社会的受容性を考慮した進化ゲーム理論による自動運転の合流意思決定
An Evolutionary Game-Theoretic Merging Decision-Making Considering Social Acceptance for Autonomous Driving
自動運転車の高速道路合流において、人間ドライバーの限定合理性に基づく進化ゲーム理論を用い、周囲車両の運転スタイルをリアルタイム推定しながら効率・快適性・安全性のバランスを取る意思決定手法を提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Haolin Liu, Zijun Guo, Yanbo Chen, Jiaqi Chen, Huilong Yu, Junqiang Xi
分類: cs.RO, cs.AI
原文アブストラクト
Highway on-ramp merging is of great challenge for autonomous vehicles (AVs), since they have to proactively interact with surrounding vehicles to enter the main road safely within limited time. However, existing decision-making algorithms fail to adequately address dynamic complexities and social acceptance of AVs, leading to suboptimal or unsafe merging decisions. To address this, we propose an evolutionary game-theoretic (EGT) merging decision-making framework, grounded in the bounded rationality of human drivers, which dynamically balances the benefits of both AVs and main-road vehicles (MVs). We formulate the cut-in decision-making process as an EGT problem with a multi-objective payoff function that reflects human-like driving preferences. By solving the replicator dynamic equation for the evolutionarily stable strategy (ESS), the optimal cut-in timing is derived, balancing efficiency, comfort, and safety for both AVs and MVs. A real-time driving style estimation algorithm is proposed to adjust the game payoff function online by observing the immediate reactions of MVs. Empirical results demonstrate that we improve the efficiency, comfort and safety of both AVs and MVs compared with existing game-theoretic and traditional planning approaches across multi-object metrics.
