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.

自主驾驶需要准确的局部场景理解信息，为此，我们提出了一种架构和损失形式来通过使用三维物体检测输出来改善局部车道图估计的准确性。所提出的方法通过将车道线作为簇中心，将物体视为要被分配到簇中心概率分布的数据点，从而学习将物体分配给车道中心线，以确保车道和物体之间的直接关系的监督，从而提高性能。该方法显著改善了车道图估计，且通过使用现有三维物体检测方法的输出，我们的方法可以实现显著的性能提升。由于我们的方法使用的是检测输出而不是检测方法的中间表示，因此我们的方法的单一模型可以在测试时使用任何检测方法。