Improving human-in-the-loop decision making in multi-mode driver assistance systems using hidden mode stochastic hybrid systems

Existing commercial driver assistance systems, including automatic braking systems and lane-keeping systems, may monitor the state of the vehicle or the environment to determine whether the systems should intervene. However, the state of the human driver is not typically included in the decision making process. In this paper, we propose to use hidden mode stochastic hybrid systems to model the interaction between the human driver and the vehicle. We show that by monitoring the human behavior as well as the vehicle state, we can infer the human state and enhance the quality of decision making in a driver assistance system. The resulting control policy is obtained by solving an optimal planning problem of the proposed hidden mode hybrid system. The policy can automatically balance the decision making about when to give warning to the driver and when to actually intervene in the control of the vehicle.