Computationally efficient fail-safe trajectory planning for self-driving vehicles using convex optimization

Abstract

Ensuring the safety of self-driving vehicles is a challenging task, especially if other traffic participants severely deviate from the predicted behavior. One solution is to ensure that the vehicle is able to execute a collision-free evasive trajectory at any time. However, a fast method to plan these socalled fail-safe trajectories does not yet exist. Our new approach plans fail-safe trajectories in arbitrary traffic scenarios by incorporating convex optimization techniques. By integrating safety verification in the planner, we are able to generate fail-safe trajectories in real-time, which are guaranteed to be safe. At the same time, we minimize jerk to provide enhanced comfort for passengers. The proposed benefits are demonstrated in different urban and highway scenarios using the CommonRoad benchmark suite and compared to a widely-used sampling-based planner.