運転行動予測のための階層型適応・転移ネットワーク
Hierarchical Adaptable and Transferable Networks (HATN) for Driving Behavior Prediction
人間の認知を模した階層型フレームワークで、意図識別と行動生成を分けて多エージェント密集交通での運転行動を予測し、交差点やラウンドアバウトの実データで精度と転移性の高さを示した論文。
著者: Letian Wang, Yeping Hu, Liting Sun, Wei Zhan, Masayoshi Tomizuka, Changliu Liu
分類: cs.RO
原文アブストラクト
When autonomous vehicles still struggle to solve challenging situations during on-road driving, humans have long mastered the essence of driving with efficient transferable and adaptable driving capability. By mimicking humans' cognition model and semantic understanding during driving, we present HATN, a hierarchical framework to generate high-quality driving behaviors in multi-agent dense-traffic environments. Our method hierarchically consists of a high-level intention identification and low-level action generation policy. With the semantic sub-task definition and generic state representation, the hierarchical framework is transferable across different driving scenarios. Besides, our model is also able to capture variations of driving behaviors among individuals and scenarios by an online adaptation module. We demonstrate our algorithms in the task of trajectory prediction for real traffic data at intersections and roundabouts, where we conducted extensive studies of the proposed method and demonstrated how our method outperformed other methods in terms of prediction accuracy and transferability.
