We present a new method for multi-agent planning involving human drivers and autonomous vehicles (AVs) in unsignaled intersections, roundabouts, and during merging. In multi-agent planning, the main challenge is to predict the actions of other agents, especially human drivers, as their intentions are hidden from other agents. Our algorithm uses game theory to develop a new auction, called \model, that directly determines the optimal action for each agent based on their driving style (which is observable via commonly available sensors like lidars and cameras). GamePlan assigns a higher priority to more aggressive or impatient drivers and a lower priority to more conservative or patient drivers; we theoretically prove that such an approach, although counter-intuitive, is game-theoretically optimal. Our approach successfully prevents collisions and deadlocks. We compare our approach with prior state-of-the-art auction techniques including economic auctions, time-based auctions (first-in first-out), and random bidding and show that each of these methods result in collisions among agents when taking into account driver behavior. We additionally compare with methods based on deep reinforcement learning, deep learning, and game theory and present our benefits over these approaches. Finally, we show that our approach can be implemented in the real-world with human drivers.

中文翻译：

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GamePlan：在交叉路口、环形交叉路口和合并处使用人类驾驶员的博弈论多智能体规划

我们提出了一种新的多智能体规划方法，涉及人类驾驶员和自动驾驶汽车 (AV) 在无信号交叉路口、环形交叉路口和合并期间。在多智能体规划中，主要挑战是预测其他智能体的行为，尤其是人类驾驶员，因为他们的意图对其他智能体是隐藏的。我们的算法使用博弈论开发了一种名为 \model 的新拍卖，该拍卖会根据每个代理的驾驶风格（可通过激光雷达和摄像头等常用传感器观察到）直接确定他们的最佳行动。GamePlan 为更具侵略性或不耐烦的司机分配更高的优先级，为更保守或耐心的司机分配较低的优先级；我们从理论上证明，这种方法虽然违反直觉，但在博弈论上是最优的。我们的方法成功地防止了冲突和死锁。我们将我们的方法与先前最先进的拍卖技术进行比较，包括经济拍卖、基于时间的拍卖（先进先出）和随机投标，并表明这些方法中的每一种都会导致代理之间的冲突帐户驱动程序行为。我们还与基于深度强化学习、深度学习和博弈论的方法进行了比较，并展示了我们对这些方法的好处。最后，我们表明我们的方法可以在现实世界中与人类驾驶员一起实施。和随机出价，并表明在考虑驾驶员行为时，这些方法中的每一种都会导致代理之间的冲突。我们还与基于深度强化学习、深度学习和博弈论的方法进行了比较，并展示了我们对这些方法的好处。最后，我们表明我们的方法可以在现实世界中与人类驾驶员一起实施。和随机出价，并表明在考虑驾驶员行为时，这些方法中的每一种都会导致代理之间的冲突。我们还与基于深度强化学习、深度学习和博弈论的方法进行了比较，并展示了我们对这些方法的好处。最后，我们表明我们的方法可以在现实世界中与人类驾驶员一起实施。