统一评测自动驾驶占位预测与预报，提升模型评估可靠性
UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving
- 整合真实数据与高保真仿真数据，构建统一评测基准
- 引入无需真实标签的新评估指标，更全面衡量占位质量
- 适合自动驾驶感知与规划研究者使用
我们提出UniOcc，一个全面、统一的基准与工具包，用于自动驾驶中的占位预测（基于历史信息预测未来占位）与占位预报（从图像预测当前帧占位）。UniOcc融合了多个真实世界数据集（nuScenes、Waymo）与高保真驾驶模拟器（CARLA、OpenCOOD）的数据，提供2D/3D占位标签，并标注了创新的逐体素运动流。不同于以往依赖次优伪标签的评估方式，UniOcc引入新型评估指标，不依赖真实标签，可对占位质量的额外维度进行稳健评估。通过在主流模型上的广泛实验，我们证明大规模、多样化训练数据及显式运动信息能显著提升占位预测与预报性能。数据与代码已公开于https://uniocc.github.io/。
原文摘要 · Abstract (English)
We introduce UniOcc, a comprehensive, unified benchmark and toolkit for occupancy forecasting (i.e., predicting future occupancies based on historical information) and occupancy prediction (i.e., predicting current-frame occupancy from camera images. UniOcc unifies the data from multiple real-world datasets (i.e., nuScenes, Waymo) and high-fidelity driving simulators (i.e., CARLA, OpenCOOD), providing 2D/3D occupancy labels and annotating innovative per-voxel flows. Unlike existing studies that rely on suboptimal pseudo labels for evaluation, UniOcc incorporates novel evaluation metrics that do not depend on ground-truth labels, enabling robust assessment on 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. Our data and code are available at https://uniocc.github.io/.
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