Predictive Traffic Rule Compliance using Reinforcement Learning
This work addresses safety and regulatory compliance for autonomous vehicles, representing an incremental improvement in motion planning methods.
The paper tackles the problem of ensuring traffic rule compliance in autonomous vehicle path planning by integrating a motion planner with a deep reinforcement learning model to predict and prevent violations. Experiments on a German highway dataset show that the model increases safety and rule compliance in challenging scenarios.
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.