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.

3D 基于占据的感知管道通过捕捉详细的场景描述并且展现出在各种对象类别和形状上的强泛化能力，显著推进了自动驾驶。为了提高感知的稳健性，我们利用车载雷达的最新进展，引入一种利用 4D 成像雷达传感器进行 3D 占据预测的新方法 RadarOcc。该方法通过直接处理 4D 雷达张量而绕过了稀疏雷达点云的限制，从而保留了关键场景细节。雷达的 4D 数据的体量庞大且具有噪声，RadarOcc 通过采用多普勒频率描述符、侧瓣感知空间稀疏化和范围自注意机制，创新地解决了相关挑战。为了最小化与直接坐标转换相关的插值误差，我们还设计了基于球面的特征编码，随后进行球面到笛卡尔特征聚合。我们在公开的 K-Radar 数据集上使用不同模态的各种基准方法进行基准测试，结果表明 RadarOcc 在基于雷达的 3D 占据预测方面具有最先进的性能，即使与基于 LiDAR 或摄像头的方法相比，结果仍然令人满意。此外，我们还提供了 4D 雷达在恶劣天气条件下卓越性能的定性证据，并通过消融研究探讨了关键管道组件的影响。