Stochastic predictive control for semi-autonomous vehicles with an uncertain driver model

In this paper a robust control framework is proposed for the lane-keeping and obstacle avoidance of semi-autonomous ground vehicles. A robust Model Predictive Control framework (MPC) is used in order to enforce safety constraints with minimal control intervention. A stochastic driver model is used in closed-loop with a vehicle model to obtain a distribution over future vehicle trajectories. The uncertainty in the prediction is converted to probabilistic constraints. The robust MPC computes the smallest corrective steering action needed to satisfy the safety constraints, to a given probability. Simulations of a driver approaching multiple obstacles, with uncertainty obtained from measured data, show the effect of the proposed framework.

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