# Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation
## 5 Discussion
### Data  
This section discusses the particularities of radar data and the nuScenes data set. Data sparsity is a general difficulty when dealing with radar data. Though [21] shows favorable results on a private data set, it becomes clear that even with high-quality-high-resolution input data, the challenge of general semantic segmentation on automotive radar data is far from solved. In 2019, a review [30] stated that they expect more radar data sets to be released in the future. However, at the start of 2021, only nuScenes [2], Astyx HiRes2019 [31] and DENSE [32] data sets provide point level radar data with 3D bounding box annotations. The HiRes2019 data consists of only 546 frames, which makes training of a learning model on this data impractical. The DENSE data set uses an outdated radar model with even greater sparsity than the nuScenes data set while providing fewer labels. The nuScenes data set consists of 40,000 annotated frames of a production radar. It is the only data set that contains the consecutive samples needed for training time-dependent models. In conclusion, this is the only reasonable public data set for deep-learning applications of radar data. Despite its unique position, nuScenes has some major drawbacks regarding its radar data. [PERSON] [33] compared the performance of their models on high-resolution and production radar data. They state that the nuScenes radar density is far worse than that of their production radar. A comparison of the specifications of the radars of [2,33] and shows that they use a comparable or even the same radar model as nuScenes. The huge differences in data density between the two data sets cannot be explained by the radar  
Figure 10: Even in the overfitting scenario, misclassifications are present when static objects are segmented. NuScenes sample token: 205c60f1248343a09 bf4c6b6c05d8525. hardware alone. We calculated that over 40% of the annotated vehicles are not covered by any radar detection when considering three radar sweeps for a nuScenes sample. For vehicles labeled as moving, the same holds true for over 24% of the labeled bounding boxes. Consequently, bounding boxes of objects detected by the radar sensor, include fewer point returns than expected from this sensor type. In the following, we look at some factors that decrease the suitability of the nuScenes data set for the development of radar algorithms and conclude with the aspects that a radar data set should consider. The nuScenes data set includes information about the number of radar points per annotation. However, this number does not distinguish between its five different radar modules. The labels include information about whether or not a vehicle is moving. Although this information is correct for many cases, by manually examining random samples of the data set, we encountered a significant number of annotations where moving objects were labeled as static objects and vice versa. This not only negatively impacts the training, but also impacts the evaluation scores of moving object detection, which is the main application of radar data. Figure 11 shows the ground-truth classes from a scene of a bus moving towards the sensor vehicle. Even though the bus is approaching at a high speed and is close to the sensor vehicle, it is wrongly labeled as a non-moving vehicle. The radar data is limited to 125 detections per sweep by the sensor interface. Additional detection filters are applied in the standard configuration of the data set API of nuScenes. The hardware limitation to 125 detections is enforced by cutting off detection signals at an \(RCS\) value of less than \(-5\,\mathrm{dB}\,\mathrm{m}^{2}\). The works of [PERSON] and [PERSON] [34; 35] measure the expected \(RCS\) value of a pedestrian at a distance of less than \(10\,\mathrm{m}\) to be less than \(-8\,\mathrm{dB}\,\mathrm{m}^{2}\) for 76 GHz and 79 GHz radar sensors, respectively.