M. S. M. Hashim, A. H. Ismail, S. Abu Bakar, M. S. Muhamad Azmi, Z. Mohamad Razlan, A. Harun1, N. S. Kamarrudin, I. Ibrahim, M. K. Faizi, M. A. M. Saad, M. F. H. Rani, A. R. Mahayadin, M. A. Rojan, M. S. Azizizol and M. H. Md Isa
Abstract: A person driving a passenger car depends on the rear view mirror and two side-mounted mirrors to observe the surrounding in order to see vehicles approaching from behind. However, the approaching vehicle may enter a region outside the driver’s field of view, making it inconspicuous to the driver. Such a region is known as the blind spot zone (BSZ). Although driving schools emphasize the importance of checking for vehicles in BSZ before attempting to change lane, many fatal collisions have occurred during lane changing. Thus, it is important to understand BSZ particularly its corresponding parameters in order to develop an effective system to detect approaching vehicles and provide warning to the driver. In this paper, a systematic approach using a grid-based technique is proposed to model the BSZ. An experiment was conducted using a commonly used passenger car in Malaysia as a test bed to model the BSZ. Controlled experimental parameters were introduced, and the final results showed that BSZ can be identified using the grid-based technique.
Keywords:Blind spot zone identification, grid-based technique, passenger cars
Liu, G., Zhou, M., Wang, L., Wang, H., & Guo, X. (2017). A blind spot detection and warning system based on millimeter wave radar for driver assistance. Optik – International Journal for Light and Electron Optics, 135, 353-365.
McNelly, B., Monaco, C., & Parkinson, M. (2015). Using population models to validate Platzer’s methodology for overcoming vehicle side mirror blind spots. Proceedings 19th Triennial Congress of the International Ergonomics Association (IEA), 1812. Retrieved from https://www.iea.cc/congress/2015/1812.pdf
Md Isa, M.H., Md Deros, B., Mohd Jawi, Z., & Abu Kassim, K.A. (2016). An anthropometric comparison of current Anthropometric Test Devices (ATDs) with Malaysian adults. Malaysian Journal of Public Health Medicine. Special Volume 1, 15-21.
Pandian, P., Sundaram, V.D., & Sivaprakasam, R. (2016). Development of fuzzy based intelligent decision model to optimize the blind spots in heavy transport vehicles. Transport Engineering, 28(1), 1-10.
Raj, S., Ezhilarasie, R., & Umamakeswari, A. (2016). Zigbee-based collision avoidance system in blind spot and heavy traffic using ultrasonic sensor. Indian Journal of Science and Technology, 9(48), 1-5.
Sivaraman, S., & Trivedi, M.T. (2013). A review of recent developments in vision-based vehicle detection. Paper presented at 2013 IEEE Intelligent Vehicles Symposium (IV), Gold Coast, Australia.
Summerskill, S. (2011). The identification of ‘blind spots’ in direct and indirect vision for category N2 & N3 vehicles using digital human modelling. Presented at Working Party on General Safety Provisions – 100th session, ECE/TRANS/WP.29/GRSG/2011-01.