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 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.
翻译:我们提出了一个新的多试剂规划方法,涉及未标志十字路口、环形路口和合并期间的人类驾驶者和自主车辆。在多试剂规划中,主要的挑战是如何预测其他代理人,特别是人类驾驶者的行动,因为其意图隐藏于其他代理人之外。我们的算法理论使用游戏理论来开发一个新的拍卖,称为GamePlan,直接决定每个代理人根据其驾驶风格采取的最佳行动(通过现有普通传感器观测到)。 GamePlan更优先重视较积极或不耐烦的驾驶者,而较保守或有耐心的驾驶者则较不优先;我们理论上证明,这种方法在游戏理论上是最佳的,可以防止碰撞和僵局。我们比较我们的方法与以前最先进的拍卖技术,包括经济拍卖、基于时间的拍卖(首次拍卖)和随机投标,并表明每种方法在考虑驾驶者行为时都会造成代理人之间的碰撞。我们还比较了基于深度强化学习、深入学习和游戏理论的方法,并展示了这些方法的好处。最后,我们证明我们的方法可以在现实世界中与人类驾驶者一起实施。