In autonomous driving, LiDAR and radar are crucial for environmental perception. LiDAR offers precise 3D spatial sensing information but struggles in adverse weather like fog. Conversely, radar signals can penetrate rain or mist due to their specific wavelength but are prone to noise disturbances. Recent state-of-the-art works reveal that the 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, then align and aggregate the two branch features to predict object detection results. However, these methods have low accuracy of predicted bounding boxes 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 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. Furthermore, 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 state-of-the-art method by 13.1% and 19.0% at Intersection of Union (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至关重要。激光雷达能提供精确的三维空间感知信息，但在雾天等恶劣天气下表现欠佳；而雷达信号因其特定波长可穿透雨雾，却易受噪声干扰。最新研究表明，雷达与激光雷达的融合可在恶劣天气下实现稳健检测。现有工作采用卷积神经网络架构分别提取各传感器数据特征，随后对齐并融合两分支特征以预测目标检测结果。然而，此类方法因标签分配与融合策略设计简单，导致预测边界框精度较低。本文提出一种基于鸟瞰视角融合学习的无锚框目标检测系统，通过融合雷达距离-方位角热图特征与激光雷达点云特征进行目标估计。我们设计了差异化标签分配策略，以促进前景/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，进一步提升了所提检测器的性能。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"晴朗+雾天"联合训练条件下，当交并比阈值为0.8时，本系统在"晴朗"与"雾天"测试场景中的平均精度分别较现有最优方法提升13.1%与19.0%。