Evaluation of Automotive Radar Simulation Tools for Adverse Weather Conditions: A Comparative Analysis of Real Measurement, HFSS and Monte-Carlo GO Methods

Weather-related crashes continue to be a significant cause of fatalities and injuries each year in the automotive field, with adverse weather conditions affecting such properties as the radar detection range, vehicle speed, and detection accuracy. Advanced driver assistance systems (ADAS) rely on clear and accurate environmental perception; automotive radar sensors play a crucial role in these systems, especially in challenging weather. Simulation has emerged as a valuable tool to validate driving functions and sensors in a virtual environment, providing the ability to simulate critical scenarios and adjust weather conditions reproducibly. However, the simulation tool requires validation against real-world scenarios to ensure reliability. Creating a near-to-reality radar model is complex because of the various factors influencing electromagnetic wave propagation, such as surface roughness, temperature, material composition, and thickness. These factors contribute to the complexity of multi-path propagation in radar simulations. This study conducted real measurements and simulations with a high-frequency structure simulator (HFSS) and a Monte-Carlo geometrical optics (Monte-Carlo GO) implementation on various dry and wet samples. The results were calibrated and compared, and they revealed that HFSS moderately outperformed the Monte-Carlo GO method's overall accuracy; nevertheless, the latter requires less computing capability and fewer triangles compared with HFSS; therefore, it may still be a vital choice for specific applications that require a lot of simulation data. Furthermore, the results showed that some materials change reflectivity because of humidity, and they must be adjusted in the simulations.