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Drive-HWM: Hierarchical World Models for Dynamic-Latent Guided Autonomous Driving

arXiv · AI, language, vision and robotics · article · Sep 3, 2026 · UTC

World models offer a promising paradigm for autonomous driving by predicting how traffic scenes may evolve and using such predictions to support action generation. However, existing approaches either separate future prediction from action generation or jointly predict them at the same temporal scale, making it difficult to simultaneously achieve long-horizon anticipation and responsive, observation-grounded decision making. We present Drive-HWM, a hierarchical slow--fast world modeling framework that organizes future representation prediction and action generation at complementary temporal sca

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