In autonomous driving systems, LiDAR and radar play important roles in the perception of the surrounding environment. LiDAR provides accurate 3D spatial sensing information but cannot work in adverse weather like fog. On the other hand, the radar signal can be diffracted when encountering raindrops or mist particles thanks to its wavelength, but it suffers from large noise. Recent state-of-the-art works reveal that fusion of radar and LiDAR can lead to robust detection in adverse weather. The existing works adopt convolutional neural network architecture to extract features from each sensor data stream, then align and aggregate the two branch features to predict object detection results. However, these methods have low accuracy of bounding box estimations due to a simple design of label assignment and fusion strategies. In this paper, we propose a bird's-eye view fusion learning-based anchor box-free object detection system, which fuses the feature derived from the radar range-azimuth heatmap and the LiDAR point cloud to estimate the possible objects. Different label assignment strategies have been designed to facilitate the consistency between the classification of foreground or background anchor points and the corresponding bounding box regressions. In addition, the performance of the proposed object detector is further enhanced by employing a novel interactive transformer module. The superior performance of the methods proposed in this paper has been demonstrated using the recently published Oxford Radar RobotCar dataset. Our system's average precision significantly outperforms the best state-of-the-art method by 13.1% and 19.0% at IoU of 0.8 under 'Clear+Foggy' training conditions for 'Clear' and 'Foggy' testing, respectively.

翻译：在自动驾驶系统中，激光雷达与雷达对环境感知发挥着重要作用。激光雷达可提供精准的三维空间感知信息，但在雾天等恶劣天气条件下无法正常工作。而雷达信号凭借其波长特性，能在遭遇雨滴或雾霾颗粒时发生衍射，但存在较大噪声干扰。近期前沿研究表明，雷达与激光雷达的融合可在恶劣天气下实现鲁棒检测。现有工作采用卷积神经网络架构从各传感器数据流中提取特征，随后对齐并聚合双分支特征以预测目标检测结果。然而，由于标签分配与融合策略设计简单，这些方法的边界框估计精度较低。本文提出基于鸟瞰图融合学习的无锚框目标检测系统，通过融合雷达距离-方位角热图与激光雷达点云的特征实现目标估计。我们设计了差异化标签分配策略，以增强前景/背景锚点分类与对应边界框回归之间的一致性。此外，通过引入新型交互式Transformer模块，所提检测器的性能得到进一步提升。采用最新发布的Oxford Radar RobotCar数据集验证了本文方法的优越性能。在'晴天+雾天'训练条件下，针对'晴天'与'雾天'测试场景，当IoU为0.8时，本系统平均精度分别超越最优现有方法13.1%与19.0%。