## 2 Related Work
To obtain the four types of dangerous accident scenarios for T-junction intersections, accident data were clustered. Additionally, dangerous collision domains were identified for passenger vehicles and commercial vehicles turning at T-junction intersections [32]. Scholars have proposed various methods for classifying intersection scenarios and evaluating indicators using naturalistic driving data and dynamic parameters. Intersection conflict types are divided into three categories and seven dangerous scenarios based on classification methods such as SHRP2, NHTSA, and InteractIVe. Factors such as road density, surrounding environmental impact, visual-field obstruction, and insufficient information were found to account for a high proportion of the factors inducing intersection accidents, according to the Driving Reliability and Error Analysis Method (DREAM) induction analysis of the logical relationship of factors [33]. Researchers proposed indicators, such as the intrusion area, estimated passing-time difference, and estimated collision time, based on the movement trajectory of vehicles passing through intersections. They analyzed common scenarios at intersections, such as Host Vehicle go Straight, Remote Vehicle turn Left in the Opposite direction (HVS/RVLO), to evaluate the potential risk of intersection vehicle traffic [34]. The features of the scenario are summarized based on 277 normal scenarios and 780 dangerous scenarios from the natural driving database. The intersection scenarios are classified based on geometric type, lane composition, and signal control. A logistic regression model was used to determine that road type, lane composition, and vehicle density significantly affect illegal driving [35]. The concept of a dynamic dilemma zone (DDZ) road segment is used in car-following theory to describe vehicle driver behavior at four-quadrant gate high-rail intersections [36]. In addition, researchers have studied three machine learning techniques to model drivers'speed in intersection scenarios during the vehicle's braking stage. The goal is to estimate a safe, comfortable, and human-like speed at the minimum distance for autonomous vehicles [37; 38]. A microsimulation platform was created to evaluate the improved intersection car-following model and simulate the influenced saturation flow rate, based on an enhanced intelligent driver model [39]. To measure traffic conflicts in intersection areas, several indicators are proposed, including post-encroachment time (PET) and time to collision (TTC) [40]. In general, the study found that TTC and PET are the most accurate measures for analyzing intersection safety, considering ease of measurement and application to different conflict types [41]. TTC is recommended for rear-end conflicts, while PET is recommended for angle conflicts. Additionally, PET is proposed as a measure for safety performance during intergreen intervals, also known as change intervals [42]. Intergreen interval refers to the combined duration of the yellow and all-red intervals between phases. In general, PET is defined as the time difference between two successive vehicles, where the first vehicle clears the conflict area and the second arrives at the conflict area. During a change of phases, PET is defined as the elapsed time from when the last clearing vehicle in the previous phase passes the conflict point until the first entering vehicle of the subsequent phase arrives there. The study uses PET to estimate the number of conflicts, which are defined as encroachments between two vehicles with a PET of less than critical time [43]. While these indicators successfully model the possibility of a collision, they cannot directly measure conflict severity, which is defined by the outcome of the collision in terms of vehicle damage, injuries, and fatalities. PET cannot fully assess the safety of angle collisions. For example, safety assessments based on PET may not align with those based on vehicle speeds at conflict points.