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Studying the Response of Drivers against Different Collision Warning Systems: A Review

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.
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Contributeur : LE2I - université de Bourgogne Connectez-vous pour contacter le contributeur
Soumis le : jeudi 24 août 2017 - 15:55:01
Dernière modification le : vendredi 5 août 2022 - 14:54:00


  • HAL Id : hal-01577013, version 1


M Muzammel,, M. Zuki Yusoff,, Aamir Saeed Malik, Mohamad Naufal Mohamad Saad, Fabrice Meriaudeau. Studying the Response of Drivers against Different Collision Warning Systems: A Review. 13th International Conference on Quality Control by Artificial Vision, May 2017, Tokyo, Japan. pp.UNSP 1033816. ⟨hal-01577013⟩



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