驾驶世界模型如何能进行反事实预测？

中文摘要 AI 辅助

驾驶世界模型通常被解释为针对观测到的驾驶片段的反事实模拟器：给定一段事实驾驶日志，它们需回答在不同的自车动作下会发生什么。本文指出该目标与直接的动作条件预测之间存在根本不匹配。直接预测使用共享历史和替代动作，但不使用该历史后观测到的事实延续，因此它可以生成看似合理的未来，却不保留该片段中实际发生的情况。我们利用溯因、行动和预测的因果公式形式化此差距，并在短时间范围设定下研究，其中替代自车动作不会改变周围智能体的演化。为使该差距可测量，我们构建了带有事实结果和匹配反事实结果的受控模拟基准。在两个代表性世界模型上，直接预测未能匹配反事实真实值，验证了我们的分析。作为该分析的建设性验证，我们引入了一个刻意简单的无训练流程，将观测到的证据移入反事实视角，并让冻结模型完成剩余未知部分。即便这种简单构造也大幅提升了整体恢复比例，并降低了两个模型上与匹配反事实的感知距离。我们希望这项工作能引起对该差距的关注，并推动开发更好的驾驶世界模型反事实预测方法。

英文摘要

Driving world models are often interpreted as counterfactual simulators for observed driving episodes: given a factual driving log, they are asked what would have happened under an alternative ego action. In this paper, we identify a fundamental mismatch between this goal and direct action-conditioned prediction. The direct prediction uses the shared history and the alternative action but not the factual continuation observed after that history. It can therefore generate a plausible future without preserving what actually happened in this episode. We formalize this gap using the causal recipe of abduction, action, and prediction and study it in a setting with a short time horizon, where the alternative ego action does not alter how surrounding agents evolve. To make the gap measurable, we construct a controlled simulation benchmark with factual outcomes and matched counterfactual outcomes. Across two representative world models, direct predictions fail to match the counterfactual ground truth, supporting our analysis. As a constructive check of this analysis, we introduce a deliberately simple, training-free pipeline that moves observed evidence into the counterfactual view and lets the frozen model complete what remains unknown. Even this simple construction raises the overall recovered fraction substantially and reduces perceptual distance to the matched counterfactual on both models. We hope this work draws attention to this gap and motivates better counterfactual prediction methods for driving world models.

发表机构

• Purdue University（普渡大学）
• Bosch Center for Artificial Intelligence（博世人工智能中心）

机构由 AI 辅助整理，请以论文原文为准。