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J. Hefei Univ. Tech. (Nat. Sci.)

合肥工业大学学报(自然科学版)

JOURNAL OF HEFEI UNIVERSITY OF TECHNOLOGY (NATURAL SCIENCE)

Competent Authorities: Ministry of Education of the People's Republic of China

Sponsor by: Hefei University of Technology

ISSN 1000-1646, CN 34-1001/N

Driving style classification considering risk distribution in car-following condition

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跟驰工况下考虑风险分布的驾驶风格分类

Journal Information

J. Hefei Univ. Tech. (Nat. Sci.), Nov.2024, Vol.47 No.11: 1514-1518

DOI: 10.3969/j.issn.1003-5060.2024.11.012

Authors

JIANG Ping, FAN Honghui, HUANG He, SHI Qin, ZHOU Yu

(School of Automobile and Traffic Engineering, Hefei University of Technology, Hefei 230009, China)

Abstract and Keywords

Abstract: In order to explore the driver characteristics through the risk distribution of various factors in car-following condition, and complete the driving style classification under vehicle-road interaction, this paper proposes a driving style classification method based on improved fuzzy comprehensive evaluation method. Firstly, the test data are collected through driving simulator. The motion parameters of the vehicle and the driving safety field are selected as characteristic parameters of driving style classification. Secondly, the combination weighting method is used to optimize the weight of the fuzzy comprehensive evaluation method, so that the corresponding weight is given to each characteristic parameter, and by using the improved fuzzy comprehensive evaluation method, drivers are divided into cautious drivers, common drivers and aggressive drivers. Finally, K-means clustering algorithm is used to verify the rationality of the above method. The result shows that the difference rate between the K-means clustering and the improved fuzzy comprehensive evaluation method is only 2%, and when the number of clusters is 3, the silhouette coefficient is up to 0.685, that is to say, just like unsupervised learning algorithm, the fuzzy comprehensive evaluation method can effectively classify driving styles.

Keywords: driving style classification; driving safety field; fuzzy comprehensive evaluation method; combination weighting method; K-means clustering algorithm

作者信息

姜平，范虹慧，黄鹤，石琴，周宇

（合肥工业大学汽车与交通工程学院，安徽合肥230009）

摘要和关键词

摘要: 车辆跟驰工况下, 为通过驾驶场景中各因素的风险分布研究驾驶员特性, 实现车路交互下的驾驶风格分类, 文章提出一种基于改进的模糊综合评价法的驾驶风格分类方法。通过驾驶模拟器采集试验数据, 并将车辆行驶参数和安全势场作为分类的特征参数; 使用组合权重法对模糊综合评价法的权重集进行改进, 从而对各特征参数赋予相应的权重, 再通过改进的模糊综合评价法将驾驶风格分为冷静型、普通型、激进型3类; 最后通过 K-means 聚类算法验证上述方法的合理性。改进的模糊综合评价法分类结果与 K-means 聚类结果的对比表明, 两者的差异率仅为 2%, 且当聚类簇数为 3 时, 轮廓系数高达 0.685, 即与无监督学习算法相同。研究结果表明, 使用该文模糊综合评价法可以实现对驾驶风格的有效分类。

关键词: 驾驶风格分类；安全势场；模糊综合评价法；组合权重法；K-means聚类算法

Funding

国家自然科学基金资助项目（71971073）

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