# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
## 5 Radar Object Detection
### Classical Detection Pipeline  
As shown in Figure 10, the conventional radar detection pipeline consists of four steps: CFAR detection, clustering, feature extraction, and classification. Firstly, a CFAR detector is applied to detect peaks in the RD heat map as a list of targets. Then, the moving targets are projected to Cartesian coordinates and clustered by DBSCAN [19]. Static targets are usually filtered out before clustering because they are indistinguishable from environmental clutter. Within each cluster, hand-crafted features, such as the statistics of measurements and shape descriptors, are extracted and sent to a machine learning classifier. Improvements can be made upon each of these four steps. CFAR is usually executed in an on-chip DSP, so the choice of method is restricted by hardware support. Cell-averaging (CA) CFAR [18] is widely used due to its efficiency. It estimates the noise as the average power of neighbouring cells around the cell under test (CUT) within a CFAR window. A threshold is set to achieve a constant false alarm rate for Rayleigh-distributed noise. The next-generation high-resolution radar chips also support order-statistics (OS) CFAR [18]. It sorts neighbouring cells around the CUT according to the received power and selects the k-th cell to represent the noise value. OS-CFAR has advantages in distinguishing close targets, but introduces a slightly increased false alarm rate and additional computational costs. More sophisticated CFAR variants are summarised in [128], but are rarely used in automotive applications. Deep learning methods can be used to improve noise estimation [129] and peak classification [128] in CFAR. Clustering is the most important stage in the radar detection pipeline, especially for the next-generation high-resolution radar [130]. DBSCAN is favoured for several reasons: it does not require a pre-specified number of clusters; it fits arbitrary shapes; it runs fast [131]. Some works improved DBSCAN by explicitly considering the characteristics of radar point clouds. Grid-based DBSCAN [132] suggests clustering radar points in an RA grid map to avoid the range-dependent resolution variations in Cartesian coordinates. Multi-stage clustering [133] proposes a coarse-to-fine two-stage framework to alleviate the negative impact of clutter. It applies a second cluster merging based on the velocity and spatial trajectory of clusters estimated from the first stage. With the improvement of automotive radar resolution, radar target classification has become a hot research topic. For moving objects, the micro-Doppler velocity of moving components such as wheels and arms can be useful for classification. To better observe these micro-motions, short-time Fourier transform (STFT) is applied to extract Doppler spectrograms. Different types of VRUs can be classified according to their micro-Doppler signatures [134; 135]. For static objects, [PERSON] et al. [136] suggest the use of statistical RCS and time-domain RCS as useful features for classification of vehicles and pedestrians. Some researchers work on exploiting a large number of features for better classification. [PERSON] et al. [137] considered a large set of 98 features and used the heuristic-guided backward elimination for feature selection. They found that range and Doppler features are most important for classification, while angle and shape features are usually discarded, probably because of the low angular resolution. [PERSON] et al. [138] compared the performance of random forest and LSTM for radar classification. Experiments showed that LSTM with an input of eight-frame sequences performs slightly better than random forests, especially in the classification of classes with a similar shape, such as pedestrians and pedestrian groups, and for false alarms. However, LSTM is more sensitive to the amount of training examples. To cope with class imbalance in radar datasets, [PERSON] et al. [139] suggest using classifier binarisation techniques, which can be divided into two variants: one-vs.-all (OVA) and one-vs.-one (OVO).