发表机构
The Hong Kong University of Science and Technology; Southern University of Science and Technology; The Chinese University of Hong Kong, Shenzhen（香港科技大学; 南方科技大学; 香港中文大学（深圳））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
EditWM通过将未来预测分解为正常演变和事件驱动的增量校正，在紧凑特征空间中实现有界更新，提升了端到端自动驾驶的未来特征预测和轨迹选择性能。
AI 中文摘要
世界模型通过预测与候选轨迹相关的场景演变来支持自动驾驶。驾驶动态的可预测性各不相同，这促使我们区分常规演变和需要选择性校正的事件引发的偏差。我们提出了EditWM，一种世界模型，它将未来预测分解为紧凑视觉特征空间中的正常演变和事件驱动的增量校正。一个轨迹条件的正常预测器提供基础预测，然后被冻结用于校正学习。一个校正解码器将该预测与观测历史和计划动作进行比较，产生一个有界的特征更新，其贡献由学习到的门控调节。校正后的未来特征通过候选特定的交叉注意力来调节轨迹评分，将世界建模与规划选择联系起来。在推理时，EditWM仅使用过去和当前的观测、自我状态和候选轨迹。在所有12,146个NAVSIM navtest场景中，专家轨迹条件下的评估显示，未来特征MSE相比Normal降低了5.35%，在83.54%的场景中有所改进。该系统使用官方EPDMS评估器在100分制上达到91.05 EPDMS。这些结果表明，当校正后的未来表示被整合到规划中时，未来特征预测得到改进，轨迹选择具有竞争力。
英文摘要
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.
