自動運転における説明の具体性が乗員に与える影響
Effects of Explanation Specificity on Passengers in Autonomous Driving
自動運転車の乗員に対し、抽象的な説明と具体的な説明を音声で提示し、安全性の知覚や不安、運転を引き継ぎたいという欲求にどう影響するかを実験した。
著者: Daniel Omeiza, Raunak Bhattacharyya, Nick Hawes, Marina Jirotka, Lars Kunze
分類: cs.RO, cs.AI, cs.CY, cs.HC, cs.LG
原文アブストラクト
The nature of explanations provided by an explainable AI algorithm has been a topic of interest in the explainable AI and human-computer interaction community. In this paper, we investigate the effects of natural language explanations' specificity on passengers in autonomous driving. We extended an existing data-driven tree-based explainer algorithm by adding a rule-based option for explanation generation. We generated auditory natural language explanations with different levels of specificity (abstract and specific) and tested these explanations in a within-subject user study (N=39) using an immersive physical driving simulation setup. Our results showed that both abstract and specific explanations had similar positive effects on passengers' perceived safety and the feeling of anxiety. However, the specific explanations influenced the desire of passengers to takeover driving control from the autonomous vehicle (AV), while the abstract explanations did not. We conclude that natural language auditory explanations are useful for passengers in autonomous driving, and their specificity levels could influence how much in-vehicle participants would wish to be in control of the driving activity.
