Accurate and robust object detection is critical for autonomous driving. Image-based detectors face difficulties caused by low visibility in adverse weather conditions. Thus, radar-camera fusion is of particular interest but presents challenges in optimally fusing heterogeneous data sources. To approach this issue, we propose two new radar preprocessing techniques to better align radar and camera data. In addition, we introduce a Multi-Task Cross-Modality Attention-Fusion Network (MCAF-Net) for object detection, which includes two new fusion blocks. These allow for exploiting information from the feature maps more comprehensively. The proposed algorithm jointly detects objects and segments free space, which guides the model to focus on the more relevant part of the scene, namely, the occupied space. Our approach outperforms current state-of-the-art radar-camera fusion-based object detectors in the nuScenes dataset and achieves more robust results in adverse weather conditions and nighttime scenarios.

翻译：精确且鲁棒的目标检测对于自动驾驶至关重要。基于图像的检测器在恶劣天气条件下因能见度低而面临困难。因此，雷达-相机融合备受关注，但在融合异构数据源时存在优化难题。针对这一问题，我们提出了两种新的雷达预处理技术，以更好地对齐雷达与相机数据。此外，我们引入了一种用于目标检测的多任务跨模态注意力融合网络（MCAF-Net），该网络包含两个新的融合模块，能够更全面地利用特征图信息。所提算法联合检测目标并分割自由空间，引导模型聚焦于场景中更相关的部分，即占据空间。我们的方法在nuScenes数据集上优于当前最先进的基于雷达-相机融合的目标检测器，并在恶劣天气条件和夜间场景中取得了更鲁棒的结果。