学習ベースの自動運転行動計画：実世界統合と展開
Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment
機械学習と古典的手法を組み合わせたハイブリッドな自動運転計画アーキテクチャを提案し、実車両での展開結果を報告した論文。
著者: Jean-Pierre Busch, Guido Linden, Jan Bergmann, Lutz Eckstein
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Recent research in machine and deep learning has shown the potential of learningbased motion planning approaches to improve the driving behavior of automated vehicles, especially in complex environments. However, their complex nature and lack of transparency can hinder explainability and trustworthiness and complicate safety assurance. Motivated by these challenges, we propose a hybrid planning architecture that combines the advantages of machine learning with the verifiability and the determinism of classical approaches. Specifically, we developed a deep neural network to interpret complex traffic scenes and propose driving behavior, while an optimization-based supervision layer validates this proposal and enforces explicit drivability and safety constraints. We evaluate the learned planner's driving behavior in open-loop studies on real-world urban data, discuss system integration aspects for stable closed-loop operation, and report results from real-world deployment on our research vehicle karl..