Stochastic Model Predictive Control With a Safety Guarantee for Automated Driving

作     者：Brudigam, Tim Olbrich, Michael Wollherr, Dirk Leibold, Marion 

作者机构：Tech Univ Munich Chair Automat Control Engn D-80333 Munich Germany Univ Augsburg Dept Comp Sci D-86135 Augsburg Germany 

出 版 物：《IEEE TRANSACTIONS ON INTELLIGENT VEHICLES》 (IEEE Trans. Intell. Veh.)

年 卷 期：2023年第8卷第1期

页      面：22-36页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0823[工学-交通运输工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：BMW Group 

主　　题：Trajectory Trajectory planning Uncertainty Safety TV Probabilistic logic Predictive control Model predictive control stochastic model predictive control failsafe trajectory planning automated vehicles 

摘      要：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. This backup is used to overwrite the SMPC input if necessary for safety. Recursive feasibility of the safe SMPC algorithm is proved. Highway simulations show the effectiveness of the proposed method regarding performance and safety.