Safety and efficiency analysis of intelligent truck platooning: A comprehensive simulation-based evaluation framework

In this study, appropriate car-following models are selected to simulate the following behaviors of manual driving vehicles and intelligent driving vehicles. Based on the HighD dataset, the parameters of the manual driving vehicle are calibrated using the VISSIM-MATLAB joint simulation environment, and the parameters of the car-following model of the intelligent driving vehicle are calibrated using the OpenACC dataset and genetic algorithm. To realistically capture the operational characteristics of intelligent truck platooning (ITP) under mixed traffic conditions, a state-based platoon formation mechanism is incorporated to describe dynamic transitions among different vehicle roles. This study comprehensively analyzes the stability and safety of ITP and their impact on road capacity. Findings indicate that ITP maintains good stability at high speeds but experiences instability at low speeds. During the emergency braking situation, it is crucial to enhance the safety measures of ITP to reduce the risk of collision. Through the analysis of the fundamental diagram and time-space diagram, it is found that when the proportion of trucks is low and moderate, ITP can improve the road traffic capacity. Nevertheless, when the proportion of trucks and the proportion of intelligent driving trucks among them are high, they instead constrain road traffic capacity. Because these results are derived from a simulated environment with specific modeling abstractions, the conclusions should be interpreted as mechanism-oriented insights rather than direct quantitative predictions. However, the research results still provide theoretical support and practical references for improving the stability and safety of intelligent driving truck platooning.