Automotive radar simulator creates accurate signals for moving scenes
AuRaSim: A Ray-Tracing Based Automotive FMCW Radar Simulator for Dynamic Scenarios
Robotics
Summary
Automotive radars help cars detect objects by sending and receiving radio waves, but simulating these signals is hard because they involve complex wave behaviors and moving targets. The authors created AuRaSim, a tool that uses 3D scene modeling and advanced ray-tracing to simulate radar signals realistically in dynamic scenes. This simulator matches real radar measurements closely, making it useful to develop and test radar technologies without costly physical tests. Their work combines graphics tools with radar physics to generate detailed synthetic radar data.
What this means in practice
- •For automotive radar engineers: Simulate detailed radar signals for developing and testing radar algorithms on varied driving scenarios without physical prototypes.
- •For robotics system developers: Generate synthetic radar data for robots to improve environment sensing and navigation in dynamic conditions.
Authors
Zhaoze Wang, Changxu Zhang, Tai Fei, Christopher Grimm, Yi Jin, Claas Tebruegge, Markus Gardill
Abstract
Modern simulators can generate synthetic camera and LiDAR data with accurate annotations for downstream tasks. In contrast, automotive radar simulation remains less mature because it requires not only a 3D engine to model dynamic objects in the scenes but also electromagnetic material properties, antenna configurations, wave propagation mechanisms, and coherent signal synthesis. Existing frameworks are either proprietary or overly simplified, limiting the scalability of radar raw measurements. This paper presents AuRaSim, a ray-tracing-based automotive frequency-modulated continuous wave (FMCW) radar simulation framework for dynamic scenarios. Rather than implementing all components from scratch, AuRaSim builds on Blender for efficient dynamic-scene construction and Sionna RT for graphics processing unit (GPU) accelerated electromagnetic ray tracing, while introducing radar-specific adaptations for path-resolved propagation with complex coefficients, motion-aware Doppler modeling, and subsequently coherent multi-channel analog-to-digital converter (ADC) cube synthesis based on waveform configurations. Quantitative comparison between simulated and measured range-Doppler maps in digital-twin scenarios achieves a root-mean-square error (RMSE) of 0.147 and a peak signal-to-noise ratio (PSNR) of 16.93 dB, demonstrating the accuracy and fidelity of the synthetic signal. AuRaSim therefore provides a complete and extensible platform for reproducible automotive radar simulation and research. Project homepage: https://wangzhaoze.github.io/aurasim.