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.
@article{arxiv.2207.09619,
title = {Learning Latent Traits for Simulated Cooperative Driving Tasks},
author = {Jonathan A. DeCastro and Deepak Gopinath and Guy Rosman and Emily Sumner and Shabnam Hakimi and Simon Stent},
journal= {arXiv preprint arXiv:2207.09619},
year = {2022}
}