吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (3): 737-744.doi: 10.13229/j.cnki.jdxbgxb201603009

• 论文 • 上一篇    下一篇

换道过程中越线时刻预测模型

王畅, 杜康, 郑楚清   

  1. 长安大学 汽车学院, 西安 710064
  • 收稿日期:2014-11-24 出版日期:2016-06-20 发布日期:2016-06-20
  • 作者简介:王畅(1984),男,副教授,博士.研究方向:车辆主动安全技术.E-mail:wangchang@chd.edu.cn
  • 基金资助:
    长江学者和创新团队发展计划项目(IRT1286); 国家自然科学基金项目(61374196,61473046); 中央高校基本科研业务费专项项目(310822151028).

Prediction model time-to-line-crossing during lane change process

WANG Chang, DU Kang, ZHENG Chu-qing   

  1. School of Automobile,Chang'an University,Xi'an 710064,China
  • Received:2014-11-24 Online:2016-06-20 Published:2016-06-20

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摘要： 分析了车辆换道过程中的运动几何特征,提出了车辆相对于车道线的偏航角度估算方法。在此基础上,分别建立了直道路段和弯道路段的越线时刻预测模型。利用视觉传感器、车载陀螺仪、车载CAN总线数据采集卡设计开发了测试平台,对多名被测试人员进行了驾驶测试,获取了大量的换道状态表征数据。利用实测换道数据对越线时刻预测模型进行验证,结果表明,直道路段预测结果中87.0%的误差绝对值小于等于0.1 s,弯道路段相应的比例达到了93.7%,且两类模型的预测误差值均符合正态分布规律。

关键词: 车辆工程, 越线时刻, 几何分析, 偏航角度估计, 车辆主动安全

Abstract: To solve the time-to-line-crossing (TLC) prediction problem during lane change process, the geometrical motion feature of vehicle during lane change was analyzed and a yaw angle estimation algorithm between vehicle and lane mark was proposed. Based on this, TLC prediction models for straight and curve roads were established. A test platform was developed which consists of vision sensor, vehicle gyro and onboard CAN bus data capture card. Real-road driving test of several drivers was carried out, and representative data of lane change state were collected. The TLC prediction models were tested using the real lane change data. Results show that 87.0% prediction errors on straight road were less than or equal 0.1 s, and the corresponding rate on curve road was 93.7%. The prediction errors of two models meet the normal distribution.

Key words: vehicle engineering, time to line crossing, geometry analysis, yaw angle estimate, vehicle active safety

中图分类号: 

  • U461.91
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