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Journals / Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering / 2020 / Cilt: 21 - Sayı: 2

IDENTIFICATION OF DRIVING CHARACTERISTICS USING MARKOV PROCESS MODEL

Tuba Nur SERTTAŞ
Ömer Nezih GEREK
Fatih Onur HOCAOĞLU
Pages
314–321
Publication Date
2020-06-15
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

By applying Markov process method to the data, the drives are classified with 73% accuracy.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Considering the role of drivers in traffic, it is an important advantage to know the driver characteristics in advance.
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Key Findings & Data:Taking this characteristic into consideration, the driver can be warned socially and economically.
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Conclusion & Impact:By applying Markov process method to the data, the drives are classified with 73% accuracy.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Driver classification · Markov Process · Drive data analysis

Abstract

Considering the role of drivers in traffic, it is an important advantage to know the driver characteristics in advance. Taking this characteristic into consideration, the driver can be warned socially and economically. In this study, taking into account this situation, driving characteristics are removed within certain drivers and the drivers are divided into two classes as calm and aggressive. The data recorded via the smartphone is used directly when classification. By applying Markov process method to the data, the drives are classified with 73% accuracy.

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Cite This Article

SERTTAŞ, T. N., GEREK, Ö. N., HOCAOĞLU, F. O. (2020). IDENTIFICATION OF DRIVING CHARACTERISTICS USING MARKOV PROCESS MODEL. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering, Cilt: 21 - Sayı: 2, 314–321. https://ajindex.com/articles/196598

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References (16)

  1. Huang C, Li L, Liu Y, Xiao L. Robust Observer Based Intermittent Forces Estimation for Driver Intervention Identification. IEEE Trans. Veh. Technol., Apr. 2020; 69(4): 3628–3640
  2. Jia S, Hui F, Li S, Zhao X, Khattak AJ. Long short-term memory and convolutional neural network for abnormal driving behaviour recognition. IET Intell. Transp. Syst., May 2020; 14(5): 306–312
  3. Lin X, Zhang K, Cao W, Zhang L. Driver Evaluation and Identification Based on Driving Behavior Data. In Proceedings - 2018 5th International Conference on Information Science and Control Engineering, 2019; 718–722
  4. Liu X, Liang J, Xu B. A Deep Learning Method for Lane Changing Situation Assessment and Decision Making. IEEE Access, 2019; 7:133749–133759
  5. Rahim MA, Liu J, Zhang Z, Zhu L, Li X, Khan S. Who is driving? Event-driven Driver Identification and Impostor Detection through Support Vector Machine. IEEE Sens. J., Jun. 2020; 20(12):1
  6. Rahim MA, Zhu L, Li X, Liu J, Zhang Z, Qin Z, Khan S, Gai K. Zero-to-Stable Driver Identification: A Non-Intrusive and Scalable Driver Identification Scheme. IEEE Trans. Veh. Technol.,Jan. 2020; 69(1): 163–171
  7. Regani SD, Xu Q, Wang B, Wu M, Ray Liu KJ. Driver Authentication for Smart Car Using Wireless Sensing. IEEE Internet Things J., Mar. 2020; 7(3): 2235–2246
  8. Van der El K, Pool DM, Van Paassen MRM, Mulder M. A Unifying Theory of Driver Perception and Steering Control on Straight and Winding Roads. IEEE Trans. Human-Machine Syst., Apr. 2020; 50(2): 165–175
  9. Aycard O, Charpillet F, Foht D and Mari JF. Place learning and recognition using hidden Markov models. In Proc, IEEE Int. Robots Syst., 1997, Grenoble, France, pp. 1741-1746
  10. Yang L, Widjaja BK and Prasad R. Application of hidden Markov models for signature verification. Pattern Recognition, 1995; 28:161-169
  11. Yang Y Xu Y. Human Action Learning via Hidden Markov Model. IEEE Trans. Syst. Man and Cyber.-Part A: Syst. And Humans, 1997; 27:1
  12. Pentland A, Liu A. Modeling and Prediction of Human Behavior. Neural Computation, 1999; 11:229-242
  13. Mitrovic D. Reliable Method for Events Recognition. IEEE Trans. on Intelligent Transp. Syst., June 2005; 6(2):198-205
  14. Torkkola K, Venkatesan S, Liu H. Sensor Sequence Modeling for Driving. FLAIRS Conference, 2005, Clearwater Beach, Florida, USA, pp. 721-727
  15. Zou X. Modeling Intersection Driving Behaviors: A Hidden Markov Model Approach-I. Jrnl. of Transportation Res. Board, 2006: 16-23
  16. Boyraz P, A