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Driver Profiling and Identification Based on Time Series Analysis

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Abstract

As more and more cars are connected to the internet, the threat of cyber-attacks and illegal access to one’s automobile increases expeditiously. Illegal access to automobiles via. the car’s integrated network system is quite a common phenomenon nowadays. Thus, it is essential to identify drivers on the basis of their driving patterns. Henceforth, this paper presents a driver profiling and identification method based on data acquired from car sensors. In-vehicle sensors generate dozens of operational data streams, and identifying the right representative features for driver profiling is a challenging task. Therefore, in our work to capture human driving behavior dynamics, we have designed a framework based on Long Short Term Memory. Moreover, for extracting relevant and independent features from the Controller Area Network (CAN) dataset, we suggested using feature selection algorithms. The proposed framework is evaluated on the publicly available vehicle CAN OBD-II dataset. While we demonstrate the effectiveness of the proposed architecture, an essential objective of this study is to verify that inter-driver heterogeneity and intra-driver homogeneity can be modeled using time series dependency.

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Availability of Data and Materials

Publicly available in vehicle CAN OBD-II dataset is used in this work.

Abbreviations

ADP:

Automatic Driver Profiling

ADAS:

Advanced Driver Assistance System

NCRB:

National Crime Records Bureau

SOTA:

State-of-the-art results

CAN OBD:

Controller Area Network On Board Diagnostics

KNN:

K-Nearest Neighbour

HMM:

Hidden Markov Model

DBA:

Driving Behaviour Analysis

MLP:

Multilayer Perceptron

RNN:

Recurrent Neural Networks

GPS:

Global Positioning System

GMM:

Gaussian Mixture Model

mRMR:

Maximum Relevance Minimum Redundancy

LSTM:

Long Short Term Memory

References

  1. Greenberg, A.: Hackers remotely kill a Jeep on the highway-with me in it. Conde Nast (2015). https://www.wired.com/2015/07/hackers-remotely-kill-jeep-highway/

  2. Centri technology. https://www.cybersecurityintelligence.com/centri-technology-2455.html

  3. Global Technology Company. https://www.aptiv.com/

  4. About ERM. https://ermtelematics.com/

  5. Self-driving cars in the news: Nvidia Automotive. https://www.nvidia.com/en-in/self-driving-cars/news/

  6. Harman Shield. https://car.harman.com/solutions/cybersecurity/cybersecurity-automotive-harman-shield

  7. Choi, S., Thalmayr, F., Wee, D., Weig, F.: Advanced driver-assistance systems: Challenges and opportunities ahead. McKinsey & Company. (2016)

  8. Burton, A., Parikh, T., Mascarenhas, S., Zhang, J., Voris, J., Artan, N.S., Li, W.: Driver identification and authentication with active behavior modeling. In: 12th International Conference on Network and Service Management, CNSM, pp. 388–393 (2016). IEEE

  9. Iea: Trends and developments in Electric Vehicle Markets - Global EV outlook 2021 - analysis. https://www.iea.org/reports/global-ev-outlook-2021/trends-and-developments-in-electric-vehicle-markets

  10. Wang, F., Bai, D., Liu, Z., Yao, Z., Weng, X., Xu, C., Fan, K., Zhao, Z., Chang, Z.: A two-step e-nose system for vehicle drunk driving rapid detection. MDPI. (2023)

  11. H. Itkonen, A.: Characterisation of motorway driving style using naturalistic driving data. Pergamon (2020). https://www.sciencedirect.com/science/article/pii/S136984781930419X

  12. Ly, M.V., Martin, S., Trivedi, M.M.: Driver classification and driving style recognition using inertial sensors. In: IEEE Intelligent Vehicles Symposium (IV), Australia, pp. 1040–1045 (2013). IEEE

  13. Satzoda, R.K., Martin, S., Ly, M.V., Gunaratne, P., Trivedi, M.M.: Towards automated drive analysis: A multimodal synergistic approach. In: 16th International IEEE Conference on Intelligent Transportation Systems, ITSC, pp. 1912–1916 (2013). IEEE

  14. https://ncrb.gov.in/sites/default/files/CII-2021/CII_2021Volume%201.pdf

  15. Lárusdóttir, E., Ulfarsson, G.: Effect of driving behavior and vehicle characteristics on energy consumption of road vehicles running on alternative energy sources. International Journal of Sustainable Transportation. 9 (2014)

  16. Wowo, K.S., Nolting, M., Tempelmeier, N.: Towards sub-maneuver selection for automated driver identification. In: 25th IEEE International Conference on Intelligent Transportation Systems, ITSC, pp. 2422–2427 (2022)

  17. Xu, F., Wang, Z., Wang, H., Lin, L., Liang, H.: Dynamic vehicle pose estimation and tracking based on motion feedback for lidars. Applied Intelligence. 53(2), 2362–2390 (2023)

