基于KL散度的驾驶员驾驶习性非监督聚类*

doi:10.19562/j.chinasae.qcgc.2018.011.010

Abstract

Abstract: In order to understand the driving style features of different drivers, an unsupervised clustering algorithm for the driving styles of drivers is proposed in this paper based on Kullback-Leibler (KL) divergence. Firstly an acquisition platform for the driving data of drivers in real vehicle test is built, and 84 drivers are tested. Then the driving data of each driver are regarded as a specific Gaussian mixture model (GMM), and whose parameters are estimated by using expectation maximization algorithm. Finally Monte Carlo algorithm is employed to estimate the KL divergence between GMMs, hence the quantitative description on the discrepancies of different drivers is obtained and drivers are clustered into different catagories of style. The results of statistical analysis on the driving data of drivers in each category after clustering show that the unsupervised clustering algorithm proposed can effectively achieve the clustereing of drivers with different driving styles

Key words: driving style, clustering, KL divergence, GMM, Monte Carlo algorithm

Zhu Bing, Jiang Yuande, Deng Weiwen, Yang Shun, He Rui, Su Chen. Unsupervised Clustering of Driving Styles Based on KL Divergence[J]., 2018, 40(11): 1317-1323.

References

[1] WANG W, XI J, CHEN H. Modeling and recognizing driver behavior based on driving data: a survey[J]. Mathematical Problems in Engineering,2014(1):1-20.
[2] 张磊.基于驾驶员特性自学习方法的车辆纵向驾驶辅助系统[D].北京:清华大学,2009.

[3] BIFULCO G N, PARIOTA L, SIMONELLI F, et al. Development and testing of a fully adaptive cruise control system[J]. Transportation Research Part C: Emerging Technologies,2013,29:156-170.
[4] 严伟.仿驾驶员速度跟随行为的自适应巡航控制算法研究[D].长春:吉林大学,2016.
[5] BIFULCO G N, PARIOTA L, BRACKSTIONE M, et al. Driving behaviour models enabling the simulation of advanced driving assistance systems:revisiting the action point paradigm[J]. Transportation Research Part C: Emerging Technologies,2013,36:352-366.
[6] BUTAKOV V A, IOANNOU P. Personalized driver/vehicle lane change models for ADAS[J]. IEEE Transactions on Vehicular Technology,2015,64(10):4422-4431.
[7] WANG W, XI J. A rapid pattern-recognition method for driving styles using clustering-based support vector machines[C]. American Control Conference (ACC),2016. IEEE,2016:5270-5275.
[8] 吴坚,姚琳琳,朱冰,等.基于高斯混合模型的驾驶员个人特质辨识[J].吉林大学学报(工学版),2015(1):38-43.
[9] XU L, HU J, JIANG H, et al. Establishing style-oriented driver models by imitating human driving behaviors[J]. IEEE Transactions on Intelligent Transportation Systems,2015,16(5):2522-2530.
[10] 胡满江,边有钢,许庆,等.自适应驾驶员行为特征的车道偏离防范系统[J].汽车工程,2017,39(10):1152-1157.
[11] MURPHEY Y L, MILTON R, KILIARIS L. Driver's style classification using jerk analysis[C]. Computational Intelligence in Vehicles and Vehicular Systems,2009. CIVVS'09. IEEE Workshop on. IEEE,2009:23-28.
[12] WANG W, XI J. A rapid pattern-recognition method for driving styles using clustering-based support vector machines[C]. American Control Conference (ACC),2016. IEEE,2016:5270-5275.
[13] 林娜.“车适应人”线控汽车驾驶员行为特性辨识算法研究[D].长春:吉林大学,2015.
[14] 张淼,刘明周,扈静,等.基于K-均值聚类的汽车驾驶姿态偏好研究[J].汽车工程,2017,39(10):1187-1191.
[15] DHILLON I S, MALLELA S, KUMAR R. A divisive information-theoretic feature clustering algorithm for text classification[J]. Journal of Machine Learning Research,2003,3:1265-1287.

[1]	XU Cheng-Shan, JIANG Fa-Chao, SONG Sen-Nan, TIAN Guang-Yu. [J]. , 2017, 39(1): 9 -14 .
[2]	ZHANG Yi-Bing, 吕Jian-Jun , YUAN Guang-Hui. [J]. , 2016, 38(12): 1467 -1470 .
[3]	JIA Ji-De, WU Chun-Zhi, JIA Xiang-Yu, REN Gang, HAN Jia-Jia. [J]. , 2017, 39(1): 97 -101 .
[4]	RONG Bing, XIAO Pan, ZHOU Jian-Wen, LIU Yi-Lu. [J]. , 2017, 39(2): 214 -219 .
[5]	WANG Zhen, CUI Ya-Hui-.Liu-Kai, XU Lin. [J]. , 2017, 39(3): 304 -310 .
[6]	GUAN Xin, XU Liang, GUO Rui, WANG Bo, SONG Hai-Yan. [J]. , 2017, 39(3): 311 -316 .
[7]	JIN Geng-Ri, GUAN Xin, ZHAN Jun, GUO Rui. [J]. , 2017, 39(3): 364 -368 .
[8]	XIONG Xing-Wang, GAO Jun-Hua, ZHOU Tao, YU Jin-Tao. [J]. , 2017, 39(4): 381 -385 .
[9]	CAO Li-Feng, ZHOU Shou-Qin, XIE Xiao-Peng. [J]. , 2017, 39(4): 401 -406 .
[10]	Chen Wenqiang, Xiong Hui, Li Keqiang, Li Xiaofei& Zhang Dezhao. Concurrent Pedestrian and Cyclist Detection Based on Deep Neural Networks[J]. , 2018, 40(6): 726 .

