Identification method for electric bus pedal misoperation based on natural driving data

被引：0
|
作者：
Yuan W. [1 ]
Yuan X.-H. [1 ,2 ]
Gao Y. [3 ]
Li K.-C. [1 ]
Zhao D.-F. [4 ]
Liu Z.-H. [4 ]
机构：
[1] School of Automobile, Chang'an University, Xi'an
[2] School of Vehicle Engineering, Xi'an Aeronautical Institute, Xi'an
[3] Key Laboratory of Ministry of Public Security for Road Traffic Safety, Wuxi
[4] Zhengzhou Yutong Bus Company Ltd., Zhengzhou
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2023年 / 53卷 / 12期
关键词：
highway transportation; machine learning; natural driving; pedal misoperation behavior; pure electric bus;
D O I：
10.13229/j.cnki.jdxbgxb.20220173
中图分类号：
学科分类号：
摘要：
Aiming at the problem of electric bus out of control caused by driver's wrong stepping on the pedal, natural driving test and simulated driving test of wrong stepping on the pedal were carried out. The pedal misoperation identification methods were established based on One-Class SVM and iForest respectively. The verification results show that the four parameters could be used to reflect pedal misoperation behavior, vehicle speed, motor torque, accelerator pedal opening and brake pedal opening. The recognition accuracy of one class SVM and iForest identification methods are 94.9% and 99.5% respectively, but iForest identification method has outstanding advantages in the recognition accuracy of pedal misoperation. The iForest method can be used to identify the running data of electric bus, which can provide theoretical support for reducing the accidents related to abnormal braking. © 2023 Editorial Board of Jilin University. All rights reserved.
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页码：3342 / 3350
页数：8
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