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.
翻译:本文建议了一种基于多种产出的百日咳过程的人类驱动者互动模式的模型方法。拟议方法的制定是对称为“水平推理”的游戏理论等级推理方法的完善,该方法通常将不同层次的行为分配给代理人。虽然它被证明是一个有效的模型工具,但水平推理方法可能对预测人类决策构成不理想的制约,原因是它所推出的驱动政策数量有限(通常为2或3个)。拟议方法是为了通过引入一个能够提供无限政策空间的连续域框架来填补文献中的这一空白。通过使用本文中介绍的方法,可以获取更准确的驱动器模型,然后用于创建高忠诚的模拟平台,以验证自主的车辆控制算法。拟议方法在真实的流量数据集上得到验证,并与用于证明其贡献和影响的常规水平方法相比较。