A game theoretical model of traffic with multiple interacting drivers for use in autonomous vehicle development
A game theoretical model of traffic with multiple interacting drivers for use in autonomous vehicle development
复制标题
用于自动驾驶汽车开发的具有多个交互驾驶员的交通博弈论模型
DOI：
10.1109/acc.2016.7525162
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
I. Kolmanovsky
中科院分区：
文献类型：
--
作者：
Dave W. Oyler;Y. Yildiz;A. Girard;Nan I. Li;I. Kolmanovsky
本文描述了一种交通博弈理论模型，其中多个驾驶员相互作用。该模型是利用分层推理（一种人类行为的博弈理论模型）以及强化学习构建的。假设驾驶员只能观察到他们所处交通状况的部分状态，因此尽管环境满足马尔可夫性质，但对驾驶员来说它似乎是非马尔可夫的。因此，每个驾驶员都隐含地必须为一个部分可观测马尔可夫决策过程找到一个策略，即从观测到行动的映射。在本文中，通过将分层推理与一种合适的强化学习算法相结合，为这个问题提供了一种计算上易于处理的解决方案。文中给出了模拟结果，这些结果表明所得到的驾驶员模型对于给定的交通场景能提供合理的行为。
This paper describes a game theoretical model of traffic where multiple drivers interact with each other. The model is developed using hierarchical reasoning, a game theoretical model of human behavior, and reinforcement learning. It is assumed that the drivers can observe only a partial state of the traffic they are in and therefore although the environment satisfies the Markov property, it appears as non-Markovian to the drivers. Hence, each driver implicitly has to find a policy, i.e. a mapping from observations to actions, for a Partially Observable Markov Decision Process. In this paper, a computationally tractable solution to this problem is provided by employing hierarchical reasoning together with a suitable reinforcement learning algorithm. Simulation results are reported, which demonstrate that the resulting driver models provide reasonable behavior for the given traffic scenarios.