--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. HE field of autonomous driving has advanced substantially over the past five years. Although perception and prediction modules have become more reliable, planning systems still face challenges, particularly regarding safety assurance and operational robustness. Furthermore, traffic rule compliance remains a fundamental prerequisite for autonomous vehicles, both to protect road users and to satisfy legal certification standards. Recent research has effectively applied temporal logic to formalize traffic rules, enabling automated online monitoring systems [1]-[3] to continuously monitor the compliance of traffic rules. These approaches use the concept of rule robustness--a quantitative metric indicating how thoroughly specific traffic rules are satisfied or violated.