论文标题
人类机器人相互作用和自动驾驶的人类行为模型：人类行为的模型需要多么准确？
Models of human behavior for human-robot interaction and automated driving: How accurate do the models of human behavior need to be?
论文作者
论文摘要
在许多情况下，有许多例子，可以访问改进的人类行为和认知模型，从而可以创建可以更好地与人类互动的机器人，而在道路车辆自动化中，这是一个快速增长的研究领域。因此，人类机器人相互作用（HRI）为人类行为建模提供了重要的应用设置 - 但是鉴于人类行为的严重复杂性，这些模型需要多么完整和准确？在这里，我们概述了一些可能的思考这个问题的方法，从建模者需要保持正确目标的建议开始：在安全，绩效和人类满意方面，成功的人类机器人互动。建模完整性和准确性的努力应集中在人类行为的那些方面，而互动成功最敏感。我们强调，确定这些方面是一个困难的科学目标本身是一个艰难的科学目标，在每个给定的HRI背景下都不同。在此问题的情况下，我们提出并举例说明了一种在此问题上制定先验假设的方法，如果机器人将参与目前在人类之间发生的相互作用（例如自动驾驶中）。我们的观点还强调了对HRI中人类行为模型过高依赖的一些可能风险，以及如何减轻这些风险。
There are many examples of cases where access to improved models of human behavior and cognition has allowed creation of robots which can better interact with humans, and not least in road vehicle automation this is a rapidly growing area of research. Human-robot interaction (HRI) therefore provides an important applied setting for human behavior modeling - but given the vast complexity of human behavior, how complete and accurate do these models need to be? Here, we outline some possible ways of thinking about this problem, starting from the suggestion that modelers need to keep the right end goal in sight: A successful human-robot interaction, in terms of safety, performance, and human satisfaction. Efforts toward model completeness and accuracy should be focused on those aspects of human behavior to which interaction success is most sensitive. We emphasise that identifying which those aspects are is a difficult scientific objective in its own right, distinct for each given HRI context. We propose and exemplify an approach to formulating a priori hypotheses on this matter, in cases where robots are to be involved in interactions which currently take place between humans, such as in automated driving. Our perspective also highlights some possible risks of overreliance on machine-learned models of human behavior in HRI, and how to mitigate against those risks.
