Effect of road geometry on driver fatigue in monotonous environments: A simulator study

Farahmand, Behzad; Boroujerdian, Amin Mirza

doi:10.1016/j.trf.2018.06.021

Cited by 57 publications

(22 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also did not consider other types of freeway layouts. Future research should create more realistic driving conditions with higher complexity and different freeway layouts to increase the validity and generalizability of the findings (Farahmand & Boroujerdian, 2018; Oron-Gilad & Ronen, 2007). Future research could also find ways to measure drivers’ mental status in real driving conditions.…”

Section: Discussionmentioning

confidence: 99%

“…These "passive" passengers' experience the landscape differently than "active" drivers who must process information and make rapid decisions. Moreover, automated driving technologies might free people's attention from driving tasks, which will likely change how people respond to roadside landscapes (Farahmand & Boroujerdian, 2018). Future researchers should explore these passive ways of experiencing high-speed environments and examine the interactive impacts of speed and landscape on humans' mental status.…”

Section: Directions For Future Researchmentioning

confidence: 99%

See 1 more Smart Citation

Perceived Green at Speed: A Simulated Driving Experiment Raises New Questions for Attention Restoration Theory and Stress Reduction Theory

Jiang

Chen

et al. 2020

Environment and Behavior

View full text Add to dashboard Cite

Few studies have investigated the impact of landscapes on humans’ mental status while they are moving at high speeds, such as driving on the freeway. This study used a simulation system to measure drivers’ mental responses to six different freeway landscapes. Each of the 33 participants completed six different 90-minute simulated driving tasks in a randomly assigned sequence. The six landscape conditions consisted of an identical freeway infrastructure, with different roadside landscapes. Results show significant differences between landscape conditions and drivers’ mental responses. Landscape conditions with greater greenness, in general, had a greater positive impact on drivers’ mental status. The barren and tree regular landscapes yielded the worst and best results, respectively. Further, higher complexity was associated with a higher level of negative mental status. We argue that the speed of human’s active movement should be considered as an essential factor in the Attention Restoration Theory and Stress Reduction Theory.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Directions For Future Researchmentioning

confidence: 99%

Perceived Green at Speed: A Simulated Driving Experiment Raises New Questions for Attention Restoration Theory and Stress Reduction Theory

Jiang

Chen

et al. 2020

Environment and Behavior

View full text Add to dashboard Cite

show abstract

“…The design indexes of long straight lines and reverse circular curves of each part were consistent and conformed to the Technical Standards of Highway Engineering (JTG B01-2014) (27). Although the effects of time on task can be observed only after 1 h of driving, symptoms of driving fatigue in a monotonous environment can arise rather fast (about 30 min) (11). Therefore, the experimental road should be long enough to ensure that the driving time is at least 30 min.…”

Section: Scenariosmentioning

confidence: 99%

“…Although sleep deprivation and prolonged driving work might only be relieved with proper sleep or rest, passive fatigue can be prevented by giving extra stimulus or increasing the driver's mental workload (11). Merat et al (1) found that three low-cost facilities-variable message signs, chevrons and rumble strips-can effectively alleviate passive driving fatigue.…”

mentioning

confidence: 99%

Exploring the Different Patterns for Generation Process of Driving Fatigue Based on Individual Driving Behavior Parameters

Peng

Rong

et al. 2021

Transportation Research Record

View full text Add to dashboard Cite

Driving fatigue is one of the main causes of traffic accidents in monotonous environments such as grassland highways. However, the process of generation of driving fatigue on grassland highways is still not clear. A driving simulation experiment with 23 participants was performed to collect data on driving behavior, reaction time and electrocardiogram (ECG) results when driving on a grassland highway. The effective feature indicators of driving fatigue based on driving behavior data were calculated by Pearson correlation coefficient and principal component analysis method. The matter-element model based on entropy weight method was used to quantify the generation process of driving fatigue (GPDF). GPDF was classified as different patterns by the eigenvalue of GPDF curves. Reaction time and ECG data were utilized to verify the rationality of GPDF. Results show that there were 13 feature indicators of driving behavior suitable for driving fatigue description. GPDF was not completely consistent among different participants and was classified into three patterns (i.e., mild, moderate and severe fatigue). The mean similarity for GPDF in each pattern was 0.87, 0.61 and 0.50. Validation test demonstrated that driving fatigue detection accuracy by GPDF was 72%. The mean similarity of the GPDF between driving behavior and ECG was 0.72. Driving fatigue tended to occur with driving time of 19 min or 33 min. This study is helpful to understand GPDF on grassland highways from the perspective of individual driving behavior, which would provide suggestions for the reasonable setting of anti-fatigue devices.

show abstract

“…Ting et al (2008) [7] identified time-on-task as the most well-known cause of driver fatigue. Additionally, other studies correlated an increasing driving time with increasing subjective sleepiness and fatigue, longer blink durations, and increasing steering wheel movements [8][9][10][11]. Sleep deprivation is also a strong factor that clearly induces higher sleepiness levels, and even mild levels of sleep deprivation (2 h) can result in driving performance impairment [12].…”

Section: Introductionmentioning

confidence: 97%

Analyzing Driver Drowsiness: From Causes to Effects

et al. 2020

View full text Add to dashboard Cite

Drowsiness and fatigue are major safety issues that cannot be measured directly. Their measurements are sustained on indirect parameters such as the effects on driving performance, changes in physiological states, and subjective measures. We divided this study into two distinct lines. First, we wanted to find if any driver’s physiological characteristic, habit, or recent event could interfere with the results. Second, we aimed to analyze the effects of subjective sleepiness on driving behavior. On driving simulator experiments, the driver information and driving performance were collected, and responses to the Karolinska Sleepiness Scale (KSS) were compared with these parameters. The results showed that drowsiness increases when the driver has suffered a recent stress situation, has taken medication, or has slept fewer hours. An increasing driving time is also a strong factor in drowsiness development. On the other hand, robustness, smoking habits, being older, and being a man were revealed to be factors that make the participant less prone to getting drowsy. From another point of view, the speed and lane departures increased with the sleepiness feeling. Subjective drowsiness has a great correlation to drivers’ personal aspects and the driving behavior. In addition, the KSS shows a great potential to be used as a predictor of drowsiness.

show abstract

Effect of road geometry on driver fatigue in monotonous environments: A simulator study

Cited by 57 publications

References 24 publications

Perceived Green at Speed: A Simulated Driving Experiment Raises New Questions for Attention Restoration Theory and Stress Reduction Theory

Perceived Green at Speed: A Simulated Driving Experiment Raises New Questions for Attention Restoration Theory and Stress Reduction Theory

Exploring the Different Patterns for Generation Process of Driving Fatigue Based on Individual Driving Behavior Parameters

Analyzing Driver Drowsiness: From Causes to Effects

Contact Info

Product

Resources

About