s) 1604.In at least one embodiment, ADAS system 1638 may include a secondary computer that performs ADAS functionality using traditional computer vision rules. In at least one embodiment, this secondary computer may use classic computer vision rules (if-then), and presence of one or more neural networks in a parent MCU may improve reliability, security, and performance. For example, in at least one embodiment, an overall system becomes more fault tolerant, particularly to faults caused by software (or software-hardware interface) functionality, through different implementation and intentional non-identity. For example, in at least one embodiment, if a software bug or fault is present in software running on a host computer and non-identical software code running on a secondary computer provides a constant overall result, a parent MCU may have greater confidence that an overall result is correct and a bug in software or hardware on that host computer does not cause a significant fault.In at least one embodiment, an output of ADAS system 1638 may be fed to host perception block and / or host dynamic driving task block. For example, in at least one embodiment, if ADAS system 1638 indicates a forward impact warning due to an object immediately ahead, a perception block may use this information in identifying objects. In at least one embodiment, a secondary computer may have its own neural network that is trained to reduce a risk of false positive results as described herein.In at least one embodiment, vehicle 1600 may further include an infotainment SoC 1630 (e.g., an in-vehicle infotainment system (IVI)). Although illustrated and described as SoC, in at least one embodiment, infotainment system 1630 may not be SoC and may include, among other things, two or more discrete components. In at least one embodiment, infotainment SoC 1630 may include, among other things, a combination of hardware and software that may be used to provide vehicle 1600 with audio (e.g., music, personal digital assistant, navigation instructions, messages, radio, etc.), video (e.g., television, movies, streaming, etc.), telephone (e.g., hands-free calls), network connectivity (e.g., LTE, WiFi, etc.), and / or information services (e.g., navigation systems, rear parking assist, a radio data system, vehicle-related information such as fuel level, total distance travelled, brake fluid level, oil level, door open / close, air filter information, etc.). The infotainment SoC 1630 may include, for example, radios, disk players, navigation systems, video players, USB and Bluetooth connectivity, carputers, in-car entertainment, WiFi, steering wheel audio controls, hands-free voice control, a heads-up display ("HUD"), HMI display 1634, a telematics device, a control panel (e.g., for control and / or interaction with various components, functions, and / or systems), and / or other components. In at least one embodiment, infotainment SoC 1630 may be further used to provide information (e.g., visual and / or audible) to vehicle 1600 users, such as information from ADAS system 1638, autonomous driving information such as scheduled vehicle maneuvers, trajectories, environmental information (e.g., intersection information, vehicle information, road information, etc.), and / or other information.In at least one embodiment, infotainment SoC 1630 may include any amount and type of GPU functionality. In at least one embodiment, infotainment SoC 1630 may communicate with other devices, systems, and / or components of vehicle 1600 via bus 1602. In at least one embodiment, infotainment SoC 1630 may be coupled to a parent MCU such that a GPU of an infotainment system may perform some self-controlling functions if primary controller(s) 1636 (e.g., vehicle 1600 primary and / or backup computers) fail. In at least one embodiment, infotainment system SoC 1630 may place vehicle 1600 in a chauffer to safe stop (Chauffer to safe stop) mode, as described herein.In at least one embodiment, vehicle 1600 may further include an instrument cluster 1632 (e.g., a digital dashboard, an electronic instrument cluster, a digital instrument panel, etc.). In at least one embodiment, instrument cluster 1632 may include, but is not limited to, a controller and / or supercomputer (e.g., a discrete controller or supercomputer). In at least one embodiment, instrument cluster 1632 may include, but is not limited to, any number and combination of a set of instruments such as tachometer, fuel level, oil pressure, tachometer, odometer, turn signal indicator, gear change indicator, belt warning light(s), parking brake warning light(s), engine fault light(s), additional restraint system information (e.g., airbag) information, lighting controls, safety system controls, navigation information, etc. In some examples, information may be displayed and / or shared between infotainment SoC 1630 and instrument cluster 1632. In at least one embodiment, an instrument cluster 1632 may be included as part of an infotainment SoC 1630, or vice versa.Inference and / or training logic 715 is used to perform inferencing and / or training operations associated with one or more embodiments. Details regarding inference and / or training logic 715 are provided herein in connection with FIGS. 7A and / or 7B. In at least one embodiment, inference and / or training logic 715 in system of FIG. 16C may be used to inferencing or predict operations based at least in part on weight parameters calculated using neural network training operations, neural network functions and / or architectures, or neural network usage cases as described herein.Such components may be used to automatically determine irregular eye or gaze behavior of a person, which may be indicative of impairment due to drug or alcohol consumption, and may take one or more actions based on the determination.FIG. 16D is an illustration of a system for communication between one / more cloud-based server(s) and the autonomous vehicle 1600 of