# A Vehicle Conflict Risk Identification Method Based on an Improved Intelligent Driver Model
## 1 Introduction
[32] proposed an improved composite network based on the residual dense module to output the following speed through driving-state analysis. By integrating the IDM through optimal weight theory, a hybrid-drive car-following model was established, demonstrating higher traffic efficiency and the ability to capture asymmetric driving characteristics. In studies incorporating driver characteristics, the influence of human factors on following behavior has been analyzed by quantifying psychological states and driving behavior traits. For instance, [PERSON], [PERSON] et al. [33] incorporated a quantified driver panic factor into the IDM to simulate acceleration behavior under panic conditions, revealing a gradual decrease in driver speed as the panic factor increases. Furthermore, [PERSON], [PERSON] et al. [34] developed an IDM tailored to simulate bus-driver behavior, accounting for multi-vehicle preposition and stimulus perception. Parameter calibration results confirmed that the modified IDM provides realistic interpretations of bus-driving behavior. Key factors such as reaction delays in hazardous situations, psychological pressure, and external environmental interference remain inadequately addressed. Particularly in mixed traffic flows with significant speed variations, the IDM model often exhibits unrealistic sharp speed fluctuations, leading to distorted simulation results and compromised risk assessment accuracy. To address these limitations, this study proposes an enhanced vehicle conflict risk identification method based on an improved intelligent driver model (IDM). First, leveraging VISSIM secondary development and scenario calibration techniques, a simulation platform aligned with the actual road test environment (Zhongyuan Avenue in Harbin, China) was constructed, with optimized speed distribution and driving behavior parameters. Second, the driver-state factor and the rear vehicle braking variable were incorporated into the traditional IDM to refine the calculation of the following vehicle's acceleration, thereby correcting the safety distance calculation logic. The driver-state variable quantifies the driver's cognitive and physiological states during driving, simulating acceleration and deceleration behaviors under different conditions. The braking dynamics variable models the vehicle's response to deceleration or stopping of the leading vehicle. These enhancements improve the dynamic adaptability of the model in complex traffic flows and better capture the uncertainty inherent in real-world driving scenarios. Finally, a conflict risk classification index was developed based on the inverse time to collision (ITTC), and statistical verification combined with threshold analysis was employed to achieve quantitative risk level identification. The remainder of this paper is organized as follows: Section 2 introduces the test section and the methodology for constructing the VISSIM simulation environment, along with the theoretical framework of the improved IDM model. Section 3 presents the experimental results and discusses the effectiveness of the proposed risk identification method. Section 4 concludes the study and outlines both the limitations and future research directions.