publication . Article . Other literature type . Preprint . 2017

Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches

Wang, Wenshuo; Xi, Junqiang; Zhao, Ding;
Open Access
  • Published: 15 Aug 2017 Journal: IEEE Transactions on Intelligent Transportation Systems, volume 20, pages 2,986-2,998 (issn: 1524-9050, eissn: 1558-0016, Copyright policy)
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Analysis and recognition of driving styles are profoundly important to intelligent transportation and vehicle calibration. This paper presents a novel driving style analysis framework using the primitive driving patterns learned from naturalistic driving data. In order to achieve this, first, a Bayesian nonparametric learning method based on a hidden semi-Markov model (HSMM) is introduced to extract primitive driving patterns from time series driving data without prior knowledge of the number of these patterns. In the Bayesian nonparametric approach, we utilize a hierarchical Dirichlet process (HDP) instead of learning the unknown number of smooth dynamical mode...
Subjects
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION
free text keywords: Machine learning, computer.software_genre, computer, Semantics, Vehicle dynamics, Engineering, business.industry, business, Computer vision, Software deployment, Artificial intelligence, Bayesian nonparametrics, Perception, media_common.quotation_subject, media_common, Hierarchical Dirichlet process, Hidden Markov model, Style analysis, Computer Science - Computer Vision and Pattern Recognition
Related Organizations
43 references, page 1 of 3

[1] S. Di Cairano, D. Bernardini, A. Bemporad, and I. V. Kolmanovsky, “Stochastic MPC with learning for driver-predictive vehicle control and its application to HEV energy management,” IEEE Transactions on Control Systems Technology, vol. 22, no. 3, pp. 1018-1031, 2014.

[2] F. Sagberg, Selpi, G. F. Bianchi Piccinini, and J. Engstro¨m, “A review of research on driving styles and road safety,” Human factors, vol. 57, no. 7, pp. 1248-1275, 2015.

[3] C. M. Martinez, M. Heucke, F.-Y. Wang, B. Gao, and D. Cao, “Driving style recognition for intelligent vehicle control and advanced driver assistance: A survey,” IEEE Transactions on Intelligent Transportation Systems, DOI:10.1109/TITS.2017.2706978, 2017.

[4] M. Rafael, M. Sanchez, V. Mucino, J. Cervantes, and A. Lozano, “Impact of driving styles on exhaust emissions and fuel economy from a heavyduty truck: Laboratory tests,” International Journal of Heavy Vehicle Systems, vol. 13, no. 1-2, pp. 56-73, 2006. [OpenAIRE]

[5] Y. L. Murphey, R. Milton, and L. Kiliaris, “Driver's style classification using jerk analysis,” in Computational Intelligence in Vehicles and Vehicular Systems, 2009. CIVVS'09. IEEE Workshop on. IEEE, 2009, pp. 23-28.

[6] L. Xu, J. Hu, H. Jiang, and W. Meng, “Establishing style-oriented driver models by imitating human driving behaviors,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 5, pp. 2522-2530, 2015.

[7] B. Shi, L. Xu, J. Hu, Y. Tang, H. Jiang, W. Meng, and H. Liu, “Evaluating driving styles by normalizing driving behavior based on personalized driver modeling,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 12, pp. 1502-1508, 2015.

[8] W. Wang, J. Xi, A. Chong, and L. Li, “Driving style classification using a semi-supervised support vector machine,” IEEE Transactions on HumanMachine Systems, DOI:10.1109/THMS.2017.2736948, 2017.

[9] V. Vaitkus, P. Lengvenis, and G. Zˇ ylius, “Driving style classification using long-term accelerometer information,” in Methods and Models in Automation and Robotics (MMAR), 2014 19th International Conference On. IEEE, 2014, pp. 641-644. [OpenAIRE]

[10] B. Higgs and M. Abbas, “Segmentation and clustering of car-following behavior: Recognition of driving patterns,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 1, pp. 81-90, 2015.

[11] H. Okuda, N. Ikami, T. Suzuki, Y. Tazaki, and K. Takeda, “Modeling and analysis of driving behavior based on a probability-weighted ARX model,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 1, pp. 98-112, 2013.

[12] S. Sekizawa, S. Inagaki, T. Suzuki, S. Hayakawa, N. Tsuchida, T. Tsuda, and H. Fujinami, “Modeling and recognition of driving behavior based on stochastic switched ARX model,” IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 4, pp. 593-606, 2007.

[13] D. A. Johnson and M. M. Trivedi, “Driving style recognition using a smartphone as a sensor platform,” in Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on. IEEE, 2011, pp. 1609-1615.

[14] C. MacAdam, Z. Bareket, P. Fancher, and R. Ervin, “Using neural networks to identify driving style and headway control behavior of drivers,” Vehicle System Dynamics, vol. 29, no. S1, pp. 143-160, 1998.

[15] Q. H. Do, H. Tehrani, S. Mita, M. Egawa, K. Muto, and K. Yoneda, “Human drivers based active-passive model for automated lane change,” IEEE Intelligent Transportation Systems Magazine, vol. 9, no. 1, pp. 42- 56, 2017.

