To construct effective teaming strategies between humans and AI systems in complex, risky situations requires an understanding of individual preferences and behaviors of humans. Previously this problem has been treated in case-specific or data-agnostic ways. In this paper, we build a framework capable of capturing a compact latent representation of the human in terms of their behavior and preferences based on data from a simulated population of drivers. Our framework leverages, to the extent available, knowledge of individual preferences and types from samples within the population to deploy interaction policies appropriate for specific drivers. We then build a lightweight simulation environment, HMIway-env, for modelling one form of distracted driving behavior, and use it to generate data for different driver types and train intervention policies. We finally use this environment to quantify both the ability to discriminate drivers and the effectiveness of intervention policies.

翻译：在复杂、危险的情况下,为了在人类和人工智能系统之间建立有效的团队战略,需要了解个人偏好和人类行为。以前,这一问题是以个案或数据不可知的方式处理的。在本文中,我们建立了一个框架,能够根据模拟的驾驶员群体提供的数据,从行为和偏好方面捕捉人类的隐含潜在代表。我们的框架尽可能利用人口样本中个人偏好和类型的知识,运用适合特定驾驶员的互动政策。然后,我们建立一个轻量级模拟环境,即HMIway-env,以模拟一种分散驾驶行为的形式,并利用它为不同的驾驶员类型生成数据,并培训干预政策。我们最后利用这一环境来量化歧视驾驶员的能力和干预政策的有效性。