## 2 Literature Review
The TTC was selected among the surrogate safety measures and analyzed with different thresholds. An autonomous vehicle MPR of 100% reduced the number of conflicts by 20% compared to an MPR of 0% when a conflict was defined as a vehicle-following event with a TTC of 1 s or less at a signalized intersection. Additionally, the number of conflicts consistently decreased as the MPR of autonomous vehicles increased when a TTC of 0.7 s or less was defined as a conflict. In addition, [PERSON] et al. analyzed the impact of MPR on the traffic mobility and safety of CAVs at signalized intersections [17]. The evaluation indicators were average delay and TTC, where the two thresholds of TTC-based conflicts were 0.75 s and 1 s. The analysis showed that, as the MPR increased, the road capacity of CAVs increased and the travel time decreased. While the number of conflicts did not change significantly depending on the threshold value of the TTC, the results showed that the number of conflicts was 2% lower when the threshold value was 1 s than when it was 0.75 s for an MPR of 50. [PERSON] and [PERSON] analyzed the mobility and safety impacts of MPR for autonomous vehicles [1]. The parameters of the Wiedemann 99 model were adjusted to implement the behavior of autonomous vehicles based on existing studies. Then, mobility and safety analyses were performed at unsignalized intersections and roundabouts. The average delay decreased as the MPR increased at unsignalized intersections, and the number of crashes decreased when there was less variation in the traffic direction. The average delay per vehicle decreased as the MPR increased at roundabouts, while rear-end and lane change conflicts decreased and intersection conflicts increased. [PERSON] et al. implemented the behavior of autonomous vehicles in VISSIM using the Wiedemann 74 and 99 models and conducted a safety analysis using TTC, PET, and conflict points [18]. The results showed that the number of collisions was greater for manual vehicles than for autonomous vehicles, the number of rear-end collisions was greater for conventional vehicles, and the number of lane change collisions was greater for autonomous vehicles. Although simulation-based driving safety analyses of autonomous vehicle MPRs have been continuously conducted, the basis for selecting the evaluation indicators selected for these driving safety analyses is not clear. Thus, a rational basis that indicators should be able to represent the real-world safety level of mixed traffic streams consisting of AVs and MVs is required. Therefore, this study collected and analyzed real-world autonomous vehicle data and utilized them in the simulation of autonomous vehicle behavior. Existing studies have mainly used TTC and TTC-based conflict counts to perform safety analyses. This study derived inter-vehicle interaction indicators such as the average spacings and SDI-basedcollision counts to evaluate driving safety. Furthermore, simulation-based driving safety evaluation indicators that effectively identify hazardous road sections in the real world via autonomous vehicle MPR were derived. In addition, an integrated evaluation index was developed in this study.