论文标题
带有车道相关性改进的行李雷达巷检测网络
Row-wise LiDAR Lane Detection Network with Lane Correlation Refinement
论文作者
论文摘要
车道检测是自动驾驶的最重要功能之一。近年来，带有RGB摄像机图像的基于深度学习的车道检测网络已显示出令人鼓舞的性能。但是，基于摄像机的方法本质上容易受到不利的照明条件的影响，例如较差或令人眼花signer乱的照明。与摄像头不同，LiDAR传感器在照明条件下具有鲁棒性。在这项工作中，我们提出了一种新型的两阶段激光雷达泳道检测网络，采用划分检测方法。第一阶段网络通过全局功能相关主链和行范围检测头产生车道建议。同时，第二阶段网络通过在车道提案周围的本地特征之间的基于注意力的机制来完善第一阶段网络的特征图，并输出一组新的车道建议。 K-lane数据集的实验结果表明，拟议的网络在F1分数方面提高了最新的gflops，而Gflops却少了30％。此外，发现第二阶段网络对车道的闭塞特别健壮，因此证明了在拥挤的环境中驾驶的拟议网络的鲁棒性。
Lane detection is one of the most important functions for autonomous driving. In recent years, deep learning-based lane detection networks with RGB camera images have shown promising performance. However, camera-based methods are inherently vulnerable to adverse lighting conditions such as poor or dazzling lighting. Unlike camera, LiDAR sensor is robust to the lighting conditions. In this work, we propose a novel two-stage LiDAR lane detection network with row-wise detection approach. The first-stage network produces lane proposals through a global feature correlator backbone and a row-wise detection head. Meanwhile, the second-stage network refines the feature map of the first-stage network via attention-based mechanism between the local features around the lane proposals, and outputs a set of new lane proposals. Experimental results on the K-Lane dataset show that the proposed network advances the state-of-the-art in terms of F1-score with 30% less GFLOPs. In addition, the second-stage network is found to be especially robust to lane occlusions, thus, demonstrating the robustness of the proposed network for driving in crowded environments.
