Learning-based Stochastic Model Predictive Control for Autonomous Driving

Autonomous driving in urban environments requires safe control policies that account for the non-determinism of moving\ud obstacles, for instance, the intention of other vehicles while crossing an uncontrolled intersection. This thesis addresses the aforementioned problem by proposing a stochastic model predictive control (SMPC) approach. In this approach, we consider robust collision avoidance as a constraint to guarantee safety and a stochastic performance index that will increase the quality of the closed-loop tracking by ignoring the unlikely obstacle configurations that could occur. We compute the probabilities associated with different obstacle trajectories by training a classifier on a realistic dataset generated by the microscopic traffic simulator SUMO and show the benefits of the proposed stochastic MPC formulation in a simulated real intersection. This thesis is divided into two parts: first, discuss the formulation of the existing control algorithm and our proposed approach, and second, the scenario prediction of the obstacle vehicles