An online estimation of driving style using data-dependent pointer model

Akena, R., Schmid, F., Burrow, M., 2017. Driving style for better fuel economy. Proc. Inst. Civil Eng. - Transp., 170(3), 131–139. Amata, H., Miyajima, C., Nishino, T., Kitaoka, N., Takeda, K., 2009. Prediction model of driving behavior based on traffic conditions and driver types. In: 12th IEEE International Conference on Intelligent Transportation Systems ITSC’09, 2009 October 3–7, St. Louis, MO, USA, pp. 747–752. Bellem, 2016, Objective metrics of comfort: developing a driving style for highly automated vehicles, Transport. Res. Part F: Traffic Psychol. Behav., 41, 45, 10.1016/j.trf.2016.05.005 Cheng, B., Fujioka, T., 1997. A hierarchical driver model. In: IEEE Conference on Intelligent Transportation System ITSC’97, 1997 November 12, Boston, MA, USA, pp. 960–965. Cohen, 1960, A coefficient of agreement for nominal scales, Education. Psychol. Measur., 20, 37, 10.1177/001316446002000104 Constantinescu, 2010, Driving style analysis using data mining techniques, Int. J. Comp. Commun. Control, 5, 654, 10.15837/ijccc.2010.5.2221 Dia, 2002, An agent-based approach to modelling driver route choice behaviour under the influence of real-time information, Transport. Res. Part C: Emerg. Technol., 10, 331, 10.1016/S0968-090X(02)00025-6 Dörr, D., Grabengiesser, D., Gauterin, F., 2014. Online driving style recognition using fuzzy logic. In: 17th EEE International Conference on Intelligent Transportation Systems (ITSC), 2014, October 8–11. Qindao, China, pp. 1021–1026. Eboli, 2017, The influence of physical and emotional factors on driving style of car drivers: a survey design, Travel Behav. Soc., 7, 43, 10.1016/j.tbs.2017.02.001 Ehsani, 2015, Conscientious personality and young drivers’ crash risk, J. Safety Res., 54, 83, 10.1016/j.jsr.2015.06.015 Elander, 1993, Behavioral correlates of individual differences in road-traffic crash risk: an examination of methods and findings, Psychol. Bull., 113, 279, 10.1037/0033-2909.113.2.279 Evans, 1996, The dominant role of driver behavior in traffic safety, Am. J. Public Health, 86, 784, 10.2105/AJPH.86.6.784 Ferdinand, 2014, Associations between driving performance and engaging in secondary tasks: a systematic review, Am. J. Public Health, 104, e39, 10.2105/AJPH.2013.301750 Ferreira, 2015, The impact of driving styles on fuel consumption: a data-warehouse-and-data-mining-based discovery process, IEEE Trans. Intell. Transport. Syst., 16, 2653, 10.1109/TITS.2015.2414663 Gallus, 2017, Impact of driving style and road grade on gaseous exhaust emissions of passenger vehicles measured by a Portable Emission Measurement System (PEMS), Transport. Res. Part D: Transp. Environ., 52, 215, 10.1016/j.trd.2017.03.011 Ghosh, 2013, Comparative analysis of k-means and fuzzy c-means algorithms, Int. J. Adv. Comp. Sci. Appl., 3, 45 Gil, 2016, A multidimensional intergenerational model of young males’ driving styles, Acc. Anal. Prevent., 97, 141, 10.1016/j.aap.2016.09.004 Guo, 2013, Individual driver risk assessment using naturalistic driving data, Acc. Anal. Prevent., 61, 3, 10.1016/j.aap.2012.06.014 Gupta, 2011, Theory and use of the EM algorithm, Found. Trends Sig. Process., 4, 223, 10.1561/2000000034 Jain, 2010, Data clustering: 50 years beyond K-means, Patt. Recog. Lett., 31, 651, 10.1016/j.patrec.2009.09.011 Kamal, M.A.S., Kawabe, T., Murata, J., Mukai, M., 2007. Driver-adaptive assist system for avoiding abnormality in driving. In: 16th IEEE International Conference on Control Applications CCA, 2007 November 27. Singapore, Singapore, pp. 1247–1252. Kárný, 2006 Kárný, M., Kadlec, J., Sutanto, E.L.,1998. Quasi-Bayes estimation applied to normal mixture. In: Rojíček, J., Valečková, M., Kárný, M., Warwick, K. (Eds.), 3rd European IEEE Workshop on Computer-Intensive Methods in Control and Data Processing, September. Prague, Czech Republic, pp. 77–82. Kárný, M., Nedoma, P., Khailova, N., Pavelková, L., 2003. Prior information in structure estimation. IEE Proc. - Control Theory Appl. 150(6), 643–653. Kerridge, 1961, Inaccuracy and inference, J. R. Statist. Soc. Ser. B (Methodol.), 23, 184 Kruskal, 1952, Use of ranks in one-criterion variance analysis, J. Am. Statist. Assoc., 47, 583, 10.1080/01621459.1952.10483441 Lajunen, 2011, Self-report instruments and methods, 43 Li, 2015, Field operational test of advanced driver assistance systems in typical Chinese road conditions: the influence of driver gender, age and aggression, Int. J. Autom. Technol., 16, 739, 10.1007/s12239-015-0075-5 Li, 2017, Estimation of driving style in naturalistic highway traffic using maneuver transition probabilities, Transport. Res. Part C: Emerg. Technol., 74, 113, 10.1016/j.trc.2016.11.011 Ma, 2015, Effects of driving style on the fuel consumption of city buses under different road conditions and vehicle masses, Transport. Res. Part D: Transp. Environ., 41, 205, 10.1016/j.trd.2015.10.003 Malikopoulos, A.A., Aguilar, J.P., 2012. Optimization of driving styles for fuel economy improvement. In: 15th