UPSI Digital Repository (UDRep)
|
UPSI Digital Repository (UDRep)
|
|
|
|
||||||||||||||||||||||||||||
| Abstract : Universiti Pendidikan Sultan Idris |
| Nighttime driving presents a critical challenge to road safety due to insufficient lighting and increased risk of driver fatigue. Existing methods for monitoring driver fatigue, mainly focusing on behavioral analysis and biometric monitoring, face significant challenges under low-light conditions. Their effectiveness, especially in dynamic lighting environments, is limited by their dependency on specific environmental conditions and active driver participation, leading to reduced accuracy and practicality in real-world scenarios. This study introduces a novel _Illumination Intelligent Adaptation and Analysis Framework (IIAAF)_, aimed at addressing these limitations and enhancing the accuracy and practicality of driver fatigue monitoring under nighttime low-light conditions. The IIAAF framework employs a multidimensional technology integration, including comprehensive body posture analysis and facial fatigue feature detection, per-pixel dynamic illumination adjustment technology, and a light variation feature learning system based on Convolutional Neural Networks (CNN) and time-series analysis. Through this integrated approach, the framework is capable of accurately capturing subtle fatigue signals in nighttime driving environments and adapting in real-time to rapid changes in lighting conditions. Experimental results on two independent datasets indicate that the IIAAF framework significantly improves the accuracy of fatigue detection under nighttime low-light conditions. This breakthrough not only enhances the effectiveness of driving assistance systems but also provides reliable scientific support for reducing the risk of accidents caused by fatigued driving. These research findings have significant theoretical and practical implications for advancing intelligent driving assistance technology and improving nighttime road safety. _ 2024 Tian et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
| References |
Mello M. T., Narciso F. V., Tufik S., Paiva T., Spence D. W., BaHammam A. S., et al. (2013). Sleep disorders as a cause of motor vehicle collisions. International Journal of Preventive Medicine, 4(3), 246. PMID: 23626880. Zhang, Q., Wang, Y., Wu, C., & Zhang, H. (2019). Research on Maximum Driving Time Based on Driving Fatigue Model from Field Experiment. In 2019 5th International Conference on Transportation Information and Safety (ICTIS) (pp. 1068-1073). IEEE. Rezaee, K., Khosravi, M. R., Attar, H., & Almatarneh, S. (2022). EEG-Based Driving Fatigue Recognition Using Hybrid Deep Transfer Learning Approach. In 2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) (pp. 1-6). IEEE. You F., Li Y.-h., Huang L., Chen K., Zhang R.-h., & Xu J.-m. (2017). Monitoring drivers' sleepy status at night based on machine vision. Multimedia Tools and Applications, 76, 14869-14886. https://doi.org/10.1007/s11042-016-4103-x. Ma, X., Chau, L.-P., & Yap, K.-H. (2017). Depth video-based two-stream convolutional neural networks for driver fatigue detection. In 2017 International Conference on Orange Technologies (ICOT) (pp. 155-158). IEEE. Sikander G., & Anwar S. (2018). Driver fatigue detection systems: A review. IEEE Transactions on Intelligent Transportation Systems, 20(6), 2339-2352. https://doi.org/10.1109/TITS.2018.2868499. Matthews, G., Wohleber, R., Lin, J., Funke, G., & Neubauer, C. (2019). Monitoring task fatigue in contemporary and future vehicles: a review. In G. Stanton (Ed.), Advances in Human Factors in Simulation and Modeling: Proceedings of the AHFE 2018 International Conferences on Human Factors and Simulation and Digital Human Modeling and Applied Optimization, Held on July 21-25, 2018, in Loews Sapphire Falls Resort at Universal Studios, Orlando, Florida, USA (pp. 101-112). Springer. Doudou M., Bouabdallah A., & Berge-Cherfaoui V. (2020). Driver drowsiness measurement technologies: Current research, market solutions, and challenges. International Journal of Intelligent Transportation Systems Research, 18, 297-319. https://doi.org/10.1007/s13177-019-00199-w. Karuppusamy N. S., & Kang B.-Y. (2020). Multimodal system to detect driver fatigue using EEG, gyroscope, and image processing. IEEE Access, 8, 129645-129667. https://doi.org/10.1109/ACCESS.020.3009226. Bakker B., Zablocki B., Baker A., Riethmeister V., Marx B., Iyer G., et al. (2021). A multi-stage, multifeature machine learning approach to detect driver sleepiness in naturalistic road driving conditions. IEEE Transactions on Intelligent Transportation Systems, 23(5), 4791-4800. https://doi.org/10.1109/TITS.2021.3090272. Yogesh, R., Ritheesh, V., Reddy, S., & Rajan, R. G. (2022). Driver Drowsiness Detection and Alert System using YOLO. In 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) (pp. 1-6). IEEE. Gu H., Ji Q., & Zhu Z. (2002). Active facial tracking for fatigue detection. In Sixth IEEE Workshop on Applications of Computer Vision, 2002 (WACV 2002). Proceedings. (pp. 137-142). IEEE. Ferna_ndez-Sotos P., Garci_a A. S., Vicente-Querol M. A., Lahera G., Rodriguez-Jimenez R., & Ferna_ndez-. Caballero A. (2021). Validation of dynamic virtual faces for facial affect recognition. Plos One, 16 (1), e0246001. https://doi.org/10.1371/journal.pone.0246001 PMID: 33493234. Valeriani D., & Poli R. (2019). Cyborg Astrobiologists: The Next Step in the Evolution of Exobiology. Frontiers in Astronomy and Space Sciences, 6, 75. Du G., Li T., Li C., Liu P. X., & Li D. (2020). Vision-based fatigue driving recognition method integrating heart rate and facial features. IEEE Transactions on Intelligent Transportation Systems, 22(5), 3089-3100. https://doi.org/10.1109/TITS.2020.2979527. Qiao, Y., Zeng, K., Xu, L., & Yin, X. (2016). A smartphone-based driver fatigue detection using fusion of multiple real-time facial features. In 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC) (pp. 230-235). IEEE. Wu, J., Chen, R., & Chen, W. (2019). Intelligent Adjusting System of Headlights based on Machine Vision. In 3rd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2019) (pp. 513-519). Atlantis Press. Yang R., Wang Z., Lin P.-S., Li X., Chen Y., Hsu P. P., et al. (2019). Safety effects of street lighting on roadway segments: Development of a crash modification function. Traffic Injury Prevention, 20(3), 296-302. https://doi.org/10.1080/15389588.2019.1573317 PMID: 30971143. Waldner M., Mu_ ller N., & Bertram T. (2022). Energy-Efficient Illumination by Matrix Headlamps for Nighttime Automated Object Detection. International Journal of Electrical and Computer Engineering Research, 2(3), 8-14. https://doi.org/10.53375/ijecer.2022.295. Shen J., Li G., Yan W., Tao W., Xu G., Diao D., et al. (2018). Nighttime driving safety improvement via image enhancement for driver face detection. IEEE Access, 6, 45625-45634. https://doi.org/10.1109/ACCESS.2018.2864629. Mo Y., Han G., Zhang H., Xu X., & Qu W. (2019). Highlight-assisted nighttime vehicle detection using a multi-level fusion network and label hierarchy. Neurocomputing, 355, 13-23. https://doi.org/10.1016/j.neucom.2019.04.005. Schamm T., von Carlowitz C., & Zo_ llner J. M. (2010). On-road vehicle detection during dusk and at night. In 2010 IEEE Intelligent Vehicles Symposium (pp. 418-423). IEEE. https://doi.org/10.1109/IVS.2010.5548013. |
| This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |