## 2 Literature Review  
In recent years, vehicle interaction models based on the field theory have garnered significant attention due to their objectivity, versatility, flexibility, and interpretability. [PERSON] et al. [7] proposed a simplified stimulus-response car-following model based on artificial potential fields, which was validated using the NGSIM dataset, revealing a new direction for addressing vehicle control issues that balance safety in complex traffic environments. [PERSON] et al. [8] modeled traffic factors such as lane markings and connected vehicles to construct a control model for autonomous vehicles, demonstrating its effectiveness. [PERSON] et al. [9] established a car-following model grounded in the driving risk field theory and validated its efficacy through numerical simulations, subsequently proposing a novel risk perception warning strategy to mitigate driving risks for connected autonomous vehicles. [PERSON] et al. [10] introduced a driving safety potential field that characterizes the comprehensive risks associated with drivers, connected vehicles, and roadways. [PERSON] et al. [11] investigated the impact of connected auxiliary devices on the efficiency of mixed-traffic flow. [PERSON] et al. [12] combined the Lennard-Jones potential function with safety potential fields, incorporating acceleration parameters and considering the combined effects of lane markings and road boundaries to develop an optimized safety potential car-following model in connected environments, validated using the Shanghai natural driving research dataset. [PERSON] et al. [13] approached the problem from a molecular force field perspective, introducing a velocity coordination term to propose a new framework for systematically analyzing the collaborative relationships and safety dynamics of heterogeneous connected vehicle groups. Similarly, [PERSON] et al. designed an emergency takeover scenario on urban expressways, utilizing a risk field to assess the risk for experimental vehicles. This approach effectively analyzed the driving risks during the takeover process of L3 autonomous vehicles under different cognitive levels. The results indicate that the model outperforms TTC in terms of accuracy in representing risk [14]. In order to understand the dynamic characteristics of surrounding vehicles and avoid potential driving risks in mixed traffic, [PERSON] et al. proposed a probabilistic driving risk assessment framework based on driving intent recognition and vehicle risk evaluation within the system. An LSTM network was used to build the intent recognition model to identify the driving intentions of surrounding vehicles, and then the driving safety field theory was applied to output potential risks [15]. [PERSON] et al. conducted a predictive study on future driving risks by reading historical vehicle trajectory data to calculate the relative position and future motion trajectory of the target vehicle with respect to the experimental vehicle. The simulation results indicate the safety, continuity, and dynamic feasibility of the proposed algorithm [16]. [PERSON] et al. also considered both comfort and safety, setting up static and dynamic risk fields. By selecting the optimal driving trajectory, they achieved lane-change optimization, ensuring that passengers were in a comfortable state for 97.5% of the time during the lane-change process [17]. In summary, existing research on driving safety fields and risk quantification theories is primarily based on the field theory, utilizing artificial potential fields to analyze driving safety issues in the human-vehicle-road system, achieving substantial results. However, some studies conducted in intelligent connected environments lack a comprehensive consideration of vehicle dynamics, driver psychological characteristics, and the interactions between multiple vehicles.