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

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

|Authors: Alexei Odinokov, Rostislav Yavorskiy

|Venue: arXiv preprint | Year: 2026

|arXiv: 2608.14481v1

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.

Key Contributions

• As heterogeneous robotic systems deploy across diverse urban zones, maintaining safety amid compl…
• We present a unified framework that bridges systematic hazard analysis and runtime enforcement us…
• Rather than treating safety as a static constraint isolated within individual software modules, w…
• We show how this representation naturally interfaces with physical AI runtime harnesses to guaran…

Topics

• reinforcement-learning
• human-robot-interaction

Code & Data

No code repository linked in paper metadata.

BibTeX

@article{Odinokov2026_260814481v1,
  title     = {Ensuring Safe Physical AI in Urban Mobility via Hazard-Informed Synthesized Envelopes},
  author    = {Alexei Odinokov and Rostislav Yavorskiy},
  year      = {2026},
  eprint    = {2608.14481v1},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  url       = {https://arxiv.org/abs/2608.14481v1}
}