Road safety: The influence of vibration frequency on driver drowsiness, reaction time, and driving performance

Zhang, N.; Fard, M.; Xu, J.; Davy, J.L.; Robinson, S.R.

doi:10.1016/j.apergo.2023.104148

Cited by 3 publications

(1 citation statement)

References 56 publications

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“…From an economic perspective, these accidents can result in substantial financial costs associated with vehicle repairs, medical bills, insurance premiums, and legal fees. On a broader scale, driver drowsiness contributes to traffic congestion due to accidents [3], emergency response, and clean-up efforts, further affecting economic productivity and the efficiency of transportation networks.…”

Section: Introductionmentioning

confidence: 99%

Novel Transfer Learning Approach for Driver Drowsiness Detection Using Eye Movement Behavior

Madni,

Raza,

Sehar

et al. 2024

IEEE Access

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Driver drowsiness detection is a critical field of research within automotive safety, aimed at identifying signs of fatigue in drivers to prevent accidents. Drowsiness impairs a driver's reaction time, decision-making ability, and overall alertness, significantly increasing the risk of collisions. Nowadays, the challenge is to detect drowsiness using physiological signals, which often require direct contact with the driver's body. This can be uncomfortable and distracting. This study aimed at detecting driver drowsiness through eye movement behavior imagery of the driver. We utilized a standard image dataset based on the eye movement behavior of drivers to conduct this research experiment. We proposed a novel transfer learning-based features generation which combined the strengths of the Visual Geometry Group (VGG-16) and Light Gradient-Boosting Machine (LGBM) methods. The proposed VGLG (VGG16-LGBM) approach first extracts spatial features from input eye image data and then generates salient transfer features using LGBM. Experimental evaluations reveal that the k-neighbors classifier outperformed the state-of-the-art approach with a high-performance accuracy of 99%. The computational complexity analysis shows that the proposed approach detects driver drowsiness in 0.00829 seconds. We have enhanced the performance through hyperparameter tuning and validations using k-fold validation. This research has the potential to revolutionize driver drowsiness detection, aiming to prevent road accidents and save precious lives.

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Section: Introductionmentioning

confidence: 99%

Novel Transfer Learning Approach for Driver Drowsiness Detection Using Eye Movement Behavior

Madni,

Raza,

Sehar

et al. 2024

IEEE Access

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Modal and dynamic response analysis of shock absorber for small commercial vehicles

Cui,

Wang,

Zhao

2024

Vib. proced.

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The dynamic response characteristics of shock absorber have a crucial influence on the stability and safety performance of vehicles. Based on the finite element method, the modal simulation of the mechanical structure of shock absorber was conducted, and the natural frequencies and modal shapes were obtained. According to the dynamic stiffness test scheme, the first four natural frequencies were set as the excitation frequencies, and the response characteristics of displacement, damping force and dynamic stiffness were tested and analyzed. In the experimental loading scheme, shear force was applied to the damper, and the shear amplitude gradually increased from 0.5 mm. Three cycle tests were conducted to obtain the average evaluation value. The results show that although the natural frequencies of the mechanical structure of the shock absorber do not avoid the excitation frequency of the road surface, it still shows good dynamic response effects at low-order excitation frequencies. The elastic stiffness and damping stiffness of the shock absorber decrease with the increase of the shear amplitude, while the damping stiffness increases with the increase of the excitation frequency.

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Leveraging Wearable Sensors in Virtual Reality Driving Simulators: A Review of Techniques and Applications

Boboc,

Butilă,

Butnariu

2024

Sensors

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Virtual reality (VR) driving simulators are very promising tools for driver assessment since they provide a controlled and adaptable setting for behavior analysis. At the same time, wearable sensor technology provides a well-suited and valuable approach to evaluating the behavior of drivers and their physiological or psychological state. This review paper investigates the potential of wearable sensors in VR driving simulators. Methods: A literature search was performed on four databases (Scopus, Web of Science, Science Direct, and IEEE Xplore) using appropriate search terms to retrieve scientific articles from a period of eleven years, from 2013 to 2023. Results: After removing duplicates and irrelevant papers, 44 studies were selected for analysis. Some important aspects were extracted and presented: the number of publications per year, countries of publication, the source of publications, study aims, characteristics of the participants, and types of wearable sensors. Moreover, an analysis and discussion of different aspects are provided. To improve car simulators that use virtual reality technologies and boost the effectiveness of particular driver training programs, data from the studies included in this systematic review and those scheduled for the upcoming years may be of interest.

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Road safety: The influence of vibration frequency on driver drowsiness, reaction time, and driving performance

Cited by 3 publications

References 56 publications

Novel Transfer Learning Approach for Driver Drowsiness Detection Using Eye Movement Behavior

Novel Transfer Learning Approach for Driver Drowsiness Detection Using Eye Movement Behavior

Modal and dynamic response analysis of shock absorber for small commercial vehicles

Leveraging Wearable Sensors in Virtual Reality Driving Simulators: A Review of Techniques and Applications

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