交通規制データベースと視覚言語モデルを活用した規制対応経路計画
Traffic Regulation-aware Path Planning with Regulation Databases and Vision-Language Models
RGBカメラと視覚言語モデル、機械可読な交通規制データベースを組み合わせ、自動運転車が法規を守りながら経路を計画・判断できるフレームワークを提案し、シミュレーションと実車で検証した。
著者: Xu Han, Zhiwen Wu, Xin Xia, Jiaqi Ma
分類: cs.RO, cs.SY, eess.IV, eess.SY
原文アブストラクト
This paper introduces and tests a framework integrating traffic regulation compliance into automated driving systems (ADS). The framework enables ADS to follow traffic laws and make informed decisions based on the driving environment. Using RGB camera inputs and a vision-language model (VLM), the system generates descriptive text to support a regulation-aware decision-making process, ensuring legal and safe driving practices. This information is combined with a machine-readable ADS regulation database to guide future driving plans within legal constraints. Key features include: 1) a regulation database supporting ADS decision-making, 2) an automated process using sensor input for regulation-aware path planning, and 3) validation in both simulated and real-world environments. Particularly, the real-world vehicle tests not only assess the framework's performance but also evaluate the potential and challenges of VLMs to solve complex driving problems by integrating detection, reasoning, and planning. This work enhances the legality, safety, and public trust in ADS, representing a significant step forward in the field.
