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Transport Problems
Silesian University of Technology
Subject: Economics, Transportation, Transportation Science & Technology
eISSN: 2300-861X
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DRIVING STYLE ANALYSIS AND DRIVER CLASSIFICATION USING OBD DATA OF A HYBRID ELECTRIC VEHICLE
Andrzej PUCHALSKI / Iwona KOMORSKA *
Keywords : hybrid electric vehicle; driving styles; eco-driving; cluster analysis
Citation Information : Transport Problems. Volume 15, Issue 4, Part 1, Pages 83-94, DOI: https://doi.org/10.21307/tp-2020-050
License : (CC BY 4.0)
Received Date : 13-May-2019 / Accepted: 27-November-2020 / Published Online: 31-December-2020
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ARTICLE
ABSTRACT
Ensuring the effectiveness of adaptive algorithms for advanced driver assistance systems (ADAS) requires online recognition of driving styles. The article discusses studies carried out during real driving cycles based on the GPS parameters and OBD system data of a hybrid vehicle. The work focuses on the search for measures of the speed and acceleration signals of the car and the measures determined on their basis that best describe the driving style responsible for the vehicle traffic safety and ecological safety. Relations between the type of driver, driving dynamics, and fuel consumption were studied. The driver's categorization was based on a statistical analysis of input signals and mean tractive force (MTF) by clustering.
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REFERENCES
1. Sagberg, F. & Selpi & Piccinini, G. F. B. & Engstrom, J. A review of research on driving styles and road safety. Human Factors. 2015. Vol. 57. No. 7. P. 1248-1275.
2. Wang, W. & Xi, J. & Chen, H. Modelling and recognizing driver behavior based on driving data: a survey. Mathematical Problem in Engineering. 2014. Vol. 2014. P. 1-20.
3. Augustynowicz, A. Preliminary classification of driving style with objective rank method. International Journal of Automotive Technology. 2009. Vol. 10. No. 5. P. 607-610.
4. Constantinescu, Z. & Marinoiu, C. & Vladoiu, M. Driving style analysis using data mining techniques. Int. J. of Computers, Communications & Control. 2010. Vol. 5. No. 5. P. 654-663.
5. Sundbom, M. & Falcone, P. & Sjoberg, J. Online driver behavior classification using probabilistic ARX Models. In: Proceedings of the 16th International IEEE Annual Conference on Intelligent Transportation Systems. Hague, Netherlands. 2013. P. 1107-1112.
6. Higgs, B. & Abbas, M. Segmentation and clustering of car-following behavior: recognition of driving pattern. IEEE Transactions on Intelligent Transportation Systems. 2015. Vol. 16. No. 1. P. 81-90.
7. Qi, G. & Wu, J. & Zhou, Y. & Du, Y. & Jia, Y. & Hounsell, N. & Stanton, N.A. Recognizing driving styles based on topic models. Transportation Research Part D: Transport and Environment. 2019. Vol. 66. P. 13-22.
8. Van Mierlo, J. & Maggetto, G. & Van de Burgval, E. & Gense, R. Driving style and traffic measures - Influence on vehicle emissions and fuel consumption. In: Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering. 2004. Vol. 218. No. 1. P. 43-50.
9. Merkisz, J. & Andrzejewski, M. & Merkisz-Guranowska, A. & Jacyna-Gołda, I. The influence of the driving style on the CO2 emissions from a passenger car. Journal of KONES. 2014. Vol. 21. No. 3. P. 219-226.
10. Lois, D. & Wang, Y. & Boggio-Marzet, A. & Monzon, A. Multivariate analysis of fuel consumption related to eco-driving: Interaction of driving patterns and external factors. Transportation Research Part D: Transport and Environment. 2019. Vol. 72. P. 232-242.
11. Jiang, Q. & Ossart, F. & Marchand, C. Comparative Study of Real-Time HEV Energy Management Strategies. IEEE Transactions on Vehicular Technology. 2017. Vol. 66. No. 12. P. 10875-10888.
12. Granovskii, M. & Dincer, I. & Rosen, M.A. Economic and environmental comparison of conventional, hybrid, electric and hydrogen fuel cell vehicles. Journal of Power Sources. 2006. Vol. 159. No. 2. P. 1186-1193.
13. Koossalapeerom, T. & Satiennam, T. & Satiennam, W & et al. Comparative study of real-world driving cycles, energy consumption, and CO2 emissions of electric and gasoline motorcycles driving in a congested urban corridor. Sustainable Cities and Society. 2019. Vol. 45. P. 619-627.
14. Burdzik, R. & Konieczny, Ł. & Jaworski, R. & Laskowski, D. & Polak, R. Comparison of energy consumption of short and long city buses in terms of assessing the needs for e-mobility. In: Siergiejczyk, M. & Krzykowska, K. Research methods and solutions to current transport problems. ISCT21 2019. Advances in Intelligent Systems and Computing. 2020. Vol. 1032. Springer, Cham. P. 74-83.
15. Nyberg, P. & Frisk, E. & Nielsen, L. Generation of equivalent driving cycles using markov chains and mean tractive force components. in: IFAC Proceedings Volumes. 2014. Vol. 47. No. 3. P. 8787-8792.
16. Nyberg, P. & Frisk, E. & Nielsen, L. Driving cycle adaption and design based on mean tractive force. In: IFAC Proceedings Volumes. 2013. Vol. 46. No. 21. P. 689-694.
17. Huertas, J.I. & Giraldo, M. & Quirama, L.F. & Diaz, J. Driving cycles based on fuel consumption. Energies. 2018. Vol. 11. No. 11. P. 1-13.
18. Puchalski, A. & Komorska, I. & Ślęzak, M. & Niewczas, A. Synthesis of naturalistic vehicle driving cycles using the Markov Chain Monte Carlo method. Eksploatacja i Niezawodnosc - Maintenance and Reliability. 2020. Vol. 22. No. 2. P. 316-322.
19. Lloyd, S.P. Least squares quantization in PCM. IEEE Transactions on Information Theory. 1982. Vol. 28. P. 129-137.
20. Bishop, C.M. Pattern recognition and machine learning, information science and statistics. New York: Springer. 2006. 740 p.