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

arXiv: https://arxiv.org/abs/2608.14481 · HTML: https://mlsanity.com/paper/2608.14481/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 14 Aug 2026.

## Builds on (most cited first, 6 of 6)

- [Cosmos World Foundation Model Platform for Physical AI](https://mlsanity.com/paper/2501.03575/) (2025)
- [Securing Vehicle-to-Everything (V2X) Communication Platforms](https://mlsanity.com/paper/2003.07191/) (2020)
- [NVIDIA Isaac Sim: Enabling Scalable, GPU-Accelerated Simulation for Robotics](https://mlsanity.com/paper/2606.03551/) (2026)
- [Harness Engineering for Physical AI: Robot Middleware Is the Harness Layer](https://mlsanity.com/paper/2606.09416/) (2026)
- [A Hazard-Informed Data Pipeline for Robotics Physical Safety](https://mlsanity.com/paper/2603.06130/) (2026)
- [Learning of Robot Safety Policies via Adversarial Synthetic Scenarios](https://mlsanity.com/paper/2606.05952/) (2026)

## Abstract (arXiv)

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.