[Submitted on 29 Oct 2026]
Title:Let LiDAR Decide Where to Look: Geometry-Measured Token Selection for Efficient Sparse Latent World Models
View a PDF of the paper titled Let LiDAR Decide Where to Look: Geometry-Measured Token Selection for Efficient Sparse Latent World Models, by Zeping Zhang and Zhuoya Zhao and Robert Lagani`ere
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Abstract:Vision-centric autonomous driving models encode thousands of image tokens per frame, yet most depict sky, buildings, or empty road---content irrelevant to planning. Worse, these tokens dilute the planner's attention: we show that randomly discarding 75% of tokens already outperforms the dense model, because the planner can concentrate on the remaining driving-critical content. Building on this finding, we present Geometry-Aware Token Selection (GATS), which uses LiDAR and radar point projections to select a small fraction of image tokens before the vision backbone runs. Because the selector operates on measured 3D geometry rather than visual features, the backbone encodes only the selected patches, cutting end-to-end FLOPs by up to $18\times$. Each kept token receives a 3D anchor from its LiDAR point and is fed to a self-supervised latent world model that predicts future features and 3D displacements without box annotations. At 25% budget, GATS achieves a 3-sec L2 displacement error of 0.520 m on nuScenes and an EPDMS of 88.9 on NAVSIM---surpassing the dense baseline (0.573 m / 85.9) at $6.6\times$ fewer total FLOPs. At an extreme 10% budget (24 GFLOPs, $18\times$ reduction), it still beats the dense baseline on both nuScenes (0.537 m vs. 0.573 m L2) and NAVSIM (87.9 vs. 85.9 EPDMS). On 739 intent-critical scenarios, GATS covers 95.8% of hazardous vehicles and achieves a 0.09% collision rate---73.5% lower than the dense model (0.34%).
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.40935 [cs.CV]
(or arXiv:2610.40935v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.40935
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zeping Zhang [view email] [v1] Thu, 29 Oct 2026 20:11:34 UTC (23,400 KB)
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