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

Summary

Abstract Summary

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.

Key Points

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.

Urban Safety Envelope

We believe that safe urban mobility requires a broad universal framework aimed at maintaining sufficient safety margins before dangerous situations develop. The urban safety envelope is learned using the hazard-informed pipeline introduced in [ 14 , 6 ] . Rather than collecting only normal driving data, synthetic training scenarios are generated specifically around known safety-critical situations.

Safety Across Hierarchical World Models

Autonomous urban mobility requires reasoning across multiple levels of abstraction. No single representation of the environment is sufficient to support strategic decision making, trajectory planning, and real-time vehicle control simultaneously.

Instead, autonomous systems employ a hierarchy of world models, where each layer maintains a specialized representation of the environment that captures only the information relevant to its function, see [ 12 ] . From a safety perspective, each layer defines its own notion of safe operation.

Safety Across Hierarchical World Models — Ensuring Safe Physical AI in Urban Mobility via Hazard-Informed Synthesized Envelopes — Fig. 2: Symbolic safety layer

Safety Transformation Across Layers

The three world models described in the previous section represent the same physical environment but at different levels of abstraction. As information propagates downward through the hierarchy, safety is progressively refined from high-level symbolic objectives into low-level physical commands.

Conversely, feedback from the real world is sent upward, updating representations at each layer and enabling closed-loop adaptation.

Safety Transformation Across Layers — Ensuring Safe Physical AI in Urban Mobility via Hazard-Informed Synthesized Envelopes — Fig. 3: Spatial safety layer

Runtime Safety Enforcement through the Physical AI Harness

The previous sections describe how safety is represented and progressively transformed across multiple abstraction layers. However, preserving safety semantics during execution requires more than correct perception, planning, and control.

Once machine learning models begin generating decisions in real time, an additional mechanism is required to ensure that their outputs remain consistent with the safety constraints established during system design. Recent work by Lee et al.

Runtime Safety Enforcement through the Physical AI Harness — Ensuring Safe Physical AI in Urban Mobility via Hazard-Informed Synthesized Envelopes — Fig. 4: Dynamic safety layer

Sources and Demonstrations

Figures are reproduced from Odinokov et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.