  High-definition (HD) semantic map generation of the environment is an
essential component of autonomous driving. Existing methods have achieved good
performance in this task by fusing different sensor modalities, such as LiDAR
and camera. However, current works are based on raw data or network
feature-level fusion and only consider short-range HD map generation, limiting
their deployment to realistic autonomous driving applications. In this paper,
we focus on the task of building the HD maps in both short ranges, i.e., within
30 m, and also predicting long-range HD maps up to 90 m, which is required by
downstream path planning and control tasks to improve the smoothness and safety
of autonomous driving. To this end, we propose a novel network named
SuperFusion, exploiting the fusion of LiDAR and camera data at multiple levels.
We use LiDAR depth to improve image depth estimation and use image features to
guide long-range LiDAR feature prediction. We benchmark our SuperFusion on the
nuScenes dataset and a self-recorded dataset and show that it outperforms the
state-of-the-art baseline methods with large margins on all intervals.
Additionally, we apply the generated HD map to a downstream path planning task,
demonstrating that the long-range HD maps predicted by our method can lead to
better path planning for autonomous vehicles. Our code and self-recorded
dataset will be available at https://github.com/haomo-ai/SuperFusion.
