EditWM: Event-Decomposed World Modeling with Incremental Correction for End-to-End Autonomous Driving

2026-09-15

AI Summary

本文提出EditWM模型，通过区分常规演变和事件驱动的修正来改进自动驾驶中的场景预测，减少未来特征MSE，提高轨迹选择准确性。

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.

Problem

• World Modeling
• Autonomous Driving
• Event-Driven Correction
• Future Prediction
• Trajectory Planning

Innovation

• Event-Decomposed World Modeling
• Incremental Correction
• Compact Visual Feature Space
• Trajectory-Conditioned Normal Predictor
• Correction Decoder