Autonomous driving requires accurate local scene understanding information. To this end, autonomous agents deploy object detection and online BEV lane graph extraction methods as a part of their perception stack. In this work, we propose an architecture and loss formulation to improve the accuracy of local lane graph estimates by using 3D object detection outputs. The proposed method learns to assign the objects to centerlines by considering the centerlines as cluster centers and the objects as data points to be assigned a probability distribution over the cluster centers. This training scheme ensures direct supervision on the relationship between lanes and objects, thus leading to better performance. The proposed method improves lane graph estimation substantially over state-of-the-art methods. The extensive ablations show that our method can achieve significant performance improvements by using the outputs of existing 3D object detection methods. Since our method uses the detection outputs rather than detection method intermediate representations, a single model of our method can use any detection method at test time.

翻译：自动驾驶需要精确的局部场景理解信息。为此，自主智能体将目标检测和在线BEV车道图提取方法作为其感知堆栈的一部分。本文提出了一种架构与损失函数设计，通过利用3D目标检测输出来提升局部车道图估计的准确性。所提方法将车道中心线视为聚类中心，将目标视为待分配概率分布的数据点，从而学习将目标分配至各中心线。该训练方案直接监督车道与目标之间的关系，进而获得更优性能。本方法在车道图估计上显著超越现有最优方法。大量消融实验表明，通过直接利用现有3D目标检测方法的输出，本方法即可实现显著的性能提升。由于本方法使用检测输出而非检测方法的中间表征，单一模型在测试时可兼容任意检测方法。