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.

翻译：环境的高清语义地图生成是自动驾驶的重要组成部分。现有方法通过融合不同传感器模态（如激光雷达和相机）在该任务上取得了良好性能。然而，当前研究基于原始数据或网络特征级融合，仅考虑短距离高清地图生成，限制了其在真实自动驾驶应用中的部署。本文聚焦于构建短距离（30米以内）及预测远距离（最高90米）高清地图任务，后者是下游路径规划与控制任务提升自动驾驶平顺性与安全性的关键。为此，我们提出名为SuperFusion的新型网络，在多个层级利用激光雷达与相机数据的融合。我们采用激光雷达深度改进图像深度估计，并利用图像特征引导远距离激光雷达特征预测。在nuScenes数据集及自记录数据集上对SuperFusion进行基准测试，结果表明该方法在所有距离区间内均大幅超越现有最优基线方法。此外，我们将生成的高清地图应用于下游路径规划任务，验证了所预测的远距离高清地图可优化自动驾驶车辆路径规划。代码及自记录数据集将在https://github.com/haomo-ai/SuperFusion 公开。