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.
翻译:我们提出了一个新的多试剂规划方法,涉及人驾驶员和自主车辆(AVs),涉及未标志十字路口、环形路口和合并期间的多试剂规划。在多试剂规划中,主要挑战在于预测其他代理人,特别是人类驾驶员的行动,因为其意图隐藏在其它代理人的手中。我们的算法理论利用游戏理论来开发一个新的拍卖,称为模型,直接决定每个代理人根据其驾驶风格(通过Lidars和相机等现有普通传感器观测到的)的最佳行动。 GamePlan更优先重视较积极或不耐烦躁的驾驶员,而较保守或有耐心的驾驶员则较不优先;我们在理论上证明,这种方法虽然是反直观的,但却是博学最佳的。我们的方法成功地防止了碰撞和僵局。我们将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(首选)以及随机招标和显示,在考虑驾驶员行为时,每种方法都会造成代理人之间的碰撞。我们还可以与基于深度强化学习、深层次学习和游戏理论的方法相比较,我们最终可以展示这些对世界的效益。