Abstract
This paper aims to use driving data to create a profile of the driver behavior, which can be then added as an additional layer to the Local Dynamic Map of the vehicle. The main contribution of the paper consists of using the Spherical KMeans Clustering, an unsupervised clustering algorithm for multidimensional datasets, to segment the continuous driving data into multiple segments (hyperspheres). Unlike the state of the art, this helps in studying the behavior since all the data will be processed at the same time regardless of the number of features. The generated hyperspheres are an abstract form of the initial numerical values, and can be contribute to a better representation of the driver behavior. We used the UAH Dataset [9] to present the proposed approach, and the cross-validation technique to evaluate the segmentation results.
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References
Babulal, G.M., Traub, C.M., Webb, M., et al.: Creating a driving profile for older adults using GPS devices and naturalistic driving methodology. F1000Research 5, 2376 (2016). https://doi.org/10.12688/f1000research.9608.2
Divya, G., Sabitha, A., Sudha, D.S., Spandana, K., Swapna, N., Hepsiba, J.: Advanced vehicle security system with theft control and accident notification using GSM and GPS module. Int. J. Innov. Res. Electr. Electron. Instrum. Control Eng. 4(3), 64–68 (2016)
Andreone, L., Brignolo, R., Damiani, S., Sommariva, F., Vivo, G., Marco, S.: Safespot final report. Technical report D8.1.1 (2010)
SAFESPOT Integrated Project (2012). http://www.safespot-eu.org/
Zott, C., Yuen, S.Y., Brown, C.L., Bertels, C., Papp, Z., Netten, B.: Safespot local dynamic maps: context-dependent view generation of a platform’s state and environment. In: 15th Intelligent Transport Systems World Congress, November 2008
Shimada, H., Yamaguchi, A., Takada, H., Sato, K.: Implementation and evaluation of local dynamic map in safety driving systems. J. Transp. Technol. 5, 102–112 (2015)
ETSI TR 102 863 V1.1.1: Intelligent Transport Systems (ITS); Vehicular Communications; Basic Set of Applications; Local Dynamic Map (LDM); Rationale for and guidance on standardization (2011)
Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31, 651–666 (2010)
Romera, E., Bergasa, L.M., Arroyo, R.: Need data for driver behaviour analysis? presenting the public UAH-DriveSet (Brazil). In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)
Bouhoute, A., Oucheikh, R., Boubouh, K., Berrada, I.: Advanced driving behavior analytics for an improved safety assessment and driver fingerprinting. IEEE Trans. Intell. Transp. Syst. 20(6), 2171–2184 (2019). https://doi.org/10.1109/TITS.2018.2864637
Car-2-car.org. 2020. About C-ITS. https://www.car-2-car.org/about-c-its/. Accessed 14 Oct 2020
Bergasa, L.M., AlmerÃa, D., Almazán, J., Yebes , J.J., Arroyo, R.: DriveSafe: an app for alerting inattentive drivers and scoring driving behaviors. In: IEEE Intelligent Vehicles Symposium (IV), pp. 240–245, Dearborn, Michigan, USA, June 2014
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Ouardini, A., El Ouazzany Ech-chahedy, I., Bouhoute, A., Berrada, I., El Kamili, M. (2021). Analyzing Driving Behavior: Towards Dynamic Driver Profiling. In: Foschini, L., El Kamili, M. (eds) Ad Hoc Networks. ADHOCNETS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-030-67369-7_13
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