AI RESEARCH
Predicting Time Pressure of Powered Two-Wheeler Riders for Proactive Safety Interventions
arXiv CS.LG
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ArXi:2601.03173v2 Announce Type: replace Time pressure critically influences risky maneuvers and crash proneness among powered two-wheeler riders, yet its prediction remains underexplored in intelligent transportation systems. We present a large-scale dataset of 129,000+ labeled multivariate time-series sequences from 153 rides by 51 participants under No, Low, and High Time Pressure conditions. Each sequence captures 63 features spanning vehicle kinematics, control inputs, behavioral violations, and environmental context.