Pre-crash injury risk prediction with guaranteed confidence level: a conformal and interpretable framework

Proposed framework demonstrates significant potential in pre-crash injury risk prediction by introducing conformal prediction techniques to machine learning models. In addition to enhancing predictive performance to nearly 90% prediction coverage and a 70.3% recall rate for serious injuries, this framework also provides enhanced interpretability by quantifying prediction uncertainty and identifying key risk factors. Unlike traditional methods, the framework remains valid under distribution shifts and combines uncertainty estimation with model interpretability. These advantages collectively lay a foundation for developing proactive traffic safety applications and formulating data-driven road safety policies.