協調運転タスクのための潜在特性学習
Learning Latent Traits for Simulated Cooperative Driving Tasks
シミュレーション上の運転者集団から個人の行動・選好を表す潜在表現を学習し、運転者タイプに応じた介入方策を訓練・評価する枠組みを構築した。
著者: Jonathan A. DeCastro, Deepak Gopinath, Guy Rosman, Emily Sumner, Shabnam Hakimi, Simon Stent
分類: cs.HC, cs.AI, cs.RO
原文アブストラクト
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.
