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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...
Highway ramp merging zones represent a typical dense traffic scenario where complex vehicle interactions make accurate trajectory prediction particularly challenging. This paper proposes a novel dual-encoder model with an interactive time horizon mechanism to address these challenges. The model employs independent enco...
A World Model-based Risk-aware Mixture-of-Experts (WM-RMoE) framework is proposed, which uniquely exhibits the ability to generate foresighted and semantically distinct overtaking maneuvers across diverse traffic densities.
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