为高速自动驾驶赛车设计容错安全架构，保障170mph下的运行安全
HALO: Fault-Tolerant Safety Architecture For High-Speed Autonomous Racing
- 基于故障模式分析构建感知、规划、控制等模块的容错机制
- 针对节点、数据、行为三类故障实现实时监控与响应
- 在真实多车竞赛场景中验证了架构的有效性
近年来，高精度自动驾驶赛车领域取得显著进展，如RoboRace和印第安纳波利斯自动驾驶挑战赛（Indy Autonomous Challenge）为研究者提供了开发高速自动驾驶车辆软件栈的平台，使车辆速度突破170 mph。确保这些车辆的安全性要求软件在高速运行期间持续监测各类故障与异常操作状态，以减轻子系统或组件故障带来的不合理风险。本文介绍了在印第安纳波利斯自动驾驶挑战赛中部署于全尺寸自动驾驶赛车上的HALO安全架构。首先对感知、规划、控制及通信模块进行故障模式与危害性分析，重点关注节点健康、数据健康和行为安全性三类故障。随后提出HALO安全范式及运行时监控方法。最后通过多智能体场景下的真实赛车试验数据，验证了该架构对各类故障的有效应对能力。
原文摘要 · Abstract (English)
The field of high-speed autonomous racing has seen significant advances in recent years, with the rise of competitions such as RoboRace and the Indy Autonomous Challenge providing a platform for researchers to develop software stacks for autonomous race vehicles capable of reaching speeds in excess of 170 mph. Ensuring the safety of these vehicles requires the software to continuously monitor for different faults and erroneous operating conditions during high-speed operation, with the goal of mitigating any unreasonable risks posed by malfunctions in sub-systems and components. This paper presents a comprehensive overview of the HALO safety architecture, which has been implemented on a full-scale autonomous racing vehicle as part of the Indy Autonomous Challenge. The paper begins with a failure mode and criticality analysis of the perception, planning, control, and communication modules of the software stack. Specifically, we examine three different types of faults - node health, data health, and behavioral-safety faults. To mitigate these faults, the paper then outlines HALO safety archetypes and runtime monitoring methods. Finally, the paper demonstrates the effectiveness of the HALO safety architecture for each of the faults, through real-world data gathered from autonomous racing vehicle trials during multi-agent scenarios.
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