A Study on Driving Load While Overtaking on Mountainous Two‐Lane Highways Based on Physiological Characteristics

超车 运输工程 环境科学 海洋工程 工程类 汽车工程 计算机科学 模拟
作者
Tangzhi Liu,G. Wang,Wangxia Luo,Ruihang Liu,Xingliang Liu,Tang Xiang,Tong Liu
出处
期刊:Journal of Advanced Transportation [Hindawi Publishing Corporation]
卷期号:2024 (1)
标识
DOI:10.1155/atr/5348875
摘要

The traffic environment of mountainous highways is more complex than that of nonmountainous highways, with higher driving loads, which increases the risk in overtaking. The changes in the driver’s pupils, eye gaze behavior, and heart rate can be used to evaluate the level of driving tension and safety. To analyze the driving load while overtaking on two‐lane highways in mountainous areas, an actual vehicle test was conducted. Twenty‐one drivers were divided into a skilled group and an unskilled group. The gaze time, gaze transfer characteristics, heart rate changes, and pupil area changes during the three stages of overtaking (intention, execution, and return) were compared and analyzed. The comprehensive evaluation of driving load during the overtaking process used the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method and Rank Sum Ratio (RSR) method. The results show that the two groups of drivers had the highest driving load during the overtaking execution stage and the lowest driving load during the intention stage. The driving load of overtaking on sections with poor‐sight distance was significantly higher than that on sections with good‐sight distance, and the risk in overtaking during the execution and return stages was highest on sections with poor‐sight distance. It is possible to reduce the driving load if the driver is familiar with the road conditions or has a rich driving experience. Compared to the unskilled group, the skilled group had lower driving loads at all stages of overtaking. The research results can provide a theoretical basis for optimizing traffic safety prevention and control technology on mountainous highways and for designing intelligent driving assistance.
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