Online lane graph construction is a promising but challenging task in autonomous driving. Previous methods usually model the lane graph at the pixel or piece level, and recover the lane graph by pixel-wise or piece-wise connection, which breaks down the continuity of the lane. Human drivers focus on and drive along the continuous and complete paths instead of considering lane pieces. Autonomous vehicles also require path-specific guidance from lane graph for trajectory planning. We argue that the path, which indicates the traffic flow, is the primitive of the lane graph. Motivated by this, we propose to model the lane graph in a novel path-wise manner, which well preserves the continuity of the lane and encodes traffic information for planning. We present a path-based online lane graph construction method, termed LaneGAP, which end-to-end learns the path and recovers the lane graph via a Path2Graph algorithm. We qualitatively and quantitatively demonstrate the superiority of LaneGAP over conventional pixel-based and piece-based methods on challenging nuScenes and Argoverse2 datasets. Abundant visualizations show LaneGAP can cope with diverse traffic conditions. Code and models will be released at \url{https://github.com/hustvl/LaneGAP} for facilitating future research.

翻译：在线车道图构建是自动驾驶领域中一项具有前景但富有挑战性的任务。以往方法通常在像素级或片段级对车道图进行建模，并通过逐像素或逐片段连接恢复车道图，这破坏了车道的连续性。人类驾驶员关注并沿连续完整的路径行驶，而非考虑车道片段。自动驾驶车辆同样需要车道图中面向特定路径的轨迹规划指导。我们认为，表征交通流量的路径是车道图的基本要素。受此启发，我们提出一种新颖的路径级车道图建模方法，该方法能够良好保持车道连续性并编码规划所需的交通信息。我们提出基于路径的在线车道图构建方法LaneGAP，该方法通过端到端方式学习路径，并利用Path2Graph算法恢复车道图。我们在具有挑战性的nuScenes和Argoverse2数据集上，定性与定量地证明了LaneGAP相比传统像素级与片段级方法的优越性。丰富的可视化结果表明LaneGAP能应对多样化的交通场景。代码与模型将在\url{https://github.com/hustvl/LaneGAP}发布，以促进未来研究。