FIG. 16A, in accordance with at least one embodiment. In at least one embodiment, system may include, but is not limited to, one / more servers 1678, one / more network(s) 1690, and any number and types of vehicles, including vehicle 1600. In at least one embodiment, server / s 1678 may include, among other things, 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 using high speed connections such as, but not limited to, NVLink interfaces 1688 developed by NVIDIA and / or PCIe connections 1686. In at least one embodiment, GPUs 1684 are connected via an NVLink and / or NVSwitch SoC, and GPUs 1684 and PCIe switches 1682 are connected via PCIe connections. Although eight GPUs 1684, two CPUs 1680, and four PCIe switches 1682 are illustrated, this is not intended to be limiting. In at least one embodiment, each of servers 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, server(s) 1678 may include eight, sixteen, thirty-two, and / or more GPUs 1684, respectively.In at least one embodiment, server / s 1678 may receive, via network(s) 1690 and from vehicles, image data representing images showing unexpected or changed road conditions, such as recently begun paving operations. In at least one embodiment, server / s 1678 may transmit neural networks 1692, updated or otherwise, and / or map information 1694 including, but not limited to, traffic and road condition information, via network(s) 1690 and to vehicles. In at least one embodiment, updates to map information 1694 may include, but are not limited to, updates to a HD map 1622, e.g., information about worksites, holes, diversions, floods, and / or other obstacles. In at least one embodiment, neural networks 1692 and / or map information 1694 may result from new training and / or experience represented in data received from any number of vehicles in an environment, and / or based at least in part on training performed at a data center (e.g., using one / more server(s) 1678 and / or other servers).In at least one embodiment, servers 1678 may be used to train machine learning models (e.g., neural networks) based at least in part on training data. In at least one embodiment, training data may be generated by vehicles and / or in a simulation (e.g., using a game engine). In at least one embodiment, any number of training data are tagged (e.g., when associated neural network benefits from supervised learning) and / or subjected to other preprocessing. In at least one embodiment, any set of training data is not tagged and / or preprocessed (e.g., if associated neural network does not require supervised learning). In at least one embodiment, after training machine learning models, machine learning models of vehicles may be used (e.g., transmitted to vehicles via network(s) 1690), and / or machine learning models may be used by one / more server(s) 1678 for remote monitoring of vehicles.In at least one embodiment, servers 1678 may receive data from vehicles and apply data to actual real-time neural networks for intelligent real-time inferencing. In at least one embodiment, server / s 1678 may include super computers for deep learning and / or dedicated AI computers operating with GPU(s) 1684, such as DGX and DGX station machines developed by NVIDIA. However, in at least one embodiment, servers 1678 may include deep learning infrastructure that uses CPU-powered data centers.In at least one embodiment, deep learning infrastructure from server(s) 1678 may be capable of fast real-time inferencing and use this capability to assess and verify state of processors, software, and / or associated hardware in vehicle 1600. For example, in at least one embodiment, deep learning infrastructure may receive periodic updates from vehicle 1600, such as a sequence of images and / or objects that vehicle 1600 has located in that sequence of images (e.g., by computer vision and / or other machine learning object classification techniques). In at least one embodiment, deep learning infrastructure may operate its own neural network to identify objects and compare them to objects identified by vehicle 1600, and if results do not match and deep learning infrastructure concludes that AI in vehicle 1600 is faulty, servers 1678 may send a signal to vehicle 1600 and instruct a vehicle 1600 fail safe computer to take control of notifying passengers and making a safe parking maneuver.In at least one embodiment, server / s 1678 may include GPU(s) 1684, and one or more programmable inference accelerators (e.g., NVIDIAs TensorRT 3 devices). In at least one embodiment, a combination of GPU-powered servers and inferencing acceleration may enable real-time responsiveness. In at least one embodiment, such as when performance is less critical, servers operated by CPUs, FPGAs, and other processors may be used for inferencing purposes.Various embodiments may be described by the following clause:Clause 1. A method comprising:presenting a pattern of light motion using an automated light device including one or more light sources;acquiring image data of at least one eye region of a person while presenting the pattern of light motion;providing at least a subset of the image data as input to a neural network;receiving an indication of impairment of the subject calculated using the neural network based at least in part on an eye response of the subject to the pattern of light motion; andperforming a responsive action corresponding to the indication of the impairment.Clause 2. the method of Clause 1, wherein the responsive action includes preventing the person from performing an operation or providing a recommendation to the person not to perform the operation in a current state of the impairment.Clause 3. The method of any preceding clause, wherein the automated lighting device is mounted in a vehicle or machine and is configured to present the pattern of light motion at a location above the eye region of the person over at least a particular horizontal angle range.Clause 4th The method of