Multimodal feature-assisted continuous driver behavior analysis and solving for edge-enabled internet of connected vehicles using deep learning

The emerging technology of internet of connected vehicles (IoCV) introduced many new solutions for accident prevention and traffic safety by monitoring the behavior of drivers. In addi-tion, monitoring drivers’ behavior to reduce accidents has attracted considerable attention from industry and academic researchers in recent years. However, there are still many issues that have not been addressed due to the lack of feature extraction. To this end, in this paper, we propose the multimodal driver analysis internet of connected vehicles (MODAL-IoCV) approach for analyzing drivers’ behavior using a deep learning method. This approach includes three consecutive phases. In the first phase, the hidden Markov model (HMM) is proposed to predict vehicle motion and lane changes. In the second phase, SqueezeNet is proposed to perform feature extraction from these classes. Lastly, in the final phase, tri-agent-based soft actor critic (TA-SAC) is proposed for recommendation and route planning, in which each driver is precisely handled by an edge node for personalized assistance. Finally, detailed experimental results prove that our proposed MOD-AL-IoCV method can achieve high performance in terms of latency, accuracy, false alarm rate, and motion prediction error compared to existing works. © 2021 by the author. Licensee MDPI, Basel, Switzerland.

Aboulola O.1 , Khayyat M.1 , Al-Harbi B.2 , Muthanna M.S.A.3 , Muthanna A. 4, 5 , Fasihuddin H.1 , Alsulami M.H.6
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  • 1 Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, 23218, Saudi Arabia
  • 2 Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, 21959, Saudi Arabia
  • 3 Department of Mathematics & Mechanics, Saint Petersburg State University, St. Petersburg, 199178, Russian Federation
  • 4 Department of Communication Networks and Data Transmission, St. Petersburg State University of Telecommunications, St. Petersburg, 193232, Russian Federation
  • 5 Department of Applied Probability and Informatics, Peoples’ Friendship, University of Russia (RUDN University), 6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation
  • 6 Computer Science Department, Community College, Shaqra, Shaqra University, Shaqra, 11961, Saudi Arabia
Ключевые слова
Driver behavior analysis; Edge node; Hidden Markov model (HMM); Internet of connected vehicles (IoCV); Recommendations; Tri-agent-based soft actor critic (TA-SAC)
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