## 2 Literature Review  
Due to the inherent uncertainty in driver decision-making behavior when approaching signalized intersections during the yellow light phase, most researchers have adopted statistical models to describe stop-or-go decisions and to estimate the likelihood of different choices. Discrete Choice Models [18; 19] have been widely used to establish the relationship between driver decisions and various influencing factors after the onset of the yellow signal, most commonly through binary logistic regression models. In addition, Fuzzy Logic Models [20], Bayesian Networks [21], and Agent-based Models [22] have also been employed to capture and predict driver behavior. However, the hesitation of human drivers to enter an intersection during the yellow phase can affect traffic flow, potentially increasing the risk of collisions, fuel consumption, and travel time [23; 24; 25]. This calls for greater attention on models of uncertain driving behavior during the yellow phase to bridge the gap between simulated environments and actual traffic conditions. The above models primarily focus on data-driven approaches to identifying decision-making patterns, without incorporating the risk perception process or exploring the fundamental causes behind decisions. In current research involving human-driven or autonomous vehicles, scholars often use models such as the IDM (Intelligent Driver Model) [26; 27], Gipps model [28; 29; 30], optimal velocity model [31; 32], and [PERSON]'s car-following model [26; 33] to simulate the behavior of human-driven vehicles following other vehicles. However, when describing behavior when approaching signalized intersections, only a few studies have considered the yellow light phase. For instance, [PERSON] et al. considered a yellow duration of 3 s, allowing vehicles to pass the stop line within the first second of the yellow phase during trajectory planning [33]. [PERSON] et al. separated the yellow duration into effective green and red phases [28; 29]. [PERSON] et al. allowed human-driven vehicles to stop before the stop line during the yellow phase [34]. These studies typically simplify or idealize the treatment and assumptions regarding the yellow phase. In fact, many researchers overlook the impact of the yellow phase on driving behavior, assuming that vehicles can only pass through the intersection during the green phase. However, this assumption does not align with real-world traffic conditions and may lead to errors in simulation results. In existing simulation software, driving behavior models built on different logical frameworks have been applied to various traffic scenarios. Table 1 presents a comparative overview of the models adopted by several widely used traffic simulation tools, highlighting the diversity in modeling strategies. However, this may hinder consistency and scalability when applying the same simulation across multiple scenarios. In contrast, models developed under a unified logical framework may be more conducive to smoother and more convenient transitions between structured simulation scenarios, as they enable standardized interpretation of driving behaviors, especially when studying complex, multi-phase traffic conditions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Software** & **Car-Following Model** & **Lane-Changing Model** & **Yielding Model at Intersections** \\ \hline VISSIM [10; 35; 36; 37] & Physio-psycho model & Rule-based discrete choice model & Decision tree and priority rule-based model \\ \hline SUMO [31; 38; 39; 40; 41] & Krauss model & Krauss model & Default priority rule and custom traffic \\ \hline AIMSUN [42; 43; 44; 45] & Gipps model & Dynamic decision-making model & Priority rule and signal control-based model \\ \hline PARAMICS [46; 47; 48; 49] & Gipps model and fuzzy logic-based model & Fuzzy logic-based dynamic model & Fuzzy logic and priority rule-based model \\ \hline TRANSIMS [50; 51; 52] & Custom agent-based model & Custom strategy-based model & Custom Rule-based models \\ \hline \hline \end{tabular}
\end{table}