# A Vehicle Conflict Risk Identification Method Based on an Improved Intelligent Driver Model
## 1 Introduction
This framework leverages extreme value modeling and deep learning to predict crash risks within the next 20-25 min, enabling proactive safety management. [PERSON], [PERSON] et al. [24] proposed a spatial-temporal framework using XGBoost and random forest to predict rural road crash risks, identifying traffic flow, road type, and weather as key factors, with 70% accuracy in high-risk segment-day forecasting. Similarly, [PERSON] et al. [25] proposed a hybrid framework that combines machine learning and extreme value theory to estimate crash risk from traffic conflicts. This framework employs isolation forest algorithms to identify extremes more efficiently than traditional sampling methods, significantly improving estimation accuracy and enabling practical applications in traffic safety. While these AI-driven methods demonstrate significant promise in enhancing conflict detection precision, their reliance on large-scale data and computational resources, combined with limited physical interpretability, may restrict their applicability in scenarios prioritizing real-time adaptability and model transparency. In contrast, the intelligent driver model (IDM) [26] provides a computationally efficient and physically interpretable framework for simulating vehicle behavior in traffic flows, particularly in mixed traffic environments. Research has investigated its asymptotic stability and damping characteristics. By incorporating factors such as driving environment adaptability, vehicle performance coupling, and driver behavior integration, numerous scholars have enhanced the traditional IDM model, enabling it to more accurately replicate real-world vehicle-following behavior during simulations, as summarized in Table 1. Research on IDM models integrated with driving environment fitness optimization seeks to enhance dynamic responsiveness to complex traffic scenarios, particularly addressing environmental interaction challenges such as mixed traffic flow and lane-changing behavior. For instance, [PERSON], Z.-w. et al. [27] introduced a back-looking distance driver model to improve perception of the overall driving environment. This model incorporates the expected following distance in homogeneous connected and autonomous vehicle (CAV) environments and employs linear stability theory to analyze stability conditions against small perturbations in homogeneous traffic flow, demonstrating advantages in maintaining string stability. Similarly, [PERSON] et al. [28] developed an adaptive IDM that dynamically adjusts its parameters based on real-time traffic conditions, enhancing the simulated driving behavior of autonomous vehicles, especially in modeling main-lane vehicle responses to lane-changing maneuvers on highways. Additionally, [PERSON] et al. [29] implemented the IDM constant acceleration heuristic as the acceleration output, targeting both the lead actor and related merge actors to determine the maximum induced deceleration, thereby improving the realism of simulated vehicle responses to lane-changing scenarios. In the study of IDM modeling incorporating vehicle performance, researchers have enhanced the model's capability to represent the mechanical responses of heterogeneous vehicles by integrating vehicle dynamics. For example, based on a comprehensive analysis of the IDM's asymptotic stability and damping properties, [PERSON] et al. [30] introduced adaptive coefficients to dynamically adjust the acceleration control function of following vehicles in real time, using the speed and travel time of the preceding vehicle, thereby improving platoon safety. Similarly, [PERSON], [PERSON] et al. [31] combined driving force concepts, the IDM, and the generalized autoregressive conditional heteroskedasticity model to develop a novel car-following model. This model's forward predictions closely match actual acceleration changes and can effectively predict dangerous driving behaviors. Additionally, [PERSON] et al.