## 4 Discussion  
In the above sections, this work demonstrates the optimization effect of the proposed severity parameters based on the pre-crash information in the IR assessment of two-vehicle crashes. According to the results in Table 3, the PCIs significantly improve the prediction performance compared with the underlying crash information, regardless of the ML model used. This finding not only verifies the validity of a priori severity evaluation for latent traffic conflicts but also corroborates the positive effect of the proposed PCIs on crash risk assessment. In terms of model selection, the advanced bagging-boosting model (LCE) exhibits a higher applicability, higher accuracy, and lower misclassification rate than other models. More accurate models usually better capture the underlying relationship between injury severity and risk factors. Accordingly, the LCE model combining PCIs obtains satisfactory prediction performance and can acquire complex patterns under the pre-crash conditions. This model offers a robust alternative for the further analysis of roadway safety. ML methods are frequently criticized as \"black boxes\" because of their lack of clarity and explicability [51]. These drawbacks have affected the widespread use of non-parametric models, even though they are flexible in data adaptation and have advantages in outstanding predictive accuracy. This study calculates the marginal effects of different features based on BOL and ranks the feature importance with SHAP value to better interpret the model results. Partial dependence plots were applied to visualize the injury probability for the different impact positions for the key PCIs. The proposed framework provides a new perspective on the interpretability of predictive models, which effectively quantify the corresponding effects between crash indicator values and IR (Figures 5-8). The SHAP value suggests that all six PCIs have the largest contribution in the injury severity assessment. Such a priori PCIs are derived from the well-established crash reconstruction theory, which has been proven to reflect the crash risk to a certain extent [52]. Based on the results, \(\Delta\)V provides the best interpretation for all severity levels. We considered a sideswipe collision between two low-speed vehicles, in which the likelihood of injury is relatively minor, and the \(\Delta\)V of both vehicles during the crash is equally low. When two vehicles with large differences in mass and speed have potential conflicts in the approach trajectory, the higher \(\Delta\)V increases the IR in this case. With regard to system-based dimensionless parameters, the predictive performance of the CMI is higher than that of the CSI. Both indicators are related to vehicle geometry and structural stiffness. However, CSI mainly measures the energy absorbed during the crash, while different forces and deformations may correspond to the same energy loss [53]. By contrast, CMI is directly related to the impact eccentricity, and the increase in the impact eccentricity results in a decrease in the rate of kinetic energy converted into translation. Thus, CMI outperforms most of the other variables in side-impact crashes (Figure 7c). The partial dependence plots depict a nonlinear relationship between PCIs and IR with intuitive visualization compared with the marginal effects of logit-based estimation. The effect of PCIs varies for the different crash types due to the variation in crash mechanisms, especially for head-on and side-impact crashes. In the side-impact cases, the passengers are impacted in a complex way with the safety protection systems and the internal cabin, and the high-dimensional parameters are instrumental in optimizing crash analysis. This finding also demonstrates that different analytical approaches should be applied considering crash types and impact position. Therefore, the thresholds of IR with \(\Delta\)V, CMI, and EES are presented for different impact positions based on the prevailing threshold setting guidelines.