  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.
