AI RESEARCH
Synergistic Simplex: Cooperative Runtime Assurance for Safety-Critical Autonomous Systems
arXiv CS.LG
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ArXi:2605.08190v1 Announce Type: new 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.