融合K-means与高斯混合模型的驾驶风格聚类研究

doi:10.16265/j.cnki.issn1003-3033.2019.12.007

中国安全科学学报 ›› 2019, Vol. 29 ›› Issue (12): 40-45.doi: 10.16265/j.cnki.issn1003-3033.2019.12.007

融合K-means与高斯混合模型的驾驶风格聚类研究

刘通¹, 付锐^1,2 教授, 张名芳³, 田顺¹

1 长安大学汽车学院,陕西西安 710064;
2 长安大学汽车运输安全保障技术交通行业重点实验室,陕西西安 710064;
3 北方工业大学电气与控制工程学院,北京 100144

收稿日期:2019-09-20 修回日期:2019-11-11 出版日期:2019-12-28
作者简介:刘通 (1989—),男,山西怀仁人,博士研究生,研究方向为人-车-路系统安全、驾驶行为分析、驾驶员类型划分。E-mail: liutong@chd.edu.cn。
基金资助:
国家自然科学基金资助(51775053);国家重点研发计划(2018YFB1600500);教育部长江学者与创新团队发展计划项目(IRT_17R95);汽车运输安全保障技术交通行业重点试验室开放课题 (300102229505)。

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

LIU Tong¹, FU Rui^1,2, ZHANG Mingfang³, TIAN Shun¹

1 School of Automobile, Chang'an University, Xi'an Shaanxi 710064, China;
2 Key Laboratory of Automobile Transportation Safety Technology, Ministry of Transport, Chang'an University, Xi'an Shaanxi 710064, China;
3 School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China

Received:2019-09-20 Revised:2019-11-11 Published:2019-12-28

摘要/Abstract

摘要： 为研究驾驶员的跟车特性,探究驾驶员风格划分方法,采集50名驾驶员的实车试验数据,选取平均跟车时距和平均制动时距为二维向量,建立基于K-means聚类结果的高斯混合模型(GMM)并分析不同风格驾驶员的聚类结果。研究表明:样本数据聚为3类时的平均轮廓系数为0.45,将驾驶员划分为冒进型、平稳型、保守型3类时聚类效果较好;冒进型驾驶员倾向于选择较小的跟车时距和制动时距,保守型驾驶员的跟车及制动时距则普遍较大,模型聚类结果更加柔性,样本区分度更高。

关键词: 驾驶风格, K-means聚类, 高斯混合模型(GMM), 跟车特性, 制动特点

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

中图分类号:

刘通, 付锐, 张名芳, 田顺. 融合K-means与高斯混合模型的驾驶风格聚类研究[J]. 中国安全科学学报, 2019, 29(12): 40-45.

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.

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融合K-means与高斯混合模型的驾驶风格聚类研究

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

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