3D lane detection has emerged as a critical challenge in autonomous driving, encompassing identification and localization of lane markings and the 3D road surface. Conventional 3D methods detect lanes from dense birds-eye-viewed (BEV) features, though erroneous transformations often result in a poor feature representation misaligned with the true 3D road surface. While recent sparse lane detectors have surpassed dense BEV approaches, they completely disregard valuable lane-specific priors. Furthermore, existing methods fail to utilize historic lane observations, which yield the potential to resolve ambiguities in situations of poor visibility. To address these challenges, we present SparseLaneSTP, a novel method that integrates both geometric properties of the lane structure and temporal information into a sparse lane transformer. It introduces a new lane-specific spatio-temporal attention mechanism, a continuous lane representation tailored for sparse architectures as well as temporal regularization. Identifying weaknesses of existing 3D lane datasets, we also introduce a precise and consistent 3D lane dataset using a simple yet effective auto-labeling strategy. Our experimental section proves the benefits of our contributions and demonstrates state-of-the-art performance across all detection and error metrics on existing 3D lane detection benchmarks as well as on our novel dataset.

翻译：三维车道线检测已成为自动驾驶领域的一项关键挑战，其任务包括识别和定位车道标记以及三维路面。传统的三维方法从密集的鸟瞰图特征中检测车道线，但错误的变换常常导致特征表示不佳，与真实的三维路面不对齐。尽管近期出现的稀疏车道检测器已超越密集鸟瞰图方法，但它们完全忽略了有价值的车道特定先验。此外，现有方法未能利用历史车道观测信息，而这些信息在能见度较差的情况下具有解决模糊性的潜力。为应对这些挑战，我们提出了SparseLaneSTP，这是一种新颖的方法，它将车道结构的几何特性和时序信息集成到一个稀疏车道Transformer中。该方法引入了一种新的车道特定时空注意力机制、一种专为稀疏架构设计的连续车道表示以及时序正则化。针对现有三维车道数据集的不足，我们还通过一种简单而有效的自动标注策略，引入了一个精确且一致的三维车道数据集。我们的实验部分证明了所提贡献的益处，并在现有三维车道检测基准以及我们的新数据集上，展示了所有检测和误差指标上的最先进性能。