BrainWAM: Action-Space Coordination of Semantic Priors and Predictive Dynamics for Autonomous Driving

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

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ethics.ai (13 August 2026), “BrainWAM: Action-Space Coordination of Semantic Priors and Predictive Dynamics for Autonomous Driving,” evidence record 19187, https://ethics.ai/record/19187 (originally published by arXiv).

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