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.

翻译：基于三维占据的感知流程通过捕捉详细的场景描述并在不同物体类别和形状上展现出强大的泛化能力，显著推动了自动驾驶技术的发展。当前方法主要依赖激光雷达或摄像头输入进行三维占据预测，但这些方法易受恶劣天气条件影响，限制了自动驾驶车辆的全天候部署。为提升感知鲁棒性，我们利用汽车雷达的最新进展，提出了一种利用4D成像雷达传感器进行三维占据预测的新方法。我们的方法RadarOcc通过直接处理4D雷达张量规避了稀疏雷达点云的限制，从而保留了必要的场景细节。RadarOcc创新性地应对了4D雷达数据量大且噪声高的挑战，采用多普勒箱描述符、旁瓣感知空间稀疏化以及距离维度自注意力机制。为最小化直接坐标变换带来的插值误差，我们还设计了基于球面的特征编码以及球面到笛卡尔坐标的特征聚合方法。我们在公开的K-Radar数据集上对基于不同模态的多种基线方法进行了基准测试。结果表明，RadarOcc在基于雷达的三维占据预测中达到了最先进水平，并且在与基于激光雷达或摄像头的方法比较时也展现出有竞争力的结果。此外，我们通过定性实验展示了4D雷达在恶劣天气条件下的优越性能，并通过消融研究探讨了关键流程组件的影响。