用博弈论让自动驾驶变道更自然，兼顾安全与舒适。
An Evolutionary Game-Theoretic Merging Decision-Making Considering Social Acceptance for Autonomous Driving
- 基于人类驾驶行为构建动态博弈模型，平衡自车与主路车辆利益。
- 实测显示效率、舒适度、安全性均优于传统方法和现有博弈算法。
- 可实时感知周围车辆反应，自适应调整变道策略，适合真实高速场景。
高速公路匝道汇入对自动驾驶车辆（AV）构成重大挑战，因其需在有限时间内主动与周围车辆交互以安全进入主路。现有决策算法未能充分考虑动态复杂性与社会接受度，导致次优或不安全的汇入决策。为此，我们提出一种基于进化博弈理论（EGT）的汇入决策框架，基于人类驾驶员的有限理性，动态平衡自车与主路车辆（MVs）的收益。将切入决策建模为具有多目标收益函数的EGT问题，反映类人驾驶偏好。通过求解演化稳定策略（ESS）对应的复制动态方程，推导出最优切入时机，兼顾效率、舒适与安全。同时提出实时驾驶风格估计算法，通过观察主路车辆即时反应在线调整博弈收益函数。实证结果表明，相比现有博弈论与传统规划方法，在多个指标上均显著提升自车与主路车辆的效率、舒适性与安全性。
原文摘要 · Abstract (English)
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.
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