any preceding clause, wherein the automated lighting device is mounted on or positioned on a sun visor to present the light pattern on a windshield of the vehicle or machine.Clause 5. the method of any preceding clause, wherein the automated lighting device includes at least one of a light bar, a two-dimensional light array, or a head-up display projector to present the pattern of the light motion.Clause 6. the method of any preceding clause, further comprising:instructing the person to avoid head movement for the duration of the presenting and to track the pattern of light movement using only a change in the viewing direction.Clause 7. the method of any preceding clause, wherein the neural network is trained to infer the indication of impairment of the subject based in part on eye response, thereby demonstrating at least one of: lack of smooth tracking, nystagmus at maximum deviation, entry of nystagmus at a 45-degree viewpoint, lack of eye coverage, pupil size response to light changes that is slower than a particular response speed, or convergence of the instant based on data for two eyes of the subject.Clause 8th The method of any preceding clause, further comprising:Dynamically adjusting the pattern of light movement based in part on a particular response or condition of the person during presenting.Clause 9th The method of any preceding clause, further comprising:comparing a frequency of eye movement of the person with a frequency of movement of the pattern of light movement.Clause 10. A processor comprising:one or more circuits operable to:causing an automated light source to present a pattern of light motion;causing image data of a person to be captured during presentation of the pattern of light movement;providing a portion of the image data corresponding to an eye region as an input to a neural network;receiving an inferred impairment value for the person, wherein the inferred impairment value is calculated using the neural network based on the portion of the image data; and performing a responsive action corresponding to the indication of the impairment.Clause 11th The processor of Clause 10, wherein the responsive action includes preventing the person from performing an operation or providing a recommendation to the person not to perform the operation in a current state of the impairment.Clause 12th The processor of Clause 10 or Clause 11, wherein the automated lighting device is mounted to present the pattern of light motion to a location above the eye region of the subject over at least a particular horizontal angle range.Clause 13 The processor of clause 10-12, wherein the one or more circuits further serve:providing instructions for presentation to the person to avoid head movement during presentation and to track the pattern of light movement using only a change in gaze direction.The clause 14th processor of clause 10-13, wherein the one or more circuits further serve:Dynamically adjusting the pattern of light movement based in part on a particular response or condition of the person during presenting.Clause 15. The processor of Clause 10-14, wherein the processor consists of at least one of:a system for performing simulation operations;a system for performing simulation operations for testing or validating autonomous machine applications;a system for performing digital twining operations;a system for performing a light transport simulation;a system for rendering a graphical output;a system for performing deep learning operations;a system implemented using an edge device; a system for generating or presenting virtual reality (VR) content; a system for generating or presenting augmented reality (AR) content; a system for generating or presenting mixed reality (MR) content; a system involving one or more virtual machines (VMs);a system implemented at least partially in a data center; a system for performing hardware tests using simulation; a system for generating synthetic data;a collaborative content creation platform for 3D assets; or a system implemented at least in part using cloud computing resources.Clause 16. A system comprising:one or more processors to acquire image data representative of a gaze change of a person during presentation of a pattern of light movement by an automated lighting device and infer an impairment value for the person using a neural network receiving the image data as input.Clause 17th The system of Clause 16, wherein the one or more processors further serve:performing a responding action corresponding to the degradation value.Clause 18th System of Clause 16 or Clause 17, 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.Clause 19th System of Clause 16-18, wherein the impairment value is inferred using at least one neural network trained to infer at least one user behavior from user eye movement.The clause 20th system of any of clause 16-19, wherein the system comprises at least one of:a system for performing simulation operations;a system for performing simulation operations for testing or validating autonomous machine applications;a system for performing digital twining operations;a system for performing a light transport simulation;a system for rendering a graphical output;a system for performing deep learning operations;a system implemented using an edge device;a system for generating or presenting virtual reality (VR) content;a system for generating or presenting augmented reality (AR) content;a system for generating or presenting mixed reality (MR) content;a system involving one or more virtual machines (VMs);a system implemented at least partially in a data center;a system for performing hardware tests using simulation;a system for generating synthetic data;a collaborative content creation platform for 3D assets; or a system implemented at least in part using cloud computing resources.Other variations are within the spirit of the present disclosure. Thus, while the disclosed techniques are susceptible to various modifications and