# Making Automotive Radar Sensor Validation Measurements Comparable
## 5 Conclusions  
This paper presents a methodology to make radar measurements comparable in the context of the validation of radar sensor models. The two metrics \(d_{\text{bias}}\) and \(d_{\text{CAVM}}\) are used to evaluate the similarity of the measured distribution functions. For this purpose, the number of data points is first analyzed in order to be able to make any comparisons using DVM. The analysis of the level of the radar cuboid simplifies the explanation of the different effects, especially in combination with the representation of the range azimuth bins using a satellite image. Finally, the research questions raised in Section 1.2 are answered, and the applicability of the findings to other radar sensors remains to be shown. _Research Question 1_: The analysis of the measurements with a CCR shows in direct comparison that there is good repeatability of the measurements, especially near an object in radar measurements. Only vegetation and the road surface in close proximity are characterized by larger deviations in \(d_{\text{bias}}\) and \(d_{\text{CAVM}}\). Furthermore, the dismantling and repositioning shows that smaller influences occur. Nevertheless, the measurements can be performed reproducibly with the right tools (e.g., ground markers). _Research Question 2_: Extended objects can be analyzed in detail with the presented methodology, and deviations can be quantified accordingly. However, it can be seen that  
Figure 27: 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 in rainy and dry conditions. The red circle visualizes the center of the vehicle bounding box. objects have a direct impact on the overall FoV of the sensor, depending on the orientation and type of object. _Research Question 3_: Based on our methodology, environmental influences are comparable, and their influence is quantitatively detectable. In particular, the close-range of the radar sensor shows larger deviations due to rain on average. With increasing distance, rain leads to larger scattering, whereas the effect decreases again after a certain distance and the rain merges with the background noise of the sensor. Therefore, for validation measurements of radar sensors, the following aspects can be derived from the results:  
* Measurements of static vehicles and point targets are well reproducible on radar cuboid and detection levels. * Vegetation and the close-range of the radar sensor lead to large deviations, especially on the detection level, which is why this interface complicates the validation of radar sensor models with the methodology described here. * The presented methodology can be used to quantify deviations between validation measurements and identify measurement outliers. * Using ideal laboratory measurements, effects in real measurements can be identified based on the deviations in \(d_{\text{bias}}\) and \(d_{\text{CAVM}}\), and their influence can also be quantified (e.g., multipath propagation and interference). * With the presented methodology, a maximum achievable quality of simulation models can be determined with a quantitative value in the unit of the measurand with respect to the repeatability and reproducibility of experiments. The previous considerations refer only to static scenarios. In particular, dynamic tests are of great importance for the radar sensor due to the Doppler effect and the associated direct velocity measurement. Therefore, in the next step, the approach and analysis will be transferred to dynamic scenarios. So far, only the usefulness of the methodology for the sensor used has been demonstrated. In the next step, the knowledge gained has to be proven with other radar sensors. Conceptualization, L.E. ; methodology, L.E. ; software, L.E. and J.P.S. ; validation, L.E. and J.P.S. ; formal analysis, L.E. ; investigation, L.E. and J.P.S. ; data curation, L.E. ; writing--original draft preparation, L.E. and J.P.S. ; writing--review and editing, L.E., J.P.S. and S.P.