Stochastic Model Predictive Control with a Safety Guarantee for Automated Driving: Extended Version

TL;DR

This paper introduces a stochastic model predictive control framework with safety guarantees for automated driving, combining probabilistic planning with backup safety trajectories to ensure safety despite uncertainties in surrounding vehicle behaviors.

Contribution

The paper presents a novel SMPC-based planning method that guarantees safety through backup trajectories using reachable sets, addressing the limitations of purely probabilistic approaches.

Findings

Effective in highway simulations

Ensures safety with probabilistic constraints

Maintains recursive feasibility

Abstract

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.