Ensuring Safe Physical AI in Urban Mobility via Hazard-Informed Synthesized Envelopes
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 sa
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 60%Safety and alignment in an era of long-horizon models →
- PossiblePossibly related (embedding) · 53%Nemotron 3.5 Content Safety: Customizable Multimodal Safety for Global Enterprise AI →
- PossiblePossibly related (embedding) · 53%After spooking Trump into safety testing, Anthropic AI models get global release →
- PossiblePossibly related (embedding) · 50%Autonomous AI attacks pose 'clear and present danger' to critical infrastructure →
- PossiblePossibly related (embedding) · 50%"Dangerous" AI models are coming no matter what →
- LinkedLinked via arxiv author · 85%Alexei Odinokov →
“Ensuring Safe Physical AI in Urban Mobility via Hazard-Informed Synthesized Envelopes”
- LinkedLinked via arxiv author · 85%Rostislav Yavorskiy →
“Ensuring Safe Physical AI in Urban Mobility via Hazard-Informed Synthesized Envelopes”