Stochastic Model Predictive Control With a Safety Guarantee for Automated Driving

Automated vehicles require efficient and safe planning to maneuver in uncertain environments. Largely this uncertainty is caused by other traffic participants, e.g., surrounding vehicles. Future motion of often difficult predict. Whereas robust control approaches achieve safe, yet conservative for automated vehicles, Stochastic Model Predictive Control (SMPC) provides the presence uncertainty. Probabilistic constraints are applied ensure that maximal risk remains below a predefined level. However, safety cannot be ensured as probabilistic may violated, which not acceptable Here, we propose an trajectory framework with guarantees SMPC obtain vehicle trajectories finite horizon. Based on first optimized input, guaranteed backup planned using reachable sets. This used overwrite input if necessary safety. Recursive feasibility algorithm proved. Highway simulations show effectiveness proposed method regarding performance