Compliance with traffic laws is a fundamental requirement for human drivers on the road, and autonomous vehicles must adhere to traffic laws as well. However, current autonomous vehicles prioritize safety and collision avoidance primarily in their decision-making and planning, which will lead to misunderstandings and distrust from human drivers and may even result in accidents in mixed traffic flow. Therefore, ensuring the compliance of the autonomous driving decision-making system is essential for ensuring the safety of autonomous driving and promoting the widespread adoption of autonomous driving technology. To this end, the paper proposes a trigger-based layered compliance decision-making framework. This framework utilizes the decision intent at the highest level as a signal to activate an online violation monitor that identifies the type of violation committed by the vehicle. Then, a four-layer architecture for compliance decision-making is employed to generate compliantly trajectories. Using this system, autonomous vehicles can detect and correct potential violations in real-time, thereby enhancing safety and building public confidence in autonomous driving technology. Finally, the proposed method is evaluated on the DJI AD4CHE highway dataset under four typical highway scenarios: speed limit, following distance, overtaking, and lane-changing. The results indicate that the proposed method increases the vehicle's overall compliance rate from 13.85% to 84.46%, while reducing the proportion of active violations to 0%, demonstrating its effectiveness.

翻译：遵守交通法规是人类驾驶员上路行驶的基本要求，自动驾驶车辆同样需要遵循交通法规。然而，当前自动驾驶车辆的决策与规划主要侧重于安全性与避碰功能，这将导致人类驾驶员产生误解与不信任，甚至可能在混合交通流中引发事故。因此，确保自动驾驶决策系统的合规性对于保障自动驾驶安全性、推动自动驾驶技术广泛应用至关重要。为此，本文提出了一种基于触发的分层合规决策框架。该框架以最高层级的决策意图作为信号，激活在线违规监控器，识别车辆所实施的违规类型。随后通过四层合规决策架构生成合规轨迹。借助该系统，自动驾驶车辆能够实时检测并纠正潜在违规行为，从而提升安全性，建立公众对自动驾驶技术的信任。最后，本文在DJI AD4CHE公路数据集上，针对限速、跟车距离、超车和变道四种典型公路场景对所提方法进行了评估。结果表明，该方法将车辆整体合规率从13.85%提升至84.46%，同时将主动违规比例降至0%，验证了其有效性。