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：基于三维占据栅格的感知框架通过捕获精细的场景描述，并在多种物体类别与形态上展现出强大的泛化能力，显著推动了自动驾驶的发展。当前方法主要依赖激光雷达或相机输入进行三维占据栅格预测。这些方法易受恶劣天气条件影响，限制了自动驾驶汽车的全天候部署。为提升感知的鲁棒性，我们利用汽车雷达领域的最新进展，提出了一种利用4D成像雷达传感器进行三维占据栅格预测的新方法。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局限性，从而保留了关键的场景细节。RadarOcc创新性地通过采用多普勒通道描述符、旁瓣感知的空间稀疏化以及距离维自注意力机制，解决了海量且含噪的4D雷达数据带来的挑战。为了最小化直接坐标变换带来的插值误差，我们还设计了一种基于球坐标的特征编码，随后进行球坐标到笛卡尔坐标的特征聚合。我们在公开的K-Radar数据集上，对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栅格预测中达到了最先进的性能，即使与基于激光雷达或相机的方法相比也展现出有前景的结果。此外，我们提供了4D雷达在恶劣天气条件下性能优越的定性证据，并通过消融研究探讨了关键流程组件的影响。