Ensuring Safe Physical AI in Urban Mobility via Hazard-Informed Synthesized Envelopes

Abstract

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.

The 2026 International Conference on Control, Robotics Engineering and Technology (CRET 2026), https://www.cret.net/