学習ヒューリスティクスとモンテカルロ木探索による自動運転車の協調計画の高速化
Accelerating Cooperative Planning for Automated Vehicles with Learned Heuristics and Monte Carlo Tree Search
自動運転車の協調計画において、学習したヒューリスティクスでモンテカルロ木探索を誘導し、有望な行動領域へ探索を絞ることで計算コストを抑えつつ良い解を得る手法を提案した。
著者: Karl Kurzer, Marcus Fechner, J. Marius Zöllner
分類: cs.LG, cs.MA, cs.RO, stat.ML
原文アブストラクト
Efficient driving in urban traffic scenarios requires foresight. The observation of other traffic participants and the inference of their possible next actions depending on the own action is considered cooperative prediction and planning. Humans are well equipped with the capability to predict the actions of multiple interacting traffic participants and plan accordingly, without the need to directly communicate with others. Prior work has shown that it is possible to achieve effective cooperative planning without the need for explicit communication. However, the search space for cooperative plans is so large that most of the computational budget is spent on exploring the search space in unpromising regions that are far away from the solution. To accelerate the planning process, we combined learned heuristics with a cooperative planning method to guide the search towards regions with promising actions, yielding better solutions at lower computational costs.
