SenseRAG: LLMベース自動運転のための能動的クエリによる環境知識ベース構築
SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving
マルチモーダルなセンサデータをLLMが読める知識ベースに統合し、能動的RAGと思考連鎖プロンプトで自動運転の状況認識・予測性能を向上させるフレームワークを提案。
著者: Xuewen Luo, Fan Ding, Fengze Yang, Yang Zhou, Junnyong Loo, Hwa Hui Tew, Chenxi Liu
分類: cs.AI, cs.RO
原文アブストラクト
This study addresses the critical need for enhanced situational awareness in autonomous driving (AD) by leveraging the contextual reasoning capabilities of large language models (LLMs). Unlike traditional perception systems that rely on rigid, label-based annotations, it integrates real-time, multimodal sensor data into a unified, LLMs-readable knowledge base, enabling LLMs to dynamically understand and respond to complex driving environments. To overcome the inherent latency and modality limitations of LLMs, a proactive Retrieval-Augmented Generation (RAG) is designed for AD, combined with a chain-of-thought prompting mechanism, ensuring rapid and context-rich understanding. Experimental results using real-world Vehicle-to-everything (V2X) datasets demonstrate significant improvements in perception and prediction performance, highlighting the potential of this framework to enhance safety, adaptability, and decision-making in next-generation AD systems.
