Providing safety guarantees for Autonomous Vehicle (AV) systems with machine-learning-based controllers remains a challenging issue. In this work, we propose Simplex-Drive, a framework that can achieve runtime safety assurance for machine-learning enabled controllers of AVs. The proposed Simplex-Drive consists of an unverified Deep Reinforcement Learning (DRL)-based advanced controller (AC) that achieves desirable performance in complex scenarios, a Velocity-Obstacle (VO) based baseline safe controller (BC) with provably safety guarantees, and a verified mode management unit that monitors the operation status and switches the control authority between AC and BC based on safety-related conditions. We provide a formal correctness proof of Simplex-Drive and conduct a lane-changing case study in dense traffic scenarios. The simulation experiment results demonstrate that Simplex-Drive can always ensure operation safety without sacrificing control performance, even if the DRL policy may lead to deviations from the safe status.
翻译:在这项工作中,我们提议建立Flaimx-Drive(Flaimex-Drive-Drive-Drive)这一框架,这一框架可实现AV机修辅助控制器的运行时间安全保障。提议的Slaimx-Drive(Limpicx-Drive)是一个未经核实的深强化学习(DRL)高级控制器(AC),该控制器在复杂情况下达到理想性能,一个基于速度-操作控制器(VO)的基线安全控制器(BC),该控制器具有可辨别的安全保障,一个经核实的模式管理器,该控制器可监测AC和BC之间的操作状态,并根据安全相关条件转换控制权。我们为Slaimx-Drive提供了正式的正确性证明,并在密集交通情况下进行换道案例研究。模拟实验结果表明,即使Driver-Drive(L)政策可能导致偏离安全状态,但Limictal-Drive)始终可以确保操作安全性,同时又不牺牲控制性能。