[Submitted on 15 Sep 2026]
Title:EditWM: Event-Decomposed World Modeling with Incremental Correction for End-to-End Autonomous Driving
View a PDF of the paper titled EditWM: Event-Decomposed World Modeling with Incremental Correction for End-to-End Autonomous Driving, by Junjie Yang and 7 other authors
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Abstract:World models support autonomous driving by predicting the scene evolution associated with candidate trajectories. Driving dynamics differ in predictability, motivating a distinction between regular evolution and event-induced deviations that call for selective correction. We propose EditWM, a world model that decomposes future prediction into normal evolution and event-driven incremental correction in compact visual feature space. A trajectory-conditioned normal predictor provides the base forecast and is then frozen for correction learning. A correction decoder compares this forecast with observation history and planned actions, producing a bounded feature update whose contribution is regulated by a learned gate. The corrected future features condition trajectory scoring through candidate-specific cross-attention, linking world modeling to plan selection. At inference, EditWM uses only past and current observations, ego state, and candidate trajectories. Across all 12,146 NAVSIM navtest scenes, expert-trajectory-conditioned evaluation shows a 5.35\% reduction in future-feature MSE over Normal, with improvements in 83.54\% of scenes. The system achieves 91.05 EPDMS on a 100-point scale using the official EPDMS evaluator. These results demonstrate improved future-feature prediction and competitive trajectory selection when corrected future representations are integrated into planning.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2609.22317 [cs.RO]
(or arXiv:2609.22317v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.22317
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Junjie Yang [view email] [v1] Tue, 15 Sep 2026 14:47:01 UTC (4,375 KB)
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