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.
翻译:通道探测是自动驾驶的最重要功能之一。 近年来,以学习为基础的深通道探测网络与 RGB 相机图像显示有良好的性能。 但是, 以相机为基础的方法本身很容易受到灯光差或闪亮等不利照明条件的影响。 与相机不同, LiDAR 传感器与照明条件不同。 在这项工作中, 我们提出一个新的双阶段LIDAR 通道探测网络, 使用行距探测方法。 第一阶段网络通过全球特征连接主干网和行距探测头生成了通道建议。 与此同时, 第二阶段网络通过关注机制完善了第一阶段网络的特征图, 包括车道周围的地方特征和一系列新的通道建议。 K- Lane 数据集的实验结果显示, 拟议的网络在F1芯方面提高了最新水平, 减少了GFLFOPs 的30% 。 此外, 第二阶段网络被认为对车道隔离特别强大, 从而显示了拟议在拥挤环境中驾驶的网络的稳健性。