Semiautonomous Vehicular Control Using Driver Modeling

Threat assessment during semiautonomous driving is used to determine when correcting a driver´s input is required. Since current semiautonomous systems perform threat assessment by predicting a vehicle´s future state while treating the driver´s input as a disturbance, autonomous controller intervention is limited to a restricted regime. Improving vehicle safety demands threat assessment that occurs over longer prediction horizons wherein a driver cannot be treated as a malicious agent. In this paper, we describe a real-time semiautonomous system that utilizes empirical observations of a driver´s pose to inform an autonomous controller that corrects a driver´s input when possible in a safe manner. We measure the performance of our system using several metrics that evaluate the informativeness of the prediction and the utility of the intervention procedure. A multisubject driving experiment illustrates the usefulness, with respect to these metrics, of incorporating the driver´s pose while designing a semiautonomous system.