# Making Automotive Radar Sensor Validation Measurements Comparable
## 4 Evaluation
### Whole Radar Cuboid
#### 4.2.3 Comparison of Volvo XC90 and Mercedes Vito  
Finally, the Volvo XC90 is compared with a Mercedes Vito. The effects of different object geometries and sizes are analyzed regarding their effects on validation measurement campaigns. The relative deviations are comparable to the previous findings. The deviations are higher at the detection level than those at the radar cuboid level, as seen in Figure 20. However, there is a difference in the deviations of all detections compared with the deviations for the detections in the ROI, which are significantly higher. Thus, also for this comparison, an evaluation is performed on the radar cuboid level with a relative deviation for the number of data points below \(1\,\mathrm{\char 37}\) and the whole detections. Figure 19: Satellite image fused with \(|d_{\text{bias}}|\) and \(d_{\text{CAVM}}\) coded in colors for each range azimuth bin. The object under examination is a Volvo XC90 with different yaw angles. The red circle visualizes the center of the vehicle’s bounding box. Figure 20: Plot of the relative deviation for the number of the data points of all measurements of the two experiments (XC90 and Vito). The WRC, WD, the ROI for the ROI RC and the ROI D, as well as EB RC are visualized. Experiment 1 is denoted as and experiment 2 as the gradations of the respective color delineate the different measurements. Whole Radar Cuboid  
Furthermore, the entire radar cuboid is analyzed. From Figure 21, it can be seen that the object has an influence on all bins, as in the comparison of CCR and XC90. This can be seen from the higher power distribution of the EDFs in the Mercedes Vito. The shape of the object (hatchback compared with vertical rear) therefore has a direct influence on the measured environment. In addition to the rear, the underbody can also influence the measurement result. The overall effect can be seen in \(d_{\mathrm{bias}}\), which shows a relatively high deviation with a low spread. For both metrics, it can be seen that the repeatability of the measurements of the different objects is very high, as the scatter is low. Whole Detections  
Figure 22 shows the EDFs for the distance, azimuth, and RCS, as well as a corresponding box plot for them. A look at the EDFs for the distance shows that these are similar to each other, but a deviation is still visible. In the distance of the vehicles at 29.65 m, a small step and thus an accumulation of detections can be seen. All detections extend over a maximum distance of 150 m. In the vicinity of the sensor, these detections result from reflections of the road surface and, with increasing distance, from the transition between road and vegetation. Whereby it can be seen from the course that at distances over 115 m, fewer detections can be recognized. Above 160 m, only isolated detections with a low RCS are present, which argues for purely stochastic causes. The median values for \(d_{\mathrm{bias}}\) and \(d_{\mathrm{CAVM}}\) are similar when considered as absolute values. However, the scatter of \(d_{\mathrm{bias}}\) at 5 m is significantly larger than the scatter of \(d_{\mathrm{CAVM}}\) at 1 m.  
Looking at the EDFs for the azimuth, it is noticeable that the EDFs overlap better for angles below 0 than for angles above 0. Furthermore, the steps for the accumulation of detections by the two vehicles are different, so the step for the XC90 is at 1.5\({}^{\circ}\) and not as significant as the one for the Vito at 0. Basically, however, there is an accumulation of detections around 0 for both vehicles, which corresponds to the vehicle position and reflections from the road surface near the sensor. For angles below \(-5^{\circ}\) and above \(5^{\circ}\), detections can be seen by the transition from the road surface to vegetation. The plateaus between \(-5^{\circ}\) and 0, and between 0 and \(5^{\circ}\), are due to the road surface. The median values of \(d_{\mathrm{bias}}\) and \(d_{\mathrm{CAVM}}\) are similar for an absolute observation.