Effect of highway directional signs on driver mental workload and behavior using eye movement and brain wave

Yang, Yanqun; Chen, Yubin; Wu, Changxu; Easa, Said M.; Lin, Wei; Zheng, Xinyi

doi:10.1016/j.aap.2020.105705

Cited by 52 publications

(23 citation statements)

References 29 publications

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“…Analyzing the six items of the NASA-TLX questionnaire, as the amount of information on the signs increases, the mental demand and temporal demand were found to increase during driving, and the degree of effort invested also increases. e results indicate that the drivers' cognition of freeway directional signs will lead to increased driving workload, which is consistent with other studies [25]. On the other hand, an increase in driver effort is conducive to maintaining appropriate driving performance and achieving better sign recognition [41].…”

Section: Influence Of Factors On Subjectivesupporting

confidence: 89%

“…Studies have shown that drivers' recognition time increases with increasing sign information, and when the sign information reaches a certain threshold, the recognition time will increase significantly [24]. Multiboard signs present information more dispersedly, which requires the drivers to search for longer periods of time to find the target, which undoubtedly increases the task processing demand [25]. In addition to a longer reaction time, a complex visual environment will lead to a higher workload and lower driving speed, as shown in simulated driving conditions [26].…”

Section: Introductionmentioning

confidence: 99%

“…In addition to a longer reaction time, a complex visual environment will lead to a higher workload and lower driving speed, as shown in simulated driving conditions [26]. According to the comprehensive efficiency of the cognitive response of road signs, the number of place name thresholds of highway direction signs should be 6 to 7 [25,27].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Freeway Safety Influencing Factors on Driving Workload and Performance Based on the Gray Correlation Method

Xie

Deng³

et al. 2021

Journal of Advanced Transportation

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Human-related factors are a crucial inducement of traffic accidents. Understanding the influence of freeway environments on the driving behavior and workload experienced by drivers has been demonstrated to be of primary importance for improving traffic safety. To study the effect of alignment, traffic flow, and sign information on drivers’ mental workload and behavior, 16 scenarios were constructed using the orthogonal design method, and simulated driving experiments were carried out with 45 participants. During driving, indicators such as the mean and standard deviation of vehicle speed and lane departure were collected, and the NASA-TLX questionnaire was adopted to measure workload. Analysis of variance results indicated that the radius of the horizontal curve, gradient, flow, and sign information level have a significant influence on drivers’ workload and speed keeping ability. In addition, the horizontal curve radius has a significant effect on lane keeping ability. The importance of safety influencing factors on driving workload and performance was quantitatively ranked by integrating the trend of Deng’s correlation degree, comprehensive correlation degree, and similar correlation degree, whose weight was calculated using the entropy method. Traffic sign information was found to have the greatest impact on workload. In terms of driving performance, traffic volume has the greatest influence on the mean and standard deviation of vehicle speed, followed by the amount of sign information. Lane departure is most affected by the radius of the horizontal curve. These findings provide guidance for freeway traffic safety regulation, including workload control and road facility optimization.

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Section: Influence Of Factors On Subjectivesupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of Freeway Safety Influencing Factors on Driving Workload and Performance Based on the Gray Correlation Method

Xie

Deng³

et al. 2021

Journal of Advanced Transportation

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“…Haak et al [43] use EEG to detect high-frequency eye blinks triggered by attention-seeking billboards. Yang et al [44] measured EEG alpha power in response to increased driver mental workload. This was done by comparing the response to a single-board sign and multi-board sign.…”

Section: Introductionmentioning

confidence: 99%

Examination of Driver Visual and Cognitive Responses to Billboard Elicited Passive Distraction Using Eye-Fixation Related Potential

Wang

Clifford

Markham

et al. 2021

Sensors

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Distractions external to a vehicle contribute to visual attention diversion that may cause traffic accidents. As a low-cost and efficient advertising solution, billboards are widely installed on side of the road, especially the motorway. However, the effect of billboards on driver distraction, eye gaze, and cognition has not been fully investigated. This study utilises a customised driving simulator and synchronised electroencephalography (EEG) and eye tracking system to investigate the cognitive processes relating to the processing of driver visual information. A distinction is made between eye gaze fixations relating to stimuli that assist driving and others that may be a source of distraction. The study compares the driver’s cognitive responses to fixations on billboards with fixations on the vehicle dashboard. The measured eye-fixation related potential (EFRP) shows that the P1 components are similar; however, the subsequent N1 and P2 components differ. In addition, an EEG motor response is observed when the driver makes an adjustment of driving speed when prompted by speed limit signs. The experimental results demonstrate that the proposed measurement system is a valid tool in assessing driver cognition and suggests the cognitive level of engagement to the billboard is likely to be a precursor to driver distraction. The experimental results are compared with the human information processing model found in the literature.

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“…Many researchers have studied essential driver characteristics, such as driver sleepiness (Resalat and Saba, 2015 ), drowsiness (Lin et al, 2005 ), fatigue (Jap et al, 2009 ; Arakawa et al, 2019 ), alertness level (Kiymik et al, 2004 ), and cognitive load (Barua et al, 2017 ). Other studies have used EEG, as the effect of color scheme (Yang et al, 2020a ), situation awareness (Yang et al, 2020b ), and the effect of directional signs (Yang et al, 2020c ). Other studies on brain activities, driver aggressiveness, music and emotion, and EEG classification have also been conducted (Fan et al, 2010 ; Liu et al, 2013 ; Lin et al, 2014 ; Yang et al, 2018 , 2019a ; Zeng et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Mental Load of Drivers in Long Highway Tunnel Based on Electroencephalograph

et al. 2021

Self Cite

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In recent years, the mileage of the tunnels has substantially increased with the rapid highway construction that led to increasing highway tunnels. Most studies on tunnel accidents have mainly focused on the external environments, such as tunnel structure, traffic volume, and lighting. In addition, although many studies on mental load of drivers have been conducted for public roads, such studies for highway tunnels have been limited. In this study, three scenarios with different front vehicle speeds (60, 45, and 30 km/h) in a two-lane long tunnel (one lane in each travel direction) were evaluated using a driving simulator. The experiment involved 24 participants (14 men and 10 women) with an average age of 25.8 years and an average experience of 3.2 years. The electroencephalogram (EEG) technology was used to collect the leading EEG indicators during the driving simulation of the scenarios: α, β, and θ waves and the wave ratio, (α + θ)/β. According to the β-wave energy measurements, the alertness of drivers was the lowest at 45 km/h after adapting to the tunnel environment, indicating that the drivers were more comfortable at this speed. This preliminary finding should help in determining the speed limit in this type of tunnel.

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Effect of highway directional signs on driver mental workload and behavior using eye movement and brain wave

Cited by 52 publications

References 29 publications

Analysis of Freeway Safety Influencing Factors on Driving Workload and Performance Based on the Gray Correlation Method

Analysis of Freeway Safety Influencing Factors on Driving Workload and Performance Based on the Gray Correlation Method

Examination of Driver Visual and Cognitive Responses to Billboard Elicited Passive Distraction Using Eye-Fixation Related Potential

Evaluation of Mental Load of Drivers in Long Highway Tunnel Based on Electroencephalograph

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