43 references, page 1 of 3
Related research
Abstract
Analysis and recognition of driving styles are profoundly important to intelligent transportation and vehicle calibration. This paper presents a novel driving style analysis framework using the primitive driving patterns learned from naturalistic driving data. In order to achieve this, first, a Bayesian nonparametric learning method based on a hidden semi-Markov model (HSMM) is introduced to extract primitive driving patterns from time series driving data without prior knowledge of the number of these patterns. In the Bayesian nonparametric approach, we utilize a hierarchical Dirichlet process (HDP) instead of learning the unknown number of smooth dynamical mode...
Subjects
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION
free text keywords: Machine learning, computer.software_genre, computer, Semantics, Vehicle dynamics, Engineering, business.industry, business, Computer vision, Software deployment, Artificial intelligence, Bayesian nonparametrics, Perception, media_common.quotation_subject, media_common, Hierarchical Dirichlet process, Hidden Markov model, Style analysis, Computer Science - Computer Vision and Pattern Recognition
Related Organizations
43 references, page 1 of 3

[1] S. Di Cairano, D. Bernardini, A. Bemporad, and I. V. Kolmanovsky, “Stochastic MPC with learning for driver-predictive vehicle control and its application to HEV energy management,” IEEE Transactions on Control Systems Technology, vol. 22, no. 3, pp. 1018-1031, 2014.

[2] F. Sagberg, Selpi, G. F. Bianchi Piccinini, and J. Engstro¨m, “A review of research on driving styles and road safety,” Human factors, vol. 57, no. 7, pp. 1248-1275, 2015.

[3] C. M. Martinez, M. Heucke, F.-Y. Wang, B. Gao, and D. Cao, “Driving style recognition for intelligent vehicle control and advanced driver assistance: A survey,” IEEE Transactions on Intelligent Transportation Systems, DOI:10.1109/TITS.2017.2706978, 2017.

[4] M. Rafael, M. Sanchez, V. Mucino, J. Cervantes, and A. Lozano, “Impact of driving styles on exhaust emissions and fuel economy from a heavyduty truck: Laboratory tests,” International Journal of Heavy Vehicle Systems, vol. 13, no. 1-2, pp. 56-73, 2006. [OpenAIRE]

[5] Y. L. Murphey, R. Milton, and L. Kiliaris, “Driver's style classification using jerk analysis,” in Computational Intelligence in Vehicles and Vehicular Systems, 2009. CIVVS'09. IEEE Workshop on. IEEE, 2009, pp. 23-28.

[6] L. Xu, J. Hu, H. Jiang, and W. Meng, “Establishing style-oriented driver models by imitating human driving behaviors,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 5, pp. 2522-2530, 2015.

[7] B. Shi, L. Xu, J. Hu, Y. Tang, H. Jiang, W. Meng, and H. Liu, “Evaluating driving styles by normalizing driving behavior based on personalized driver modeling,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 12, pp. 1502-1508, 2015.

[8] W. Wang, J. Xi, A. Chong, and L. Li, “Driving style classification using a semi-supervised support vector machine,” IEEE Transactions on HumanMachine Systems, DOI:10.1109/THMS.2017.2736948, 2017.

[9] V. Vaitkus, P. Lengvenis, and G. Zˇ ylius, “Driving style classification using long-term accelerometer information,” in Methods and Models in Automation and Robotics (MMAR), 2014 19th International Conference On. IEEE, 2014, pp. 641-644. [OpenAIRE]

[10] B. Higgs and M. Abbas, “Segmentation and clustering of car-following behavior: Recognition of driving patterns,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 1, pp. 81-90, 2015.

[11] H. Okuda, N. Ikami, T. Suzuki, Y. Tazaki, and K. Takeda, “Modeling and analysis of driving behavior based on a probability-weighted ARX model,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 1, pp. 98-112, 2013.

[12] S. Sekizawa, S. Inagaki, T. Suzuki, S. Hayakawa, N. Tsuchida, T. Tsuda, and H. Fujinami, “Modeling and recognition of driving behavior based on stochastic switched ARX model,” IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 4, pp. 593-606, 2007.

[13] D. A. Johnson and M. M. Trivedi, “Driving style recognition using a smartphone as a sensor platform,” in Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on. IEEE, 2011, pp. 1609-1615.

[14] C. MacAdam, Z. Bareket, P. Fancher, and R. Ervin, “Using neural networks to identify driving style and headway control behavior of drivers,” Vehicle System Dynamics, vol. 29, no. S1, pp. 143-160, 1998.

[15] Q. H. Do, H. Tehrani, S. Mita, M. Egawa, K. Muto, and K. Yoneda, “Human drivers based active-passive model for automated lane change,” IEEE Intelligent Transportation Systems Magazine, vol. 9, no. 1, pp. 42- 56, 2017.

43 references, page 1 of 3
Related research
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publication . Article . Other literature type . Preprint . 2017

Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches

Wang, Wenshuo; Xi, Junqiang; Zhao, Ding;