Influence of dynamic highway tunnel lighting environment on driving safety based on eye movement parameters of the driver

He, Shiyong; Liang, Bo; Pan, Guobing; Wang, Feng; Cui, Lulu

doi:10.1016/j.tust.2017.04.020

Cited by 114 publications

(52 citation statements)

References 18 publications

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“…In addition, the numbers of gazes and scans in the tunnel are significantly less than those of driving in normal situations (P < 0.01), while the average gaze time is higher than normal (219.60 ms). ere is no significant difference in average scan time between the two groups (the tunnel is 79.79 ms, normal 80.24 ms), which indicates that the driving environment in the tunnel is more monotonous than in the normal environment [22], and drivers need to observe the surroundings more frequently in the normal environment than in the tunnel environment, and their sight will move more frequently. e pupil diameter is an important indicator of the driver's sensitivity to light source [22].…”

Section: Data Collectionmentioning

confidence: 92%

“…ere is no significant difference in average scan time between the two groups (the tunnel is 79.79 ms, normal 80.24 ms), which indicates that the driving environment in the tunnel is more monotonous than in the normal environment [22], and drivers need to observe the surroundings more frequently in the normal environment than in the tunnel environment, and their sight will move more frequently. e pupil diameter is an important indicator of the driver's sensitivity to light source [22]. e size of the pupil is affected by the illuminance: the larger the illuminance value, the smaller the pupil diameter.…”

Section: Data Collectionmentioning

confidence: 92%

“…Cho et al calibrated the threshold of illumination intensity in tunnels based on the driver's visual characteristics [21]. He et al recorded the eye movement parameters of drivers passing through the tunnel and analyzed the influence of tunnel lighting environment on driving safety [22]. Feng et al studied changes in physiological and behavioral characteristics in longitudinal segments of urban underpass tunnels by conducting a real-vehicle experiment [23].…”

Section: Introductionmentioning

confidence: 99%

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Model of Driver’s Eye Movement and ECG Index under Tunnel Environment Based on Spatiotemporal Data

Shen

Wang

2020

Journal of Advanced Transportation

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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. e 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. e 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. e 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.

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Section: Data Collectionmentioning

confidence: 92%

Section: Data Collectionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Model of Driver’s Eye Movement and ECG Index under Tunnel Environment Based on Spatiotemporal Data

Shen

Wang

2020

Journal of Advanced Transportation

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“…Due to the great progress in the field of artificial intelligence and computer technology, the visual search mode of driver was studied by traffic scholars of relevant field in different road environments such as sharp turn section of mountain road, 4 road construction section, 5 tunnel entrance section, 6 tunnel lighting environment, 7 traffic sign and billboard, [8][9][10] roundabout, 11 curved road, 12,13 grassland road, 14 and urban road. 15 In terms of the relationship between emotion and visual search pattern, Dickins et al [16][17][18] found that eye gaze time was lengthened when learning under the negative emotion.…”

Section: Introductionmentioning

confidence: 99%

Analysis of female novice extroversion driver’s eye movement characteristics under anxiety

Liu

Wang

Liu

et al. 2019

Advances in Mechanical Engineering

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Anxiety is a common emotion of driver, which always affects the safety of driving. Eye movement characteristics can be used to understand the true emotion state of human beings. It is of great significance to study the law of eye movement for realizing active vehicle safety warning and human-machine cooperation. In this article, anxiety-induction experiment, real-vehicle driving experiments, and virtual driving experiments were designed and used to obtain the eye movement data of female novice extroversion driver under calm and anxiety, and mathematical statistics analysis was made on the fixation count, fixation duration, and visit duration in the area of interest within the driver horizon. The results showed that there are significant differences in fixation count and fixation duration of drivers (p\0:05, p is the accompanying probability), and the main effect of emotion is significant (p\0:05).Compared with the situation of calm, fixation area, fixation count, and fixation duration of drivers under anxiety were more focused on the middle area, the fixation count and visit duration on the left area were relatively more, the fixation duration on the right area was relatively longer, and anxiety was more likely to cause driver's attention bias.

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“…Thus, the lighting lamps should be installed in order to provide adequate luminance at the tunnel entrance and the threshold zone to allow time for eye adaptation. Currently, energy consumption of tunnel lighting is larger than half of tunnel total energy consumption [5,6]. Hence, research on tunnel lighting energy saving has received significant attention in the last few years [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Luminance Control Method for Tunnel Lighting Based on Traffic Volume

Qin

Dong

et al. 2017

Sustainability

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This paper presents an intelligent control method for tunnel lighting based on traffic volume. The monitoring data for a period of 12 days of the Chibai tunnel (located in the Jilin province of China) under different weather conditions was selected as the case study. The data used in the analysis included traffic volume, vehicle speed, the time of light-emitting diodes (LEDs) operating at their lowest luminance level, and the average time interval between two consecutive vehicles. The traffic flow analysis indicated that the tunnel has a relatively heavy traffic volume in the daytime (7:00 a.m. to 6:00 p.m.) and a relatively low traffic volume in the nighttime (12:00 a.m. to 6:00 a.m. and 7:00 p.m. to 12:00 a.m.). Thus, we propose a tunnel lighting control method that distinguishes day and night operational strategies. In the daytime, the luminance of tunnel zones depends on tunnel exterior luminance, traffic volume and vehicle speed regardless of vehicle presence. In the night, the "vehicle in, light brightens; vehicle out, light darkens" control method is adopted for the tunnel luminance, which depends on vehicle presence.

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Influence of dynamic highway tunnel lighting environment on driving safety based on eye movement parameters of the driver

Cited by 114 publications

References 18 publications

Model of Driver’s Eye Movement and ECG Index under Tunnel Environment Based on Spatiotemporal Data

Model of Driver’s Eye Movement and ECG Index under Tunnel Environment Based on Spatiotemporal Data

Analysis of female novice extroversion driver’s eye movement characteristics under anxiety

An Intelligent Luminance Control Method for Tunnel Lighting Based on Traffic Volume

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