  The road network graph is a critical component for downstream tasks in
autonomous driving, such as global route planning and navigation. In the past
years, road network graphs are usually annotated by human experts manually,
which is time-consuming and labor-intensive. To annotate road network graphs
effectively and efficiently, automatic algorithms for road network graph
detection are demanded. Most existing methods either adopt a post-processing
step on semantic segmentation maps to produce road network graphs, or propose
graph-based algorithms to directly predict the graphs. However, these works
suffer from hard-coded algorithms and inferior performance. To enhance the
previous state-of-the-art (SOTA) method RNGDet, we add an instance segmentation
head to better supervise the training, and enable the network to leverage
multi-scale features of the backbone. Since the new proposed approach is
improved from RNGDet, we name it RNGDet++. Experimental results show that our
RNGDet++ outperforms baseline methods in terms of almost all evaluation metrics
on two large-scale public datasets. Our code and supplementary materials are
available at \url{https://tonyxuqaq.github.io/projects/RNGDetPlusPlus/}.
