[Submitted on 29 Oct 2026]
Title:UFMF: Radar-Guided Kinematic Priors for Real-Time 4D Occupancy Forecasting
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Abstract:Real-time 4D occupancy forecasting requires reliably extrapolating both ego motion and surrounding actor motion under strict latency budgets. Recent occupancy world models improve long-horizon fidelity through generative decoders and 3D volumetric modeling, yet their computational cost makes real-time deployment challenging. Light-weight BEV forecasters meet latency requirements but often blur or suppress fast-moving actors at longer horizons. We present UFMF (Ultra-Fast Multi-modal Forecaster), a real-time forecaster that keeps an attention-free diagonal state-space temporal core while injecting targeted kinematic and geometric structure where lightweight models typically fail. A radar-guided velocity-aware dynamic prior (VADP) decomposes future displacement into a linear extrapolation plus a learned horizon-dependent residual. The resulting per-cell flow fields transport a compact dynamic latent subspace during rollout. Two auxiliary training constraints complement this branch: world-frame dynamic supervision enforces inter-horizon trajectory coherence, and class-conditioned vertical-profile supervision regularizes BEV-to-3D lifting. UFMF operates at 122 FPS on a single RTX 4090 and reaches 0.45 fast-displacement mIoU on nuScenes, outperforming recent occupancy world models at a fraction of their computational cost.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.40947 [cs.CV]
(or arXiv:2610.40947v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.40947
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zeping Zhang [view email] [v1] Thu, 29 Oct 2026 20:24:44 UTC (1,457 KB)
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