Post accident analysis of driver affection

In this research paper, we proposed to understand and analyzed the driver behavior through affective space model which allows the emotion to be represented in valance(V) and arousal(A). Through this analysis, we can determine correlation between driver behavior and basics emotion which will gain such agreement by psychologists in this area. Besides, through the VA, it will let us to see the driver behavior post accident. This paper presented, the data which has been collected by using Electroencephalogram (EEG) machine and classified into two parts which consist of 4 subjects (2 females and 2 males). First part will be the three basic emotions for each driver which are happy, calm and sad, whereas the second part contains of 3 driving tasks. We use the Mel-frequency cepstral coefficients (Mfcc) as tools to extract features and together with neural network classifier, multi layer perceptron (MLP as a classifier. According to the preliminary experiment, the results show the reasonable accuracy for verifying emotions and identifying subjects. The understanding of driver's behavior will assist us to develop a system which can easily detect highly emotional agitated driver so that we can prevent the accident.