# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
## 7 Challenges
### Ghost Object Detection  
Multi-path is a phenomenon in the physics of waves where a wave from a target travels to a detector through two or more paths. Because of multi-path propagation, the radar receives both direct reflections and indirect time-shifted reflections of targets. If the target reflections and the multi-path reflections occupy the same RD cell, the performance of DOA estimation is affected. Otherwise, if they occupy different cells, it can produce ghost targets in multi-path directions. In the latter case, since ghost detection has similar dynamics to the real target, it is difficult to eliminate them in the traditional detection pipeline. The multi-path effect can be classified into three types [196]. The first type is the reflection between ego-vehicle and targets. Therefore, the distance and velocity of clutter should be multiple times the true measurement. The second type is the underbody reflection. It usually happens under the truck, resulting in points with longer distances. This see-through effect is sometimes beneficial, since occluded vehicles can be detected. The third type is mirrored ghost detections caused by the reflective surface. Because of the large wavelength of automotive 77 GHz radar, many flat facilities, such as concrete walls, guardrails, and noise cancellation walls, can be regarded as reflective surfaces. As shown in Figure 12, this kind of multi-path effect can be further categorised into type 1 and type 2 depending on whether the final reflection occurs on the target or the surface [197]. The number of reflections is referred to as the order of the multi path. Usually, only orders below 3 need to be considered, since higher-order reflections return little energy due to signal diffusion. Figure 12: Multi-path effect. The solid orange box is the real object. Dotted boxes are ghost objects caused by multi-path propagation. A high-quality dataset is necessary for the performance evaluation of ghost detection. However, the labelling of ghost objects is a difficult task and requires expert knowledge. [PERSON] et al. (2018) proposed a method to automatically identify radar ghost objects by comparing with the LiDAR point cloud. However, LiDAR measurements are not perfect. They have their own inherent defects, such as sparsity, limited range, and holes with no reflections. Therefore, using LiDAR as the ground truth could be sometimes problematic. In the Radar Ghost dataset ([PERSON] et al., 2017), ghost objects are manually annotated with the help of a helper tool. This tool can automatically calculate the locations of potential ghosts based on real objects and reflective surfaces. As a result, four types of multi-path effects are annotated, including type-1 second-order bounces, type-2 second-order bounces, type-2 third-order bounces, and the other higher-order bounces. In addition, they also provide a synthetic dataset by overlaying objects from different frames within the same scene. Unlike clutter, ghost objects cannot be filtered by temporal tracking because they have the same kinematic properties as real targets. Instead, they can be detected by geometric methods ([PERSON] et al., 2016; [PERSON] et al., 2017). With a radar ghost dataset, it is also possible to train a neural network for ghost detection, such as PointNet-based methods ([PERSON] et al., 2017) and PointNet++-based methods ([PERSON] et al., 2017; [PERSON] et al., 2017). Because of the signal diffusion, the higher-order reflections can be safely ignored. Thus, ghost objects usually occur in a ring-shaped region with a similar distance as the real target. Accordingly, [PERSON] et al. (2017) designed a ring grouping to replace the multi-scale grouping in PointNet++. The scene structure and relationship between detections are important cues to identify ghost objects. [PERSON] et al. (2018) suggest the occupancy grid map can provide the information of the scene structure. Therefore, they used the occupancy grid map and the list of moving objects as inputs to FCN, to predict a heat map of moving ghost detections.