  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.
