Enhanced Generalization Performance in Deep Learning for Monitoring Driver Distraction: A Systematic Review

Authors

  • Joel John M. Department of CSE, Saveetha Engineering College, Chennai, Tamil Nadu 602105, India
  • Dinakaran K. Department of AI & DS, SA Engineering College, Chennai, Tamil Nadu 600077, India
  • Kavin F. Muscat Engineering Consultancy Private Limited, Chennai, Tamil Nadu 600032, India
  • Anitha P. Department of Electronics and Communication Engineering, Sri Sai Ranganathan Engineering College, Coimbatore, Tamil Nadu, India
  • Gurupandi D. Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India
  • Pradeepa K. Department of Computer Science and Engineering, St. Michael College of Engineering and Technology, Kalayarkoil, Tamil Nadu 630551, India

DOI:

https://doi.org/10.37934/araset.48.1.137151

Keywords:

Deep learning, Human interaction, Machine learning, Neural networks, Sensors

Abstract

Automatic analysis of driver behaviour is one of the most difficult subjects in the field of intelligent transportation systems. This study focuses on disturbed driver stance identification as part of the human action recognition methodology. Distracted drivers have been blamed for several vehicle crashes. Several research projects attacked the issue using various ways, including the use of invasive detectors, which are not practicable for mass production. The majority of the research done in the early 2010s relied on typical Machine Learning algorithms to complete the identification function. Many studies have been performed since the development of DL techniques to accomplish attention identification utilizing Neural Networks. Additionally, most of the study in the field has been done in a simulator or lab context, and the suggested system has not been validated in an actual situation. Most crucially, the field's study activities could be further separated into numerous sections. Many training methods, model properties, & feature choice parameters have been evaluated in this work, which seeks to give a comprehensive evaluation of machine learning methodologies employed for identifying driving distractions.

Author Biographies

Joel John M., Department of CSE, Saveetha Engineering College, Chennai, Tamil Nadu 602105, India

joeljohn1@saveetha.ac.in

Dinakaran K., Department of AI & DS, SA Engineering College, Chennai, Tamil Nadu 600077, India

dinakaran.cse78@gmail.com

Kavin F., Muscat Engineering Consultancy Private Limited, Chennai, Tamil Nadu 600032, India

fkavin03@gmail.com

Pradeepa K., Department of Computer Science and Engineering, St. Michael College of Engineering and Technology, Kalayarkoil, Tamil Nadu 630551, India

pradeepa.k94@gmail.com

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Published

2024-07-09

How to Cite

Joel John M., Dinakaran K., Kavin F., Anitha P., Gurupandi D., & Pradeepa K. (2024). Enhanced Generalization Performance in Deep Learning for Monitoring Driver Distraction: A Systematic Review. Journal of Advanced Research in Applied Sciences and Engineering Technology, 48(1), 137–151. https://doi.org/10.37934/araset.48.1.137151

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Section

Articles