Intelligent transportation in the prevention of accidents

Document Type : Original Article

Authors

1 Department of Computer Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran

2 Assistant Prof, Department of Electrical Engineering, Adiban Higher Education Institute, Garmsar, Iran

3 Department of Computer Engineering, Faculty of Technology and Engineering, Yasouj University, Yasouj, Iran

Abstract

Information technology has played an effective role in various industrial fields, the introduction of this technology in the field of transportation systems has also been considered as a suitable solution and has led to the emergence of intelligent transportation systems. One of the critical application areas in intelligent transportation is computer vision for accident prevention. In the topic of intelligent transportation, intelligent driver monitoring systems are among the things that have been taken into consideration in the safety of cars, so that these systems try to help and warn the driver by intelligently diagnosing accident-causing conditions. By using such intelligent systems, driving accidents and incidents can be significantly reduced. One of the most important concerns in intelligent transportation to prevent significant financial and life losses due to traffic accidents is the drowsiness of drivers. Therefore, intelligent systems are used to make the control systems of transportation vehicles more intelligent by analyzing different states of the driver. For this reason, a system that can intelligently detect the driver's level of alertness or sleepiness by controlling the driver's behavior and the condition of the car is important. With 96% accuracy, 94% sensitivity and 94% accuracy, the proposed system has the best performance in detecting driver drowsiness, which is one of the characteristics of this research compared to other researches.

Keywords


Rahmani-Seryasat, O., Haddadnia, J., & Ghayoumi-Zadeh, H. (2015). A new method to classify breast cancer tumors and their fractionation. Ciência e Natura, 37(4), 51-57.
Rahmani Seryasat, O., Haddadnia, J., & Ghayoumi Zadeh, H. (2016). Assessment of a novel computer aided mass diagnosis system in mammograms. Iranian Quarterly Journal of Breast Disease, 9(3), 31-41.
Seryasat, O. R., & Haddadnia, J. (2017). Assessment of a novel computer aided mass diagnosis system in mammograms. Biomedical Research, 28(7), 3129-3135.
Haddadnia, J., Seryasat, O. R., & Rabiee, H. (2013). Thyroid diseases diagnosis using probabilistic neural network and principal component analysis. Journal of Basic and Applied Science Research, 3(2), 593-598.
Zati, C., (2014). Investigation and the role of using intelligent transportation systems in managementCity traffic. National conference of architecture, civil engineering and new urban development, 8Pages.
Mehri, A.K , Ebrahimi Dehkordi, A. (2016). Planning urban intelligent transportation systems (ITS) with emphasis on multi-storey parking in coastal cities, The third annual conference on architecture, urban planning and urban management research, 18pages.
Sarikan, S. S., Ozbayoglu, A. M., & Zilci, O. (2017). Automated vehicle classification with image processing and computational intelligence. Procedia Computer Science, 114, 515–522. doi:10.1016/j.procs.2017.09.022
Pourahmad, A., Ziari, K., Hataminejad, H., & Parsa, S. (2018). Explanation of Concept and Features of a Smart City. The Monthly Scientific Journal of Bagh-e Nazar, 15(58), 5–26.
Bagloee, S. A., Tavana, M., Asadi, M., & Oliver, T. (2016). Autonomous vehicles: challenges, opportunities, and future implications for transportation policies. Journal of Modern Transportation, 24(4), 284–303. doi:10.1007/s40534-016-0117-3
Khodayari, A., Ghaffari, A., Kazemi, R., & Braunstingl, R. (2011). Modify car following model by human effects based on Locally Linear Neuro Fuzzy. 2011 IEEE Intelligent Vehicles Symposium (IV). Baden-Baden, Germany. doi:10.1109/ivs.2011.5940465
Khodayari, Al., Ghaffari, A., Kazemi, R., Nahovi, A., Salehinia, S., Jamshidi, A. (2013). Designing an intelligent control system for car chase behaviour according to the momentary behaviour of the car driver. Iranian Mechanical Engineering Research Journal, 15(1): 100-119.
Toshima, I., & Aoki, S. (2006). Effect of driving delay with an acoustical tele-presence robot, TeleHead. Proceedings of the 2005 IEEE International Conference on Robotics and Automation. Barcelona, Spain. doi:10.1109/robot.2005.1570096
Healey, J., & Picard, R. (2002). Detecting driver stress“, 15th Int. Conf. Pattern Recognition (τ. 4). Barcelona, Spain.
Kircher, A., Uddman, M., & Sandin, J. (2002). Vehicle Control and drowsiness“, Swedish National Road and Transport Research Institute.
Królak, A., & Strumiłło, P. (2012). Eye-blink detection system for human–computer interaction. Universal Access in the Information Society, 11(4), 409–419. doi:10.1007/s10209-011-0256-6