Evaluation of scenario-based automotive radar testing in virtual environment using real driving data
S. B. J. Gowdu, P. Aust, A. Schwind
Oct 8, 2022
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2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)
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Key Takeaway Key Takeaway: Our virtual automotive radar testing system effectively simulates real driving scenarios, achieving low root mean square errors in range, azimuth, and RCS for a 20-second driving scenario.
Abstract
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 over-the-air vehicle-in-the-loop test system for automotive millimeter-wave 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 pre-defined 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° and 2 dB, respectively.