CPC Class B60W

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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.

Autonomous vehicle methods and systems are described herein for communicating with an autonomous or semi-autonomous vehicle to remotely control operation of the vehicle, to detect and remove unauthorized passengers, to deliver loads, to receive registration information for the vehicle, to provide accessibility information to the vehicle, and/or to receive sensor or other environmental data to integrate with an electronic game or other extended reality experience.