Dynamic lane tracking system based on multi-model fuzzy controller

Qing Shi, Jin Zhao, Lei Han, You Ning, Guangwei Wang
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引用次数: 3

Abstract

Large amounts of information can be obtained from road images by computer vision. This paper presents a vision-based lane detection algorithm to find the lane curves in each video frame, while a multi-model fuzzy controller is also established to fulfill lane following, which is based on fuzzy logic. In the proposed detecting algorithm, a series of algorithms, including image preprocessing and lane extraction algorithm, are done to extract the edge features, then lane-fitting is done successfully inside the region of interest(ROI) based on the coordinate transformation. Furthermore, some real-time simulation tests had been done successfully for verification, and the lateral position error was calculated. Meanwhile, the multi-model fuzzy controller, which inherits the advantages of both the multi-model control and fuzzy control, has been used at a low speed. In the end, the integrated simulation results validate the good tracking performance of this algorithm.
基于多模型模糊控制器的车道动态跟踪系统
计算机视觉可以从道路图像中获得大量的信息。本文提出了一种基于视觉的车道检测算法,在每个视频帧中找到车道曲线,并建立了一个基于模糊逻辑的多模型模糊控制器来实现车道跟踪。在该检测算法中,通过图像预处理和车道提取算法等一系列算法提取边缘特征,然后基于坐标变换在感兴趣区域内成功进行车道拟合。此外,还成功地进行了实时仿真试验验证,并计算了横向位置误差。同时,多模型模糊控制器继承了多模型控制和模糊控制的优点,在低速下得到了应用。最后,综合仿真结果验证了该算法的良好跟踪性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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