Literature DB >> 22765914

An electrophysiological study of the impact of a Forward Collision Warning System in a simulator driving task.

Mercedes Bueno1, Colette Fabrigoule, Philippe Deleurence, Daniel Ndiaye, Alexandra Fort.   

Abstract

Driver distraction has been identified as the most important contributing factor in rear-end collisions. In this context, Forward Collision Warning Systems (FCWS) have been developed specifically to warn drivers of potential rear-end collisions. The main objective of this work is to evaluate the impact of a surrogate FCWS and of its reliability according to the driver's attentional state by recording both behavioral and electrophysiological data. Participants drove following a lead motorcycle in a simplified simulator with or without a warning system which gave forewarning of the preceding vehicle braking. Participants had to perform this driving task either alone (simple task) or simultaneously with a secondary cognitive task (dual task). Behavioral and electrophysiological data contributed to revealing a positive effect of the warning system. Participants were faster in detecting the brake light when the system was perfect or imperfect, and the time and attentional resources allocation required for processing the target at higher cognitive level were reduced when the system was completely reliable. When both tasks were performed simultaneously, warning effectiveness was considerably affected at both performance and neural levels; however, the analysis of the brain activity revealed fewer differences between distracted and undistracted drivers when using the warning system. These results show that electrophysiological data could be a valuable tool to complement behavioral data and to have a better understanding of how these systems impact the driver.
Copyright © 2012 Elsevier B.V. All rights reserved.

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Year:  2012        PMID: 22765914     DOI: 10.1016/j.brainres.2012.06.027

Source DB:  PubMed          Journal:  Brain Res        ISSN: 0006-8993            Impact factor:   3.252


  1 in total

1.  Blind-Spot Collision Detection System for Commercial Vehicles Using Multi Deep CNN Architecture.

Authors:  Muhammad Muzammel; Mohd Zuki Yusoff; Mohamad Naufal Mohamad Saad; Faryal Sheikh; Muhammad Ahsan Awais
Journal:  Sensors (Basel)       Date:  2022-08-15       Impact factor: 3.847

  1 in total

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