FORTE：动态环境中面向时空风险感知规划的占用预测

中文摘要 AI 辅助

在动态环境中进行安全导航需要预测未来的环境状态，以考虑时空风险，特别是碰撞可能发生的时间和地点。为此，占用网格图（OGM）预测已被广泛采用作为一种有效的方法。然而，现有的基于OGM的导航方法往往难以实现准确且高效的预测，并且在规划过程中未能充分利用预测OGM中的时间信息。为了应对这些挑战，我们提出了FORTE，一个导航框架，它从时空占用重叠和占用方向性的角度直接利用预测占用的时空演化。基于这些特性，FORTE评估多条拓扑不同的路径，并在无需显式目标检测或跟踪的情况下选择合适的路径。为了支持在线规划，我们构建了一个基于潜在扩散模型的OGM预测器，它以非自回归方式生成整个预测范围，同时通过时间移位模块保持时间一致性。广泛的评估表明，FORTE优于最先进的基线。在预测方面，FORTE实现了高达215.3%的IoU提升和5.24倍的推理速度提升；在导航方面，它实现了高达3.5倍的成功率提升。

英文摘要

Safe navigation in dynamic environments requires anticipating future environmental states to account for spatiotemporal risks, specifically when and where collisions may occur. To this end, occupancy grid map (OGM) prediction has been widely adopted as an effective approach. However, existing OGM-based navigation methods often struggle to achieve accurate and efficient forecasting and fail to fully exploit the temporal information in predicted OGMs during planning. To address these challenges, we propose FORTE, a navigation framework that directly exploits the spatiotemporal evolution of predicted occupancy from the perspectives of spatiotemporal occupancy overlap and occupancy directivity. Based on these properties, FORTE evaluates multiple topology-distinct paths and selects the suitable one without explicit object detection or tracking. To support online planning, we formulate a latent diffusion model-based OGM predictor that generates the entire forecast horizon in a non-autoregressive manner while maintaining temporal consistency through temporal shift modules. Extensive evaluations demonstrate that FORTE outperforms state-of-the-art baselines. For prediction, FORTE achieves up to 215.3% higher IoU and 5.24x faster inference; for navigation, it yields up to a 3.5x higher success rate.

发表机构

• Ewha Womans University（梨花女子大学）

机构由 AI 辅助整理，请以论文原文为准。