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A Robust Lane Detection and Verification Method for Intelligent Vehicles

机译:智能车辆的鲁棒车道检测与验证方法

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Robust lane detection is important to the lane departure warning (LDW) for driver assistant system. In this study, lane marks are extracted by searching the lane model parameters in a special defined parameter space without thresholding. The proposed method is based on the lateral inhibition property of human vision system to clear up the edges of lane marks in variant weather conditions; moreover, the lane detected results are verified by the proposed conjugate Gaussian model such that there are no false alarm on the edges of shadow and other vehicles. The proposed lane detection method can gain precise lane-mark information for a lane departure warning system.
机译:稳健的车道检测对于驾驶员辅助系统的车道偏离警告(LDW)至关重要。在这项研究中,通过在没有定义阈值的特殊定义的参数空间中搜索车道模型参数来提取车道标记。所提出的方法基于人类视觉系统的横向抑制特性,以在变化的天气条件下清除车道标记的边缘。此外,通过提出的共轭高斯模型验证了车道检测结果,从而在阴影和其他车辆的边缘没有误报。所提出的车道检测方法可以为车道偏离警告系统获得精确的车道标志信息。

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