气候变化领域集成服务门户(CCPortal): Prediction of driving stress on high-altitude expressway using driving environment features: A naturalistic driving study in Tibet

CCPortal

DOI	10.1080/15389588.2024.2305420
	Prediction of driving stress on high-altitude expressway using driving environment features: A naturalistic driving study in Tibet
	Qin, Pengcheng; He, Jie; Sun, Shuang; Yan, Xintong; Wang, Chenwei; Ye, Yuntao; Yan, Guanfeng; Yan, Tao; Wang, Mingnian
发表日期	2024
ISSN	1538-9588
EISSN	1538-957X
英文摘要	ObjectiveOwing to the harsh environment in high-altitude areas, drivers experience significant driving stress. Compared with urban roads or expressways in low-altitude areas, the driving environment in high-altitude areas has distinct features, including mountainous environments and a higher proportion of trucks and buses. This study aims to investigate the feasibility of predicting stress levels through elements in the driving environment.MethodsNaturalistic driving tests were conducted on an expressway in Tibet. Driving stress was assessed using heart rate variability (HRV)-based indicators and classified using K-means clustering. A DeepLabv3 model was built to conduct semantic segmentation and extract environment elements from the driving scenarios recorded through a camera next to the driver's eyes. A decision tree and 4 other ensemble learning models based on decision trees were built to predict driving stress levels using the environment elements.ResultsFifty-six indicators were extracted from the driving environment. Results of the prediction models demonstrate that extreme gradient boosting has the best overall performance with the F1 score (harmonic mean of the precision and recall) and G-mean (geometric mean of sensitivity and specificity) reaching 0.855 and 0.890, respectively. Indicators based on the variation rate of trucks and buses have high feature importance and exhibit positive effects on driving stress. Indicators reflecting the proportion of mountain, road, and sky features negatively affect the expected levels of driving stress. Additionally, the mountain feature demonstrates multidimensional effects, because driving stress is positively affected by indicators of the variation rate for mountain elements.ConclusionsThis study validates the prediction of driving stress using environment elements in the driver's field of view and extends its application to high-altitude expressways with distinct environmental characteristics. This method provides a real-time, less intrusive, and safer method of driving stress assessment and prediction and also enhances the understanding of the environmental determinants of driving stress. The results hold promising applications, including the development of a driving state assessment and warning module as well as the identification of high-risk road sections and implementation of control measures.
关键词	High-altitude expressway driving stress prediction naturalistic driving test driving environment element machine learning
英文关键词	HEART-RATE-VARIABILITY; ALGORITHM; SIMULATOR; WORKLOAD
WOS研究方向	Public, Environmental & Occupational Health ; Transportation
WOS记录号	WOS:001163455700001
来源期刊	TRAFFIC INJURY PREVENTION
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/283275
作者单位	Southeast University - China; BYD; Sichuan Normal University; Southwest Jiaotong University; Southeast University - China
推荐引用方式 GB/T 7714	Qin, Pengcheng,He, Jie,Sun, Shuang,et al. Prediction of driving stress on high-altitude expressway using driving environment features: A naturalistic driving study in Tibet[J],2024.
APA	Qin, Pengcheng.,He, Jie.,Sun, Shuang.,Yan, Xintong.,Wang, Chenwei.,...&Wang, Mingnian.(2024).Prediction of driving stress on high-altitude expressway using driving environment features: A naturalistic driving study in Tibet.TRAFFIC INJURY PREVENTION.
MLA	Qin, Pengcheng,et al."Prediction of driving stress on high-altitude expressway using driving environment features: A naturalistic driving study in Tibet".TRAFFIC INJURY PREVENTION (2024).