alternative constructions, certain illustrated embodiments thereof are illustrated in the drawings and have been described in detail above. It should be understood, however, that there is no intention to limit the disclosure to any specific form or forms disclosed, but on the contrary, the intention is to cover all modifications, alternative constructions and equivalents as may come within the spirit and scope of the disclosure as defined in the appended claims.The use of the terms "a" and "an" and "the" and reference terms in the context of describing the disclosed embodiments (particularly in the context of the following claims) are to be construed to cover both singular and plural unless otherwise stated herein or clearly conflicting with the context and not as a definition of a term. The terms "comprising", "having", "including" and "containing" are to be construed as open ended terms (meaning "including but not limited to") unless otherwise noted. The term "connected," when unmodified and related to physical connections, is to be construed as partially or wholly contained within, attached to, or joined together, even if there is something in between. The denomination herein is intended to be a quick method of individually referring to each separate value falling within the range unless otherwise specified herein, and each separate value is incorporated into the specification as though individually specified herein. The use of the term "set" (e.g., "a set of things") or "subset", unless otherwise noted or contradicted by context, is to be construed as a non-empty collection comprising one or more elements. Furthermore, unless otherwise noted or contradicted by context, the term "subset" of a corresponding set does not necessarily denote a proper subset of the corresponding set, however subset and corresponding set may be the same.A conjunctival phrase, such as terms in the form of "at least one of A, B, and C" or "at least one of A, B, and C", unless specifically stated otherwise or otherwise in significant contradiction by the context, is to be otherwise understood with the context as generally used to present that a ding, term, etc., may be either A or B or C or a non-empty subset of the set of A and B and C. For example, in an illustrative example of a set having three elements, the conjunctival expressions "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}. Such a conjunctival phrase is therefore generally not intended to imply that certain embodiments require that at least one of A, at least one of B and at least one of C be present. Moreover, unless otherwise noted or contradicted by context, the term "plurality" indicates a state that is plural (e.g., "a plurality of things" indicates multiple things). A plurality is at least two things, but may be more when indicated either explicitly or by context. Furthermore, unless otherwise indicated or clear from context, the term "based on" means "based at least in part on" and not "based exclusively on.".Operations of processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. In at least one embodiment, a process such as those described herein (or variants and / or combinations thereof) is performed under control of one or more computer systems configured with executable instructions and implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) executed together on one or more processors, by hardware, or combinations thereof. In at least one embodiment, code is stored on a computer readable storage medium, for example, in the form of a computer program comprising a plurality of instructions executable by one or more processors. In at least one embodiment, a computer readable storage medium is a non-transitory computer readable storage medium that excludes transitory signals (e.g., propagating transient electrical or electromagnetic transmission) but includes non-transitory data storage circuitry (e.g., buffers, caches, and queues) within transceivers of transitory signals. 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 having stored thereon (or on other memory to store executable instructions) executable instructions that, when executed by (i.e., as a result of) one or more processors of a computing system, cause a computing system to perform operations described herein. A set of non-transitory computer readable storage media, in at least one embodiment, comprises a plurality of non-transitory computer readable storage media, and in one or more individual non-transitory storage media of a plurality of non-transitory computer readable storage media, all code is absent, while a plurality of non-transitory computer readable storage media collectively store all code. In at least one embodiment, executable instructions are executed such that various instructions are executed by various processors - for example, a non-transitory computer readable storage medium stores instructions and a main central processing unit ("CPU") executes some instructions while a graphical processing unit ("GPU") executes other instructions. In at least one embodiment, different components of a computing system include separate processors, and different processors execute different subsets of instructions.Accordingly, in at least one embodiment, computing systems are configured to implement one or more services that alone or together perform operations of processes described herein, and such computing systems are configured with applicable hardware and / or software that enable operations to be performed. Further, a computing system implementing at least one embodiment of the present disclosure is a single device, and in another embodiment is a distributed computing system that includes multiple devices that operate differently, such that distributed computing systems perform operations described herein, and such that a single device does not perform all operations.The use of an example and all examples or exemplary terminology (e.g., "such as") as provided herein is intended to make embodiments of the disclosure more fully understood and not to limit the scope of the disclosure unless otherwise claimed. No language in the specification is intended to mean that an unclaimed element is essential to the practice of the disclosure.