Abstract
In order to improve the driver’s physiological and psychological state, the driver’s mental load which is caused by sight distance, lighting, and other factors in the tunnel environment should be quantified via modeling the spatiotemporal data. The experimental schemes have been scientifically designed based on methods of traffic engineering and human factor engineering, which aims to test the driver’s spatiotemporal data of eye movement and ECG (electrocardiogram) index in the tunnel environment. Firstly, the changes in the driver’s spatiotemporal data are analyzed to judge the changing trend of the driver’s workload in the tunnel environment. The results show that the cubic spline interpolation function model can fit the dynamic changes of average pupil diameter and heart rate (HR) growth rate well, and the goodness of fit for the model group is above 0.95. So, tunnel environment makes the driver’s typical physiological indicators fluctuate in the coordinates of time and space, which can be modeled and quantified. Secondly, in order to analyze the classification of tunnel risk level, a fusion model has been built based on the functions of average pupil diameter and HR growth rate. The tunnel environmental risk level has been divided into four levels via the fusion model, which can provide a guidance for the classification of tunnel risk level. Furthermore, the fusion model allows tunnel design and construction personnel to adopt different safety design measures for different risk levels, and this method can effectively improve the economy of tunnel operating safety design.
Highlights
Tunnel is a typical bad visual environment, and driving in tunnel environment is a relatively dangerous activity
A survey from Italy shows that severe accident rates and cost rates in tunnels were higher than those on the corresponding motorways [2]
Driving performance in tunnels is different from freeway driving, and darker lighting conditions and enclosed space will make drivers nervous and increase the effort required to maintain lateral control of the vehicle, which will affect drivers’ psychological state and driving behavior [3, 4]
Summary
Tunnel is a typical bad visual environment, and driving in tunnel environment is a relatively dangerous activity. Related research shows that visual intervention is an effective method in vehicle trajectories’ intervention [5, 6], and the tunnel safety can be improved by using visual intervention method to affect the driver’s driving behavior in tunnel section. Erefore, it is of practical significance to study the driver’s physiological and psychological state in tunnel environment, which reduces the accident rate in tunnel environment. Ere are many factors that affect the safety of tunnel driving: road alignment, transition of antisliding performance, traffic states, and differences in internal and external environments [7]. Scholars have carried out lots of research about the highway traffic safety to improve the driving safety, especially for the tunnel environment [8, 9]. Meng et al proposed a novel quantitative risk assessment model to assess the risks in the nonhomogeneous urban road tunnels
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