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首页> 外文期刊>Intelligent Transport Systems, IET >Dynamic integration and online evaluation of vision-based lane detection algorithms
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Dynamic integration and online evaluation of vision-based lane detection algorithms

机译：基于视觉的车道检测算法的动态集成和在线评估

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摘要

Lane detection techniques have been widely studied in the last two decades and applied in many advance driver assistance systems. However, the development of a robust lane detection system, which can deal with various road conditions and efficiently evaluate its detection results in real time, is still of great challenge. In this study, a vision-based lane detection system with dynamic integration and online evaluation is proposed. To increase the robustness of the lane detection system, the integration system dynamically processes two lane detection modules. First, a primary lane detection module is designed based on the steerable filter and Hough transform algorithm. Then, a secondary algorithm, which combines the Gaussian mixture model for image segmentation and random sample consensus for lane model fitting, will be activated when the primary algorithm encounters a low detection confidence. To detect the colour and line style of the ego lanes and evaluate the lane detection system in real time, a lane sampling and voting technique is proposed. By combining the sampling and voting system system with prior lane geometry knowledge, the evaluation system can efficiently recognise the false detections. The system works robustly in various complex situations (e.g. shadows, night, and lane missing scenarios) with a monocular camera.
机译：在过去的二十年里，车道检测技术已经得到了广泛的研究，并应用于许多先进的驾驶员辅助系统中。但是，开发能够应对各种道路状况并实时有效地评估其检测结果的鲁棒车道检测系统仍然是巨大的挑战。在这项研究中，提出了一种基于视觉的具有动态集成和在线评估的车道检测系统。为了提高车道检测系统的鲁棒性，集成系统动态处理两个车道检测模块。首先，基于可转向滤波器和霍夫变换算法设计了一个主要车道检测模块。然后，当主要算法遇到低检测置信度时，将激活结合了用于图像分割的高斯混合模型和用于车道模型拟合的随机样本共识的辅助算法。为了检测自我通道的颜色和线条样式并实时评估通道检测系统，提出了一种通道采样和投票技术。通过将采样和投票系统系统与先前的车道几何知识相结合，评估系统可以有效地识别错误的检测结果。该系统可通过单眼相机在各种复杂情况（例如阴影，夜晚和车道丢失场景）中稳定运行。

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