Nvidia Corporation

Nuri Murat Arar

Niranjan Avadhanam

Nishant Puri

Hairong Jiang

Rajath Shetty

Shagan Sah

Sujay Yadawadkar

Pavlo Molchanov

Yuzhuo Ren

In various examples, estimated field of view or gaze information of a user may be projected external to a vehicle and compared to vehicle perception information corresponding to an environment outside of the vehicle. As a result, interior monitoring of a driver or occupant of the vehicle may be used to determine whether the driver or occupant has processed or seen certain object types, environmental conditions, or other information exterior to the vehicle. For a more holistic understanding of the state of the user, attentiveness and/or cognitive load of the user may be monitored to determine whether one or more actions should be taken. As a result, notifications, AEB system activations, and/or other actions may be determined based on a more complete state of the user as determined based on cognitive load, attentiveness, and/or a comparison between external perception of the vehicle and estimated perception of the user.

Occupant attentiveness and cognitive load monitoring for autonomous and semi-autonomous driving applications

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.

Neural network based facial analysis using facial landmarks and associated confidence values

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.

Personalized calibration functions for user gaze detection in autonomous driving applications

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.

Gaze determination using glare as input

In various examples, an adaptive eye tracking machine learning model engine ('adaptive-model engine') for an eye tracking system is described. The adaptive-model engine may include an eye tracking or gaze tracking development pipeline ('adaptive-model training pipeline') that supports collecting data, training, optimizing, and deploying an adaptive eye tracking model that is a customized eye tracking model based on a set of features of an identified deployment environment. The adaptive-model engine supports ensembling the adaptive eye tracking model that may be trained on gaze vector estimation in surround environments and ensemble based on a plurality of eye tracking variant models and a plurality of facial landmark neural network metrics.

Adaptive eye tracking machine learning model engine

Systems and methods for determining the gaze direction of a subject and projecting this gaze direction onto specific regions of an arbitrary three-dimensional geometry. In an exemplary embodiment, gaze direction may be determined by a regression-based machine learning model. The determined gaze direction is then projected onto a three-dimensional map or set of surfaces that may represent any desired object or system. Maps may represent any three-dimensional layout or geometry, whether actual or virtual. Gaze vectors can thus be used to determine the object of gaze within any environment. Systems can also readily and efficiently adapt for use in different environments by retrieving a different set of surfaces or regions for each environment.

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Zurich, Switzerland

Nuri Murat Arar