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.
View on arXiv@article{wang2025_2503.24381,
title={ UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving },
author={ Yuping Wang and Xiangyu Huang and Xiaokang Sun and Mingxuan Yan and Shuo Xing and Zhengzhong Tu and Jiachen Li },
journal={arXiv preprint arXiv:2503.24381},
year={ 2025 }
}