NeuroFlow：自動運転システム向け軽量・高効率なモデル統合スケジューリング戦略の開発
NeuroFlow: Development of lightweight and efficient model integration scheduling strategy for autonomous driving system
自動運転車の計算資源制約に対応するため、深層学習モデルと非深層学習アルゴリズムのデータフローを解析し、リアルタイム性能と安全性を両立するスケジューリング戦略を提案・実車検証した。
著者: Eunbin Seo, Gwanjun Shin, Eunho Lee
分類: cs.RO, cs.AI
原文アブストラクト
This paper proposes a specialized autonomous driving system that takes into account the unique constraints and characteristics of automotive systems, aiming for innovative advancements in autonomous driving technology. The proposed system systematically analyzes the intricate data flow in autonomous driving and provides functionality to dynamically adjust various factors that influence deep learning models. Additionally, for algorithms that do not rely on deep learning models, the system analyzes the flow to determine resource allocation priorities. In essence, the system optimizes data flow and schedules efficiently to ensure real-time performance and safety. The proposed system was implemented in actual autonomous vehicles and experimentally validated across various driving scenarios. The experimental results provide evidence of the system's stable inference and effective control of autonomous vehicles, marking a significant turning point in the development of autonomous driving systems.
