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PCARNN-DCBF: Minimal-Intervention Geofence Enforcement for Ground Vehicles

arXiv 2511.15522 ↗First posted 19 Nov 2025cs.LG cs.ROcited by 0

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Abstract (arXiv)

Runtime geofencing for ground vehicles is rapidly emerging as a critical technology for enforcing Operational Design Domains (ODDs). However, existing solutions struggle to reconcile high-fidelity learning with the structural requirements of verifiable control. We address this by introducing PCARNN-DCBF, a novel pipeline integrating a Physics-encoded Control-Affine Residual Neural Network with a preview-based Discrete Control Barrier Function. Unlike generic learned models, PCARNN explicitly preserves the control-affine structure of vehicle dynamics, ensuring the linearity required for reliable optimization. This enables the DCBF to enforce polygonal keep-in constraints via a real-time Quadratic Program (QP) that handles high relative degree and mitigates actuator saturation. Experiments in CARLA across electric and combustion platforms demonstrate that this structure-preserving approach significantly outperforms analytical and unstructured neural baselines.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 39 of its 108 references that are in the map. First posted on arXiv 19 Nov 2025.

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References · 39

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27 more
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