Abstract
News editors obtained the following quote from the background information suppli ed by the inventors: “Autonomous and semi-autonomous vehicles rely on machine le arning approaches-such as those using deep neural networks (DNNs)-to analyze ima ges of an interior space (e.g., cabin, cockpit) of a vehicle or other machine. A n Occupant Monitoring System (OMS) is an example of a system that may be used wi thin a vehicle cabin to perform real-time assessments of occupant or operator pr esence, gaze, alertness, and/or other conditions. For example, OMS sensors (such as, but not limited to, RGB sensors, infrared (IR) sensors, depth sensor, camer as, and/or other optical sensors) may be used to track an occupant’s or operator ’s gaze direction, head pose, and/or blinking. This gaze information may be used to determine a level of attentiveness of the occupant or operator (e.g., to det ect drowsiness, fatigue, and/or distraction), and/or to take responsive action t o prevent harm to the occupant or operator-e.g., by redirecting their attention to a potential hazard, pulling the vehicle over, and/or the like. For example, D NNs may be used to detect that an operator is falling asleep at the wheel, based on the operator’s downward gaze toward the floor of the vehicle, and the detect ion may lead to an adjustment in the speed and direction of the car (e.g., pulli ng the vehicle over to the side of the road) or an auditory alert to the operato r. Occupant monitoring systems often rely on training DNNs with a high volume of training image data that reflects the facial features of different persons to h elp increase the accuracy of gaze predictions across all persons.”
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