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Recent success of Mixture-of-Experts (MoE) architecture in Large Language Models (LLMs) demonstrates that specialization of parameters enables strong scalability. In this work, we propose DriveMoE, a novel MoE-based E2E-AD framework, with a Scene-Specialized Vision MoE and a Skill-Specialized Action MoE. DriveMoE is built upon our $\\pi_0$ Vision-Language-Action (VLA) baseline (originally from the embodied AI field), called Drive-$\\pi_0$. Specifically, we add Vision MoE to Drive-$\\pi_0$ by training a router to select relevant cameras according to the driving context dynamically. 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