Stochastic predictive control for lane keeping assistance systems using a linear time-varying model

This paper presents a new controller for prevention of unintended roadway departures using model predictive control (MPC). The uncertainty with the driver's behavior is taken into account as the Gaussian disturbance. Correspondingly, we impose a lower bound on the probability of the vehicle remaining within the lane. Using current information of the vehicle and predicted steering of the driver, a linear time-varying (LTV) model of the human-vehicle system is obtained on-line through a successive linearization approach. The probabilistic safety constraints are converted into tightened constraints on the states of the LTV model by computing the evolution of the disturbance. Consequently, the predictive control problem is formulated as a quadratic program. The controller corrects the driver's steering, wherever there is a risk of unintended roadway departure, to keep the vehicle within the lane. Simulations and experiments implemented on a passenger vehicle were performed. The results indicate that the proposed controller improves safety performance compared to previous works.