自動運転のための軽量な信頼度認識言語モデルによる意思決定
Decision-Making with Lightweight Confidence-Aware Language Model for Autonomous Driving
大規模言語モデルの推論能力を軽量モデルに蒸留し、信頼度付きの意思決定を高速に行う自動運転フレームワークを提案した。
詳しい要約
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著者: Ruoyu Yao, Ruiguo Zhong, Pei Liu, Mingxing Peng, Rui Yang, Jun Ma
分類: cs.RO
原文アブストラクト
Large Language Models (LLMs) and Multimodal LLMs (MLLMs) have demonstrated immense potential in autonomous driving (AD) by offering human-like reasoning and open-world generalization. However, the excessive computational overhead and high inference latency of these massive models severely hinder their deployment in resource-constrained AD systems. To address this challenge, we propose a novel decision-making framework utilizing a lightweight confidence-aware language model, which bridges the gap between complex multimodal intention reasoning and efficient inference. Specifically, we design a multi-agent collaborative workflow, comprising action voting, confidence assessment, and summarization agents, to generate high-quality, confidence-annotated decision demonstrations via explicit Chain-of-Thought (CoT) reasoning. These demonstrations are then distilled into a lightweight language model featuring a dual-head architecture, enabling the joint prediction of decision probabilities and the generation of textual rationales. The distillation is realized via a confidence-aware fine-tuning strategy coupled with Retrieval Augmented Generation (RAG) to enhance the model's adaptability and data efficiency. Comprehensive closed-loop experiments on the nuPlan benchmark demonstrate that our approach achieves state-of-the-art (SOTA) success rates in both regular and long-tail scenarios while maintaining low inference latency.
