Robust Lane Departure Warning System for ADAS on Highways
Robust Lane Departure Warning System for ADAS on Highways
复制标题
适用于高速公路 ADAS 的强大车道偏离警告系统
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
A. M. Aly
中科院分区：
文献类型：
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作者：
Yara A. Ahmed;A. T. Mohamed;A. M. Aly
车道检测在高级驾驶员辅助系统（ADAS）中具有重要作用，必须处理好。车道检测是车道偏离警告系统（LDWS）的核心，它在驾驶员无意中离开车道时发出警告。不幸的是，诸如直线和弯曲道路的道路类型条件、诸如雾、低照明的天气条件会增加找到车道线的难度。本文实现了一种基于图像处理技术的车道线检测的车道偏离预警系统。视频帧被馈送到预处理阶段，在预处理阶段调整图像的亮度和对比度，然后红绿色蓝（RGB）图像被改变为色调饱和度亮度（HSL）颜色空间。高斯模糊过滤器，以消除噪音。Sobel边缘检测，检测和过滤车道线。透视扭曲将视图转换为平面2D表面。滑动窗口技术，以区分左右车道边界。最后，在拟合表示车道边界的多项式方程之后，可以计算车辆相对于车道中心的偏移。利用自制的数据集和埃及高速公路上的Raspberry pi 4视频帧（包括白天、夜间和雾天的视频帧）对算法进行了测试，结果表明该算法需要改进。
Lane detection in Advanced Driver Assistance System (ADAS) has a significant role and must be handled well. Lane detection is the core for Lane Departure Warning System (LDWS) that warns the driver upon unintendedly leaving the lane. Unfortunately, road type conditions such as straight and curved road, weather conditions such as fog, low illumination can increase the difficulty of finding the lane lines. In this paper, lane departure warning system that is based on image processing technique for detecting lane lines is implemented. Video frames are fed to the pre-processing phase where brightness and contrast of images are adjusted, then Red Green Blue (RGB) images are changed to Hue Saturation Lightness (HSL) colour space. Gaussian blur filter to remove noise. Sobel edge detection to detect and filter lane lines. Perspective warp to convert view to flat 2D surface. Sliding window technique to differentiate between the left and right lane boundaries. Finally, after fitting polynomial equations that represents lane boundaries, the vehicle offset from lane center can be calculated. The algorithm is tested using self-made dataset as well as on Raspberry pi 4 on highway in Egypt that includes daytime, nighttime, and foggy video frames, Results showed that it needs improvements.