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This paper introduces BrainWAM, a structured action-space coordination framework that integrates semantic reasoning and predictive dynamics for autonomous driving by creating specialized pathways for each. The approach addresses the limitations of existing models that either focus on semantic priors or predictive world modeling, which can lead to attention-allocation mismatches. By employing an asynchronous rectified-flow inference strategy, BrainWAM achieves state-of-the-art performance on NAVSIM benchmarks, outperforming traditional Vision-Language-Action and World Action Model methods.
A novel framework that harmonizes semantic reasoning and predictive dynamics, achieving unprecedented performance in autonomous driving tasks.
Autonomous driving requires planning under both semantic constraints and predictive dynamics. Existing end-to-end driving approaches, however, typically emphasize only one side of this requirement: Vision-Language-Action (VLA) models exploit VLM priors for semantic reasoning, while World Action Models (WAMs) provide future-aware prediction through generative world modeling. This naturally motivates a unified planner that can leverage both semantic priors and predictive dynamics. However, we find that a naive combination through joint token-level attention suffers from an attention-allocation mismatch, where semantic shortcuts dominate the shared attention space and suppress predictive dynamics. Inspired by neuroscience evidence that complex behavior arises from coordination among functionally specialized systems, we propose BrainWAM, a structured action-space coordination framework that converts semantic reasoning and predictive world modeling into two specialized action-oriented pathways, and aligns them at the level of compact action representations. We further introduce an asynchronous rectified-flow inference strategy with decoupled video and action denoising, which shortens inference latency while preserving planning-relevant predictive context. BrainWAM reaches state-of-the-art performance on both NAVSIM v1 (89.5 PDMS) and NAVSIM v2 (89.6 EPDMS), consistently outperforming VLA-only or WAM-only methods, highlighting BrainWAM as a practical and promising direction for autonomous driving systems.
