RoadOcc Learns When to Persist, Transport, or Refresh Memory for Roadside Occupancy Prediction

2026-09-23

AI Summary

论文提出RoadOcc模型，通过学习何时使用固定坐标历史、速度定位历史或当前证据来解决路边占用预测中移动证据重用导致的预测错误问题。

Abstract

Fixed roadside cameras repeatedly observe a stable scene overlaid by sparse moving traffic. Temporal memory can recover weak observations, but reusing moving evidence at stale locations can corrupt occupancy predictions. Motion compensation addresses displacement, while reliance on the resulting history remains a separate learning problem. We introduce RoadOcc, which learns soft routing among fixed-coordinate history (\emph{Persist}), velocity-addressed history (\emph{Transport}), and current evidence (\emph{Refresh}). Motion state and class-consistent historical support supervise these source choices. Dynamic-aware cross-attention (DCA) updates candidate locations, multi-scale voxel velocity estimation (VVE) constructs transport addresses from multi-scale current--history correspondence, and velocity-guided dynamic sparse fusion (VDSF) combines routed evidence under fixed sparse-token budgets. On InfraOcc, RoadOcc reaches 65.29 mIoU and 32.37 dynamic mIoU, gains of 4.44 and 4.71 over STCOcc. Controlled address experiments show that VVE raises dynamic mIoU by 0.87 over fixed-coordinate reading. Across three seeds, supervised P/T/R adds 1.40 dynamic points over motion-corrected retrieval, while removing Refresh costs 0.32 points. Results from two transfer models, Occ3D-nuScenes, and longer intervals provide additional support. Code will be released.

Problem

• roadside occupancy prediction
• temporal memory
• motion compensation
• sparse moving traffic
• occupancy predictions

Innovation

• soft routing
• dynamic-aware cross-attention (DCA)
• multi-scale voxel velocity estimation (VVE)
• velocity-guided dynamic sparse fusion (VDSF)