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Driver Behavior Profiling and Recognition Using Deep-Learning Methods: In Accordance with Traffic Regulations and Experts Guidelines

Author

Listed:
  • Ward Ahmed Al-Hussein

    (Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Lip Yee Por

    (Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Miss Laiha Mat Kiah

    (Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Bilal Bahaa Zaidan

    (Department of Computing, Faculty of Arts, Universiti Pendidikan Sultan Idris, Tanjong Malim 35900, Perak, Malaysia)

Abstract

The process of collecting driving data and using a computational model to generate a safety score for the driver is known as driver behavior profiling. Existing driver profiles attempt to categorize drivers as either safe or aggressive, which some experts say is not practical. This is due to the “safe/aggressive” categorization being a state that describes a driver’s conduct at a specific point in time rather than a continuous state or a human trait. Furthermore, due to the disparity in traffic laws and regulations between countries, what is considered aggressive behavior in one place may differ from what is considered aggressive behavior in another. As a result, adopting existing profiles is not ideal. The authors provide a unique approach to driver behavior profiling based on timeframe data segmentation. The profiling procedure consists of two main parts: row labeling and segment labeling. Row labeling assigns a safety score to each second of driving data based on criteria developed with the help of Malaysian traffic safety experts. Then, rows are accumulated to form timeframe segments. In segment labeling, generated timeframe segments are assigned a safety score using a set of criteria. The score assigned to the generated timeframe segment reflects the driver’s behavior during that time period. Following that, the study adopts three deep-learning-based algorithms, namely, Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), to classify recorded driving data according to the established profiling procedure, and selects the most suitable one for a proposed recognition system. Various techniques were used to prevent the classification algorithms from overfitting. Using gathered naturalistic data, the validity of the modulated algorithms was assessed on various timeframe segments ranging from 1 to 10 s. Results showed that the CNN, which achieved an accuracy of 96.1%, outperformed the other two classification algorithms and was therefore recommended for the recognition system. In addition, recommendations were outlined on how the recognition system would assist in improving traffic safety.

Suggested Citation

  • Ward Ahmed Al-Hussein & Lip Yee Por & Miss Laiha Mat Kiah & Bilal Bahaa Zaidan, 2022. "Driver Behavior Profiling and Recognition Using Deep-Learning Methods: In Accordance with Traffic Regulations and Experts Guidelines," IJERPH, MDPI, vol. 19(3), pages 1-23, January.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:3:p:1470-:d:736386
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    References listed on IDEAS

    as
    1. Ward Ahmed Al-Hussein & Miss Laiha Mat Kiah & Lip Yee Por & Bilal Bahaa Zaidan, 2021. "Investigating the Effect of Social and Cultural Factors on Drivers in Malaysia: A Naturalistic Driving Study," IJERPH, MDPI, vol. 18(22), pages 1-18, November.
    2. Khoo, Hooi Ling & Asitha, K.S., 2016. "An impact analysis of traffic image information system on driver travel choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 88(C), pages 175-194.
    3. Jorge Tiago Bastos & Pedro Augusto B. dos Santos & Eduardo Cesar Amancio & Tatiana Maria C. Gadda & José Aurélio Ramalho & Mark J. King & Oscar Oviedo-Trespalacios, 2020. "Naturalistic Driving Study in Brazil: An Analysis of Mobile Phone Use Behavior while Driving," IJERPH, MDPI, vol. 17(17), pages 1-14, September.
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    Cited by:

    1. Ward Ahmed Al-Hussein & Wenshuang Li & Lip Yee Por & Chin Soon Ku & Wajdi Hamza Dawod Alredany & Thanakamon Leesri & Huda Hussein MohamadJawad, 2022. "Investigating the Effect of COVID-19 on Driver Behavior and Road Safety: A Naturalistic Driving Study in Malaysia," IJERPH, MDPI, vol. 19(18), pages 1-18, September.
    2. Nattawut Pumpugsri & Wanchai Rattanawong & Varin Vongmanee, 2023. "Development of a Safety Heavy-Duty Vehicle Model Considering Unsafe Acts, Unsafe Conditions and Near-Miss Events Using Structural Equation Model," Sustainability, MDPI, vol. 15(16), pages 1-20, August.

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