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Research on the Classification and Identification for Personalized Driving Styles
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 03, 2018 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Most of the Advanced Driver Assistance System (ADAS) applications are aiming at improving both driving safety and comfort. Understanding human drivers' driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system performance, in particular, the acceptance and adaption of ADAS to human drivers. The research presented in this paper focuses on the classification and identification for personalized driving styles. To motivate and reflect the information of different driving styles at the most extent, two sets, which consist of six kinds of stimuli with stochastic disturbance for the leading vehicles are created on a real-time Driver-In-the-Loop Intelligent Simulation Platform (DILISP) with PanoSim-RT®, dSPACE® and DEWETRON® and field test with both RT3000 family and RT-Range respectively. Three physical quantities, the root mean square of vehicle acceleration, the time-to-start and the time gap of each driver, are extracted from test samples, and their mean and variance are used as clustering samples. Then driving styles are defined and classified into three categories via Particle Swarm Optimization Clustering (PSO-Clustering) algorithm. The identification models are built as a Multi-dimension Gaussian Hidden Markov Process (MGHMP), and key parameters of identification models are optimized in orthogonal test method. The consistency of the classification and identification results under DILISP and field test are compared. Test results show that the stimuli set consisted of 4 kinds of non-periodic apparent transient step signals should be the prior selection for classification and identification. What’s more, driving styles can be classified clearly and identified effectively with the accuracy rate above 95% by using the proposed classification and identification strategy.
CitationSun, B., Deng, W., Wu, J., Li, Y. et al., "Research on the Classification and Identification for Personalized Driving Styles," SAE Technical Paper 2018-01-1096, 2018, https://doi.org/10.4271/2018-01-1096.
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- Phumphuang, P.,P.Wuttidittachotti, andC.Saiprasert, "Driver Identification Using Variance of the Acceleration Data," Computer Science and Engineering Conference (ICSEC), 2015 International, IEEE, 2015.
- Xiao, L. andGao, F., “A Comprehensive Review of the Development of Adaptive Cruise Control Systems,” Vehicle System Dynamics 48(10):1167-1192, 2010.
- Wang, J. et al., “A Framework of Vehicle Trajectory Replanning in Lane Exchanging with Considerations of Driver characteristics,” IEEE Transactions on Vehicular Technology 66(5):3583-3596, 2017.
- Meseguer, J.E. et al., “DrivingStyles: A Mobile Platform for Driving Styles and Fuel Consumption Characterization,” Journal of Communications and Networks 19(2):162-168, 2017.
- Hoogendoorn, S.P. andBovy, P.H.L., “State-of-the-Art of Vehicular Traffic Flow Modelling,” Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 215(4):283-303, 2001.
- Cao, B., andZ.Yang. "Car-following Models Study Progress," Knowledge Acquisition and Modeling, 2009. KAM'09. Second International Symposium on. Vol. 3. IEEE, 2009.
- Brackstone, M. andMcDonald, M., “Car-following: A Historical Review,” Transportation Research Part F: Traffic Psychology and Behaviour 2(4):181-196, 1999.
- Wang, W.,J.Xi, andH.Chen, "Modeling and Recognizing Driver Behavior based on Driving Data: a Survey," Mathematical Problems in Engineering 2014, 2014.
- Ishibashi, M., et al., "Indices for Characterizing Driving Style and their Relevance to Car Following Behavior," SICE, 2007 Annual Conference, IEEE, 2007.
- Song, C.-S.,Chun, B.-Y., andChung, H.-S., “Test-Retest Reliability of the Driving Habits Questionnaire in Older Self-driving Adults,” Journal of physical therapy science 27(11):3597-3599, 2015.
- Wang, W., andJ.Xi, "A Rapid Pattern-Recognition Method for Driving Styles Using Clustering-based Support Vector Machines," American Control Conference (ACC), 2016. IEEE, 2016.
- Brombacher, P., et al., "Driving Event Detection and Driving Style Classification Using Artificial Neural Networks," Industrial Technology (ICIT), 2017 IEEE International Conference on. IEEE, 2017.
- Aljaafreh, A.,N.Alshabatat, andM. S.Najim Al-Din, "Driving Style Recognition Using Fuzzy Logic," Vehicular Electronics and Safety (ICVES), 2012 IEEE International Conference on. IEEE, 2012.
- G. C. M.Quintero,J. A.Oñate López,J. M.Pérez Rúa, "Intelligent Erratic Driving Diagnosis based on Artificial Neural Networks," ANDESCON, 2010 IEEE. IEEE, 2010.
- Takano, W., et al. "Recognition of Human Driving Behaviors Based on Stochastic Symbolization of Time Series Signal," Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on. IEEE, 2008.
- Nishiwaki, Y., et al. "Generation of Pedal Operation Patterns of Individual Drivers in Car-following for Personalized Cruise Control," Intelligent Vehicles Symposium, 2007 IEEE. IEEE, 2007.
- Lin, N., et al., "An Overview on Study of Identification of Driver Behavior Characteristics for Automotive Control," Mathematical Problems in Engineering, 2014.
- Van Ly, M.,S.Martin, andM. M.Trivedi, “Driver Classification and Driving Style Recognition Using Inertial Sensors," Intelligent Vehicles Symposium (IV), 2013 IEEE. IEEE, 2013.
- Manzoni, V., et al., "Driving Style Estimation via Inertial Measurements," Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on. IEEE, 2010.
- Sathyanarayana, A.,S.Omid Sadjadi, andJ.HL Hansen. "Leveraging Sensor Information from Portable Devices Towards Automatic Driving Maneuver Recognition," Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on. IEEE, 2012.
- Castignani, G.,R.Frank, andT.Engel, "Driver Behavior Profiling Using Smartphones," Intelligent Transportation Systems-(ITSC), 2013 16th International IEEE Conference on. IEEE, 2013.
- Johnson, D. A., andM. M.Trivedi, "Driving Style Recognition Using a Smartphone as a Sensor Platform," Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on. IEEE, 2011.
- Paefgen, J., et al., "Driving Behavior Analysis with Smartphones: Insights from a Controlled Field Study," Proceedings of the 11th International Conference on mobile and ubiquitous multimedia. ACM, 2012.
- Lanatà, A. et al., “How the Autonomic Nervous System and Driving Style Change with Incremental Stressing Conditions during Simulated Driving,” IEEE Transactions on Intelligent Transportation Systems 16(3):1505-1517, 2015.
- Doshi, A., andM. M.Trivedi, "Examining the Impact of Driving Style on the Predictability and Responsiveness of the Driver: Real-world and Simulator Analysis," Intelligent Vehicles Symposium (IV), 2010 IEEE. IEEE, 2010.
- Rabiner, L.R., “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proceeding of the IEEE 77(2):257-286, 1989.
- Reynolds, D.A. andRose, R.C., “Robust Text-Independent Speaker Identification Using Gaussian Mixture Speaker Models,” IEEE Transaction on Speech and Audio Processing 3(1):72-83, 1995.
- Hayes, M.H.,”Statistical Digital Signal Processing and Modeling,” (New York: John Wiley & Sons, Inc, 1996).
- Baum, L.E. andPetrie, T., “Statistical Inference for Probabilistic Functions of Finite State Markov Chains,” The Annals of Mathematical Statistics 37:1554-1563, 1966.