大規模言語モデルを用いた自動運転のためのエッジ・クラウド協調動作計画
Edge-Cloud Collaborative Motion Planning for Autonomous Driving with Large Language Models
データドリフト検出を活用し、重要なデータのみをクラウドのGPT-4で処理、通常データはエッジ上の小型LLMで処理するエッジ・クラウド協調自動運転システムEC-Driveを提案した。
著者: Jiao Chen, Suyan Dai, Fangfang Chen, Zuohong Lv, Jianhua Tang
分類: cs.RO, cs.AI
原文アブストラクト
Integrating large language models (LLMs) into autonomous driving enhances personalization and adaptability in open-world scenarios. However, traditional edge computing models still face significant challenges in processing complex driving data, particularly regarding real-time performance and system efficiency. To address these challenges, this study introduces EC-Drive, a novel edge-cloud collaborative autonomous driving system with data drift detection capabilities. EC-Drive utilizes drift detection algorithms to selectively upload critical data, including new obstacles and traffic pattern changes, to the cloud for processing by GPT-4, while routine data is efficiently managed by smaller LLMs on edge devices. This approach not only reduces inference latency but also improves system efficiency by optimizing communication resource use. Experimental validation confirms the system's robust processing capabilities and practical applicability in real-world driving conditions, demonstrating the effectiveness of this edge-cloud collaboration framework. Our data and system demonstration will be released at https://sites.google.com/view/ec-drive.
