Human computer interface using electroencephalography for driver behavior classification

Vamsi K. Manchala, Alvaro V. Clara, Susheelkumar C. Subramanian, Sangram Redkar, Thomas Sugar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

It is important to know and be able to classify the drivers’ behavior as good, bad, keen or aggressive, which would aid in driver assist systems to avoid vehicle crashes. This research attempts to develop, test, and compare the performance of machine learning methods for classifying human driving behavior. It also proposes to correlate driver affective states with the driving behavior. The major contributions of this work are to classify the driver behavior using Electroencephalograph (EEG) while driving simulated vehicle and compare them with the behavior classified using vehicle parameters and affective states. The study involved both classical machine learning techniques such as k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN) and latest “unsupervised” Hybrid Deep Learning techniques, and compared the accuracy of classification across subjects, various driving scenarios and affective states.

Original languageEnglish (US)
Title of host publication21st International Conference on Advanced Vehicle Technologies; 16th International Conference on Design Education
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791859216
DOIs
StatePublished - Jan 1 2019
EventASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019 - Anaheim, United States
Duration: Aug 18 2019Aug 21 2019

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume3

Conference

ConferenceASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019
CountryUnited States
CityAnaheim
Period8/18/198/21/19

Keywords

  • Brain computer interface
  • Driver behavior classification

ASJC Scopus subject areas

  • Mechanical Engineering
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Modeling and Simulation

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  • Cite this

    Manchala, V. K., Clara, A. V., Subramanian, S. C., Redkar, S., & Sugar, T. (2019). Human computer interface using electroencephalography for driver behavior classification. In 21st International Conference on Advanced Vehicle Technologies; 16th International Conference on Design Education (Proceedings of the ASME Design Engineering Technical Conference; Vol. 3). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DETC2019-97540