The Significance of the Development of Road Safety Performance Indicators Related to Driver Fatigue

Davidović, Jelica; Pešić, Dalibor; Lipovac, Кrsto; Antić, Boris

doi:10.1016/j.trpro.2020.03.024

Cited by 24 publications

(11 citation statements)

References 15 publications

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“…This means that when teenage riders are tired, they are three times more likely to have an accident compared to adult riders, as illustrated in Figure 5. This is consistent with previous studies conducted by (Lumba et al, 2022b;Lumba, 2022c;Madvari et al, 2023;Davidovic et al, 2020). Based on the data obtained during the survey, it is shown that among the total number of drivers who experienced fatigue while driving, 59.05% slept for 6 hours or less at the night before the accident and 48.57% had an accident between 12:00 AM and 06:00 AM.…”

Section: Resultssupporting

confidence: 91%

“…Similarly, driving for extended durations can affect the driver's level of fatigue, leading to an increased risk of accidents (Vipin & Rahul, 2021) and causing more severe accidents (Zhao et al, 2023). Feeling tired while driving can reduce the driver's ability to concentrate and can also increase the likelihood of accidents (Lumba, 2022c;Madvari et al, 2023), even causing more severe accidents (Davidovic et al, 2019). Besides age, another factor that affects the risk of accidents is the gender of the rider.…”

Section: Introductionmentioning

confidence: 99%

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Strategies for Enhancing Traffic Safety among Adolescent and Adult Motorcycle Riders in Indonesia

Lumba

2024

JJCE

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This research places emphasis on strategies for enhancing traffic safety among adolescent and adult motorcycle riders. 87.36% of accidents in Indonesia involved motorcycle riders and approximately 25% of these accidents involve underage riders. Therefore, a reasonable solution is needed to reduce the number of accidents in Indonesia. The criteria for respondents in this study were adolescent and adult motorcycle riders. The respondents were interviewed for approximately 10 minutes. The total sample amounted to 530 respondents. The research was conducted in Riau province, Indonesia. The research results indicate that adolescent riders are about 4 times more likely to experience accidents compared to adult riders. Model validation demonstrates an accuracy level of up to 89% for the obtained model. The findings of this research suggest strategies for enhancing the safety of both adolescent and adult motorcycle riders, including advising riders to drive their motorcycles in good stamina and performance, advising riders to rest when fatigued, advising riders not to ride between 12:00 AM and 06:00 AM, enforcing strict measures by law enforcement to apprehend riders without a valid driving license, exercising caution when crossing straight roads and roads with non-variable roadside variability. Another finding from this research is that the model in this research took 3 factor types into consideration that cause accidents including human factors, road and environmental factors and vehicle factor. Then, these factors are modeled simultaneously. Keywords: Adolescent, Adult, Motorcycle, Riders, Safety, Strategies.

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Section: Resultssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Strategies for Enhancing Traffic Safety among Adolescent and Adult Motorcycle Riders in Indonesia

Lumba

2024

JJCE

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“…The research on driver fatigue detection can be classified into two categories: passive driver monitoring system and active driver fatigue control system. Among them, the first is the use of preventive measures, by which a driver is monitored, and an alarm is displayed to advise the driver to stop the vehicle during the fatigue stage [5][6][7]. The second is the abnormal fatigue detection in which a driver is deemed to be asleep and the system can actively control the vehicle to avoid accidents [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Research on a Real-Time Driver Fatigue Detection Algorithm Based on Facial Video Sequences

Zhu

Zhang

et al. 2022

Applied Sciences

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The research on driver fatigue detection is of great significance to improve driving safety. This paper proposes a real-time comprehensive driver fatigue detection algorithm based on facial landmarks to improve the detection accuracy, which detects the driver’s fatigue status by using facial video sequences without equipping their bodies with other intelligent devices. A tasks-constrained deep convolutional network is constructed to detect the face region based on 68 key points, which can solve the optimization problem caused by the different convergence speeds of each task. According to the real-time facial video images, the eye feature of the eye aspect ratio (EAR), mouth aspect ratio (MAR) and percentage of eye closure time (PERCLOS) are calculated based on facial landmarks. A comprehensive driver fatigue assessment model is established to assess the fatigue status of drivers through eye/mouth feature selection. After a series of comparative experiments, the results show that this proposed algorithm achieves good performance in both accuracy and speed for driver fatigue detection.

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“…Psychomotor functions required for driving are known to be impacted by inadequate sleep [4]. Specifically, reduced psychomotor functioning as a result of inadequate sleep manifests in impaired reaction time and precise motor responses [5][6][7]. With specific reference to driving, research has reported delayed response times in drivers with inadequate sleep, with drivers taking up to 44% longer to start braking [8].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving

Tuckwell

Keal

Gupta

et al. 2022

Sensors

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Prolonged sitting and inadequate sleep can impact driving performance. Therefore, objective knowledge of a driver’s recent sitting and sleep history could help reduce safety risks. This study aimed to apply deep learning to raw accelerometry data collected during a simulated driving task to classify recent sitting and sleep history. Participants (n = 84, Mean ± SD age = 23.5 ± 4.8, 49% Female) completed a seven-day laboratory study. Raw accelerometry data were collected from a thigh-worn accelerometer during a 20-min simulated drive (8:10 h and 17:30 h each day). Two convolutional neural networks (CNNs; ResNet-18 and DixonNet) were trained to classify accelerometry data into four classes (sitting or breaking up sitting and 9-h or 5-h sleep). Accuracy was determined using five-fold cross-validation. ResNet-18 produced higher accuracy scores: 88.6 ± 1.3% for activity (compared to 77.2 ± 2.6% from DixonNet) and 88.6 ± 1.1% for sleep history (compared to 75.2 ± 2.6% from DixonNet). Class activation mapping revealed distinct patterns of movement and postural changes between classes. Findings demonstrate the suitability of CNNs in classifying sitting and sleep history using thigh-worn accelerometer data collected during a simulated drive. This approach has implications for the identification of drivers at risk of fatigue-related impairment.

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The Significance of the Development of Road Safety Performance Indicators Related to Driver Fatigue

Cited by 24 publications

References 15 publications

Strategies for Enhancing Traffic Safety among Adolescent and Adult Motorcycle Riders in Indonesia

Strategies for Enhancing Traffic Safety among Adolescent and Adult Motorcycle Riders in Indonesia

Research on a Real-Time Driver Fatigue Detection Algorithm Based on Facial Video Sequences

A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving

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