# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
## 5 Radar Object Detection
### Pre-CFAR Detector
To fully exploit the information encoded in RAD tensors, some works [86; 163; 164] adopt the multi-view encoder-decoder architecture. [PERSON] et al. [163] and [PERSON] al. [164] both utilised a similar multi-view structure. The RAD tensor is projected into three 2D views. Then, three decoders extract features from these views, respectively. To fuse these features, [PERSON] et al.directly concatenated three feature maps. [PERSON] et al.recovered the tensor shape by duplicating these 2D feature maps along the missing dimension, then used a 3D convolution layer to fuse them. Next, the Doppler dimension is suppressed by pooling to recover the shape of the RA feature map. Finally, the fused feature maps are sent to a decoder for downstream segmentation tasks. Another difference is [PERSON] et al.used a skip-connection, while [PERSON] et al.adopted an ASPP [75] pathway to encode information from different resolutions. RAMP-CNN et al. [86] is also built in a multi-view architecture, but it uses three encoder-decoders for feature map extraction. Their fusion method is similar to [PERSON]'s, but in 2D. Radar pre-CFAR data are captured in polar coordinates. For object detection, polar-to-Cartesian transformation is necessary to obtain the correct bounding box. [PERSON] et al. [163] compared three configurations for coordinate transformation: preprocessed input transformation, learning from neural networks, and transformation on a middle-layer feature map. Experiments showed applying explicit polar-to-Cartesian transformation to the last-layer feature map achieves the best performance, the implicit learning-based transformation is slightly worse, and the preprocessed transformation is far inferior to the other two. They attributed this poor performance to distorted azimuth sidelobes in the input. In fact, conventional 2D convolution is not the best choice for radar pre-CFAR data, since the range, Doppler, and azimuth dimension vary in their dynamic ranges and resolutions. Instead of 2D convolution, PolarNet [165] uses a cascade of two 1D convolutions, including a columnwise convolution to extract range-dependent features, followed by a row-wise convolution to mix information from spatial neighbours. A similar idea is used in Google's RadarNet [166] for gesture recognition. They first extracted rangewise features, then summarised them together in the later stage. [PERSON] et al. [167] used an isotropic graph convolution network (GCN) [168] to encode the RAD tensor and achieved more than a 10% improvement in AP for 3D detection. They argued that the performance gain comes from the ability of GCN to aggregate information from neighbouring nodes. Incorporating temporal information is an effective way to improve the performance of pre-CFAR detectors. There are multiple ways to add temporal information to the network. [PERSON] et al. [163] used a convolutional LSTM layer to process a sequence of feature maps from the encoder network. Experiments indicated the temporal layer enables more accurate detection and significantly better velocity estimation. [PERSON] et al. [164] compared the performance between the static model with accumulated inputs and the temporal model with stacked inputs. For the static model, RAD tensors within three frames are accumulated into one single tensor and fed to a multi-view encoder-decoder for segmentation. For the temporal model, RAD tensors within five frames are stacked to form a 4D tensor and then sent to a multi-view encoder-decoder. In each branch, multiple 3D convolution layers are used to leverage spatial-temporal information. The results show that the introduction of the temporal dimension can significantly improve detection performance. [PERSON] et al. [69] discussed the effect of the number of stacked radar frames. They found too long frames will introduce many background clutter, which in turn makes it difficult for the model to learn target correspondences. According to their experiments, stacking of five frames is the most suitable choice.