DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving

Understand

End-to-end autonomous driving (E2E-AD) demands effective processing of multi-view sensory data and robust handling of diverse and complex driving scenarios, particularly rare maneuvers such as aggressive turns.

• 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-$\pi_0$.