A Machine Learning Approach Based on Automotive Engine Data Clustering for Driver Usage Profiling Classification

  • Cephas Alves da Silveira Barreto UFRN
  • João C. Xavier-Júnior UFRN
  • Anne M. P. Canuto UFRN
  • Ivanovitch M. D. da Silva UFRN


The potential for processing car sensing data has increased in recent years due to the development of new technologies. Having this type of data is important, for instance, to analyze the way drivers behave when sitting behind steering wheel. Many studies have addressed the drive behavior by developing smartphone-based telematics systems. However, very little has been done to analyze car usage patterns based on car engine sensor data, and, therefore, it has not been been explored its full potential by considering all sensors within a car engine. Aiming to bridge this gap, this paper proposes the use of Machine Learning techniques (supervised and unsupervised) on automotive engine sensor data to discover drivers’ usage patterns, and to perform classification through a distributed online sensing platform. We believe that such platform can be useful used in different domains, such as fleet management, insurance market, fuel consumption optimization, CO2 emission reduction, among others.


Al-Sultan, S., Al-Bayatti, A. H., and Zedan, H. (2013). Context-aware driver behavior detection system in intelligent transportation systems. IEEE transactions on vehicular technology, 62(9):4264–4275.

Alsmadi, I. and Alhami, I. (2015). Clustering and classification of email contents. Journal of King Saud University - Computer and Information Sciences, 27(1):46 – 57.

Amsalu, S. B., Homaifar, A., Afghah, F., Ramyar, S., and Kurt, A. (2015). Driver behavior modeling near intersections using support vector machines based on statistical feature extraction. In 2015 IEEE Intelligent Vehicles Symposium (IV), pages 1270–1275.

Araujo, R., Igreja, A., de Castro, R., and Araujo, R. E. (2012). Driving coach: A smartphone application to evaluate driving efficient patterns. In 2012 IEEE Intelligent Vehicles Symposium, pages 1005–1010.

Barreto, V. and Mooers, D. (2017). What is OBD II? History of On-Board Diagnostics.

Castignani, G., Derrmann, T., Frank, R., and Engel, T. G. (2015). Driver behavior profiling using smartphones: A low-cost platform for driver monitoring. IEEE Intelligent Transportation Systems Magazine, 7:91–102.

Eren, H., Makinist, S., Akin, E., and Yilmaz, A. (2012). Estimating driving behavior by a smartphone. In Intelligent Vehicles Symposium (IV), 2012 IEEE, pages 234–239. IEEE.

Halkidi, M., Batistakis, Y., and Vazirgiannis, M. (2002). Clustering validity checking methods: part ii. SIGMOD Rec., 31 (3):19–27.

Higgs, B. and Abbas, M. (2015). Segmentation and clustering of car-following behavior: Recognition of driving patterns. IEEE Transactions on Intelligent Transportation Systems, 16(1):81–90.

Johnson, D. and Trivedi, M. (2011). Driving style recognition using a smartphone as a sensor platform. In 14th Int. IEEE Conf. Intelligent Transportation Systems, page 1609–1615.

Kumagai, T. and Akamatsu, M. (2006). Prediction of human driving behavior using dynamic bayesian networks. IEICE - Trans. Inf. Syst., E89-D(2):857–860.

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

Meseguer, J. E., Calafate, C. T., Cano, J. C., and Manzoni, P. (2013). Drivingstyles: A smartphone application to assess driver behavior. In Computers and Communications (ISCC), 2013 IEEE Symposium on, pages 535–540. IEEE.

Mitchell, T. M. Machine learning. 1997. 45(37):870–877.

Viana, E. (2018b). Smart fleet android.

Viana, E. (Jun, 2018a). Java OBD API: An OBD-II API written in Java.

World Health Organization (2015). Global status report on road safety 2015.

You, C.-W., Montes-de Oca, M., Bao, T. J., Lane, N. D., Lu, H., Cardone, G., Torresani, L., and Campbell, A. T. (2012). Carsafe: A driver safety app that detects dangerous driving behavior using dual-cameras on smartphones. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing, UbiComp ’12, pages 671–672, New York, NY, USA. ACM.

Zhang, C., Patel, M., Buthpitiya, S., Lyons, K., Harrison, B., and Abowd, G. D. (2016). Driver classification based on driving behaviors. In Proceedings of the 21st International Conference on Intelligent User Interfaces, pages 80–84. ACM.

Como Citar

Selecione um Formato
BARRETO, Cephas Alves da Silveira; XAVIER-JÚNIOR, João C.; CANUTO, Anne M. P.; DA SILVA, Ivanovitch M. D.. A Machine Learning Approach Based on Automotive Engine Data Clustering for Driver Usage Profiling Classification. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 15. , 2018, São Paulo. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 174-185. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2018.4414.