# Making Automotive Radar Sensor Validation Measurements Comparable
## 4 Evaluation
### ROI Radar Cuboid  
Figure 6 shows the same structure as in the previous evaluations. However, in this case, only the bin of the radar cuboid and therefore the power where the CCR is located are analyzed. The distribution functions show a slight deviation from each other. This is also reflected in the two metrics. The deviations in \(d_{\mathrm{bias}}\) show a spread of 0.045 dB and a much smaller spread in \(d_{\mathrm{CAVM}}\). This shows that the repeatability of the experiments for the CCR is even higher in comparison to the whole radar cuboid. This evaluation is therefore suitable for validation measurements in the case of this sensor for point targets. Figure 5: EDFs of all five measurements of the CCR on the detection level. In the first row the distance distribution, in the second row the azimuth distribution and in the third row the RCS distribution with the corresponding box plot for \(d_{\mathrm{bias}}\) and \(d_{\mathrm{CAVM}}\) are shown. The aggregated number of detections is listed below the diagrams. Figure 7 visualizes the RCS distribution of the CCR detections based on an ROI. Due to the point target characteristics and the small, defined ROI, an analysis of the distance and azimuth is not discussed. The deviations in \(d_{\text{bias}}\) are 0.01 \(\text{dbm}^{2}\), and an even smaller scatter in \(d_{\text{CAVM}}\) appears. Finally, the ROI on detection level shows a high repeatability of the measurements. For point targets, it can therefore be concluded that a ROI can be used as a filter on the detection level for validation measurements. Each Range and Azimuth Bin on Radar Cuboid Level  
For a detailed analysis of the environmental influence on the repeatability of the radar cuboid, the EDFs are compared using \(d_{\text{bias}}\) and \(d_{\text{CAVM}}\) for each individual range azimuth bin. For this purpose, the respective maximum of \(|d_{\text{bias}}|\) and \(d_{\text{CAVM}}\) of the corresponding range azimuth bin are visualized with a satellite image and the radar position measured by the GNSS sensor. This makes it possible to interpret the causes of anomalies and assign them to areas in the radar FoV. The result is shown in Figure 8.  
It can be seen that for the CCR position, there are small deviations across all azimuths and \(\pm\) 2 range bins. This can be explained by the results of the ROI of the CCR. Due to the high back scattering power, stochastic effects are less pronounced. Due to the window functions used in the radar sensor, the power is smeared into the neighboring bins of the radar cuboid, resulting in the high repeatability in the previously identified range for \(d_{\text{bias}}\) and \(d_{\text{CAVM}}\). Only in front of and behind this area do deviations occur in both metrics in the center of the FoV. In the near range, the largest deviations occur for \(d_{\text{bias}}\) and \(d_{\text{CAVM}}\). These  
Figure 6: EDFs of all five measurements of the CCR on the radar cuboid level filterd by an ROI are shown in the diagram. The power distribution with the corresponding box plot for \(d_{\text{bias}}\) and \(d_{\text{CAVM}}\) is visualized on the right side. The aggregated number of bins is listed below the diagram. Figure 7: EDFs of all five measurements of the CCR on the detection level filtered by an ROI are shown in the diagram. The RCS distribution with the corresponding box plot for \(d_{\text{bias}}\) and \(d_{\text{CAVM}}\) is visualized on the right side. The aggregated number of detections of all timeframes is listed below the diagram. variations in the radar cuboid lead to the close-range detections identified in the whole detection analysis. In general, the transition from road to vegetation causes the largest deviations. Here, the deviations decrease with increasing azimuth space from the grass. The road surface, on the other hand, shows a very high repeatability of the measurements, with only minor deviations. Cracks and vegetation on the runway are possible explanations for this observation.