# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
## 3 Datasets, Labelling, and Augmentation
### Radar Datasets
It also provides 3D box annotations of vehicles. The Zendar dataset [38] is a high-resolution radar dataset that uses SAR for moving vehicle detection. It provides time-synchronised images, radar ADC data, 2D SAR point clouds, and projected LiDAR point clouds in BEV. Pointwise annotations of moving vehicles are applied to the SAR point cloud. It also provides an SDK for converting raw ADC data to RD maps and visualisation. Robust perception under adverse weather is a popular research topic for safe autonomous driving. Although there are some recently published datasets for adverse weather [39; 40; 41; 42], only a few include radar in their sensor suite. The Dense dataset [43] focused on evaluating multi-modal fusion algorithms under adverse weather. In addition to LiDAR and a stereo camera, it is also equipped with several all-weather sensors, including one frontal long-range radar, one gated camera working on the NIR band, one FIR camera, and one weather station sensor. The data are captured in various natural weather conditions, including rain, snow, light fog, and heavy fog, as well as in a controlled lab environment in a fog chamber. However, the dataset only provides sparse radar targets with limited FoV and poor resolution. The RADIATE dataset [44] focuses in particular on leveraging radar in adverse weather. The data-collection car is equipped with a camera, a LiDAR, and a spinning radar. The datasets are captured under different weather, such as sun, night, rain, fog, and snow. It provides annotations for 2D object detection, object tracking, and SLAM. Several datasets utilise radar sensors in short-range (SR) or ultra-short-range (USR) mode for high-resolution close-field imaging. In this mode, close objects will occupy several cells in both the range and Doppler dimension (because of the micro-Doppler motion). To fully utilise these spatially spread range and Doppler signatures, annotations are made directly on RA maps or RAD tensors. The CARRADA dataset [45] uses TI AWR1843 BOOST radar in short-range mode, with a max distance of 50 m. It provides real-valued RA maps,RD maps, and unannotated RAD tensors, as well as synchronised images, for training neural networks. In both RA and RD maps, objects are annotated at the point level with categories of pedestrian, car, or cyclist. In addition, the dilated segmentation mask and the bounding box around the cluster are also provided. The data are collected on an empty test track with at most two moving objects in the FoV. The RADDet dataset [46] also uses TI AWR1843 BOOST radar with a max distance of 50 m, as well as a stereo camera. It provides 3D bounding boxes for complex-valued RAD tensors and 2D bounding boxes for RA maps projected in Cartesian view. The data are captured using a tripod located on the sidewalks and facing the main roads. Therefore, its scenario is much more complex than the CARRADA dataset. The CRUW dataset [47] uses a TI AWR1843 radar and a stereo camera for object detection. It adopts a different signal processing pipeline, which directly outputs the RA map using range FFT and angle FFT. Then, the object-level pointwise annotation is applied to complex-valued RA maps. A probabilistic camera-0 radar fusion approach is used to improve annotation quality. The dataset contains 3.5 h and 400 K frames of camera-radar data in different driving scenarios, including parking lot, campus road, city street, and highway. The RaDICaL dataset [48] uses TI IWR1443 BOOST radar in multiple configurations for different scenarios, including indoor, parking lot, highway, and single human walking. It records radar ADC data together with the RGB-D images and IMU data using ROS. It also provides a signal processing SDK [23] to process and annotate radar data. The Ghent VRU dataset [49] collects radar data specifically for VRU detection. The sensor suite includes a TI AWR1243 radar, a camera, and a 16-beam LiDAR. The data are recorded by a vehicle driving on public roads in a crowded European city centre.