automotive radar engineers

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.

Abstract: This paper develops a channel model for scattering from a broad class of integrated sensing and communication (ISAC) targets-robots, humans, and automobiles-based on a unified measurement campaign at 28~GHz. Nine targets are characterized from numerous transmitter--receiver configurations per target, spanning full bistatic and aspect angles and thus covering backward to forward scattering. Repeated acquisitions are collected for each configuration to capture target motion and dynamics, yielding nearly 117\,000 acquisitions in total. Measurements are collected with a 2~GHz-wide channel sounder equipped with a 256-element phased-array antenna, followed by digital beamforming and super-resolution processing for reliable extraction of multiple paths in delay and 2D angle, with average estimation errors of 0.1~ns and 0.2$^\circ$ respectively. From these measurements, we reduce a \emph{path-specific} channel representation in which the scattering is modeled as a cluster of paths whose center is defined by the bistatic specular \emph{geometry}; the cluster shape is described \emph{stochastically}. To ensure physically grounded spatio--temporal consistency, extracted paths are mapped to incident points on the target, each with an associated radar cross section (RCS) that is compatible with the 3GPP TR~38.901 ISAC channel model, and are subsequently mapped back to path properties for channel synthesis. The resulting statistical distributions are well behaved, physically interpretable, and consistent across targets, geometries, and motion. Comparison between measured and synthesized channel realizations demonstrates close agreement in pathloss trends and fluctuation behavior. Together, these results yield a high-fidelity, measurement-derived channel representation suitable for ISAC simulation, target modeling, and system design.