See Tomorrow, Act Today: Foresight-Driven Autonomous Driving

2026-05-07

AI Summary

This work addresses the limitation of existing end-to-end autonomous driving approaches, which are predominantly reactive and lack proactive anticipation of future states. To overcome this, the authors propose ForeSight, a framework that reframes motion planning as a forward-looking decision-making process. ForeSight leverages a pretrained world model to generate multimodal future visual scenes, which in turn conditionally guide action planning—elevating the world model from an auxiliary component to the core planning backbone. Notably, this is the first method to explicitly incorporate imagined future scenarios as the central mechanism for action prediction. Evaluated on the NAVSIM and nuScenes datasets, ForeSight significantly outperforms current state-of-the-art methods, demonstrating the efficacy and superiority of prospective modeling in complex, dynamic traffic environments.

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.

Problem

• autonomous driving
• reactive planning
• foresight
• anticipatory decision-making
• world model

Innovation

• foresight-driven planning
• world model
• anticipatory decision-making
• future scene imagination
• autonomous driving