## 4 Results  
### The Validation of PCRI's Effectiveness Using the inD Dataset  
In this part, we perform calculations of TTC and PCRI for the inD dataset. Before conflict identification, it is necessary to determine the conflict thresholds for TTC and PCRI. In some studies, the threshold of TTC is 1.5 s [48; 49]. Therefore, we also use 1.5 s as the threshold of TTC. For PCRI, the threshold for EDR is set to one lane width. TTR threshold is based on drivers' braking reaction time of 1.14 s, and the risk region's radius is also one lane width [50]. If the distance from the lead vehicle to the subject vehicle's trajectory relative to  
Figure 11: The study area with the coordinate system and schematic diagram of five lanes. Figure 10: Shooting videos by a drone. the lead vehicle exceeds one lane width, or if the exposed distance within the risk region is less than one lane width, or if the time to reach the boundary of the risk region allows for sufficient driver reaction, the scenario is classified as conflict-free. Otherwise, a conflict is identified. According to the general calculation results, when TTC corresponds to 1.5 s, the calculated PCRI results fall within the range of 0.165 to 0.170. In this paper, the threshold of PCRI is set to be 0.167, and the conflict type is classified based on SSAM developed by FWHA [51]. The type is classified as a rear-end conflict if the absolute value of the conflict angle is less than 30\({}^{\circ}\), a crossing conflict if the absolute value of the conflict angle is greater than 85\({}^{\circ}\), or otherwise a lane-changing conflict. TTC and PCRI are calculated by frames. A conflict is identified between vehicles if any measure's minimum value in this event is below its specified threshold. The visualization tools provided by the inD dataset are used to manually observe and analyze vehicle actions, aiming to retain valid conflict frames. A total of 362 conflicts are obtained in the inD dataset, including 61 rear-end conflicts, 66 lane-changing conflicts, and 235 crossing conflicts. Thus, the average missing rates in identification for different types of conflict based on TTC and PCRI are shown in Table 1. In lane-changing conflicts, TTC and PCRI exhibit comparable accuracy with the lowest average missing rates in three conflict types. However, PCRI demonstrates superior performance over TTC in the identification of rear-end and crossing conflicts with both lower missing rates. The average missing rate of PCRI is 65% lower than the result of TTC across all conflict types. Therefore, compared to TTC, PCRI is more effective in identifying different types of conflicts with a lower identification missing rate. According to the results presented in Table 1, rear-end and crossing conflicts are identified as two critical conflict types with high rates of missed identifications for one or both measures. To demonstrate the different performance between TTC and PCRI in identifying conflicts in these conflict types, this paper selects typical conflicts of rear-end and crossing conflicts for frame-by-frame analysis. First, a car-following event with the rear-end conflict between vehicle #139 and vehicle #141 recorded in video No.22 of the inD dataset is selected. This conflict responds to the high missing rate in identification for rear-end conflict using TTC. Figure 12 shows the changes in TTC and PCRI in this event. In this scenario, PCRI and TTC exhibit similar trends with slight temporal deviations at their respective minima. TTC identifies a heightened rear-end conflict risk between frames #5930 to #6000, pinpointing frame #5973 as the most dangerous moment with the highest risk level. PCRI marks its high-risk points between frames #5960 to #6010, with frame #5986 as the critical moment of highest risk. In this video, the subject vehicle continuously decelerates upon approaching the intersection.