AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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World models in end-to-end autonomous driving predict future scene evolution to provide foresight for trajectory planning. Existing methods mainly study how to predict the future and how to use it, but less often ask which future representation is actually most useful for planning. To this end, we propose PlanWAM, a Pl...
GeoWorldAD is proposed, a geometry world action model that grounds trajectory planning in ego-aligned 3D space and anticipates short-horizon scene evolution with latent future geometry tokens and progressively aggregates multi-scale present geometry and latent future geometry through iterative trajectory refinement.
WCog-VLA is proposed, a novel dual-level World-Cognitive VLA framework that successfully bridges semantic world forecasting with generative world evolution to achieve proactive autonomous driving.
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