  3D occupancy-based perception pipeline has significantly advanced autonomous
driving by capturing detailed scene descriptions and demonstrating strong
generalizability across various object categories and shapes. Current methods
predominantly rely on LiDAR or camera inputs for 3D occupancy prediction. These
methods are susceptible to adverse weather conditions, limiting the all-weather
deployment of self-driving cars. To improve perception robustness, we leverage
the recent advances in automotive radars and introduce a novel approach that
utilizes 4D imaging radar sensors for 3D occupancy prediction. Our method,
RadarOcc, circumvents the limitations of sparse radar point clouds by directly
processing the 4D radar tensor, thus preserving essential scene details.
RadarOcc innovatively addresses the challenges associated with the voluminous
and noisy 4D radar data by employing Doppler bins descriptors, sidelobe-aware
spatial sparsification, and range-wise self-attention mechanisms. To minimize
the interpolation errors associated with direct coordinate transformations, we
also devise a spherical-based feature encoding followed by
spherical-to-Cartesian feature aggregation. We benchmark various baseline
methods based on distinct modalities on the public K-Radar dataset. The results
demonstrate RadarOcc's state-of-the-art performance in radar-based 3D occupancy
prediction and promising results even when compared with LiDAR- or camera-based
methods. Additionally, we present qualitative evidence of the superior
performance of 4D radar in adverse weather conditions and explore the impact of
key pipeline components through ablation studies.
