# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
## 3 Datasets, Labelling, and Augmentation
### Radar Datasets
The test car is equipped with a rich set of sensors, including an FMCW spinning radar, two 32-beam LiDARs, a stereo camera, three monocular cameras, two 2D LiDARs, and a GPS/IMU system. The spinning radar can provide a 360\({}^{\circ}\) high-resolution intensity map of surrounding environments. However, it has no Doppler information and is rarely used in production cars due to the high price. The Mulran dataset [54] focuses on range-sensor-based place recognition. It uses a spinning radar and a 64-beam LiDAR to capture the surrounding environment. The recorded data are temporally (monthly revisits) and structurally (multi-city) diverse. The Borea dataset [55] aims at studying the effect of seasonal variation on long-term localisation. The sensor suite includes a spinning radar, a camera, a GPS/IMU system, and a 128-beam LiDAR. The data were collected by driving a repeated route over one year, thus capturing seasonal variations and adverse weather conditions. It provides a pose ground truth for the localisation task, as well as 3D bounding box annotations for object detection in sunny weather. Similar to the Borea dataset, the EU long-term dataset [56] aims at localisation in highly dynamic environments and long-term autonomy. Its sensor suite includes two stereo cameras, two 32-beam LiDARs, two fisheye cameras, a four-beam LiDAR, a 77 GHz long-range radar, and a 2D LiDAR facing the road. The Endeavour dataset [57] adopts five multi-mode radars to cover the 360\({}^{\circ}\) surrounding environment. It is also equipped with LiDARs and RTK-GPS to provide the ground truth for radar odometry. The ColoRadar dataset [58] utilises a compact moving sensor rig, which consists of a 64-beam LiDAR, a TI AWR2243 cascaded 4D radar, a TI AWR1843 radar, and an IMU. Three levels of radar data representation are provided, including raw ADC samples, range-azimuth-elevation-Doppler (RAED) tensors from the 4D radar, and point clouds from the single-chip radar. The data are gathered in a variety of scenarios, including highly diverse indoor environments, outdoor environments, and an underground mine. There are also some radar datasets designed for specific tasks. PREVENTION [59] focuses on predicting inter-vehicle interactions. The data-collection car is equipped with one frontal long-range radar, two corner radars, one 32-beam LiDAR, and two cameras. It provides annotations of 2D bounding boxes, lane change behaviours, and trajectories. SCORP [60] is a radar dataset for open space segmentation in parking scenarios. It provides three kinds of radar data, including the RAD tensor, RA map, and BEV map. The Radar Ghost dataset [61] aims at studying the effect of multi-path propagation in autonomous driving. It provides pointwise annotations of real targets and four types of ghost targets. We summarize these datasets in Table 4. As two popular research directions, radar pre-CFAR datasets and 4D radar datasets are listed independently. With a focus on radar, we specify radar information, including type, data format, maximum range, and whether it comes with Doppler velocity. In addition, we provide information on other sensor modalities, scenarios, weathers, and size, so that researchers can select appropriate datasets for their tasks. We also maintain a website for browsing each reference and related data processing codes at [62].