Autonomous vehicles (AVs) need to interact with other traffic participants who can be either cooperative or aggressive, attentive or inattentive. Such different characteristics can lead to quite different interactive behaviors. Hence, to achieve safe and efficient autonomous driving, AVs need to be aware of such uncertainties when they plan their own behaviors. In this paper, we formulate such a behavior planning problem as a partially observable Markov Decision Process (POMDP) where the cooperativeness of other traffic participants is treated as an unobservable state. Under different cooperativeness levels, we learn the human behavior models from real traffic data via the principle of maximum likelihood. Based on that, the POMDP problem is solved by Monte-Carlo Tree Search. We verify the proposed algorithm in both simulations and real traffic data on a lane change scenario, and the results show that the proposed algorithm can successfully finish the lane changes without collisions.

中文翻译：

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实际交通数据验证的无人驾驶车辆的交互意识行为规划

自主车辆（AV）需要与其他交通参与者互动，他们可以是合作的，也可以是积极的，专心的或不专心的。这种不同的特性可能导致完全不同的交互行为。因此，为了实现安全高效的自动驾驶，AV在计划自己的行为时需要意识到这种不确定性。在本文中，我们将这种行为计划问题表述为部分可观察的马尔可夫决策过程（POMDP），其中将其他交通参与者的协作视为不可观察的状态。在不同的协作水平下，我们通过最大似然原理从实际交通数据中学习人类行为模型。在此基础上，通过蒙特卡洛树搜索解决了POMDP问题。