自动驾驶任务级运行时保证框架
Mission-Level Runtime Assurance Framework for Autonomous Driving
AI总结：
提出一种评估驾驶安全与任务完成能力的运行时保证框架，通过监控系统拒绝不可行命令，实验证明其优于仅关注平台安全的方法。
摘要：
本文研究当高级驾驶命令出现故障或不可靠时自动驾驶的运行时安全性。与主要关注即时车辆安全的传统运行时安全方法不同，所提出的框架在执行命令前评估驾驶安全以及车辆是否仍能成功完成任务。该框架通过引入任务级故障场景（如跳过必需检查点、进入受限区域、生成无法成功完成任务的未来路线）扩展了highway-env。引入运行时监控系统，在执行前检测并拒绝不安全或任务不可行的命令。作为对比，使用公开的Simplex-Drive框架实现了一个基于学习的驾驶控制、安全回退控制和运行时控制器切换的自适应Simplex-Drive运行时安全基线。实验结果表明，仅平台级运行时安全无法检测任务级规划故障，而所提出的框架成功拒绝任务不可行命令，并在随机故障条件下提高了任务成功率。
英文摘要：
This paper studies runtime safety for autonomous driving when high-level driving commands become faulty or unreliable. Unlike conventional runtime-safety approaches that mainly focus on immediate vehicle safety, the proposed framework evaluates both driving safety and whether the vehicle can still successfully complete its mission before a command is executed. The framework extends highway-env with mission-level fault scenarios such as skipping required checkpoints, entering restricted areas, and generating future routes that can no longer complete the mission successfully. A runtime monitoring system is introduced to detect and reject unsafe or mission-infeasible commands before execution. For comparison, an adapted Simplex-Drive runtime-safety baseline with learning-based driving control, safety fallback control, and runtime controller switching is implemented using the public Simplex-Drive framework. Experimental results show that platform-level runtime safety alone cannot detect mission-level planning faults, while the proposed framework successfully rejects mission-infeasible commands and improves mission success under randomized fault conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
