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  • Autor
    • Magosi, Zoltan Ferenc
    • Wellershaus, Christoph
    • Tihanyi, Viktor Roland
    • Patrick, Luley
    • Eichberger, Arno
  • TitelEvaluation Methodology for Physical Radar Perception Sensor Models Based on On-Road Measurements for the Testing and Validation of Automated Driving
  • Datei
  • DOI10.3390/en15072545
  • Erschienen inEnergies
  • Band15
  • Erscheinungsjahr2022
  • Heft7
  • LicenceCC BY 4.0
  • ISSN1996-1073
  • ZugriffsrechteCC-BY
  • Download Statistik106
  • Peer ReviewJa
  • AbstractIn recent years, verification and validation processes of automated driving systems have been increasingly moved to virtual simulation, as this allows for rapid prototyping and the use of a multitude of testing scenarios compared to on-road testing. However, in order to support future approval procedures for automated driving functions with virtual simulations, the models used for this purpose must be sufficiently accurate to be able to test the driving functions implemented in the complete vehicle model. In recent years, the modelling of environment sensor technology has gained particular interest, since it can be used to validate the object detection and fusion algorithms in Model- in-the-Loop testing. In this paper, a practical process is developed to enable a systematic evaluation for perception–sensor models on a low-level data basis. The validation framework includes, first, the execution of test drive runs on a closed highway; secondly, the re-simulation of these test drives in a precise digital twin; and thirdly, the comparison of measured and simulated perception sensor output with statistical metrics. To demonstrate the practical feasibility, a commercial radar-sensor model (the ray-tracing based RSI radar model from IPG) was validated using a real radar sensor (ARS-308 radar sensor from Continental). The simulation was set up in the simulation environment IPG CarMaker® 8.1.1, and the evaluation was then performed using the software package Mathworks MATLAB®. Real and virtual sensor output data on a low-level data basis were used, which thus enables the benchmark. We developed metrics for the evaluation, and these were quantified using statistical analysis.