Safe semi-autonomous control with enhanced driver modeling

During semi-autonomous driving, threat assessment is used to determine when controller intervention that overwrites or corrects the driver's input is required. Since today's semi-autonomous systems perform threat assessment by predicting the vehicle's future state while treating the driver's input as a disturbance, controller intervention is limited to just emergency maneuvers. In order to improve vehicle safety and reduce the aggressiveness of maneuvers, threat assessment must occur over longer prediction horizons where driver's behavior cannot be neglected. We propose a framework that divides the problem of semi-autonomous control into two components. The first component reliably predicts the vehicle's potential behavior by using empirical observations of the driver's pose. The second component determines when the semi-autonomous controller should intervene. To quantitatively measure the performance of the proposed approach, we define metrics to evaluate the infor-mativeness of the prediction and the utility of the intervention procedure. A multi-subject driving experiment illustrates the usefulness, with respect to these metrics, of incorporating the driver's pose while designing a semi-autonomous system.