[2603.14841] Real-Time Driver Safety Scoring Through Inverse Crash Probability Modeling

Computer Science > Machine Learning arXiv:2603.14841 (cs) [Submitted on 16 Mar 2026 (v1), last revised 31 Mar 2026 (this version, v2)] Title:Real-Time Driver Safety Scoring Through Inverse Crash Probability Modeling Authors:Joyjit Roy, Samaresh Kumar Singh, Sushanta Das View a PDF of the paper titled Real-Time Driver Safety Scoring Through Inverse Crash Probability Modeling, by Joyjit Roy and 1 other authors View PDF HTML (experimental) Abstract:Road crashes remain a leading cause of preventable fatalities. Existing prediction models predominantly produce binary outcomes, which offer limited actionable insights for real-time driver feedback. These approaches often lack continuous risk quantification, interpretability, and explicit consideration of vulnerable road users (VRUs), such as pedestrians and cyclists. This research introduces SafeDriver-IQ, a framework that transforms binary crash classifiers into continuous 0-100 safety scores by combining national crash statistics with naturalistic driving data from autonomous vehicles. The framework fuses National Highway Traffic Safety Administration (NHTSA) crash records with Waymo Open Motion Dataset scenarios, engineers domain-informed features, and incorporates a calibration layer grounded in transportation safety literature. Evaluation across 15 complementary analyses indicates that the framework reliably differentiates high-risk from low-risk driving conditions with strong discriminative performance. Findings further rev...