A framework for modeling human-like driving behaviors for autonomous vehicles in driving simulators
Proceedings of the fifth international conference on Autonomous agents
EEG feature extraction for classifying emotions using FCM and FKM
ACACOS'08 Proceedings of the 7th WSEAS International Conference on Applied Computer and Applied Computational Science
Influence of human factor on transport system safety
ASM'10 Proceedings of the 4th international conference on Applied mathematics, simulation, modelling
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As a high rate of road accidents, a research study has been done on the causes of these accidents. Yet, driver behavior is one of the main reason for this predicament while emotion plays a vital role as it affect the driver behavior itself. However, studies on pre-cursor emotion and pre-post accident condition using Electroencephalogram (EEG) pattern are scarce. Hence, this paper proposed to analyze the pre-post accident analysis to determine the correlation between driver behavior and emotion through the 2-D affective space model: valance arousal approach (VAA). EEG machine has been applied to produce the brain waves that requires the drivers to drive in a different traffic condition by using driving simulator. The analysis results of VA for each driver exposed that pre- cursor emotion will affect the emotion in pre-accident whereas negative emotion appear frequently in post-accident compared to positive emotion. This exemplify that the understanding of pre-cursor emotion and its relationship towards driver behavior will help the driver to learn on how to control his/her emotions which can prevent an accident.