Abstract
Modern vehicles can generate up to several Gigabytes of data per day which are mostly used only for aspects directly related to the proper functioning of the vehicle itself. However, these data have an enormous value as they can be collected and analyzed to better understand additional aspects of the driving experience, such as classifying the driver’s behavior and driving style.
In this paper, we present a simple yet novel unsupervised methodology that is able to classify the behavior of a driver in a certain geographical area on the basis of the data collected from all the drivers in the same area. The proposed methodology has been tested on two different datasets involving professional truck drivers and it has been verified using human labelled ground truth data. The results obtained demonstrate the feasibility of the proposed solution. To our knowledge, this is the first study to classify driving behaviours of professional truck drivers and validate their performance on such large-scale data with actual safety scores.
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References
Bender, A., Agamennoni, G., Ward, J.R., Worrall, S., Nebot, E.M.: An unsupervised approach for inferring driver behavior from naturalistic driving data. IEEE Trans. Intell. Transp. Syst. 16(6), 3325–3336 (2015)
Brambilla, M., Mascetti, P., Mauri, A.: Comparison of different driving style analysis approaches based on trip segmentation over GPS information. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 3784–3791. IEEE (2017)
Castignani, G., Derrmann, T., Frank, R., Engel, T.: Driver behavior profiling using smartphones: a low-cost platform for driver monitoring. IEEE Intell. Transp. Syst. Mag. 7(1), 91–102 (2015)
Chan, T.K., Chin, C.S., Chen, H., Zhong, X.: A comprehensive review of driver behavior analysis utilizing smartphones. IEEE Trans. Intell. Transp. Syst. (2019)
Constantinescu, Z., Marinoiu, C., Vladoiu, M.: Driving style analysis using data mining techniques. Int. J. Comput. Commun. Control 5(5), 654–663 (2010)
Fazeen, M., Gozick, B., Dantu, R., Bhukhiya, M., González, M.C.: Safe driving using mobile phones. IEEE Trans. Intell. Transp. Syst. 13(3), 1462–1468 (2012). https://doi.org/10.1109/TITS.2012.2187640
Ferreira, J.C., de Almeida, J., da Silva, A.R.: The impact of driving styles on fuel consumption: a data-warehouse-and-data-mining-based discovery process. IEEE Trans. Intell. Transp. Syst. 16(5), 2653–2662 (2015). https://doi.org/10.1109/TITS.2015.2414663
Fugiglando, U., et al.: Driving behavior analysis through CAN bus data in an uncontrolled environment. IEEE Trans. Intell. Transp. Syst. (2019). https://doi.org/10.1109/TITS.2018.2836308
Fugiglando, U., Santi, P., Milardo, S., Abida, K., Ratti, C.: Characterizing the “driver DNA” through can bus data analysis. In: Proceedings of the 2nd ACM International Workshop on Smart, Autonomous, and Connected Vehicular Systems and Services, pp. 37–41. CarSys 2017, Association for Computing Machinery, New York, NY, USA (2017). https://doi.org/10.1145/3131944.3133939
Hlasny, T., Fanti, M.P., Mangini, A.M., Rotunno, G., Turchiano, B.: Optimal fuel consumption for heavy trucks: a review. In: 2017 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), pp. 80–85, September 2017. https://doi.org/10.1109/SOLI.2017.8120974
Linkov, V., Zaoral, A., Řezáč, P., Pai, C.W.: Personality and professional drivers’ driving behavior. Transp. Res. Part F Traffic Psychol. Behav. 60, 105–110 (2019)
Marina Martinez, C., Heucke, M., Wang, F., Gao, B., Cao, D.: Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey. IEEE Trans. Intell. Transp. Syst. 19(3), 666–676 (2018). https://doi.org/10.1109/TITS.2017.2706978
McCall, J.C., Trivedi, M.M.: Driver behavior and situation aware brake assistance for intelligent vehicles. Proc. IEEE 95(2), 374–387 (2007)
Miyajima, C., et al.: Driver modeling based on driving behavior and its evaluation in driver identification. Proc. IEEE 95(2), 427–437 (2007)
Mudgal, A., Hallmark, S., Carriquiry, A., Gkritza, K.: Driving behavior at a roundabout: a hierarchical Bayesian regression analysis. Transp. Res. Part D: Transp. Environ. 26, 20–26 (2014)
O’Keeffe, K., Santi, P., Wang, B., Ratti, C.: Urban sensing as a random search process. Phys. A Statist. Mech. Appl. 562, 125307 (2021)
Simma Software Inc: Understanding SAE j1939 (2021). http://www.simmasoftware.com/j1939-presentation.pdf
Wang, W., Xi, J., Zhao, D.: Driving style analysis using primitive driving patterns with Bayesian nonparametric approaches. IEEE Trans. Intell. Transp. Syst. 20(8), 2986–2998 (2019). https://doi.org/10.1109/TITS.2018.2870525
Wang, W., Xi, J., Chen, H.: Modeling and recognizing driver behavior based on driving data: a survey. Mathematical Problems in Engineering 2014 (2014)
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Milardo, S., Rathore, P., Santi, P., Buteau, R., Ratti, C. (2022). An Unsupervised Approach for Driving Behavior Analysis of Professional Truck Drivers. In: Martins, A.L., Ferreira, J.C., Kocian, A. (eds) Intelligent Transport Systems. INTSYS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 426. Springer, Cham. https://doi.org/10.1007/978-3-030-97603-3_4
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DOI: https://doi.org/10.1007/978-3-030-97603-3_4
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