Grounding What Shapes the Plan: Rethinking Groundedness for Physical Intelligence in Autonomous Driving

2026-10-05

AI Summary

This study addresses the "correct reasoning yet suboptimal action" problem in autonomous driving by redefining the groundedness of physical intelligence and proposing the GroundAct framework. GroundAct establishes the concept of grounded planning, which treats entities as fundamental units and employs lightweight reference tokens to associate states with interactions. This design enables an explicit mapping from symbolic reasoning to planning, effectively bridging the gap between reasoning and action. By integrating closed-loop simulation with open-loop planning algorithms, experimental results demonstrate that the proposed method exhibits strong open-loop planning capabilities across routine, out-of-distribution, and safety-critical scenarios. Furthermore, the framework's effectiveness is validated through closed-loop driving evaluations, confirming its potential for robust and reliable deployment in real-world autonomous driving applications.

Abstract

Driving models increasingly ground reasoning in causal relations, spatial structure, perceptual evidence, and predicted futures. These advances make reasoning more faithful to the driving scene, but leave a fundamental question unresolved: what should groundedness mean when the model ultimately outputs an action? Correctly grounded reasoning does not, by itself, ensure desirable driving outcomes. We introduce GroundAct, which starts from a simple premise: driving unfolds through physical entities and their interactions. Entities therefore become the unit of grounding; a lightweight reference token keeps each selected entity's continuous state addressable through symbolic reasoning; and only the referenced entities' interactions with the evolving proposal correct the plan. The result is an explicit path from what reasoning grounds to what the plan does, which we call grounded planning. To assess its practical value, we evaluate GroundAct in both open- and closed-loop settings. GroundAct shows strong open-loop planning across normal, out-of-distribution, and safety-critical scenarios, with closed-loop results extending this evidence to driving in simulation.

Problem

• autonomous driving
• groundedness
• physical intelligence
• action generation
• planning

Innovation

• Grounded Planning
• Physical Intelligence
• Autonomous Driving
• Reference Token
• Entity Interaction