Study on driving style clustering based on K-means and Gaussian mixture model

doi:10.16265/j.cnki.issn1003-3033.2019.12.007

Abstract

Abstract: In order to study drivers' car-following characteristics and explore an effective method to classify driving styles, 50 participants were recruited to carry out a real road driving test. A GMM with results of K-means clustering was established based on two-dimensional variables: average car-following time gap and average braking time gap. And then results of different types of drivers were analyzed. The research shows that clustering result is better with three categories (aggressive drivers, steady drivers, and conservative drivers) with an average contour value of 0.45. It is found that aggressive drivers tend to choose a smaller car-following time gap or braking time gap while conservative drivers usually take a larger value, and a much softer clustering result with a high separability between samples would be achieved.

Key words: driving style, K-means clustering, Gaussian mixture model (GMM), car-following characteristics, braking characteristics

CLC Number:

LIU Tong, FU Rui, ZHANG Mingfang, TIAN Shun. Study on driving style clustering based on K-means and Gaussian mixture model[J]. China Safety Science Journal, 2019, 29(12): 40-45.

References

[1] 吕能超,任泽远,段至诚,等. Near-crash事件中驾驶人行为特征分析[J]. 中国安全科学学报, 2017, 27(6): 19-24.
LYU Nengchao, REN Zeyuan, DUAN Zhicheng, et al. Analysis of driving behavior characteristics of drivers in Near-crash events [J]. China Safety Science Journal, 2017, 27(6):19-24.
[2] 楚文慧,吴超仲,张晖,等. 基于个性化行为模型的驾驶疲劳识别方法[J]. 中国安全科学学报, 2018, 28(6): 43-48.
CHU Wenhui, WU Chaozhong, ZHANG Hui, et al. Driver behavior model and its application in driver fatigue identification [J]. China Safety Science Journal, 2018, 28(6):43-48.
[3] MARTINEZ C M, HEUCKE M, WANG F Y, et al. Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 19(3): 666-676.
[4] ELANDER J, WEST R, FRENCH D. Behavioral correlates of individual differences in road-traffic crash risk: an examination of methods and findings[J]. Psychological Bulletin, 1993, 113(2): 279-294.
[5] GONZALEZ A B R, WILBY M R, DIAZ J J V, et al. Modeling and detecting aggressiveness from driving signals[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(4): 1 419-1 428.
[6] JEONG E. Detection of lateral hazardous driving events using in-vehicle gyro sensor data[J]. KSCE Journal of Civil Engineering, 2013, 17(6): 1 471-1 479.
[7] LAJUNEN T, SUMMALA H. Driving experience, personality, and skill and safety-motive dimensions in drivers' self-assessments[J]. Personality and Individual Differences, 1995, 19(3): 307-318.
[8] 庄明科, 白海峰, 谢晓非. 驾驶人员风险驾驶行为分析及相关因素研究[J]. 北京大学学报:自然科学版, 2008, 44(3): 475-482.
ZHUANG Mingke, BAI Haifeng, XIE Xiaofei. A study on risky driving behavior and related factors [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2008, 44(3): 475-482.
[9] LI Pengfei, SHI Jianjun, LIU Xiaoming. Driving style recognition based on driver behavior questionnaire[J]. Open Journal of Applied Sciences, 2017, 7(4): 115-128.
[10] 孙龙,常若松. 营运驾驶员驾驶风格研究[J]. 人类工效学, 2014, 20(3): 10-12.
SUN Long, CHANG Ruosong. A study on driving styles of commercial driver [J]. Chinese Journal of Ergonomics,2014, 20(3): 10-12.
[11] 侯海晶,金立生,关志伟,等. 驾驶风格对驾驶行为的影响[J]. 中国公路学报, 2018, 31(4): 22-31.
HOU Haijing, JIN Lisheng, GUAN Zhiwei, et al. Effects of driving style on driver behavior [J]. China Journal of Highway and Transport, 2018, 31(4): 22-31.
[12] WOO H, JI Yonghoon, TAMURA Y, et al. Driver classification in vehicle following behavior by using dynamic potential field method[C]. 2017 IEEE 20^th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2017: 1-6.
[13] 王畅,付锐,彭金栓,等. 应用于换道预警的驾驶风格分类方法[J]. 交通运输系统工程与信息, 2014, 14(3): 187-193.
WANG Chang, FU Rui, PENG Jinshuan, et al. Driving style classification method for lane change warning system [J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(3): 187-193.
[14] PENG Guanghan, HE Hongdi, LU Weizhen. A new car-following model with the consideration of incorporating timid and aggressive driving behaviors[J]. Physica A: Statistical Mechanics and its Applications, 2016, 442: 197-202.
[15] LYU Chen, HU Xiaosong, ALBERTO S V, et al. Driving-style-based co-design optimization of an automated electric vehicle: a cyber-physical system approach[J]. IEEE Transactions on Industrial Electronics, 2019, 66(4): 2 965-2 975.
[16] MARTINEZ C M, HEUCKE M, WANG F, et al. Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(3), 666-676.
[17] LIU Tong, SELP I, FU Rui. The relationship between different safety indicators in car-following situations[C]. 2018 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2018: 1 515-1 520.
[18] 王雪松,朱美新,邢祎伦. 基于自然驾驶数据的避撞预警对跟车行为影响[J]. 同济大学学报:自然科学版, 2016, 44(7): 1 045-1 051.
WANG Xuesong, ZHU Meixin, XING Yilun. Impacts of collision warning system on car-following behavior based on naturalistic driving data [J]. Journal of Tongji University:Natural Science, 2016, 44(7): 1 045-1 051.
[19] HIGGS B, ABBAS M. A two-step segmentation algorithm for behavioral clustering of naturalistic driving styles[C]. 2013 IEEE 16^th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2013: 857-862.
[20] LU Guangquan, CHENG Bo, LIN Qingfeng, et al. Quantitative indicator of homeostatic risk perception in car following[J]. Safety Science, 2012, 50(9): 1 898-1 905.