競争レースにおける開ループおよびフィードバックナッシュ軌道のiLQGamesによる生成
Open-Loop and Feedback Nash Trajectories for Competitive Racing with iLQGames
レースを動的ゲームとして定式化し、iLQRの変種であるiLQGamesを用いて開ループとフィードバックのナッシュ均衡軌道を生成し、その挙動を比較評価した。
著者: Matthias Rowold, Alexander Langmann, Boris Lohmann, Johannes Betz
分類: cs.RO, cs.GT
原文アブストラクト
Interaction-aware trajectory planning is crucial for closing the gap between autonomous racing cars and human racing drivers. Prior work has applied game theory as it provides equilibrium concepts for non-cooperative dynamic problems. With this contribution, we formulate racing as a dynamic game and employ a variant of iLQR, called iLQGames, to solve the game. iLQGames finds trajectories for all players that satisfy the equilibrium conditions for a linear-quadratic approximation of the game and has been previously applied in traffic scenarios. We analyze the algorithm's applicability for trajectory planning in racing scenarios and evaluate it based on interaction awareness, competitiveness, and safety. With the ability of iLQGames to solve for open-loop and feedback Nash equilibria, we compare the behavioral outcomes of the two equilibrium concepts in simple scenarios on a straight track section.
