予測ヒューリスティックを用いた効率的なゲーム理論的計画法による社会的に適合する自動運転
Efficient Game-Theoretic Planning with Prediction Heuristic for Socially-Compliant Autonomous Driving
自動運転における社会的相互作用を考慮した計画問題に対し、予測アルゴリズムをヒューリスティックとして組み込んだモンテカルロ木探索に基づく効率的な軌道計画法を提案し、多様な運転行動と他車の選好推定を可能にした。
著者: Chenran Li, Tu Trinh, Letian Wang, Changliu Liu, Masayoshi Tomizuka, Wei Zhan
分類: cs.RO
原文アブストラクト
Planning under social interactions with other agents is an essential problem for autonomous driving. As the actions of the autonomous vehicle in the interactions affect and are also affected by other agents, autonomous vehicles need to efficiently infer the reaction of the other agents. Most existing approaches formulate the problem as a generalized Nash equilibrium problem solved by optimization-based methods. However, they demand too much computational resource and easily fall into the local minimum due to the non-convexity. Monte Carlo Tree Search (MCTS) successfully tackles such issues in game-theoretic problems. However, as the interaction game tree grows exponentially, the general MCTS still requires a huge amount of iterations to reach the optima. In this paper, we introduce an efficient game-theoretic trajectory planning algorithm based on general MCTS by incorporating a prediction algorithm as a heuristic. On top of it, a social-compliant reward and a Bayesian inference algorithm are designed to generate diverse driving behaviors and identify the other driver's driving preference. Results demonstrate the effectiveness of the proposed framework with datasets containing naturalistic driving behavior in highly interactive scenarios.
