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

Objective Pre-crash injury risk prediction is crucial for proactive safety measures, while traditional models, which output single-point predictions without explaining the decision reasons, often lack interpretability and reliable uncertainty estimation to reflect potential risk distributions. These drawbacks limit their practical effectiveness in mitigating injury severity. To overcome these limitations, this study develops a novel framework that outputs potential risk distributions and their corresponding probabilities using only pre-crash data, thereby delivering probabilistic outputs with a statistically guaranteed 90% confidence level. By introducing such a framework, we aim to provide a more convincing and interpretable analysis of the injury distribution and its underlying causes in traffic accidents, ultimately offering data-driven guidance for injury mitigation strategies.