稀な危険状況に対する先制的運転行動のデモンストレーション学習
Modeling Preemptive Behaviors for Uncommon Hazardous Situations From Demonstrations
路上障害物による見通しの悪い都市部の稀な危険運転シーンを対象に、24人の運転デモから安全な自動運転行動を学習し、キーフレームと線形結合で未知の状況変化へ汎化する手法を提案した。
著者: Priyam Parashar, Akansel Cosgun, Alireza Nakhaei, Kikuo Fujimura
分類: cs.RO, cs.AI
原文アブストラクト
This paper presents a learning from demonstration approach to programming safe, autonomous behaviors for uncommon driving scenarios. Simulation is used to re-create a targeted driving situation, one containing a road-side hazard creating a significant occlusion in an urban neighborhood, and collect optimal driving behaviors from 24 users. Paper employs a key-frame based approach combined with an algorithm to linearly combine models in order to extend the behavior to novel variations of the target situation. This approach is theoretically agnostic to the kind of LfD framework used for modeling data and our results suggest it generalizes well to variations containing an additional number of hazards occurring in sequence. The linear combination algorithm is informed by analysis of driving data, which also suggests that decision-making algorithms need to consider a trade-off between road-rules and immediate rewards to tackle some complex cases.
