## 2 Literature Review  
This study analyzes the driving behavior of autonomous vehicles operating in an autonomous mobility testbed to simulate the behavior of autonomous vehicles. Additionally, simulation-based driving-safety-integrated evaluation indices for each MPR of autonomous vehicles are developed. In this regard, simulation-based studies analyzing the driving safety of autonomous vehicles by MPR are reviewed to draw research opportunities. The use of simulations to analyze driving safety in mixed traffic can be an important factor in proactively enhancing traffic safety [2, 3]. [PERSON] et al. used VISSIM to evaluate the safety of connected vehicle (CV) technology at signalized intersections [4]. The indicators utilized in the analysis were time-to-collision (TTC), post-encroachment time (PET), rear-end conflict (REC) count, lane change conflict (LCC) count, and the total number of conflicts (TNCs). By comparing the evaluation index values for each MPR at adjusted and unadjusted signalized intersections, it was determined that, the lower the values for TTC and PET and the higher the values for REC, LCC, and TNC, the more dangerous the driving safety. The safety analysis showed that, at the intersections where CV technology was applied, safety increased across all the indicators, regardless of the MPR. Furthermore, the risk decreased with an increasing MPR. Similarly, [PERSON] evaluated the impact of connected and automated vehicles (CAVs) on mobility and safety in mixed traffic under adverse weather conditions, utilizing traffic safety alternative evaluation indicators such as the TTC and time-exposed time-to-collision (TET) [5]. The evaluation indicator values were compared in clear, rain, and snow weather conditions by MPR, using TTC and TET to evaluate driving safety. The analysis revealed that, as the MPR of CAVs increased, the overall traffic performance and safety under adverse weather conditions increased. In addition, the results of existing studies have shown that increasing the MPR of CAVs improves safety [6, 7, 8]. [PERSON] et al. utilized VISSIM to identify the impact of CAVs on crash frequency and severity [9]. The VISSIM programming interface was used to develop and implement CAV control algorithms to simulate CAV behavior. A signalized intersection was established in the simulation, and the MPR of CAVs was increased by 10% to evaluate the impact of CAVs on crash frequency and severity. [PERSON] et al. also evaluated the impact of autonomous vehicles on safety according to the MPR of CAVs, utilizing two driving logics (cautious and all-knowing) for CoExist and Akins' driving logic to evaluate safety impacts [10]. The travel time and number of conflicts were utilized for performance measures to evaluate mobility and safety, respectively. CoExist's driving logic showed a decrease in the number of conflicts as the MPR increased, while Akins' driving logic showed a decrease in the number of conflicts above an MPR of 50. On the other hand, [PERSON] et al. used the TTC and crash probability as evaluation indicators to evaluate the safety impact of the MPR of CAVs, showing that safety increased up to an MPR of 50, but above this value, there was an increased crash probability [11]. Similarly, existing studies have shown that safety decreases or even increases after a certain MPR [12; 13]. [PERSON] and [PERSON] evaluated the impact of CAVs on traffic safety at signalized intersections using the TTC [14]. Conflicts were reduced by 90% for an MPR of 50 and by 50% for an MPR of 30. There was no safety impact for MPRs lower than 20%. [PERSON] and [PERSON] also analyzed the safety impacts of the MPR of CAVs at signalized intersections and found that the crash rate was reduced by approximately 50% at an MPR of 100%, whereas there was no safety impact at an MPR of less than 20% [15]. [PERSON] et al. analyzed the impact on autonomous vehicle safety by utilizing surrogate safety measures [16]. The driving safety of autonomous vehicles at signalized intersections and roundabouts by MPR was evaluated via simulation.