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汽车安全与节能学报

JASE ›› 2016, Vol. 07 ›› Issue (02): 167-174.DOI: 10.3969/j.issn.1674-8484.2016.02.005

• 汽车安全 • 上一篇    下一篇

基于毫米波雷达和机器视觉的夜间前方车辆检测

金立生1，程蕾1，成波2   

  1. 1. 吉林大学 交通学院，长春130022，中国；
    2. 清华大学 汽车安全与节能国家重点实验室，北京100084，中国
  • 收稿日期:2015-10-21 出版日期:2016-06-25 发布日期:2016-07-06
  • 作者简介:金立生(1975—)，男( 汉)，山东，教授。E-mail: jinls@jlu.edu.cn
  • 基金资助:

    汽车安全与节能国家重点实验室开放基金(KF14182)

Leading vehicle detection at night based on millimeter-wave radar and machine vision

JIN Lisheng1, CHENG Lei1, CHENG Bo2   

  1. 1. School of Transportation, Jilin University, Changchun 130022, China;
    2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
  • Received:2015-10-21 Online:2016-06-25 Published:2016-07-06

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摘要：

为研究夜间追尾事故中本车智能防撞预警方法，提出了一种基于毫米波雷达和机器视觉的前方车辆检测方法。利用多传感器融合数据，检测前方车辆的距离、速度等。建立传感器之间转换关系，转换雷达目标的世界坐标到图像坐标。在图像上形成感兴趣区域，利用图像处理方法减少干扰点，运用Dempster-Shafer（D-S）证据理论，融合特征信息，得到总的信任度值检验感兴趣区域内的车辆。实验采集多段夜间道路行车视频数据，统计实现尾灯识别的帧数，与主观判断进行比较。结果表明：该方法能够实现对夜间前方车辆的检测和定位。

关键词: 车辆主动安全, 智能防撞预警, 夜间前方车辆检测, 毫米波雷达, 机器视觉, 数据融合, 动态感兴趣区域

Abstract:

A leading-vehicle night-detection method was proposed based on millimeter-wave radar-vision, using the fusion of data from multi-sensors to investigate the intelligent warning system for avoidance vehicles collision at night with preceding vehicles. A world coordinate of the preceding vehicles was built for millimeter
wave radar target to form the region of interesting image after relationship transformation from the world coordinate to image pixels coordinate. Image processing method reduced interference from the outside environment. A general value of reliability was achieved to test vehicles in interest depending on Dempster-
Shafer Evidence Theory (D-S) which fused feature information. Several sections of video for vehicles driving on road were collected during experiment. The statistical data of frames of taillight identified were achieved and compared by subjective judgment. The results show that the method effectively eliminates the influence of illumination condition at night, accurately detect leading vehicles and determine their location.

Key words: vehicles active safety, avoidance collision warning, leading vehicle detection at night, millimeter wave radar, machine vision, data fusion, dynamic region of interest