## 6 Discussion
### Comparative Analysis with Existing Indicators  
Existing risk quantification indicators primarily focus on evaluating single driving situations with conflict points. However, for scenarios where the risk is jointly influenced by the target vehicle and two or more interacting vehicles, current risk quantification indicators may not provide an effective assessment. Therefore, a comparative analysis was conducted between the _MRF_ and existing conflict point-based risk quantification indicators (post-encroachment time, _PET_; modified time to collision, _MTTC_; deceleration rate to avoid crash, _DRAC_), as well as the safety field (safety potential field, _SPF_), which is also based on field theory. The results are shown in Figure 11.  
Figure 10: Comparative analysis of the target vehicle and interacting vehicle risk field changes in the forecast horizon.  
In Figure 11a, when the target vehicle started to change lanes around 1 s, its _MRF_ gradually increased. Prior to the formation of conflict points with the forward-interacting vehicles (L0, L1, L2), the associated risk could be continuously quantified. From 1 to 4 s, during the lane change, no significant sudden changes in the _MRF_ were observed. After approximately 4 s, once the lane change was completed, the vehicle entered the following state. Since the risk of longitudinal collision with L1 is relatively high, the _MRF_ remained at a higher level.  
In Figure 11b-d, marked points represent the moments of appearance and disappearance of vehicle trajectory conflict points. Traditional conflict point-based risk quantification indicators can only assess risks when conflict points appear on the predicted vehicle trajectories. In cases where the leading vehicle's speed is higher than that of the following vehicle, these traditional indicators fail to provide a continuous risk evaluation throughout the driving process.  
As shown in Figure 11b, before the target vehicle completed the lane change, \(1/PET\) could only evaluate the risk related to the two interacting vehicles (L0, F0) in the original lane. After the target vehicle completed the lane change, it only evaluated the risk related to the two interacting vehicles (L1, F1) in the target lane.  
When the speed of the leading vehicle exceeded that of the following vehicle, as shown in Figure 11c,d, both \(1/MTTC\) and \(DRAC\) indicators also showed limitations. In following situations, the headway and speed differential between the two vehicles may both be small. If the leading vehicle suddenly decelerated, the following vehicle had no opportunity to avoid the collision risk through deceleration or lane change. Therefore, in such cases, the Morse field risk quantification method based on the field theory has the advantage of  
Figure 11: Results of the analysis of the quantitative indicators for each risk.  
continuous risk quantification, compensating for the shortcomings of traditional conflict point-based risk indicators.  
The method was compared with the safety potential field, which is also based on the field theory. By comparing Figure 11a,e, it can be observed that the overall trend of _SPF_ was generally similar to that of _MRF_. However, _SPF_ exhibited two significant fluctuations around 3 s (during the target vehicle's lane change process) and around 4 s (when switching from the lane change state to the following state). The former may be due to the consideration of lane markings, as the risk associated with the target vehicle crossing the lane lines differed before and after the lane change. The latter may arise because _SPF_ only explores the spatial distance's effect on the field, without considering the field's temporal decay process. In contrast, the Morse field risk quantification method incorporates a half-life parameter (\(t_{1/2}\)) in Equation (7), allowing it to combine both spatial and temporal dimensions, thereby making the risk quantification results more aligned with the objective reality.