See Tomorrow, Act Today: Foresight-Driven Autonomous Driving

TL;DR

The paper proposes ForeSight, a foresight-driven autonomous driving framework that imagines future scenes to improve decision-making, outperforming reactive methods in dynamic scenarios.

Contribution

It introduces a novel planning paradigm that emphasizes future scene imagination using a pretrained world model for anticipatory decision-making in autonomous driving.

Findings

ForeSight outperforms previous state-of-the-art methods on NAVSIM and nuScenes datasets.

Explicit future imagination improves navigation in dynamic, interactive scenarios.

The approach demonstrates the effectiveness of anticipatory planning over reactive strategies.

Abstract

Current end-to-end autonomous driving planners are fundamentally reactive: they condition on historical and present observations to predict future actions. We argue that autonomous agents should instead imagine future scenes before deciding, just as human drivers mentally simulate ``what will happen next" before acting. We introduce ForeSight, a foundation world model centric planning framework that reframes autonomous driving as anticipatory decision-making. Rather than treating world models as auxiliary components, ForeSight makes future scene imagination the primary driver of action prediction. Our approach operates in two stages: (1) generating plausible future visual worlds via a pretrained world model, and (2) planning actions conditioned on these imagined futures. This paradigm shift from ``what should I do now?" to ``what will happen, and how should I respond?" enables genuinely anticipatory rather than reactive planning. By grounding decisions in anticipated contexts rather than present observations alone, ForeSight navigates dynamic, interactive scenarios more effectively. Extensive experiments on NAVSIM and nuScenes demonstrate that explicit future imagination significantly outperforms previous state-of-the-art alternatives, validating our foresight-driven approach.