Pre-and postaccident emotion analysis on driving behavior
There are many contributing factors that result in high number of traffic accidents on the roads and highways today. Globally, the human (operator) error is observed to be the leading cause. These errors may be transpired by the driver’s emotional state that leads to his/her uncontrolled driving beh...
| Main Authors: | , , , |
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| Format: | Book Chapter |
| Language: | English English |
| Published: |
Springer New York
2014
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/38962/ http://irep.iium.edu.my/38962/ http://irep.iium.edu.my/38962/ http://irep.iium.edu.my/38962/3/38962_Pre-%20and%20postaccident%20emotion%20analysis%20on%20driving%20behavior_SCOPUS.pdf http://irep.iium.edu.my/38962/9/38962_Pre-%20and%20postaccident%20emotion%20analysis%20on%20driving%20behavior.pdf |
| Summary: | There are many contributing factors that result in high number of traffic accidents on the roads and highways today. Globally, the human (operator) error is observed to be the leading cause. These errors may be transpired by the driver’s emotional state that leads to his/her uncontrolled driving behavior. It has been reported in a number of recent studies that emotion has direct influence on the driver behavior. In this chapter, the pre- and postaccident emotion of the driver is studied in order to better understand the behavior of the driver. A two-dimensional Affective Space Model (ASM) is used to determine the correlation between the driver behavior and the driver emotion. A 2-D ASM developed in this study consists of the valance and arousal values extracted from electroencephalogram (EEG) signals of ten subjects while driving a simulator under three different conditions consisting of initialization, pre-accident, and postaccident. The initialization condition refers to the subject’s brain signals during the initial period where he/she is asked to open and close his/her eyes. In order to elicit appropriate precursor emotion for the driver, the selected picture stimuli for three basic emotions, namely, happiness, fear, and sadness are used. The brain signals of the drivers are captured and labeled as the EEG reference signals for each driver. The Mel frequency cepstral coefficient (MFCC) feature extraction method is then employed to extract relevant features to be used by the multilayer perceptron (MLP) classifier to verify emotion. Experimental results show an acceptable accuracy for emotion verification and subject identification. Subsequently, a two-dimensional Affective Space Model (ASM) is employed to determine the correlation between the emotion and the behavior of drivers. The analysis using the 2-D ASM provides a visualization tool to facilitate better understanding of the pre- and postaccident driver emotion. |
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