# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
## 5 Radar Object Detection
### Pre-CFAR Detector  
There are some attempts to explore the potential of pre-CFAR data for detection. Radar pre-CFAR data encode rich information of both targets and backgrounds, but this is hard to interpret by humans. Neural networks are expected to better utilise this information. One option is to use neural networks to replace CFAR [156] or DOA estimation [76; 157]. Readers can refer to [158] for a detailed survey of learning-based DOA estimation. Alternatively, there are also some efforts to perform end-to-end detection through neural networks. The deep radar detector [159] jointly trains two cascaded networks for CFAR and DOA estimation, respectively. [PERSON] et al. [160] used stacked complex RD maps as the input to an FCN for 3D detection. In order to remove the DC component in phase, they performed a phase normalisation by using RD cells in the first receiver as normalisers. They argued that phase normalisation is crucial for successful training. [PERSON] et al. [51] designed a DDM-MIMO encoder with a complex RD map as the input. In the DDM configuration, as illustrated in Figure 3, all Tx antennas transmit signals at the same time. Instead of performing waveform separation, they directly applied range FFT and Doppler FFT to ADC signals received by Rx antennas. In this way, targets detected from different Tx antennas should be located separately with fixed Doppler shifts in the RA map. To extract these features, they designed a two-layer MIMO encoder, consisting of a dilated convolutional layer to separate Tx channels, followed by a convolutional layer to mix the information. This MIMO encoder was jointly trained with the following RA encoder, detection head, and segmentation head. In close-field applications that require large bandwidth and high resolution, RD maps are not suitable because the extended Doppler profile can lead to false alarms. The RA map, on the other hand, does not suffer from the same problem. For each detection point on the RA map, the micro-Doppler information in slow time can be utilised for better classification. RODNet [47] uses complex RA maps as input for object detection. It performs range FFT followed by angle FFT to obtain a complex RA map for each sampled chirp. It is difficult to separate static clutter and moving objects using the RA map alone without Doppler dimension. To utilise the motion information, it samples a few chirps within a frame. Then, the sequences of RA maps corresponding to these chirps are sent to a temporal convolution layer. Specifically, it first uses \(1\times 1\) convolutions along the chirp dimension to aggregate temporal information. Then, a 3D convolution layer is used to extract temporal features. Finally, the features are merged along the chirp dimension by max-pooling. Experiments indicate sampling 8 chirps out of 255 can achieve a comparable performance with using the full chirp sequences. Training neural network to utilise phase information in complex RA or RD maps is a difficult task. Alternatively, some works attempt to use the real-valued RAD tensor as the input. A key issue in using the 3D RAD tensor as the input is the curse of dimensionality. Therefore, many techniques are proposed to reduce the computational cost of 3D tensor processing. RADDet [46] normalises and reshapes the RAD tensor to an image-like data structure. The Doppler dimension is treated as the channel of 2D RA maps. Then, YOLO is applied to the RA map for object detection. One disadvantage is that this method fails to utilise the spatial distribution of Doppler velocities. Alternatively, 3D convolution can be used to extract features from all three dimensions in a 3D tensor, but requires huge computation and memory overheads [161]. RODNet [47] samples chirp sequences, as described above, to reduce input dimensionality. RTCNet [162] reduces tensor size by cropping a small cube around each point detected by CFAR and then uses 3D CNN to classify these small cubes. However, its detection performance is limited by the CFAR detector.