Kim Donghyun, Jeong Yonghwan
Department of Automotive Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.
Department of Mechanical and Automotive Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.
Sensors (Basel). 2024 Dec 5;24(23):7802. doi: 10.3390/s24237802.
This study proposed a robust lane-keeping algorithm designed for snowy road conditions, utilizing a snow tire track detection model based on machine learning. The proposed algorithm is structured into two primary modules: a snow tire track detector and a lane center estimator. The snow tire track detector utilizes YOLOv5, trained on custom datasets generated from public videos captured on snowy roads. Video frames are annotated with the Computer Vision Annotation Tool (CVAT) to identify pixels containing snow tire tracks. To mitigate overfitting, the detector is trained on a combined dataset that incorporates both snow tire track images and road scenes from the Udacity dataset. The lane center estimator uses the detected tire tracks to estimate a reference line for lane keeping. Detected tracks are binarized and transformed into a bird's-eye view image. Then, skeletonization and Hough transformation techniques are applied to extract tire track lines from the classified pixels. Finally, the Kalman filter estimates the lane center based on tire track lines. Evaluations conducted on unseen images demonstrate that the proposed algorithm provides a reliable lane reference, even under heavy snowfall conditions.
本研究提出了一种专为雪地路况设计的稳健车道保持算法，该算法利用基于机器学习的雪地轮胎轨迹检测模型。所提出的算法主要由两个模块组成：雪地轮胎轨迹检测器和车道中心估计器。雪地轮胎轨迹检测器利用YOLOv5，在从雪地道路上拍摄的公共视频生成的自定义数据集上进行训练。视频帧使用计算机视觉标注工具（CVAT）进行标注，以识别包含雪地轮胎轨迹的像素。为了减轻过拟合，检测器在一个组合数据集上进行训练，该数据集包含雪地轮胎轨迹图像和来自优达学城数据集的道路场景。车道中心估计器使用检测到的轮胎轨迹来估计车道保持的参考线。检测到的轨迹进行二值化处理并转换为鸟瞰图图像。然后，应用骨架化和霍夫变换技术从分类像素中提取轮胎轨迹线。最后，卡尔曼滤波器根据轮胎轨迹线估计车道中心。对未见过的图像进行的评估表明，即使在大雪条件下，所提出的算法也能提供可靠的车道参考。
