Making Automotive Radar Sensor Validation Measurements Comparable

Abstract

Virtual validation of radar sensor models is becoming increasingly important for the safety validation of Light Detection and Rangings (lidars). Therefore, methods for quantitative comparison of radar measurements in the context of model validation need to be developed. This paper presents a novel methodology for accessing and quantifying validation measurements of radar sensor models. This method uses Light Detection and Rangings (lidars) and the so-called Double Validation Metric (DVM) to effectively quantify deviations between distributions. By applying this metric, the study measures the reproducibility and repeatability of radar sensor measurements. Different interfaces and different levels of detail are investigated. By comparing the radar signals from real-world experiments where different objects are present, valuable insights are gained into the performance of the sensor. In particular, the research extends to assessing the impact of varying rain intensities on the measurement results, providing a comprehensive understanding of the sensor’s behavior under these conditions. This holistic approach significantly advances the evaluation of radar sensor capabilities and enables the quantification of the maximum required quality of radar simulation models.