Abstract: In various examples, systems and methods are disclosed that provide highly accurate gaze predictions that are specific to a particular user by generating and applying, in deployment, personalized calibration functions to outputs and/or layers of a machine learning model. The calibration functions corresponding to a specific user may operate on outputs (e.g., gaze predictions from a machine learning model) to provide updated values and gaze predictions. The calibration functions may also be applied one or more last layers of the machine learning model to operate on features identified by the model and provide values that are more accurate. The calibration functions may be generated using explicit calibration methods by instructing users to gaze at a number of identified ground truth locations within the interior of the vehicle. Once generated, the calibration functions may be modified or refined through implicit gaze calibration points and/or regions based on gaze saliency maps.

Abstract: A neural network system leverages dual attention, specifically both spatial attention and channel attention, to jointly estimate heart rate and respiratory rate of a subject by processing images of the subject. A motion neural network receives images of the subject and estimates heart and breath rates of the subject using both spatial and channel domain attention masks to focus processing on particular feature data. An appearance neural network computes a spatial attention mask from the images of the subject and may indicate that features associated with the subject's face (as opposed to the subject's hair or shoulders) to accurately estimate the heart and/or breath rate. Channel-wise domain attention is learned during training and recalibrates channel-wise feature responses to select the most informative features for processing. The channel attention mask is learned during training and can be used for different subjects during deployment.

Abstract: In various examples, color harmonization is applied to images of an environment in a reference light space. For example, different cameras on an ego-object may use independent capturing algorithms to generate processed images of the environment representing a common time slice using different capture configuration parameters. The processed images may be transformed into deprocessed images by inverting one or more stages of image processing to transform the processed images into a reference light space of linear light, and color harmonization may be applied to the deprocessed images in the reference light space. After applying color harmonization, corresponding image processing may be reapplied to the harmonized images using corresponding capture configuration parameters, the resulting processed harmonized images may be stitched into a stitched image, and a visualization of the stitched image may be presented (e.g., on a monitor visible to an occupant or operator of the ego-object).

Abstract: In various examples, color statistic(s) from ground projections are used to harmonize color between reference and target frames representing an environment. The reference and target frames may be projected onto a representation of the ground (e.g., a ground plane) of the environment, an overlapping region between the projections may be identified, and the portion of each projection that lands in the overlapping region may be taken as a corresponding ground projection. Color statistics (e.g., mean, variance, standard deviation, kurtosis, skew, correlation(s) between color channels) may be computed from the ground projections (or a portion thereof, such as a majority cluster) and used to modify the colors of the target frame to have updated color statistics that match those from the ground projection of the reference frame, thereby harmonizing color across the reference and target frames.

Abstract: In various examples, two or more cameras in an automotive surround view system generate two or more input images to be stitched, or combined, into a single stitched image. In an embodiment, to improve the quality of a stitched image, a feedback module calculates two or more scores representing errors between the stitched image and one or more input images. If a computed score indicates structural errors in the stitched image, the feedback module calculates and applies one or more geometric transforms to apply to the one or more input images. If a computed score indicates color errors in the stitched image, the feedback module calculates and applies one or more photometric transforms to apply to the one or more input images.

Abstract: In various examples, sensor parameter calibration techniques for in-cabin monitoring systems and applications are presented. An occupant monitoring system (OMS) is an example of a system that may be used within a vehicle or machine cabin to perform real-time assessments of driver and occupant presence, gaze, alertness, and/or other conditions. In some embodiments, a calibration parameter for an interior image sensor is determined so that the coordinates of features detected in 2D captured images may be referenced to an in-cabin 3D coordinate system. In some embodiments, a processing unit may detect fiducial points using an image of an interior space captured by a sensor, determine a 2D image coordinate for a fiducial point using the image, determine a 3D coordinate for the fiducial point, determine a calibration parameter comprising a rotation-translation transform from the 2D image coordinate and the 3D coordinate, and configure an operation based on the calibration parameter.

Abstract: Systems and methods for more accurate and robust determination of subject characteristics from an image of the subject. One or more machine learning models receive as input an image of a subject, and output both facial landmarks and associated confidence values. Confidence values represent the degrees to which portions of the subject's face corresponding to those landmarks are occluded, i.e., the amount of uncertainty in the position of each landmark location. These landmark points and their associated confidence values, and/or associated information, may then be input to another set of one or more machine learning models which may output any facial analysis quantity or quantities, such as the subject's gaze direction, head pose, drowsiness state, cognitive load, or distraction state.

Abstract: State information can be determined for a subject that is robust to different inputs or conditions. For drowsiness, facial landmarks can be determined from captured image data and used to determine a set of blink parameters. These parameters can be used, such as with a temporal network, to estimate a state (e.g., drowsiness) of the subject. To improve robustness, an eye state determination network can determine eye state from the image data, without reliance on intermediate landmarks, that can be used, such as with another temporal network, to estimate the state of the subject. A weighted combination of these values can be used to determine an overall state of the subject. To improve accuracy, individual behavior patterns and context information can be utilized to account for variations in the data due to subject variation or current context rather than changes in state.

