System and method for transitioning between an autonomous and manual driving mode based on detection of a drivers capacity to control a vehicle

A method comprising:

• training a neural network of a driving control transition module using training data received from a remote source and using the trained neural network for modifying the operation of the driving control transition module based on a vehicle context in which an autonomous vehicle is operating, the context including a current vehicle location;
• receiving sensor data related to a vehicle driver's capacity to take manual control of an autonomous vehicle and receiving sensor data related to the vehicle context, the sensor data including image data from a camera in the vehicle;
• using the image data to perform facial feature analysis of a face of the driver;
• classifying the vehicle driver into at least one of a plurality of driver state classifications based on the facial feature analysis of the face of the driver;
• determining, based on the sensor data and the at least one of the plurality of driver state classifications, if the driver has the capacity to take manual control of the autonomous vehicle, the determining including prompting the driver to perform an action or provide an input;
• and
• outputting a vehicle control transition signal to a vehicle subsystem to cause the vehicle subsystem to take action based on the driver's capacity to take manual control of the autonomous vehicle, the vehicle control transition signal corresponding to the at least one of the plurality of driver state classifications, the vehicle control transition signal causing the vehicle subsystem to direct the vehicle to safely pull over to the side of a roadway, if the driver is determined to not have the capacity to take manual control of the vehicle.

A non-transitory machine-useable storage medium embodying instructions which, when executed by a machine, cause the machine to:

• train a neural network of a driving control transition module using training data received from a remote source and use the trained neural network to modify the operation of the driving control transition module based on a vehicle context in which an autonomous vehicle is operating, the context including a current vehicle location;
• receive sensor data related to a vehicle driver's capacity to take manual control of an autonomous vehicle and receive sensor data related to the vehicle context, the sensor data including image data from a camera in the vehicle;
• use the image data to perform facial feature analysis of a face of the driver;
• classify the vehicle driver into at least one of a plurality of driver state classifications based on the facial feature analysis of the face of the driver;
• determine, based on the sensor data and the at least one of the plurality of driver state classifications, if the driver has the capacity to take manual control of the autonomous vehicle, the instructions being further configured to prompt the driver to perform an action or provide an input;
• and
• output a vehicle control transition signal to a vehicle subsystem to cause the vehicle subsystem to take action based on the driver's capacity to take manual control of the autonomous vehicle, the vehicle control transition signal corresponding to the at least one of the plurality of driver state classifications, the vehicle control transition signal causing the vehicle subsystem to direct the vehicle to safely pull over to the side of a roadway, if the driver is determined to not have the capacity to take manual control of the vehicle.