吉林大学学报(工学版) ›› 2023, Vol. 53 ›› Issue (12): 3342-3350.doi: 10.13229/j.cnki.jdxbgxb.20220173

• 车辆工程·机械工程 • 上一篇    

基于自然驾驶数据的电动公交踏板误操作辨识方法

袁伟1(),袁小慧1,2,高岩3,李坤宸1,赵登峰4,刘朝辉4   

  1. 1.长安大学 汽车学院,西安 710064
    2.西安航空学院 车辆工程学院,西安 710077
    3.道路交通安全公安部重点实验室,江苏 无锡 214000
    4.郑州宇通客车股份有限公司,郑州 450016
  • 收稿日期:2022-02-23 出版日期:2023-12-01 发布日期:2024-01-12
  • 作者简介:袁伟(1975-),男,教授,博士.研究方向:新能源汽车生态驾驶和运行安全.E-mail:yuanwei@chd.edu.cn
  • 基金资助:
    道路交通安全公安部重点实验室开放基金项目(2020ZDSYSKFKT01)

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

Wei YUAN1(),Xiao-hui YUAN1,2,Yan GAO3,Kun-chen LI1,Deng-feng ZHAO4,Zhao-hui LIU4   

  1. 1.School of Automobile,Chang'an University,Xi'an 710064,China
    2.School of Vehicle Engineering,Xi'an Aeronautical Institute,Xi'an 710077,China
    3.Key Laboratory of Ministry of Public Security for Road Traffic Safety,Wuxi 214000,China
    4.Zhengzhou Yutong Bus Company Ltd. ,Zhengzhou 450016,China
  • Received:2022-02-23 Online:2023-12-01 Published:2024-01-12

摘要:

针对驾驶人误踩踏板引发电动公交失控问题,开展了自然驾驶试验和误踩踏板的模拟驾驶试验,采用One-Class SVM和iForest方法建立了踏板误操作辨识方法。试验结果表明:车速、电机转矩、加速踏板开度和制动踏板开度可作为反映踏板误操作行为的特征参数;One-Class SVM和iForest辨识方法识别准确率分别为94.9%和99.5%,但iForest辨识方法在踏板误操作行为的识别精确率方面具有突出优势。利用iForest辨识方法对电动公交运行数据进行辨识,可为降低误踩踏板相关事故发生提供理论支持。

关键词: 公路运输, 踏板误操作, 自然驾驶, 纯电动公交, 机器学习

Abstract:

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.

Key words: highway transportation, pedal misoperation behavior, natural driving, pure electric bus, machine learning

中图分类号: 

  • U471.3

表 1

纯电动公交车型参数"

参 数数值
外形尺寸(长×宽×高)/(mm×mm×mm)12 000×2550×3290
最大允许总质量/kg18 000
驱动电机额定功率/kW120
驱动电机峰值功率/kW240
动力电池额定电压/V579.6
动力电池额定容量/(A·h)604
最高时速/(km·h-169

图1

公交车OBD接口"

图2

公交车CAN高/低接口"

表 2

自然驾驶数据采集项目"

序号项目类型
1低压总开关点火系统
2点火开关接通档点火系统
3点火开关启动档点火系统
4挡位信号挡位系统
5加速踏板开度/%踏板
6制动踏板开度/%踏板
7车速/(km·h-1车速
8纵向/横向速度/(km·h-1GPS
9纵向加速度/(m·s-2GPS
10手刹信号制动系统
11制动灯信号制动系统
12前/后气压制动压力/kPa制动系统
13左/右转向灯转向系统
14横摆加速度/(m·s-2转向系统
15横向加速度/(m·s-2转向系统
16转向盘转角/(°)转向系统
17转向盘转向角速度/(rad·s-1转向系统
18纵向距离/m雷达系统
19行人数量雷达系统
20左/右车道单目距离/m雷达系统
21电池电流/A电池组
22电池电压/V电池组
23电池荷电状态/%电池组
24电机转速/(r·s-1电机组
25电机转矩/(N·m)电机组

表3

模拟制动试验方案"

模拟试验驾驶操作行为试验要求试验车速(km·h-1
正常制动松开加速踏板后踩下制动踏板驾驶人突然松开加速踏板,并进行紧急制动,车辆刹停后结束试验10/20/30/40
踏板误操作情形1松开加速踏板后再踩下加速踏板驾驶人先迅速松开加速踏板,再以紧急制动的力度和速度迅速踩下加速踏板,待车速超过试验车速10 km/h左右时,松开加速踏板,踩制动踏板停车,结束试验10/20/30
踏板误操作情形2直接踩下加速踏板驾驶人直接以紧急制动的力度和速度迅速踩下加速踏板,待车速超过试验车速10 km/h左右时,松开加速踏板,踩制动踏板停车,结束试验10/20/30
踏板误操作情形3右脚同时踩加速踏板和制动踏板驾驶人将右脚横摆后,大力踩下加速踏板和制动踏板,维持5 s左右后松开右脚,踩制动踏板停车,结束试验10/20/30
踏板误操作情形4右脚踩加速踏板,左脚踩制动踏板驾驶人右脚将加速踏板迅速踩到底,同时左脚大力踩制动踏板,维持5 s左右后松开双脚,踩制动踏板停车,结束试验10/20/30

图 3

正常制动"

图4

踏板误操作情形"

表4

One-Class SVM算法的分类混淆矩阵"

制动行为类型预测召回率/%
正常制动踏板误操作
真实正常制动10 47354195.1
踏板误操作4041091.1
精确率/%99.643.1

表5

iForest算法的分类混淆矩阵"

制动行为类型预测召回率/%
正常制动踏板误操作
真实正常制动10 9852999.7
踏板误操作3241892.9
精确率/%99.793.5

图5

训练集识别结果"

图6

测试集识别结果"

表6

降维的测试集的分类混淆矩阵"

制动行为类型预测召回率/%
正常制动踏板误操作
真实正常制动10 9763899.7
踏板误操作5539587.8
精确率/%99.591.2
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