We introduce UniOcc, a comprehensive, unified benchmark for occupancy forecasting (i.e., predicting future occupancies based on historical information) and current-frame occupancy prediction from camera images. UniOcc unifies data from multiple real-world datasets (i.e., nuScenes, Waymo) and high-fidelity driving simulators (i.e., CARLA, OpenCOOD), which provides 2D/3D occupancy labels with per-voxel flow annotations and support for cooperative autonomous driving. In terms of evaluation, unlike existing studies that rely on suboptimal pseudo labels for evaluation, UniOcc incorporates novel metrics that do not depend on ground-truth occupancy, enabling robust assessment of additional aspects of occupancy quality. Through extensive experiments on state-of-the-art models, we demonstrate that large-scale, diverse training data and explicit flow information significantly enhance occupancy prediction and forecasting performance.

本文提出了UniOcc，一个综合性的统一基准，用于占用预测与当前帧占用预测，解决了现有研究依赖于次优伪标签进行评估的问题。UniOcc引入了不依赖真实占用的评估指标，通过大规模多样化训练数据和明确的流信息，显著提升了占用预测和预报的性能，推动了自主驾驶领域的研究和应用。 

UniOcc：自主驾驶中的统一占用预测与预报基准