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/}.

翻译：道路网络图是自动驾驶中全局路径规划与导航等下游任务的关键组成部分。过去，道路网络图通常由人类专家手动标注，耗时且劳动密集。为了实现高效、有效的道路网络图标注，亟需自动化的道路网络图检测算法。现有方法大多采用对语义分割图进行后处理的方式生成道路网络图，或直接提出基于图的算法预测道路网络图。然而，这些方法受限于硬编码算法且性能较差。为改进先前的最优方法（SOTA）RNGDet，我们新增实例分割头以更好监督训练过程，并让网络能够利用骨干网络的多尺度特征。由于新提出的方法基于RNGDet改进而来，故命名为RNGDet++。实验结果表明，在两个大规模公开数据集上，我们的RNGDet++几乎所有评估指标均超越基线方法。代码与补充材料详见\url{https://tonyxuqaq.github.io/projects/RNGDetPlusPlus/}。