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A Machine Vision Approach for Geofence-based Adaptive Friction Modulation in Connected Semi-autonomous Vehicles

Ushaa Eswaran, Vivek Eswaran, Keerthna Murali, Vishal Eswaran


Safeguarding vulnerable road user (VRU) safety necessitates fail-safes preventing collisions from semi-autonomous vehicles. Geofencing allows service-zones definition but introduces friction discouraging adoption. This paper details an onboard vision model detecting VRUs by helmet status, triggering reactionary neuro-modulations of vehicle maneuverability. The architecture encompasses a YOLOv3 detector determining pedestrian/bicyclist crossing intent through path extrapolations, calibrated using lidar-fusion correcting distance estimates. The subsequent velocity governor adapts acceleration profiles based on predicted exposure risks, verified through hardware-in-loop testing. Ease-of-use implementations will encourage voluntary adoption of potentially life-saving technologies meeting pedestrian fatality reduction goals.


Geofence-based friction modulation, machine vision, semi-autonomous vehicles, vulnerable road users (VRUs), fail-safe velocity governance

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