aiqbench analysis
This research addresses the critical safety challenges of deploying heterogeneous robotic systems in complex urban environments by integrating systematic hazard analysis with runtime enforcement mechanisms. The proposed framework utilizes cross-layer transformations across symbolic, spatial, and dynamic world models to create synthesized safety envelopes that directly interface with physical AI runtimes. By moving beyond static constraints isolated within single software modules, this work offers a robust approach for guaranteeing safe human-robot interactions in real-world mobility scenarios.
Ensuring Safe Physical AI in Urban Mobility via Hazard-Informed Synthesized Envelopes
arXiv:2608.14481v1 Announce Type: new 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.
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.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2608.14481 [cs.RO]
(or arXiv:2608.14481v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.14481 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 176 words
