用危险感知的动态安全包，保障城市中机器人与人的交互安全。
Ensuring Safe Physical AI in Urban Mobility via Hazard-Informed Synthesized Envelopes
- 构建跨符号、空间、动态模型的统一安全转换框架
- 通过危险感知安全包实现运行时动态安全约束
- 适合关注城市机器人安全部署的研究者和工程师
随着异构机器人系统在多样化城市区域中的部署，复杂的人机交互环境下保障安全仍是一大挑战。本文提出一种统一框架，将系统性危险分析与运行时安全执行相结合，引入危险感知的安全包。不同于将安全视为孤立于各软件模块的静态约束，我们设计了一种跨越符号、空间与动态世界模型的跨层安全转换过程。该表示形式可自然对接物理AI运行时管控机制，确保城市移动中的安全性。
原文摘要 · Abstract (English)
As heterogeneous robotic systems deploy across diverse urban zones, maintaining safety amid complex human-robot interactions remains a critical challenge. We present a unified framework that bridges systematic hazard analysis and runtime enforcement using hazard-informed safety envelopes. Rather than treating safety as a static constraint isolated within individual software modules, we introduce a cross-layer safety transformation process spanning symbolic, spatial, and dynamic world models. We show how this representation naturally interfaces with physical AI runtime harnesses to guarantee safe urban mobility.
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