标题： 有序偏好博弈的近似解 显示英文标题

标题： Approximate solutions to games of ordered preference

摘要： 自主车辆必须平衡优先级目标，如最小化行驶时间、确保安全性和协调交通。 有序偏好博弈有效地模拟了这些交互作用，但随着时间范围、玩家数量或偏好级别数量的增加，它们变得计算上难以处理。 虽然滚动时域框架通过解决顺序的较短博弈来缓解长时域的不可行性，通常是通过热启动进行的，但它们并未解决现有方法在求解有序偏好博弈时固有的复杂度增长问题。 本文介绍了一种解决方案策略，通过在滚动时域中使用字典序迭代最佳响应（IBR）来近似解决方案，称为 “时间上的字典序IBR”。 时间上的字典序IBR利用过去的信息来加速收敛。 我们通过模拟交通场景证明，时间上的字典序IBR能够高效地计算出滚动时域有序偏好博弈的近似最优解，并收敛到广义纳什均衡。 显示英文摘要

摘要： Autonomous vehicles must balance ranked objectives, such as minimizing travel time, ensuring safety, and coordinating with traffic. Games of ordered preference effectively model these interactions but become computationally intractable as the time horizon, number of players, or number of preference levels increase. While receding horizon frameworks mitigate long-horizon intractability by solving sequential shorter games, often warm-started, they do not resolve the complexity growth inherent in existing methods for solving games of ordered preference. This paper introduces a solution strategy that avoids excessive complexity growth by approximating solutions using lexicographic iterated best response (IBR) in receding horizon, termed "lexicographic IBR over time." Lexicographic IBR over time uses past information to accelerate convergence. We demonstrate through simulated traffic scenarios that lexicographic IBR over time efficiently computes approximate-optimal solutions for receding horizon games of ordered preference, converging towards generalized Nash equilibria.