用强化学习预测并预防交通规则违规，提升自动驾驶安全性。
Predictive Traffic Rule Compliance using Reinforcement Learning
- 用运动规划模块替代传统策略网络，生成可解释轨迹
- 在德国高速数据集上实现超规划时长的违规预测
- 适合自动驾驶安全系统研发者参考
自动驾驶路径规划已进入安全与合规关键阶段。本文提出一种将运动规划器与深度强化学习结合的方法，用于预测潜在交通规则违规。核心创新在于用运动规划模块替代标准演员-评论家方法中的演员网络，确保轨迹生成稳定且可解释。我们以交通规则鲁棒性作为奖励训练评论家，其输出直接作为运动规划的成本函数，指导轨迹选择。将德国道路交通条例中的若干关键州际规则纳入规则库，并采用基于图的状态表示处理复杂交通信息。在公开的德国高速公路数据集上的实验表明，该模型能在规划时长之外预测并预防交通规则违规，在复杂交通场景中显著提升安全性和规则合规性。
原文摘要 · Abstract (English)
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.
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