    Article  Google Scholar 

  18. Liu, J., Liu, Y., Li, D., Wang, H., Huang, X., Song, L.: DSDCLA: driving style detection via hybrid CNN-LSTM with multi-level attention fusion. Applied Intelligence. 53(16), 19237–19254 (2023)

    Article  Google Scholar 

  19. Kwak, B.I., Woo, J., Kim, H.K.: Know your master: Driver profiling-based anti-theft method. In: 14th Annual Conference on Privacy, Security and Trust (PST), pp. 211–218 (2016). IEEE

  20. Azadani, M.N., Boukerche, A.: Driving behavior analysis guidelines for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems. 23(7), 6027–6045 (2021)

  21. Wahab, A., Quek, C., Tan, C.K., Takeda, K.: Driving profile modeling and recognition based on soft computing approach. IEEE transactions on neural networks. 20(4), 563–582 (2009)

    Article  Google Scholar 

  22. Tanaka, D., Baba, Y., Kashima, H., Okubo, Y.: Large-scale driver identification using automobile driving data. In: IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 3441–3446 (2019). IEEE

  23. Meng, X., Lee, K.K., Xu, Y.: Human driving behavior recognition based on hidden markov models. In: IEEE International Conference on Robotics and Biomimetics, pp. 274–279 (2006). IEEE

  24. Mekki, A.E., Bouhoute, A., Berrada, I.: Improving driver identification for the next-generation of in-vehicle software systems. IEEE Transactions on Vehicular Technology. 68(8), 7406–7415 (2019)

    Article  Google Scholar 

  25. Yang, J., Zhao, R., Zhu, M., Hallac, D., Sodnik, J., Leskovec, J.: Driver2vec: Driver identification from automotive data. CoRR. abs/2102.05234 (2021)

  26. Ding, Z., Xu, D., Tu, C., Zhao, H., Moze, M., Aioun, F., Guillemard, F.: Driver identification through heterogeneity modeling in car-following sequences. IEEE Transactions on Intelligent Transportation Systems. 23(10), 17143–17156 (2022)

    Article  Google Scholar 

  27. Dhal, P., Azad, C.: A comprehensive survey on feature selection in the various fields of machine learning. Applied Intelligence, 1–39 (2022)

  28. Mazzanti, S.: "Mrmr" explained exactly how you wished someone explained to you. Towards Data Science (2022). https://towardsdatascience.com/mrmr-explained-exactly-how-you-wished-someone-explained-to-you-9cf4ed27458b

  29. Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R.P., Tang, J., Liu, H.: Feature selection: A data perspective. ACM Computing Surveys. 50(6), 94–19445 (2018)

    Article  Google Scholar 

  30. Team, K.: Simple. flexible. powerful. https://keras.io/

  31. Di Giacomo, U., Casolare, R., Eigner, O., Martinelli, F., Mercaldo, F., Priebe, T., Santone, A.: Exploiting supervised machine learning for driver detection in a real-world environment. Procedia Computer Science. 192, 2440–2449 (2021)

    Article  Google Scholar 

  32. Cura, A., Küçük, H., Ergen, E., Öksüzoğlu, İB.: Driver profiling using long short term memory (lstm) and convolutional neural network (cnn) methods. IEEE Transactions on Intelligent Transportation Systems. 22(10), 6572–6582 (2020)

    Article  Google Scholar 

  33. Zahraoui, Y., Errajraji, K., Ramah, S., Bouhoute, A., Berrada, I.: Driver profiling: The pathway to deeper personalization. Journal of King Saud University - Computer and Information Sciences. 34(10, Part B), 9088–9101 (2022)

  34. Al-Hussein, W.A., Por, L.Y., Kiah, M.L.M., Zaidan, B.B.: Driver behavior profiling and recognition using deep-learning methods: In accordance with traffic regulations and experts guidelines. International Journal of Environmental Research and Public Health. 19(3) (2022)

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Acknowledgements

The authors wish to thank International Institute of Information Technology Naya Raipur for providing the technical support for carrying out this project.

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No funding is provided by any external agency.

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Author notes

  1. Avantika Singh, Vipulesh Tiwari and Srinivasa KG These authors contributed equally to this work.

Authors and Affiliations

  1. Dr. Shyama Prasad Mukherjee International Institute of Information Technology, Naya Raipur, Raipur, 493661, Chhattisgarh, India

    Avantika Singh, Vipulesh Tiwari & Srinivasa KG

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  1. Avantika Singh
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  2. Vipulesh Tiwari
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  3. Srinivasa KG
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Contributions

VT has formally analyze the problem and developed the source code. AS has conceptualized the methodology, supervised the project and finalized the manuscript writing. SKG has supervised the project. All authors read and approved the final manuscript.

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Correspondence to Avantika Singh.

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Singh, A., Tiwari, V. & KG, S. Driver Profiling and Identification Based on Time Series Analysis. Int. J. ITS Res. (2024). https://doi.org/10.1007/s13177-024-00404-5

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