让安全监控与机器学习互相协作，提升自动驾驶安全性与性能。
Synergistic Simplex: Cooperative Runtime Assurance for Safety-Critical Autonomous Systems
- 安全监控与机器学习双向融合，突破传统单向监督限制。
- 在保持形式化安全保证的前提下，显著提升障碍物检测性能。
- 适合关注自动驾驶安全验证与系统优化的研究者和工程师。
自动驾驶系统越来越多地依赖机器学习（ML）组件完成感知与控制等关键任务。尽管ML提供了必要能力，但其不可避免地存在长尾故障，难以直接用于安全关键场景。运行时保障（RTA）通过将ML组件与可验证的安全监控器配对来缓解此问题，如控制Simplex和感知Simplex架构。然而，安全监控器性能有限仍是主要瓶颈。本文提出的协同Simplex（Synergistic Simplex, SS）架构通过实现ML组件与安全监控器之间的双向集成，在保持形式化安全保证的同时提升了系统性能。其核心创新在于允许安全监控器使用ML输出，这在传统RTA系统中通常被禁止。我们形式化推导了该集成保持安全性的条件，并展示了性能优势。本文针对自动驾驶障碍物检测任务，完成了SS的架构设计、分析与评估。
原文摘要 · Abstract (English)
Autonomous systems increasingly rely on machine-learning (ML) components for safety-critical tasks such as perception and control in autonomous vehicles (AVs). While ML enables essential capabilities, it inevitably exhibits long-tail faults that make it unsuitable for safety-critical tasks. Runtime assurance (RTA) mitigates this issue by pairing ML components with verifiable safety monitors, e.g., Control Simplex and Perception Simplex architectures. However, the limited performance of safety monitors remains a major bottleneck. The Synergistic Simplex (SS) architecture improves system performance by enabling bidirectional integration between ML components and safety monitors while preserving formal safety guarantees. The key innovation here is allowing safety monitors to use ML outputs, which is typically prohibited in RTA systems. We formally derive conditions under which this integration preserves safety and demonstrate the performance benefits. We present the design, analysis, and evaluation of SS for AV obstacle detection.
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