Analysis of Driver Behavior in Various Events Using Electrodermal Activity Signal

Yedukondala Rao Veeranki, Toleti Sai Shanmukh Kailash, Luis Roberto Mercado Diaz, Hugo F. Posada-Quintero

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Keywords: Electrodermal Activity, Galvanic Skin Response, Decomposition, Random Forest Recursive Feature Elimination, Machine Learning

Abstract: Inappropriate driver behavior is a leading cause of traffic accidents, contributing to 94% of crashes, according to the National Motor Vehicle Crash Causation Survey. Factors such as individual driving styles, risk-taking tendencies, and non-compliance with traffic regulations increase the likelihood of conflicts and accidents, emphasizing the need for a deeper understanding of driver behavior. Despite existing studies on driving behavior, there is a lack of precise and comprehensive classification methods that effectively correlate physiological signals with driving actions. Current models do not fully capture the cognitive aspects of driving, limiting their applicability in enhancing traffic safety and accident prevention. The primary goal of this study is to identify the most relevant features that contribute to analyzing driving behavior and utilize them to classify driver actions accurately. We conducted feature extraction using 52 distinct features to analyze driving behavior. Following this, feature selection was performed using the Random Forest Recursive Feature Elimination method to identify the 10 most significant features. These important features were then used for driver behavior classification with machine learning models, ensuring improved accuracy and efficiency in identifying different driving patterns.

Track: 1. Digital Health Solutions (i.e. sensors and algorithms) for diagnosis, progress, and self-management

NominateReviewer:

Toleti Sai Shanmukh Kailash
Email: [e-mail]

Submission Number: 5