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Proceedings Paper

Studying the response of drivers against different collision warning systems: a review
Author(s): M. Muzammel; M. Zuki Yusoff; A. Saeed Malik; M. Naufal Mohamad Saad; F. Meriaudeau
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Paper Abstract

The number of vehicle accidents is rapidly increasing and causing significant economic losses in many countries. According to the World Health Organization, road accidents will become the fifth major cause of death by the year 2030. To minimize these accidents different types of collision warning systems have been proposed for motor vehicle drivers. These systems can early detect and warn the drivers about the potential danger, up to a certain accuracy. Many researchers study the effectiveness of these systems by using different methods, including Electroencephalography (EEG). From the literature review, it has been observed that, these systems increase the drivers' response and can help to minimize the accidents that may occur due to drivers unconsciousness. For these collision warning systems, tactile early warnings are found more effective as compared to the auditory and visual early warnings. This review also highlights the areas, where further research can be performed to fully analyze the collision warning system. For example, some contradictions are found among researchers, about these systems' performance for drivers within different age groups. Similarly, most of the EEG studies focus on the front collision warning systems and only give beep sound to alert the drivers. Therefore, EEG study can be performed for the rear end collision warning systems, against proper auditory warning messages which indicate the types of hazards. This EEG study will help to design more friendly collision warning system and may save many lives.

Paper Details

Date Published: 14 May 2017
PDF: 7 pages
Proc. SPIE 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017, 1033816 (14 May 2017); doi: 10.1117/12.2266632
Show Author Affiliations
M. Muzammel, Univ. Teknologi Petronas (Malaysia)
LE2I, Univ. de Bourgogne, CNRS (France)
M. Zuki Yusoff, Univ. Teknologi Petronas (Malaysia)
A. Saeed Malik, Univ. Teknologi Petronas (Malaysia)
M. Naufal Mohamad Saad, Univ. Teknologi Petronas (Malaysia)
F. Meriaudeau, Univ. Teknologi Petronas (Malaysia)
LE2I, Univ. de Bourgogne, CNRS (France)

Published in SPIE Proceedings Vol. 10338:
Thirteenth International Conference on Quality Control by Artificial Vision 2017
Hajime Nagahara; Kazunori Umeda; Atsushi Yamashita, Editor(s)

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