Research on Driving Workload Characteristics of Drivers Under Various Dangerous Scenarios Based on EEG

Because of the special traffic environment, the tunnel is called a bottleneck on the highway sections; there is a huge risk of safe operation. Tunnel interior zone lighting plays an important role in the tunnel; good lighting can eliminate depression and driving fatigue of the driver in the tunnel. In this paper, freeway tunnel interior zone lighting is as the research object. We analyzed the driver's demand for freeway tunnel interior zone lighting and transformed illumination to luminance in the model of driver workload, operating speed and the illumination. And this model is established by our group. According to comfortable and relatively comfortable driving workload intense threshold, we can get the safe and comfortable luminance threshold of tunnel interior zone. This paper proposed a detection and evaluation method in freeway tunnel interior zone luminance, and the method have been applied and verified on one freeway in south China.

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Assessment of Urban Railway Transit Driver Workload and Fatigue under Real Working Conditions

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119826071 ◽

2019 ◽

Vol 2673 (11) ◽

pp. 891-900

Author(s):

Yuan-chun Huang ◽

Lan-peng Li ◽

Zhi-gang Liu ◽

Hai-yan Zhu ◽

Lin Zhu

Keyword(s):

Reaction Time ◽

Working Conditions ◽

Night Shift ◽

Driving Mode ◽

Autonomous Mode ◽

Driver Workload ◽

Driving Workload ◽

Significant Regression ◽

Time Changes ◽

Fatigue Driving

This paper describes an experiment conducted to establish a workload model by employing physiological methods to measure driver workload and fatigue under real working conditions. Experienced healthy metro drivers were selected as subjects; they performed normal schedules during which simultaneous electrocardiogram (ECG) recording was used to assess their levels of fatigue. Then, subjective workload assessment and reaction time tests were conducted during each break interval to monitor the drivers’ physiological and psychological performance. Based on task analysis, driving workload models with time weight parameters of four types of tasks were established and the workload real-time changes during different shifts were evaluated. The results demonstrate that workload tends to increase over time and it is significantly higher during manual driving mode than autonomous mode ( p = 0.015 < 0.05). Driving fatigue occurs earlier in the night shift than in the day shift according to ECG spectrum analysis results. Although the results of reaction time tests show no significance ( p = 0.917 > 0.05), the increase in the number of reaction errors after fatigue driving indicates a reduction in drivers’ cognitive ability. Regression analysis shows a significant regression relationship with a mutual incentive effect between workload and fatigue in three shifts ( R2 > 0.4). These will be used as a future reference for fatigue research and to help develop reasonable schedules to ensure operational safety.

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An Experimental Evaluation of Using Automotive HUDs to Reduce Driver Distraction While Answering Cell Phones

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/154193120204602209 ◽

2002 ◽

Vol 46 (22) ◽

pp. 1819-1823 ◽

Cited By ~ 6

Author(s):

Christopher Nowakowski ◽

Dana Friedman ◽

Paul Green

Keyword(s):

Experimental Evaluation ◽

Response Times ◽

Cell Phones ◽

Driving Performance ◽

Driver Distraction ◽

Steering Wheel ◽

Center Console ◽

Large Influence ◽

Driving Workload ◽

Curve Radius

To examine strategies for reducing driver distraction while answering the phone, 24 participants answered calls while driving a simulator. Calls were answered using a center-console-mounted phone or one of several phone designs which utilized a HUD to display the caller ID and steering-wheel-mounted buttons to activate the phone. Driving workload was manipulated by varying the curve radius and by varying the timing of the call, either 1 second before or 5 seconds after the start of a curve. The HUD-based phones resulted in response times that were 39 percent faster than the conventional center-console phone, and they resulted in up to 62 percent fewer line crossings. Additionally, when using the center-console phone, road curvature had a large influence on response times and driving performance; however, the HUD-based phone were less sensitive to increased road curvature or driving workload.

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