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 surrounding vehicles is often difficult to predict. Whereas robust control approaches achieve safe, yet conservative motion planning for automated vehicles, Stochastic Model Predictive Control (SMPC) provides efficient planning in the presence of uncertainty. Probabilistic constraints are applied to ensure that the maximal risk remains below a predefined level. However, safety cannot be ensured as probabilistic constraints may be violated, which is not acceptable for automated vehicles. Here, we propose an efficient trajectory planning framework with safety guarantees for automated vehicles. SMPC is applied to obtain efficient vehicle trajectories for a finite horizon. Based on the first optimized SMPC input, a guaranteed safe backup trajectory is planned, using reachable sets. The SMPC input is only applied to the vehicle if a safe backup solution can be found. If no new safe backup solution can be found, the previously calculated, still valid safe backup solution is applied instead of the SMPC solution. Recursive feasibility of the safe SMPC algorithm is proved. Highway simulations show the effectiveness of the proposed method regarding performance and safety

Nachweislich sichere Bewegungsplanung für autonome Fahrzeuge durch Echtzeitverifikation

This thesis introduces fail-safe motion planning as the first approach to guarantee legal safety of autonomous vehicles in arbitrary traffic situations. The proposed safety layer verifies whether intended trajectories comply with legal safety and provides fail-safe trajectories when intended trajectories result in safety-critical situations. The presented results indicate that the use of fail-safe motion planning can drastically reduce the number of traffic accidents.Die vorliegende Arbeit führt ein neuartiges Verifikationsverfahren ein, mit dessen Hilfe zum ersten Mal die verkehrsregelkonforme Sicherheit von autonomen Fahrzeugen gewährleistet werden kann. Das Verifikationsverfahren überprüft, ob geplante Trajektorien sicher sind und generiert Rückfalltrajektorien falls diese zu einer unsicheren Situation führen. Die Ergebnisse zeigen, dass die Verwendung des Verfahrens zu einer deutlichen Reduktion von Verkehrsunfällen führt