Introducing the double validation metric for radar sensor models

In automated vehicles, environment perception is performed by various sensor types, such as cameras, radars, lidars, and ultrasonics. Simulation models of these sensors, as required in virtual validation methods, are available in various degrees of detail. However, proving the validity of such models is a subject of research. New metrics and methods for credibility assessment of simulation are needed to standardize the validation process in the future. The so-called double validation metric (DVM) has shown advantages and allows an intuitive interpretability of the validation results. The DVM has so far only been applied to lidar sensor models. In this paper, an extension to the DVM is introduced, which is called the DVM Map. A static measurement scenario is conducted in reality and transferred into simulation. The novel method is demonstrated on the obtained real and simulated radar sensor data. In this simple scenario special focus is put on the position accuracy of GNSS reference sensors. Therefore, their impact on the result of sensor model validation is discussed. The paper shows that the method provides a more detailed and accurate validation in comparison to the state of the art of a radar simulation, revealing previously undetected simulation errors. Errors due to the environment model, signal propagation, and signal processing are separated and satellite imagery is used for intuitive visualization of the results. This method is a complementary tool to existing validation techniques to improve the interpretability and judging the trustworthiness of radar simulations.