## 3 Results
### Feature Importance  
The contribution of factors to the IR is explained by global and local SHAP values based on the outperforming LCE-CF model. Figure 4 depicts the global effect of each explanatory variable on severity levels. The PCIs have greater (or comparable) importance value than BF variables. Variable \(\Delta\)V has the highest mean SHAP value, followed by CMI, EES, and Vr, indicating their superior utility in the IR assessment. In addition, most features are not sensitive to the SEV2 levels (low percentage of green bars), which indirectly explains the poor prediction performance of SEV2.  
Figure 4: Global feature importance based on SHAP values.  
Figure 5 provides a detailed interpretation of the different variables for the injury severity assessment. Each subplot provides three dimensions of information: (1) the features are ranked in a descending order according to their importance (average SHAP value); (2) the color of each point represents the corresponding feature value; and (3) the distribution of SHAP values in each row provides a local interpretation of features on a given severity level, with a positive SHAP value implying the enhancement of the corresponding variable value on the severity category, and vice versa.  
\(\Delta\)V provides the best explanation for all severity levels: the potential outcomes become more severe with the \(\Delta\)V increase, which is in agreement with relevant posterior crash studies and proves the validity of the proposed PCI method [50]. Although EES and CSI have similar effects to \(\Delta\)V, they cannot be fully applied to SEV1 and SEV2. A possible reason is that the definition of EES neglects the influence of restitution coefficient \(\epsilon\) (Equation (10)), which cannot be neglected during a low-speed impact.  
In terms of basic features, mass and Mc (referring to system mass) indirectly respond to the stiffness and structural properties of the vehicle body, which are proven to be effectively used for the identification of moderate outcomes. Moreover, occupant age and crash characteristics significantly affect the prediction of severe crashes. In particular, near-side impacts (corresponding to a high value of impact) and older occupants are prone to major injury, which is consistent with the marginal effect of BOL. Fewer lanes, higher approaching speed, skidding, and drinking also exacerbated the injury severity, but such information is relatively limited in the IR assessment.  
To better investigate the difference in crash types, Figure 6 shows the SHAP value for head-on, rear-end, and side impact crashes. The potential influence of most features on the different severity levels is similar to that in Figure 6. However, the rank of features greatly varies for different types. The results indicate that judging the potential risk of side-impact crashes only through velocity-based indicators is insufficient because the CMI and CSI provide a better interpretation of the degree of injury. The potential reason is that the eccentricity of impact significantly influences the energy conversion for the complex angle crashes, which can be directly reflected with the distribution of the system-based indicators. The impact position also shows high association with the injury severity for side-impact and rear-end crashes.  
Figure 5: Distribution of local feature importance.