Sebastian Mair

Sebastian Mair, Matthias Althoff

Cooperative Adaptive Cruise Control (CACC) is a well-studied technology for forming string-stable vehicle platoons. Ensuring collision avoidance is particularly difficult in CACC due to the small desired inter-vehicle spacing. We propose a safety protocol preventing collisions in a provably-correct manner while still maintaining a small distance to the preceding vehicle, by utilizing communicated braking capabilities. In addition, the safety of the protocol is ensured despite possible communication failures. While our concept can be applied to any CACC system, we particularly consider a class of CACCs, where the platoon vehicles successively agree on a consensus behavior. Our safety protocol is evaluated on various scenarios using the CommonRoad benchmark suite.

Yanliang Huang, Sebastian Mair, Zhuoqi Zeng et al.

Autonomous vehicle path planning has reached a stage where safety and regulatory compliance are crucial. This paper presents an approach that integrates a motion planner with a deep reinforcement learning model to predict potential traffic rule violations. Our main innovation is replacing the standard actor network in an actor-critic method with a motion planning module, which ensures both stable and interpretable trajectory generation. In this setup, we use traffic rule robustness as the reward to train a reinforcement learning agent's critic, and the output of the critic is directly used as the cost function of the motion planner, which guides the choices of the trajectory. We incorporate some key interstate rules from the German Road Traffic Regulation into a rule book and use a graph-based state representation to handle complex traffic information. Experiments on an open German highway dataset show that the model can predict and prevent traffic rule violations beyond the planning horizon, increasing safety and rule compliance in challenging traffic scenarios.