# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
## 3 Datasets, Labelling, and Augmentation
### Data Augmentation  
Data augmentation plays an essential role in improving the generalisation of deep learning models. It is well studied for images [79], LiDAR point clouds [80], and audio spectrograms [81], but overlooked in radar perception. According to the summary report of the Radar Object Detection 2021 (ROD2021) Challenge [82], data augmentation techniques significantly improve the performance of RA-map-based radar detection. Considering the radar representation, we can divide the augmentation techniques into spectral- and point-cloud-based. Augmentation methods can also be featured as local or global depending on whether the entity being augmented is a single object or the entire scene. Spectral augmentation techniques are used for radar pre-CFAR data. DANet [83] adopts several global augmentation techniques borrowed from computer vision for radar RA maps. The methods include mirroring, resizing, random combination, adding Gaussian noise, and temporal reversing. Although physical fidelity is not explicitly considered, the performance gain proves the effectiveness of these augmentation methods. RADIO [84] implements four types of spectral augmentations, including attenuation, resolution change, adding speckle noise, and background shift. The first two methods are applied to a local patch around each detected object. The attenuation effect is approximated by dampening the cells according to an empirical relationship between the received power and range. The resolution change is modelled by nearest-neighbour interpolation according to the object size. The speckle noise can be approximated as a multiplicative truncated exponential distribution [85] or multiplicative Gaussian noise [84]. Background shift is performed by adding or subtracting a constant value to background cells. RAMP-CNN [86] applies global geometric augmentations to RA maps. It translates and rotates RA maps in Cartesian coordinate, then projects back to the original polar coordinate. The out-of-boundaries areas are cropped off, and the blank areas are filled with background noises. Energy loss and antenna gain loss due to the transformation are compensated according to the radar equation. Point cloud augmentation aims to introduce invariance to geometric transformations and improve the signal-to-clutter ratio. Compared with spectral augmentation, point cloud augmentation methods can be easily extended to multiple modalities by properly handling occlusion issues [87; 88]. Geometric augmentation can be applied locally or globally, depending on whether the transformation is applied to a single target or the entire scene. For radar point clouds, the Doppler velocity and RCS need further consideration. As illustrated in Figure 6, rotating objects locally will affect the radial velocity, and rotating the radar point cloud globally will affect the ego-motion-compensated radial velocity if the ego-motion is not rotated accordingly. Therefore, [PERSON] et al. [50] advise only using mirroring and scaling along the longitudinal axis as augmentation. Another applicable technique is the copy-paste augmentation, which copies the detected object from other frames and pastes it into the same location in the current frame, as done in [89]. A limitation of these two methods is that they do not change the distribution of detections, while radar points are actually randomly distributed over the object in different frames. According to experiments [90], most of the radar detections are located in the proximity of the vehicle contour and wheel rims. The number of detections per object is inversely proportional to the distance, and the probability of detection on the contour depends heavily on the orientation. Simulation-based methods, which will be introduced in the next section, are more suitable to capture such randomness. Figure 6: Radar data augmentation techniques. The Doppler velocity measured by radar is a scalar, so local rotation of the radar detection will cause a misalignment between the Doppler velocity and the true velocity.