外观
论文arXiv 2610.1768815 天前
OccWAM：具有联合场景到自车流匹配的占用世界-动作模型
原文链接：OccWAM: Occupancy World-Action Models with Joint Scene-to-Ego Flow Matching（arXiv 2610.17688 · 发布日期：2026-10-13）
OccWAM: Occupancy World-Action Models with Joint Scene-to-Ego Flow Matching
OccWAM用场景条件流匹配联合生成未来占用与自车轨迹，并在Bench2Drive和nuScenes验证自动驾驶规划与预测。
核心要点
- 现有占用感知规划以层级或并行多任务方式耦合预测与动作，限制二者相互交互。
- OccWAM在场景表征条件下，通过流匹配联合生成稠密未来占用和连续自车轨迹。
- 两阶段scene-to-ego流程先预测场景中心占用与绝对轨迹，再重索引到自车坐标细化。
- 实验在Bench2Drive和nuScenes上报告闭环驾驶、开环规划和未来占用预测达到SOTA。
验证范围与局限：验证范围为Bench2Drive和nuScenes上的闭环驾驶、开环规划与未来占用预测。
英文摘要 (Abstract)
Occupancy world models provide a structured, metric representation of future driving scenes, making them a promising basis for end-to-end autonomous driving. However, existing occupancy-aware planners typically couple occupancy prediction and action generation through either hierarchical coupling or parallel multi-task learning. Hierarchical methods use occupancy either as a fully predicted future preceding action generation or as an action-conditioned rollout for trajectory evaluation, whereas parallel multi-task methods predict occupancy alongside planning through a separate branch or auxiliary objective. These interfaces retain either an explicit generation order or a role asymmetry, limiting reciprocal interaction between world prediction and action generation during trajectory formation. We propose OccWAM, an occupancy-native world-action model that jointly generates dense future occupancy and continuous ego trajectories via flow matching conditioned on scene representations. Building on this joint formulation, we introduce scene-to-ego world-action flow matching, a two-stage formulation. The first stage predicts scene-centric occupancy together with an absolute ego trajectory, providing a stable representation of future scene evolution. The same predicted world is then re-indexed into the future ego frames induced by the initial trajectory, where ego motion is represented through adjacent pose increments and a second joint flow refines ego-centric occupancy and motion for action-aligned planning. Unlike video-based world-action models, OccWAM preserves explicit semantic geometry for direct planning reasoning without pixel synthesis. Extensive experiments on Bench2Drive and nuScenes demonstrate state-of-the-art performance in closed-loop driving, open-loop planning, and future occupancy prediction.
作者：Qihang Huang, Wenhui Huang, Collister Chua, Shiyu Fang, Zhan Chen, Songyan Zhang, Zhidong Wang, Daiying Tian, Chen Lv
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