## 6 Discussion  
### Risk Quantification Method Evaluation  
We applied the Morse risk field (_MRF_) quantification model to continuously calculate the interaction vehicle risk field intensity at fixed intervals across different driving scenarios. In the microscopic car following scenario, which involves only one target vehicle and one interaction vehicle, the interaction vehicle drives directly in front of the target vehicle, undergoing acceleration, constant speed, deceleration, and constant speed movements sequentially. The target vehicle follows the interaction vehicle. The results of the car following scenario experiment are shown in Figure 8. Figure 8a displays the speed of each vehicle and the driving risk experienced by the target vehicle. \(v_{1}\) represents the speed of the interaction vehicle, \(v_{2}\) represents the speed of the target vehicle, and _MRF_ represents the driving risk the target vehicle experiences from the interaction vehicle. Figure 8b shows the driving risk experienced by the target vehicle, where _TTC_\({}^{-1}\) indicates the inverse of the time to collision between the two vehicles, _MRF_ represents the driving risk the target vehicle experiences from the interaction vehicle, _MRF-c_ indicates the driving risk the target vehicle experiences from the interaction vehicle at the current moment, and _MRF-p_ represents the predicted future driving risk the target vehicle will experience from the interaction vehicle. As can be seen from Figure 8b, the variation patterns of _TTC_\({}^{-1}\) and _MRF_ are generally similar, with the difference being that during the 18-30 s period, _TTC_\({}^{-1}\) reflects a lower driving risk, while _MRF_ indicates a higher risk. From Figure 8a, it is evident that the 18-30 s period corresponds to the high-speed car following phase, and after 45 s, it represents the medium-speed car following phase. _TTC_\({}^{-1}\) reflects a consistent driving risk for the target vehicle during both phases, which does not align with the actual situation. In contrast, _MRF_ shows a higher driving risk during the high-speed car following phase and a lower risk during the medium-speed car following phase. Therefore, in this scenario, _MRF_ provides a more accurate representation of the driving risk than _TTC_\({}^{-1}\). Furthermore, Figure 8b illustrates the driving risk experienced by the target vehicle at the current moment (_MRF-c_), as well as the predicted future driving risk (_MRF-p_). The root mean square error between _MRF-c_ and _MRF-p_ was 0.052, with a deviation of 5.38%. From the figure, it can be observed that the variation patterns of _MRF-c_ and _MRF-p_ were consistent, with _MRF-p_ located to the left of the _MRF-c_ curve, indicating that the _MRF-p_ curve can represent the predicted future  
Figure 8: Experimental results of the car following scenario. (**a**) The speed of each vehicle and the driving risk experienced by the target vehicle in the car following scenario. (**b**) The driving risk experienced by the target vehicle in the car following scenario. driving risk at the current moment. This demonstrates the effectiveness of the proposed model in the car following scenario. In the merging scenario (involving only one target vehicle and one interacting vehicle), the target vehicle is traveling at approximately 82 km/h in the right lane, while the interacting vehicle accelerates from behind in the left lane, overtakes the target vehicle at about 97 km/h, then changes lanes to the right and merges into the target vehicle's lane. The results of the merging scenario experiment are shown in Figure 9. Figure 9a displays the speeds of the vehicles and the driving risk experienced by the target vehicle. Here, \(v_{1}\) represents the speed of the interacting vehicle, \(v_{2}\) represents the speed of the target vehicle, and MRF indicates the driving risk experienced by the target vehicle from the interacting vehicle. Figure 9b shows the relative distance between the vehicles and the driving risk experienced by the target vehicle.