  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.