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 incorporated by reference and set forth in their entirety herein.In the specification and claims, the terms "coupled" and "connected" may be used together with their derivatives. It should be understood that these terms may not be intended as synonyms for each other. Rather, 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.Unless specifically stated otherwise, it may be appreciated throughout the specification that terms such as "processing," "computing," "computing," "determining," or the like, refer to operations and / or processes of a computer or computing system, or similar electronic computing device, that manipulate data represented as physical, such as electronic quantities within the computing system's registers and / or memories and / or transform data represented as physical quantities within the computing system's memories, registers, or other such information storage, transfer, or display devices.In a similar manner, the term "processor" may refer to a device or portion of a device that processes electronic data from registers and / or memories and transforms that electronic data into other electronic data that may be stored in registers and / or memories. As non-limiting examples, the "processor" may be a CPU or a GPU. A "computing platform" may include one or more processors. As used herein, "software" processes may include, for example, software and / or hardware entities that perform work over time, such as tasks, threads, and smart agents. Moreover, each process may refer to multiple processes for executing instructions sequentially or in parallel, continuously, or intermittently. The terms "system" and "method" are used interchangeably herein in that the system may include one or more methods and methods may be considered a system.In the present document, references may be made to obtaining, detecting, receiving, or inputting analog or digital data to a subsystem, computer system, or computer-implemented machine. Obtaining, acquiring, receiving, or inputting analog and digital data can be realized in various ways, such as by receiving data as parameters of a function call or a call to an application programming interface. In some implementations, the process of obtaining, capturing, receiving, or inputting analog and digital data may be realized by transferring data over a serial or parallel interface. In another implementation, the process of obtaining, capturing, receiving, or inputting analog and digital data may be realized by transferring data over a computing network from a providing entity to a capturing entity. References may also be made to providing, outputting, transmitting, transmitting, or presenting analog or digital data. In various examples, the process for providing, outputting, transmitting, transmitting, or presenting analog or digital data may be realized by transferring data as input or output parameters of a function call, an application programming interface parameter, or an inter-process communication mechanism.Although the discussion above sets forth example implementations of described techniques, other architectures may be used to implement the described functionality and are intended to be within the scope of this disclosure. Furthermore, although specific distributions of responsibility are defined above for the purpose of discussion, various functions and responsibility are distributed and divided in different ways depending on circumstances.Furthermore, although the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject matter claimed in the appended claims is not necessarily limited to the specific features or acts described. Rather, specific features and acts are disclosed as example forms of implementing the claims.It is to be understood that aspects and embodiments are described above by way of example only and that modifications in detail may be made within the scope of the claims.Any apparatus, method, and feature disclosed in the specification and (where applicable) in the claims and drawings may be provided independently or in any suitable combination.Reference numerals appearing in the claims are for illustration purposes only and are not intended to have any limiting effect on the scope of the claims.References included in the specificationThis list of documents cited by the applicant has been produced in an automated manner and is only included for the better information of the reader. The list is not part of the German patent application or utility model application. The DPMA does not take any adhesion for any faults or omissions.Cited Non-Patent LiteratureTaxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles" (e.g., Standard No. J3016-201806 published June 15, 2018

[0156] Standard No. J3016-20169 published September 30, 2016

[0156] ISO") 26262

[0197] IEC") 61508

[0197]

Claims

