# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
## 3 Datasets, Labelling, and Augmentation
### Radar Datasets
It provides radar RAD tensors with segmentation mask annotations for VRUs. To compensate for range-dependent power, many datasets apply logarithmic scaling or normalisation to the pre-CFAR data as the default. The CARRADA dataset [45] and RADDet dataset [46] apply logarithmic scaling to their radar data. The normalisation can be applied in different ways, including local power normalising in the Ghent VRU dataset [49], min-max scaling in the CRUW dataset [47], and Z-score standardisation in the RADDet dataset [46]. Here, we only summarize some operations that are explicitly mentioned. Further checks are needed when benchmarking the algorithm using different datasets. As 4D radar is just entering the market, only a few public datasets are available. The Astyx dataset [2] is the first publicly available 4D radar dataset. The sensors include a 16-beam LiDAR, a camera, and an Astyx 6455 HiRes 4D radar. It provides 3D bounding box annotations of seven classes of road users. Each object also features four levels of occlusion and three levels of uncertainty. The dataset is very small, with only 500 annotated frames of short clips, each clip containing less than 10 frames. The class distribution is very imbalanced, with over 90% of the annotated objects being car. The View-of-Delft (VoD) dataset [50] is a recently published 4D radar dataset especially focused on the detection of VRUs. The sensor suite includes a ZF FRGen 21 4D radar, a 64-beam LiDAR, and a stereo camera. It provides 8693 annotated frames with 3D bounding boxes and tracking IDs. Each object is also annotated with two levels of occlusion and four types of activity attributes (stopped, moving, parked, pushed, sitting). The data were collected in campus, suburb, and old-town locations, with a preference for scenarios containing VRUs. It provides fine-grained annotations of vehicles, trucks, and 10 classes of VRUs. Different classes are equally distributed (21.6% pedestrians, 8.8% cyclists, and 21.9% cars). The RADIal dataset [51] is a 4D radar dataset for vehicle detection and open space segmentation. The sensors include camera, LiDAR, 4D radar, GPS. and vehicle's CAN traces. The 4D radar is a 12 Tx 16 Rx cascaded radar. A key feature is that they also provide radar raw ADC data, which makes it possible to explore the potential of neural networks in the signal processing stage. This dataset is comparable in size to the VoD dataset, with 8252 annotated frames captured in city streets, highways, and countryside roads. Two kinds of annotations are provided, vehicle annotations and open space segmentation masks in BEV. The vehicle annotations are in the format of 2D bounding boxes for image- and object-level points for LiDAR and radar. Although they do not provide bounding box annotations for the radar point clouds,it is possible for researchers to annotate them on their own, given the LiDAR point clouds and images. TJ4 DRadSet [52] is a 4D dataset for 3D detection and tracking. The sensor suite includes a 32-beam LiDAR, a camera, a high-performance 4D radar (Oculii Eagle), and a GNSS. By utilising Oculii's virtual aperture imaging technique, this 4D radar can output a much denser point cloud than others. It has a maximum detection range of 400 m and an angular resolution of less than 1\({}^{\circ}\) in both azimuth and elevation. The data are captured in a wide range of road conditions in urban driving. The dataset contains a total of 40K frames of synchronised data, where 7757 frames of them are annotated with 3D bounding boxes and track IDs. Radar can also be utilised for localisation. Compared to camera and LiDAR, radar has the advantages of a long detection range and robustness to occlusions. Millimetre waves can penetrate certain non-metallic objects, such as glass, Polywood, and clay bricks [32] and are less affected by dust, smoke, fog, rain, snow, and ambient lighting conditions [32]. Therefore, radar has great potential for mapping and localisation in adverse weather. The Oxford radar robotcar dataset [53] is the most popular dataset for radar SLAM.