[Paper] BEVLM: Distilling Semantic Knowledge from LLMs into Bird's-Eye View Representations

Summary

The BEVLM paper introduces a novel approach to integrate Large Language Models (LLMs) into autonomous driving, addressing current inefficiencies. Existing methods feed LLMs with multi-view images independently, causing redundant computation and limited 3D spatial consistency. BEVLM proposes distilling semantic knowledge from LLMs directly into Bird's-Eye View representations, aiming to enhance reasoning and efficiency for complex driving scenarios.

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