Toss, Henrik (RISE Research Institutes of Sweden), Karlsson, Kristian (RISE Research Institutes of Sweden), Duthon, Pierre (Cerema), Poledna, Yuri (Technische Hochschule Ingolstadt)

Rain Reflectivity Distribution and Detectability in Automotive Radar

Scheduled for presentation during the Regular Session "S28a-Multi-Sensor Fusion and Perception for Robust Autonomous Driving" (TH-LM-T28), Thursday, November 20, 2025, 10:50−11:10, Stradbroke

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

In the development of automated driving and advanced driver assistance systems, it is essential that the underlying algorithms are thoroughly tested. Simulation is a valuable tool for evaluation of such algorithms, especially in safety-critical scenarios. Simulations can also provide the opportunity to generate and repeat rare, yet important, test cases and can be applied at various stages of development. For simulation to serve as a reliable substitute for real-world testing, it must offer high fidelity. Therefore, advanced models for both sensors, and the sensed environment are required. A thorough understanding of adverse weather conditions within the operational design domain is consequently crucial to maintain fidelity. This work develops statistical models of radar backscatter from rain for the perception by automotive radars which can be implemented in a simulated environment. To demonstrate the method’s validity, both simulation and real-world data are used to assess the fidelity of rain radar signal predicted or generated using the model. The work presented here offers a state-of-the-art understanding of how rain (based on drop size distributions) affects the radar model and the background signal to be expected across the radar field of view, including the velocity dimension. With the use of such models in simulation it is possible to anticipate how the radar will react in the real-world under similar conditions. Future work includes integrating these findings into a radar-in-the-loop environment and developing additional adverse weather models for radar.