Accurate and efficient lane detection in 3D space is essential for autonomous driving systems, where robust generalization is the foremost requirement for 3D lane detection algorithms. Considering the extensive variation in lane structures worldwide, achieving high generalization capacity is particularly challenging, as algorithms must accurately identify a wide variety of lane patterns worldwide. Traditional top-down approaches rely heavily on learning lane characteristics from training datasets, often struggling with lanes exhibiting previously unseen attributes. To address this generalization limitation, we propose a method that detects keypoints of lanes and subsequently predicts sequential connections between them to construct complete 3D lanes. Each key point is essential for maintaining lane continuity, and we predict multiple proposals per keypoint by allowing adjacent grids to predict the same keypoint using an offset mechanism. PointNMS is employed to eliminate overlapping proposal keypoints, reducing redundancy in the estimated BEV graph and minimizing computational overhead from connection estimations. Our model surpasses previous state-of-the-art methods on both the Apollo and OpenLane datasets, demonstrating superior F1 scores and a strong generalization capacity when models trained on OpenLane are evaluated on the Apollo dataset, compared to prior approaches.

翻译：三维空间中的精确高效车道线检测对于自动驾驶系统至关重要，其中鲁棒的泛化能力是三维车道检测算法的首要要求。考虑到全球车道结构的广泛多样性，实现高泛化能力尤为困难，因为算法必须准确识别世界各地各种不同的车道模式。传统的自上而下方法严重依赖于从训练数据集中学习车道特征，往往难以处理具有先前未见属性的车道。为应对这一泛化局限，我们提出一种方法：先检测车道的关键点，随后预测关键点之间的顺序连接以构建完整的三维车道。每个关键点对于维持车道连续性至关重要，我们通过允许相邻网格使用偏移机制预测同一关键点，为每个关键点生成多个候选点。采用PointNMS消除重叠的候选关键点，从而减少估计的鸟瞰图（BEV）图中的冗余，并最小化连接估计带来的计算开销。我们的模型在Apollo和OpenLane数据集上均超越了先前的最优方法，在OpenLane上训练的模型于Apollo数据集上评估时，相比现有方法展现出更优的F1分数和强大的泛化能力。