A Low-Cost Vehicle Assistance System for Detection of Critical Driving Situations

[1] Zhang, H., Ni, D., Ding, N., Sun, Y., Zhang, Q., Li, X. “Structural analysis of driver fatigue behavior: A systematic review”, Transportation Research Interdisciplinary Perspectives 21, 2023. DOI: 10.1016/j.trip.2023.100865 Search in Google Scholar

[2] El-Nabi, S. A., El-Shafai, W., El-Rabaie, E. S. M., Ramadan, K. F., Abd El-Samie, F. E., Mohsen, S. “Machine learning and deep learning techniques for driver fatigue and drowsiness detection: a review”, Multimed. Tools Appl., 2023. DOI: 10.1007/s11042-023-15054-0 Search in Google Scholar

[3] Ueno, H., Kaneda, M., Tsukino, M. “Development of drowsiness detection system”, 2002. DOI: 10.1109/vnis.1994.396873 Search in Google Scholar

[4] Saini, V. “Driver Drowsiness Detection System and Techniques : A Review”, Int. J. Comput. Sci. Inf. Technol. 5 (3), 2014. Search in Google Scholar

[5] Chai, M., Li, S. W., Sun, W. C., Guo, M. Z., Huang M. Y. “Drowsiness monitoring based on steering wheel status”, Transp. Res. Part D Transp. Environ. 66, 2019. DOI: 10.1016/j.trd.2018.07.007 Search in Google Scholar

[6] Rongben, W., Lie, G., Bingliang, T., Lisheng, J. “Monitoring mouth movement for driver fatigue or distraction with one camera”, 2004. DOI: 10.1109/itsc.2004.1398917 Search in Google Scholar

[7] Ji, Q., Zhu, Z., Lan, P. “Real-time nonintrusive monitoring and prediction of driver fatigue”, IEEE Trans. Veh. Technol. 53 (4), 2004. DOI: 10.1109/TVT.2004.830974 Search in Google Scholar

[8] Čolić, A., Marques, O., Furht, B., “Driver Drowsiness detection System and Solutions”, 2014. Search in Google Scholar

[9] Of, O., Carriers, M. “PERCLOS : A Valid Psychophysiological Measure of Alertness as Assessed by Psychomotor Vigilance”, October 31 (5), 1998. Search in Google Scholar

[10] Škoda auto s.r.o. “Fatigue recognition assistant”, [Online] Available at: https://eshop.skoda-auto.sk/en_SK/fatigue-recognition-assistant/p/5E0054801 Search in Google Scholar

[11] Volkswagen Group, “The Transporter 6.1 Optional Extra Brochure”, 2020. [Online]. Available at: https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwiq6sCk6IKEAxWq0AIHHWNyDBUQFnoECDQQAQ&url=https%3A%2F%2Fwww.volkswagenvans.ie%2Fidhub%2Fcontent%2Fdam%2Fonehub_nfz%2Fimporters%2Fie%2Fmodels%2Fdownloads%2Foptional-ext Search in Google Scholar

[12] I. Rear View Safety, “iVue Driver Fatigue System”. [Online]. Available at: https://www.rearviewsafety.com/driver-fatigue-system-rvs-335.html Search in Google Scholar

[13] Kumar, A., Kaur, A., Kumar, M. “Face detection techniques: a review”, Artif. Intell. Rev. 52 (2), 2019. DOI: 10.1007/s10462-018-9650-2 Search in Google Scholar

[14] Hatem, H., Beiji, Z., Majeed, R. “A Survey of Feature Base Methods for Human Face Detection”, Int. J. Control Autom. 8 (5), 2015. DOI: 10.14257/ijca.2015.8.5.07 Search in Google Scholar

[15] Anila, S., Devarajan, N. “Simple and Fast Face Detection System Based on Edges”, Int. J. Univers. Comput. Sci. 1 (2), 2010. Search in Google Scholar

[16] Yao, J., Cham, W. K. “Efficient model-based linear head motion recovery from movies”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2, 2004. DOI: 10.1109/cvpr.2004.1315193 Search in Google Scholar

[17] Jones, M., Viola, P. “Fast multi-view face detection”, Mitsubishi Electr. Res. Lab TR-20003-96 3.14, 2003. Search in Google Scholar

[18] Viola P., Jones, M. “Rapid object detection using a boosted cascade of simple features”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1, 2001. DOI: 10.1109/cvpr.2001.990517 Search in Google Scholar

[19] Ahmed, M. I. B., et al., “A Deep-Learning Approach to Driver Drowsiness Detection”, Safety 9 (3), 2023. DOI: 10.3390/safety9030065 Search in Google Scholar

[20] Guede-Fernández, F., Fernández-Chimeno, M., Ramos-Castro, J., García-González, M. A. “Driver Drowsiness Detection Based on Respiratory Signal Analysis”, IEEE Access 7, 2019. DOI: 10.1109/ACCESS.2019.2924481 Search in Google Scholar

[21] Wu, Y., Ji, Q. “Facial Landmark Detection: A Literature Survey”, Int. J. Comput. Vis. 127 (2), 2019. DOI: 10.1007/s11263-018-1097-z Search in Google Scholar

[22] Ferkova, Z., Matula, P. “Multimodal Point Distribution Model for Anthropological Landmark Detection”, in Proceedings - International Conference on Image Processing, ICIP 2019, September, 2019. DOI: 10.1109/ICIP.2019.8803252 Search in Google Scholar

[23] Wang, N., Gao, X., Tao, D., Yang, H., Li, X. “Facial feature point detection: A comprehensive survey”, Neurocomputing 275, 2018. DOI: 10.1016/j.neucom.2017.05.013 Search in Google Scholar

[24] Kazemi, V., Sullivan, J. “One millisecond face alignment with an ensemble of regression trees”, 2014. DOI: 10.1109/CVPR.2014.241 Search in Google Scholar

[25] Milesich, T., Danko, J. Bucha, J. “Neural Networks - A Way to Increase the Fuel Efficiency of Vehicles”, Strojnícky časopis – Journal of Mechanical Engineering 68 (1), pp. 81 – 88, 2018. DOI: 10.2478/scjme-2018-0008 Search in Google Scholar

[26] Mishra, A. “Machine Learning Algorithm for Surface Quality Analysis of Friction Stir Welded Joint”, Strojnícky časopis – Journal of Mechanical Engineering 70 (2), pp. 11 – 20, 2020. DOI: 10.2478/scjme-2020-0016 Search in Google Scholar

[27] Ying, X. “An Overview of Overfitting and its Solutions”, in Journal of Physics: Conference Series 1168 (2), 2019. DOI: 10.1088/1742-6596/1168/2/022022 Search in Google Scholar

[28] Luo, P., Wang, X., Tang, X. “Hierarchical face parsing via deep learning,” 2012. DOI: 10.1109/CVPR.2012.6247963 Search in Google Scholar

[29] Sun, Y., Wang, X., Tang, X. “Deep convolutional network cascade for facial point detection”, 2013. DOI: 10.1109/CVPR.2013.446 Search in Google Scholar

[30] Chen, Y., Yang, J., Qian, J. “Recurrent neural network for facial landmark detection”, Neurocomputing 219, 2017. DOI: 10.1016/j.neucom.2016.09.015 Search in Google Scholar