论文标题
通过高斯流程使用无限政策空间对人类驾驶员的互动进行建模
Modeling Human Driver Interactions Using an Infinite Policy Space Through Gaussian Processes
论文作者
论文摘要
本文提出了一种建模人类驾驶员相互作用的方法，该方法依赖于多输出高斯过程。所提出的方法是作为游戏理论层次推理方法的改进，称为“级别K推理”，通常从常规上将行为的离散级别分配给代理。尽管证明它是一种有效的建模工具，但级别K推理方法可能会构成不希望的约束，以预测其提取的驾驶员策略有限（通常为2或3）引起的人类决策。提出的方法是通过引入可实现无限政策空间的连续域框架来填补文献中的这一空白。通过使用本文介绍的方法，可以获得更准确的驱动器模型，然后可以使用该方法来创建高保真模拟平台以验证自动驾驶汽车控制算法。所提出的方法在真实的流量数据集上进行了验证，并将其与传统的级别K方法进行了比较，以证明其贡献和含义。
This paper proposes a method for modeling human driver interactions that relies on multi-output gaussian processes. The proposed method is developed as a refinement of the game theoretical hierarchical reasoning approach called "level-k reasoning" which conventionally assigns discrete levels of behaviors to agents. Although it is shown to be an effective modeling tool, the level-k reasoning approach may pose undesired constraints for predicting human decision making due to a limited number (usually 2 or 3) of driver policies it extracts. The proposed approach is put forward to fill this gap in the literature by introducing a continuous domain framework that enables an infinite policy space. By using the approach presented in this paper, more accurate driver models can be obtained, which can then be employed for creating high fidelity simulation platforms for the validation of autonomous vehicle control algorithms. The proposed method is validated on a real traffic dataset and compared with the conventional level-k approach to demonstrate its contributions and implications.
