| 网联环境下基于精简车头时距特性的驾驶风格分类 |
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| 引用本文： | 吕能超,高谨谨,王维锋,王玉刚.网联环境下基于精简车头时距特性的驾驶风格分类[J].交通信息与安全,2022,40(1):116-125. |
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| 作者姓名： | 吕能超 高谨谨 王维锋 王玉刚 |
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| 作者单位： | 1.武汉理工大学智能交通系统研究中心 武汉 430063 |
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| 基金项目： | 国家自然科学基金;国家重点研发计划;湖北省杰出青年基金项目 |
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| 摘 要： | 基于现有网联数据获取技术与条件，从车联网系统提取车头时距参数并将3 s内的车头时距特征值定义为驾驶模式，根据驾驶模式进而对驾驶风格（即驾驶人的驾驶行为习惯）进行分类。通过车头时距特性对驾驶模式进行量化分类，根据标定好的驾驶风格结果，辨识每种驾驶风格包含的典型驾驶模式；运用模糊分类方法赋予典型驾驶模式相应分值，通过计算每位驾驶人分值并结合已标定的驾驶风格结果设定每种驾驶风格的阈值；利用该阈值对测试集中的驾驶人风格进行识别，以验证识别准确率。采集了44名驾驶人网联环境行车数据将驾驶人标定为激进型、普通（即既不保守也不激进）型和保守型。按上述方法设置各驾驶风格阈值，结果表明:各驾驶风格的阈值分别为:S < 64.67为保守型，64.67 ≤ S < 181.20为普通型，S ≥ 181.20为激进型；使用所提方法来识别驾驶人风格，总体准确率为85.7%。所提出的基于车头时距的驾驶风格分类方法，使用了极精简的驾驶行为参数，为驾驶风格分类应用提供了新思路。
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| 关 键 词： | 交通工程 车联网 驾驶风格分类 车头时距 驾驶模式 模糊隶属度 |
| 收稿时间： | 2021-09-28 |
Classification of Driving Style Using Simplified Features of Headway Under the Connected Vehicles Environment |
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| LYUNengchao,GAO Jinjin,WANG Weifeng,WANG Yugang.Classification of Driving Style Using Simplified Features of Headway Under the Connected Vehicles Environment[J].Journal of Transport Information and Safety,2022,40(1):116-125. |
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| Authors: | LYUNengchao GAO Jinjin WANG Weifeng WANG Yugang |
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| Affiliation: | 1.Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China2.College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, Chinz |
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| Abstract: | Based on current data collection techniques from connected vehicles, this paper aims to classify driving styles(i.e., driving habits or behavior)by analyzing driving modes which is defined as time headway in 3 s. Specifically, driving modes are quantitatively classified by time headway and typical driving modes reflecting each driving style are identified according to the calibrated driving styles. Evaluation score is assigned to each typical driving mode using a fuzzy classification method and the thresholds of each driving style is proposed based on the evaluation scores and the calibrated driving styles. The thresholds are applied to a test data set, which includes driving behavior data of 44 drivers, to verify the accuracy of the proposed method. In summary, three types of driving styles are identified: the evaluation score S < 64.67 is seen as the conservative driving style(CDS), the score 64.67 ≤ S < 181.20 is classified as the"regular"(that is, neither-conservation-nor-aggressive(NCNA))driving style; and the score S ≥ 181.20 is grouped into the aggressive driving style(ADS). Study results show that the accuracy of the proposed method against the testing data set is 85.7%. The proposed method uses simplified driving parameters (headway)for driving-style classification, which provides a new way for driving-style classification. |
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