一句话：提出基于信号时序逻辑的通用模块化运行时强制执行方法，通过梯度驱动算法修复自动驾驶系统的规划轨迹。
自动驾驶系统集成了感知、规划与控制等关键任务，虽然在仿真器中已有大量测试与分析手段，但部署到真实道路后仍可能遇到未被覆盖的关键场景。现有运行时监控工作只能处理简单安全属性（如避免碰撞），且强制执行手段单一粗暴（如紧急制动）。本文提出 REDriver，一种通用且模块化的运行时强制执行方法：用户可用基于信号时序逻辑（STL）的规范语言描述广泛属性（如国家交通法规）；REDriver 基于 STL 的定量语义监控自动驾驶系统的规划轨迹，并在检测到可能违反规范时，使用梯度驱动算法修复轨迹。作者在流行自动驾驶系统 Apollo 的两个版本上实现了 REDriver，并采用中国交通法规违反基准进行评测。结果表明，REDriver 在极小开销下显著提升了 Apollo 对规范的符合度。
Autonomous driving systems (ADSs) integrate sensing, perception, drive control, and several other critical tasks in autonomous vehicles, motivating research into techniques for assessing their safety. While there are several approaches for testing and analysing them in high-fidelity simulators, ADSs may still encounter additional critical scenarios beyond those covered once they are deployed on real roads. An additional level of confidence can be established by monitoring and enforcing critical properties when the ADS is running. Existing work, however, is only able to monitor simple safety properties (e.g., avoidance of collisions) and is limited to blunt enforcement mechanisms such as hitting the emergency brakes. In this work, we propose REDriver, a general and modular approach to runtime enforcement, in which users can specify a broad range of properties (e.g., national traffic laws) in a specification language based on signal temporal logic (STL). REDriver monitors the planned trajectory of the ADS based on a quantitative semantics of STL, and uses a gradient-driven algorithm to repair the trajectory when a violation of the specification is likely. We implemented REDriver for two versions of Apollo (i.e., a popular ADS), and subjected it to a benchmark of violations of Chinese traffic laws. The results show that REDriver significantly improves Apollo's conformance to the specification with minimal overhead.