## 5 Conclusions  
Examination of the pre-crash factors can offer insights into the underlying relationships between crash mechanisms and injury severity. This work highlights the importance of pre-crash analysis and ML interpretation in a priori IR evaluation. The calculation of PCIs under pre-crash conditions is introduced on the basis of the impulse-momentum theory. Considering many existing criteria for injury severity, an ML-based framework is proposed to compare the explanatory variables related to the crash severity with considerable samples. Six models were used to assess the injury severity within a fusion FARS/CRSS dataset and investigate the potential influence of the different crash characteristics. The differences between the basic features and the PCIs in margin effects were also compared based on parameter estimation of BOL. The key crash factors for different crash types were then identified from the outperforming LCE-AF model by interpreting the SHAP value and partial dependence plot. The thresholds of PCIs for several impact positions are also proposed to evaluate the IR, which can be used as a reference for targeted safety strategies. The following conclusions are drawn:  
1. The PCIs derived from the pre-crash conditions display an excellent optimization effect in injury severity assessment. Among the selected models, the LCE method with PCI features is effective in handling complex pre-crash features and a priori information and can obtain the best performance with an AUC of 97.23% and an accuracy of 89.21%. 2. Based on the SHAP value and partial dependence distribution, \(\Delta\)V is identified as the most valuable factor for synthetical IR evaluation, followed by CMI, EES, Vr, and CSI. We also determined the nonlinear relationship between the built PCI features and the likelihood of IRs, which is often disregarded in traditional statistical methods. 3. With regard to the impact side, occupants subjected to near-side impacts more severely suffered from crashes, and the protective effect reflects that the back side of vehicleshas better-designed structures to reduce the impulse from the impacted vehicle. Based on the partial dependence analysis, the probability decision thresholds of \(\Delta\)V, CMI, and EES were developed considering different impact sides. The key finding from this work is that the indicators derived from the initial pre-crash information are found to be stronger than the more commonly used basic features in IR assessment. This feature may be attributed to the severity indices obtained from crash momentum analysis that can reflect energy transformations and mechanisms of vehicle-to-vehicle collision. The developed PCI formulation provides useful insights, which can serve as a reference for the transfer from post-crash passive disposal to pre-crash active prevention. Nowadays, a large amount of plausible data can be provided through the V2V communications and ADAS system due to the development of the smart transportation and information technologies. As a potential issue for further research, we aim to better model IRs and discover unobserved heterogeneity for complex crash characteristics on high-dimensional datasets based on the proposed framework. Therefore, the combination of explainable ML models and safety field knowledge is considered a promising research direction. In addition, this study only compared the expected variables of injury severity due to the lack of vehicle operational data. Further study can explore a novel framework for measuring the conflict risk considering the proximity to a crash and the severity level of the potential outcomes. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13126983/s1](https://www.mdpi.com/article/10.3390/app13126983/s1)]([https://www.mdpi.com/article/10.3390/app13126983/s1](https://www.mdpi.com/article/10.3390/app13126983/s1)), Table S1: Crash data. Conceptualization, C.G. ([PERSON]) and J.X. ; Data curation, S.L., C.G. ([PERSON]) and Y.M. ; Formal analysis, C.G.