Abstract: Apparatuses, systems, and techniques are described to determine locations of objects using images including digital representations of those objects. In at least one embodiment, a gaze of one or more occupants of a vehicle is determined independently of a location of one or more sensors used to detect those occupants.

Abstract: In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

Abstract: In various examples, systems and methods are disclosed herein for a vehicle command operation system that may use technology across multiple modalities to cause vehicular operations to be performed in response to determining a focal point based on a gaze of an occupant. The system may utilize sensors to receive first data indicative of an eye gaze of an occupant of the vehicle. The system may utilize sensors to receive second data indicative of other data from the occupant. The system may then calculate a gaze vector based on the data indicative of the eye gaze of the occupant. The system may determine a focal point based on the gaze vector. In response to determining the focal point, the system causes an operation to be performed in the vehicle based on the second data.

Abstract: In various examples, systems and methods are disclosed that provide highly accurate gaze predictions that are specific to a particular user by generating and applying, in deployment, personalized calibration functions to outputs and/or layers of a machine learning model. The calibration functions corresponding to a specific user may operate on outputs (e.g., gaze predictions from a machine learning model) to provide updated values and gaze predictions. The calibration functions may also be applied one or more last layers of the machine learning model to operate on features identified by the model and provide values that are more accurate. The calibration functions may be generated using explicit calibration methods by instructing users to gaze at a number of identified ground truth locations within the interior of the vehicle. Once generated, the calibration functions may be modified or refined through implicit gaze calibration points and/or regions based on gaze saliency maps.

Abstract: In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

Abstract: In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

Abstract: In various examples, audio alerts of emergency response vehicles may be detected and classified using audio captured by microphones of an autonomous or semi-autonomous machine in order to identify travel directions, locations, and/or types of emergency response vehicles in the environment. For example, a plurality of microphone arrays may be disposed on an autonomous or semi-autonomous machine and used to generate audio signals corresponding to sounds in the environment. These audio signals may be processed to determine a location and/or direction of travel of an emergency response vehicle (e.g., using triangulation). Additionally, to identify siren types—and thus emergency response vehicle types corresponding thereto—the audio signals may be used to generate representations of a frequency spectrum that may be processed using a deep neural network (DNN) that outputs probabilities of alert types being represented by the audio data.

Abstract: Machine learning systems and methods that learn glare, and thus determine gaze direction in a manner more resilient to the effects of glare on input images. The machine learning systems have an isolated representation of glare, e.g., information on the locations of glare points in an image, as an explicit input, in addition to the image itself. In this manner, the machine learning systems explicitly consider glare while making a determination of gaze direction, thus producing more accurate results for images containing glare.

Abstract: State information can be determined for a subject that is robust to different inputs or conditions. For drowsiness, facial landmarks can be determined from captured image data and used to determine a set of blink parameters. These parameters can be used, such as with a temporal network, to estimate a state (e.g., drowsiness) of the subject. To improve robustness, an eye state determination network can determine eye state from the image data, without reliance on intermediate landmarks, that can be used, such as with another temporal network, to estimate the state of the subject. A weighted combination of these values can be used to determine an overall state of the subject. To improve accuracy, individual behavior patterns and context information can be utilized to account for variations in the data due to subject variation or current context rather than changes in state.

Abstract: In various examples, audio alerts of emergency response vehicles may be detected and classified using audio captured by microphones of an autonomous or semi-autonomous machine in order to identify travel directions, locations, and/or types of emergency response vehicles in the environment. For example, a plurality of microphone arrays may be disposed on an autonomous or semi-autonomous machine and used to generate audio signals corresponding to sounds in the environment. These audio signals may be processed to determine a location and/or direction of travel of an emergency response vehicle (e.g., using triangulation). Additionally, to identify siren types—and thus emergency response vehicle types corresponding thereto—the audio signals may be used to generate representations of a frequency spectrum that may be processed using a deep neural network (DNN) that outputs probabilities of alert types being represented by the audio data.

Abstract: Approaches for an advanced AI-assisted vehicle can utilize an extensive suite of sensors inside and outside the vehicle, providing information to a computing platform running one or more neural networks. The neural networks can perform functions such as facial recognition, eye tracking, gesture recognition, head position, and gaze tracking to monitor the condition and safety of the driver and passengers. The system also identifies and tracks body pose and signals of people inside and outside the vehicle to understand their intent and actions. The system can track driver gaze to identify objects the driver might not see, such as cross-traffic and approaching cyclists. The system can provide notification of potential hazards, advice, and warnings. The system can also take corrective action, which may include controlling one or more vehicle subsystems, or when necessary, autonomously controlling the entire vehicle. The system can work with vehicle systems for enhanced analytics and recommendations.

Abstract: A machine learning model (MLM) may be trained and evaluated. Attribute-based performance metrics may be analyzed to identify attributes for which the MLM is performing below a threshold when each are present in a sample. A generative neural network (GNN) may be used to generate samples including compositions of the attributes, and the samples may be used to augment the data used to train the MLM. This may be repeated until one or more criteria are satisfied. In various examples, a temporal sequence of data items, such as frames of a video, may be generated which may form samples of the data set. Sets of attribute values may be determined based on one or more temporal scenarios to be represented in the data set, and one or more GNNs may be used to generate the sequence to depict information corresponding to the attribute values.