# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
## 5 Radar Object Detection
### Point Cloud Detector  
End-to-end object detectors are expected to replace the conventional pipelines based on hand-crafted features. However, the convolutional neural network is not well designed for sparse data structure [140]. It is necessary to increase the input density of the radarpoint cloud for better performance. [PERSON] et al. [141] accumulated radar points into an occupancy grid mapping (OGM), then applied YOLOv3 [142] for object detection. Some works [143; 144; 145] utilise point cloud segmentation networks, such as PointNet [146] and PointNet++ [147], followed by a bounding box regression module for 2D radar detection. The original 3D point cloud input is replaced by a 4D radar point cloud with two spatial coordinates in the x-y plane, Doppler velocity, and RCS. [PERSON] et al. [145] compared the performances of the two-stage clustering method, OGM-based method, and PointNet-based method with respect to 2D detection. Experiments showed that the OGM-based method performs best, while the PointNet-based method performs far worse than others probably due to sparsity. [PERSON] et al. [148] suggest that incorporating global information can help with the sparsity issue of the radar point cloud. Therefore, they added a gMLP [149] block to each set abstraction layer in PointNet++. The gMLP block is expected to extract global features at an affordable computational cost. Most radar detection methods only apply to moving targets, since static objects are difficult to classify due to low angular resolution. [PERSON] et al. [150] propose a scene understanding framework to detect both static and dynamic objects simultaneously. For static objects, they first built an RCS histogram grid map through the temporal integration of multiple frames and send it to a fully convolutional network (FCN) [151] for semantics segmentation. For dynamic objects, they adopted a two-branch recurrent architecture: One is the point feature generation module, which uses PointNet++ to extract features from the input point cloud. The other is the memory abstraction module, which learns temporal features from the temporal neighbours in the memorised point cloud. The resulting features are concatenated together and sent to an instance segmentation head. In addition, a memory update module is proposed to integrate targets into the memorised point cloud. Finally, static and dynamic points are combined into a single semantic point cloud. The proposed framework can successfully detect moving targets such as cars and pedestrians, as well as static targets such as parked cars, infrastructures, poles, and vegetation. As 4D radars have gradually come to the market, radar point cloud density has increased considerably. A major advantage of 4D radar is that static objects can be classified based on elevation measurements without the need to build an occupancy grid map. Therefore, it is possible to train a single detector for both static and dynamic objects. [PERSON] et al. [50] applied PointPillars [152] to 4D radar point clouds for 3D detection of multi-class road users. They found the performance can be improved by temporal integration and by introducing additional features, such as elevation, Doppler velocity, and RCS. Among them, the Doppler velocity is essential for detecting pedestrians and bicyclists. However, the performance of the proposed 4D radar detector (mAP 47.0) is still far inferior to their LiDAR detector on 64-beam LiDAR (mAP 62.1). They argue this performance gap comes from radar's poor ability in determining the exact 3D position of objects. RPFA-Net [153] improves PointPillars by introducing a radar pillar features attention (PFA) module. It leverages self-attention to extract the global context feature from pillars. The global features are then residually connected to the original feature map and sent to a CNN-based detection network. The idea behind this is to explore the global relationship between objects for a better heading angle estimation.