zaliza Md Nor

Driver identification and driver's emotion verification using KDE and MLP neural networks

A Drivers behavior is a major factor that contributes to the high accidents rates. However, if we are able to identify their behavior, it may be possible for us to detect driving idiosyncrasies that may prevent accidents. Therefore, this paper presents some simple and effective methods for an in-car data acquisition in collecting real time driving data. The data has been classified into three different drivers condition which leads into accident. They are happy expression, talking on the phone and normal driving. These data will be used to investigate the effectiveness of a drivers behavior which focusing on the drivers response towards the brake and gas pedals as well as its rate of change. From these data we will demonstrate simple yet effective technique in driver identification and driver verification. We use the kernel density estimation (KDE) as tools to extract features. Then, we use these features to recognize the emotion of the driver by using multi layer perceptron (MLP) as classifiers. The enhancement of drivers security, safety and comfort driving can be derived trough the performances of the drivers emotion verification which contribute to the development in the area of intelligent vehicle driver verification system.

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 drivers behavior will assist us to develop a system which can easily detect highly emotional agitated driver so that we can prevent the accident.

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 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.