Evaluation of scenario-based automotive radar testing in virtual environment using real driving data

Safety assurance of intended functionality through rigorous testing is a key to large-scale homologation and deployment of automated driving. It is therefore imperative to transfer real world tests into efficient and quantifiable virtual testing procedures and environments without compromising reliability. In earlier work, we presented a fully operational overthe-air vehicle-in-the-loop test system for automotive millimeterwave radar, where we generated a virtual electromagnetic environment with physically realistic radar target echoes. We evaluated the performance of the implemented test system with an exemplary scenario parameterised with analytically predefined vehicle manoeuvres. In this work, we significantly proceed with the performance evaluation through re-simulation of scenarios based on real driving data and traffic manoeuvres. We have measured a standard Euro-NCAP scenario, namely, the Car-to-Pedestrian Longitudinal Adult on a proving ground and re-simulated the ground truth parameters in the test bed. We compare the consistency of the test results at several data abstraction levels using parameter trajectories. Additionally, we introduce and evaluate quality metrics such as difference and root mean square error. For a driving scenario approximately 20 seconds long, we achieved promisingly low root mean square errors in range, azimuth and RCS of 0.3 m, 0.5 degrees and 2 dB, respectively.