Unsupervised Clustering of Driving Styles Based on KL Divergence

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 10

[1]	Zhifei Wu,Shoubiao Li. Lane Detection Algorithm Based on Instance Segmentation [J]. Automotive Engineering, 2023, 45(2): 263-272.
[2]	Junzhao Jiang,Wenhao Yang,Bin Peng,Ting Guo,Yekai Xu,Guozhuo Wang. Driving Range Prediction of Fuel Cell Vehicles Based on Energy Consumption Weighting Strategy [J]. Automotive Engineering, 2023, 45(12): 2357-2365.
[3]	Lisheng Jin,Yang He,Huanhuan Wang,Zhen Huo,Xianyi Xie,Baicang Guo. Point Cloud Segmentation Algorithm Based on Adaptive Threshold DBSCAN for Roadside LiDAR [J]. Automotive Engineering, 2022, 44(7): 987-996.
[4]	Zheyu Zhang,Lü Chao,Jinghang Li,Guangming Xiong,Shaobin Wu,Jianwei Gong. Pedestrian Trajectory Prediction and Risk Grade Assessment Based on Vehicle-Perspective Pedestrian Data [J]. Automotive Engineering, 2022, 44(5): 675-683.
[5]	Hui Jin,Haotian Li. Alarm Strategy for Frontal Crash Warning System Based on Driving Style [J]. Automotive Engineering, 2021, 43(3): 405-413.
[6]	Lei Ma,Zebin Liu,Biao Cao,Wenju Wang,Quanyuan Qiu. Roughness Measure-Based Road Drivable Region Identification [J]. Automotive Engineering, 2021, 43(11): 1594-1601.
[7]	Zhiguo Zhao,Peng Wang,Xiaorong Chen,Kaichong Liang. Obstacle Tracking of a Lidar-equipped Vehicle in Turning for Collision Avoidance [J]. Automotive Engineering, 2021, 43(11): 1611-1619.
[8]	Wen Sun,Juncai Rong,Junnian Wang,Jia Xu,Lü Siwen,Lili Liu. Research on the Construction Method of Urban Compound Driving Cycle with Curved Road Information [J]. Automotive Engineering, 2021, 43(10): 1519-1527.
[9]	Guo Jinghua, Li Keqiang, Wang Jin, Chen Tao, Li Wenchang, Wang Ban. Study on Prediction of Preceding Vehicle's Stochastic Motion Based on Risk Scenarios Clustering Analysis [J]. Automotive Engineering, 2020, 42(7): 847-853.
[10]	Zhao Han, Liu Hao, Qiu Mingming, Cao Longkai, Zhang Yilei, Yu Wei. Research on the Driving Style Identification Method Considering the Influence of Traffic Density [J]. Automotive Engineering, 2020, 42(12): 1718-1727.
[11]	Gu Xianguang, Meng Kewei, Yao Xinxin, Wang Hongbo. Judging Method for Lateral Stability of Intelligent Vehicle Based on Combined Clustering [J]. Automotive Engineering, 2020, 42(11): 1497-1505.
[12]	Zhao Zhiguo, Feng Jianxiang, Zhou Liangjie, Wang Kai, Hu Haorui, Zhang Haishan, Ning Zhonglin. K-means++ Clustering and Recognition of Driver'sCollision Avoidance Steering Behavior [J]. Automotive Engineering, 2020, 42(1): 52-58.
[13]	Zhu Wangwang, Huang Hongcheng ,Ma Jinxing. A Research on Parking Space Detection Algorithm Based on Image Recognition [J]. Automotive Engineering, 2019, 41(7): 744-749.
[14]	Jin Hui, Yu Qian. A Research on the Relationship Between Weight Coefficient Variation ofClutch Optimal Control and the Vehicle Performances [J]. Automotive Engineering, 2019, 41(5): 550-555.
[15]	Zhu Bing, Li Weinan, Wang Zhen, Zhao Jian, He Rui, Han Jiayi. Identification Strategy of Driving Style Based on Random Forest [J]. , 2019, 41(2): 213-218.