A method comprising: presenting, using an automated light machine that includes one or more light sources, a pattern of light motion; acquiring image data from at least one eye region of a person while presenting the pattern of light motion; providing at least a subset of the image data as input to a neural network; receiving an indication of impairment of the person calculated using the neural network based at least in part on an eye response of the person to the pattern of light motion; and performing a responsive action corresponding to the indication of impairment.The method of claim 1, wherein the responsive action includes preventing the person from performing an operation or providing a recommendation to the person not to perform the operation in a current state of impairment.The method of any preceding claim, wherein the automated lighting device is mounted in a vehicle or machine and is configured to present the pattern of light movement at a location above the eye region of the person over at least a particular horizontal angle range.The method of any preceding claim, wherein the automated lighting device is mounted on or positioned on a sun visor to present the light pattern on a windshield of the vehicle or machine.The method of any preceding claim, wherein the automated lighting device includes at least one of a light bar, a two-dimensional light array, or a head-up display projector for presenting the pattern of the light motion.The method of any preceding claim, further comprising: instructing the person to avoid head movement for the duration of the presenting and to track the pattern of light movement using only a change in gaze direction.The method of any preceding claim, wherein the neural network is trained to infer the indication of impairment of the subject based in part on eye response, thereby demonstrating at least one of: lack of smooth tracking, nystagmus at maximum deviation, entry of nystagmus at a 45 degree viewpoint, lack of eye coverage, pupil size response to light changes that is slower than a particular response speed, or convergence of the instant based on data for two eyes of the subject.The method of any preceding claim, further comprising: dynamically adjusting the pattern of light movement based in part on a particular response or condition of the person during presenting.The method of any preceding claim, further comprising: comparing a frequency of eye movement of the subject to a frequency of movement of the pattern of light movement.A processor comprising: one or more circuits operable to: cause an automated light source to present a pattern of light motion; cause image data to be captured to a person during a presentation of the pattern of light motion; provide a portion of the image data corresponding to an eye region as input to a neural network; receive an inferred impairment value for the person, wherein the inferred impairment value is calculated using the neural network based on the portion of the image data; and perform a responsive action corresponding to the indication of the impairment.The processor of claim 10, wherein the responsive action includes preventing the person from performing an operation or providing a recommendation to the person not to perform the operation in a current state of impairment.The processor of claim 10 or claim 11, wherein the automated lighting device is mounted to present the pattern of light movement to a location above the eye region of the subject over at least a particular horizontal angular range.The processor of claims 10-12, wherein the one or more circuits are further to: provide instructions for presentation to the person to avoid head movement during presentation and track the pattern of light movement using only a change in gaze direction.The processor of claims 10-13, wherein the one or more circuits are further to: dynamically adjust the pattern of light motion based in part on a particular response or condition of the person during presenting.The processor of claims 10-14, wherein the processor consists of at least one of: a system for performing simulation operations; a system for performing simulation operations for testing or validating autonomous machine applications; a system for performing digital twinning operations; a system for performing a light transport simulation; a system for rendering a graphical output; a system for performing deep learning operations; a system implemented using an edge device; a system for generating or presenting virtual reality (VR) content; a system for generating or presenting augmented reality (AR) content; a system for generating or presenting mixed reality (MR) content; a system involving one or more virtual machines (VMs); a system implemented at least partially in a data center; a system for performing hardware tests using simulation; a system for generating synthetic data; a platform for collaborative content creation for 3D assets; or a system implemented at least partially using cloud computing resources.A system comprising: one or more processors to acquire image data representative of a gaze change of a person during presentation of a pattern of light movement by an automated lighting device and infer an impairment value for the person using a neural network receiving the image data as input.The system of claim 16, wherein the one or more processors are further to: perform a responsive action corresponding to the impairment value.The system of claim 16 or claim 17, 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.The system of claims 16-18, wherein the impairment value is inferred using at least one neural network trained to infer at least one user behavior from the user eye motion.The system of any of claims 16-19, wherein the system comprises at least one of: a system for performing simulation operations; a system for performing simulation operations for testing or validating autonomous machine applications; a system for performing digital twinning operations; a system for performing a light transport simulation; a system for rendering a graphical output; a system for performing deep learning operations; a system implemented using an edge device; a system for generating or presenting virtual reality (VR) content; a system for generating or presenting augmented reality (AR) content; a system for generating or presenting mixed reality (MR) content; a system involving one or more virtual machines (VMs); a system implemented at least partially in a data center; a system for performing hardware tests using simulation; a system for generating synthetic data; a platform for collaborative content creation for 3D assets; or a system implemented at least partially using cloud computing resources.

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