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International Journal of Electronics Automation Review Article

Detection of Driver Emotion Using Deep Learning

  1. Sanket Patil Department of Computer Science, NBN Sinhgad School of Engineering, Ambegaon, Pune,
  2. Sujata Bhande Department of Computer Science, NBN Sinhgad School of Engineering, Ambegaon, Pune,
  3. Ankita Hannure Department of Computer Science, NBN Sinhgad School of Engineering, Ambegaon, Pune,
  4. Akramuddin Ahmed Department of Computer Science, NBN Sinhgad School of Engineering, Ambegaon, Pune

Abstract

High level Driver-Help Frameworks (ADASs) are utilized for expanding security in the auto space, yet momentum ADASs quite work without considering drivers' states, e.g., whether she/he is genuinely able to drive. Feelings are a significant way of behaving of people and may emerge in driving circumstances. Uncontrolled feelings can prompt unsafe impacts. To control and decrease the adverse consequence of conduct. In this paper we will distinguish the driver’s conduct. We are going to chip away at five classifications, for example, driver messaging, driver turning, safe driving, talking and other movement. By utilizing a convolutional brain network, we are goin to characterize driver behavior. The convolutional brain network extricates the highlights as well as arranges the classification or conduct. In this paper we are trained model on 100 epochs, and we achieve 92.23% accuracy.

Keywords

References (11)

  1. Claude Frasson, Pierre Olivier Brosseau, and Thi Hong Dung Tran “Virtual Environment for Monitoring Emotional Behaviour in Driving” Springer International Publishing Switzerland 2014.
  2. Davoli L, Martalò M, Cilfone A, Belli L, Ferrari G, Presta R, et al. On Driver Behavior Recognition for Increased Safety: A Roadmap. Safety. 2020;6(4):55. doi:10.3390/safety6040055
  3. AYMAN ALTAMEEM, ANKIT KUMAR, RAMESH CHANDRA POONIA, SANDEEP KUMAR, AND ABDUL KHADER JILANI SAUDAGAR “Early Identification and Detection of Driver Drowsiness by Hybrid Machine Learning” ACCESS.2021.3131601.
  4. indu Verma “Deep Learning Based Real-Time Driver Emotion Monitoring” 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES) September 12-14, 2018, Madrid, Spain.
  5. Kowalczuk, M. Czubenko, T. Merta “Emotion monitoring system for drivers” IFAC PapersOnLine 52-8 (2019) 200–205.
  6. Li G, Lee BL, Chung WY. Smartwatch-Based Wearable EEG System for Driver Drowsiness Detection. IEEE Sensors Journal. 2015;15(12):7169-7180. doi:10.1109/jsen.2015.2473679
  7. Sunagawa M, Shikii SI, Nakai W, Mochizuki M, Kusukame K, Kitajima H. Comprehensive Drowsiness Level Detection Model Combining Multimodal Information. IEEE Sensors Journal. 2020;20(7):3709-3717. doi:10.1109/jsen.2019.2960158
  8. Dasgupta A, Rahman D, Routray A. A Smartphone-Based Drowsiness Detection and Warning System for Automotive Drivers. IEEE Transactions on Intelligent Transportation Systems. 2019;20(11):4045-4054. doi:10.1109/tits.2018.2879609
  9. Ramzan M, Khan HU, Awan SM, Ismail A, Ilyas M, Mahmood A. A Survey on State-of-the-Art Drowsiness Detection Techniques. IEEE Access. 2019;7:61904-61919. doi:10.1109/access.2019.2914373
  10. Kaplan S, Guvensan MA, Yavuz AG, Karalurt Y. Driver Behavior Analysis for Safe Driving: A Survey. IEEE Transactions on Intelligent Transportation Systems. 2015;16(6):3017-3032. doi:10.1109/tits.2015.2462084
  11. You F, Li X, Gong Y, Wang H, Li H. A Real-time Driving Drowsiness Detection Algorithm With Individual Differences Consideration. IEEE Access. 2019;7:179396-179408. doi:10.1109/access.2019.2958667