GamePlan：交叉口、圆形交叉口和合并处有人类驾驶员的博弈论多智能体规划
GamePlan: Game-Theoretic Multi-Agent Planning with Human Drivers at Intersections, Roundabouts, and Merging
摘要：
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）在无信号交叉口，圆形，并在合并。在多智能体规划中，主要的挑战是预测其他智能体的行动，特别是人类驾驶员，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来开发一种新的拍卖，称为GamePlan，它根据每个代理的驾驶风格（通过常用的传感器可以观察到）直接确定每个代理的最佳行动。GamePlan为更具侵略性或不耐烦的驾驶员分配更高的优先级，为更保守或耐心的驾驶员分配更低的优先级;我们从理论上证明了这种方法是游戏理论上最佳的，可以防止碰撞和死锁。我们比较我们的方法与现有的国家的最先进的拍卖技术，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，这些方法中的每一个导致代理之间的碰撞时，考虑到司机的行为。我们比较了基于DRL，深度学习和博弈论的方法，并提出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实现与人类司机。
英文摘要：
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 GamePlan, that directly determines the optimal action for each agent based on their driving style (which is observable via commonly available sensors). 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 is game-theoretically optimal 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 compare with methods based on DRL, 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
