基于BP神经网络的雷达与视觉前车信息融合研究
Research on ahead vehicle information fusion of radar and vision based on BP neural network
针对智能汽车单一传感器对前方车辆运动信息和方位识别误差较大的问题，在Prescan仿真软件中建立起摄像头和毫米波雷达识别车辆的智能车模型，用雷达和摄像头采集被跟踪车辆在多种运动模式下数据，使用BP神经网络实现视觉和雷达对被跟踪车辆的运动信息的融合，准确识别前方车辆的车速、车距、相对角度信息。研究结果表明：视觉和雷达单独识别的前方车辆车距、车速、相对角度信息与真值相比存在较大误差，而经过BP神经网络融合视觉和雷达信息后输出的识别结果与真值相比误差较小，有效提高智能汽车对前方车辆的运动信息识别精度。
雷达电子技术应用自动化技术、自动化技术设备
车辆工程BP神经网络雷达视觉信息融合
郑玲,曾杰.基于BP神经网络的雷达与视觉前车信息融合研究[EB/OL].(2016-07-18)[2025-10-30].http://www.paper.edu.cn/releasepaper/content/201607-183.点此复制
The motion and position information of ahead vechicle obtained by single sensor contain large error information. In this paper,intellegent vehicle with radar and camera is established in Prescan software,and the motion and position information ofahead vechile in different motion condition are captured .The information fusion of radar and vision based on BP neural network is uesd to obtain speed, distance and angle with higher accuracy of ahead vechile.The result shows that the speed, distance and angle obtained by single radar or vision have relative large difference compared with ture information value,and the fusion information output by BP nerual network have relative small difference compared with ture information value.The information fusion of radar and vision can effectively improve the motion and position recognition accuracy of ahead vehicle.
automobile engineeringBP neural networkradravisioninformation fusion
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