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Lane detection algorithm based on Hough transform for high-speed self driving vehicles

机译:基于霍夫变换的高速自驾车道检测算法

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This study proposes a lane detection method based on expressway driving videos through a computer vision-based image processing system without using sensors. Both straight and curved sections can occur on a road, and thus, lanes must be detected by quickly determining such sections. The proposed method detects straight and curved sections that are estimated to be lanes using the Hough transform. When lanes are detected from actual images, the scope of left and right lanes is limited to reduce computational load. In this paper, we propose a lane-detection algorithm using the colour space and a stepwise algorithm for accurate lane detection. To verify the proposed algorithms, we developed a small self-driving vehicle model using a TX-2 board. The experiment results when applying the proposed Hough transform algorithm and lane-detection algorithm using the colour space show that the lane detection rate of vehicles driving on curves at high speed is approximately 96%. Through the extensive simulation results, the proposed algorithm to vehicle black boxes or autonomous driving will help prevent lane departure and reduce accident rates.
机译:这项研究提出了一种基于高速公路驾驶视频的车道检测方法,该方法是通过基于计算机视觉的图像处理系统,而不使用传感器。直线段和弯曲段都可能出现在道路上,因此,必须通过快速确定此类段来检测车道。所提出的方法使用霍夫变换检测估计为车道的直线和曲线部分。当从实际图像中检测到车道时,左右车道的范围受到限制,以减少计算量。在本文中,我们提出了一种使用颜色空间的车道检测算法和一种用于精确车道检测的逐步算法。为了验证提出的算法,我们使用TX-2板开发了小型自动驾驶汽车模型。通过在色彩空间上应用提出的霍夫变换算法和车道检测算法的实验结果表明,高速行驶在弯道上的车辆的车道检测率约为96%。通过广泛的仿真结果,提出的针对车辆黑匣子或自动驾驶的算法将有助于防止车道偏离并降低事故率。

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