IEEE International Conference on Intelligent Transportation Systems (ITSC), 2012, September 16–19. Anchorage, AK, USA, pp. 194–199. Manzoni, V., Corti, A., De Luca, P., Savaresi, S.M., 2010. Driving style estimation via inertial measurements. In: 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2010, September 19–22. Funchal, Portugal, pp. 777–782. Mensing, 2014, Eco-driving: An economic or ecologic driving style?, Transport. Res. Part C: Emerg. Technol., 38, 110, 10.1016/j.trc.2013.10.013 Mudgal, 2014, Driving behavior at a roundabout: a hierarchical Bayesian regression analysis, Transport. Res. Part D: Transp. Environ., 26, 20, 10.1016/j.trd.2013.10.003 Murphey, Y.L., Milton, R., Kiliaris, L., 2009. Driver’s style classification using jerk analysis. In: IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems, 2009, March 30–April 2. Nashville, TN, USA, pp. 23–28. Nagy, 2011, Bayesian estimation of dynamic finite mixtures, Int. J. Adapt. Control Sig. Process., 25, 765, 10.1002/acs.1239 Nikulin, 2016, Driving style identification with unsupervised learning, vol. 9729, 155 Pal, 2005, A possibilistic fuzzy c-means clustering algorithm, IEEE Trans. Fuzzy Syst., 13, 517, 10.1109/TFUZZ.2004.840099 Pampel, 2015, How I reduce fuel consumption: an experimental study on mental models of eco-driving, Transport. Res. Part C: Emerg. Technol., 58, 669, 10.1016/j.trc.2015.02.005 Park, 2010, Bayesian mixture modeling approach to account for heterogeneity in speed data, Transport. Res. Part B: Methodol., 44, 662, 10.1016/j.trb.2010.02.004 Peterka, 1981, Bayesian system identification, 239 Plöchl, 2007, Driver models in automobile dynamics application, Veh. Syst. Dyn., 45, 699, 10.1080/00423110701432482 Rangaraju, 2015, Impacts of electricity mix, charging profile, and driving behavior on the emissions performance of battery electric vehicles: a Belgian case study, Appl. Energy, 148, 496, 10.1016/j.apenergy.2015.01.121 Reilly, 2005, A rapid method for the comparison of cluster analyses, Statistica Sinica, 15, 19 Sagberg, 2015, A review of research on driving styles and road safety, Human Factors, 57, 1248, 10.1177/0018720815591313 Sentoff, 2015, Implications of driving style and road grade for accurate vehicle activity data and emissions estimates, Transport. Res. Part D: Transp. Environ., 35, 175, 10.1016/j.trd.2014.11.021 Son, 2016, Comparative study between Korea and UK: relationship between driving style and real-world fuel consumption, Int. J. Autom. Technol., 17, 175, 10.1007/s12239-016-0017-x Suzdaleva, 2014, Data-based speed-limit-respecting eco-driving system, Transport. Res. Part C: Emerg. Technol., 44, 253, 10.1016/j.trc.2014.04.009 Suzdaleva, E., Nagy, I., Mlynářová, T., 2016. Expert-based initialization of recursive mixture estimation. In: 8th IEEE International Conference on Intelligent Systems, 2016, September 4–6. Sofia, Bulgaria, pp. 308–315. Taubman-Ben-Ari, 2016, Parents’ perceptions of the Family Climate for Road Safety: associations with parents’ self-efficacy and attitudes toward accompanied driving, and teens’ driving styles, Transport. Res. Part F: Traffic Psychol. Behav., 40, 14, 10.1016/j.trf.2016.04.006 Taubman-Ben-Ari, 2016, The multidimensional driving style inventory a decade later: review of the literature and re-evaluation of the scale, Acc. Anal. Prevent., 93, 179, 10.1016/j.aap.2016.04.038 Toledo, 2007, Integrated driving behavior modeling, Transport. Res. Part C: Emerg. Technol., 15, 96, 10.1016/j.trc.2007.02.002 Toledo, 2008, In-vehicle data recorders for monitoring and feedback on drivers’ behavior, Transport. Res. Part C: Emerg. Technol., 16, 320, 10.1016/j.trc.2008.01.001 Vaitkus, V., Lengvenis, P., Žylius, G., 2014. Driving style classification using long-term accelerometer information. In: 19th IEEE International Conference On Methods and Models in Automation and Robotics (MMAR), 2014, September 2–5. Miedzyzdroje, Poland, pp. 641–644. Wang, R., Lukic, S.M., 2011. Review of driving conditions prediction and driving style recognition based control algorithms for hybrid electric vehicles. In: IEEE Conference on Vehicle Power and Propulsion Conference, 2011, September 6–9. Chicago, IL, USA, pp. 1–7. Wang, W., Xi, J., Li, X., 2016. Statistical pattern recognition for driving styles based on Bayesian probability and kernel density estimation. arXiv:1606.01284 [stat.ML]. Xu, 2015, Establishing style-oriented driver models by imitating human driving behaviors, IEEE Trans. Intell. Transport. Syst., 16, 2522, 10.1109/TITS.2015.2409870 Yu, 2012, A particle filter driven dynamic Gaussian mixture model approach for complex process monitoring and fault diagnosis, J. Process Control, 22, 778, 10.1016/j.jprocont.2012.02.012 Zhang, 2010, A pattern-recognition approach for driving skill characterization, IEEE Trans. Intell. Transport. Syst., 11, 905, 10.1109/TITS.2010.2055239 Zou, 2014, Analyzing different functional forms of the varying weight parameter for finite mixture of negative binomial regression models, Anal. Meth. Acc. Res., 1, 39