Haptic Shared Control Systems And A Neuroergonomic Approach To Measuring System Trust

触觉共享控制系统和测量系统信任的神经工学方法

• 批准号: EP/Y00194X/1
• 负责人: James Blundell
• 金额: $ 17.62万
• 依托单位: CRANFIELD UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY00194X%2F1
• 关键词: Haptic; Shared; Control; Systems; Neuroergonomic

With sufficient R&D investment the UK is forecasted to experience significant economic growth across a range of sectors by 2035 delivered through automation and robotics. Relevant application areas include the design and safety of future connected autonomous vehicles, remotely monitored teleoperations, and human-robot interactions involved in surgery, nursing, manufacturing, construction, and maintenance. Achieving sufficient user trust in automation is pivotal to realising this ambition in the UK. Measuring trust in automation is critical for determining automation acceptance and its correct usage. In addition, the act of deciding to trust is based on a mixture of analytic and emotional decisions. This complexity means that the widely used questionnaire-based methods of measuring trust are insufficient. Not only do they struggle to measure the full complexity of trust, but they cannot measure changes in trust as they occur in real-time in response to automation experience.Measuring neural responses during interaction with automation is a potential objective means to measuring trust in real-time. Our own published research has advocated using functional near infrared spectroscopy (fNIRS) where areas of the brain associated with emotional trust judgements were identified. Having a real-time marker of emotional trust is important as it allows for a measure of trust that is uncoupled from analytical decision processes; processes that are involved in a range of cognitive processes, including mental workload. The primary goal of this proposal is to identify a unique neural measurement of automation trust. We will tackle this challenge through the in parallel measurement and analysis of neural correlates of trust (fNIRS) and physiological correlates of mental workload (heart rate variability, pupillometry, galvanic skin response) during experiments where participants interact with automated teammates of varying reliable.To demonstrate the application of a unique neural marker of automation trust we will examine how trust changes in response to the communication method between humans and automation. Conventionally, responsibility between humans and automation is "traded" from one to another as a lumped whole. For instance, the way adaptive cruise control functions in modern cars. The driver can transfer whole responsibility to the car, typically initiated by a button press. Likewise, transfers are rapid and whole in automatic emergency braking, and initiated by the automation when an imminent collision is sensed. These transitions are often called "bumpy" and are implicated in compromises to safety. A promising alternative communication method is "haptic shared control". It offers greater transparency through the continuous force feedback communication of the automation's behaviour via the system's control input (e.g., steering wheel, accelerator pedal). This means that the user is better kept "in the loop", supporting "smooth" shifts of authority in response to automation-induced faults. However, no studies have been conducted providing a comparison of trust between traded and haptic shared control. Hence, the current proposal aims to provide not an only a demonstration of the application of a neural measure of automation trust, but also addresses the fundamental lack of knowledge surrounding haptic shared control and trust.To realise this ambitious research, we will focus on initiating a collaborative academic relationship between Coventry University and TU Delft, respective world-experts in operator physiological monitoring and haptic shared control. Together we will establish neural markers of automation trust in a series of laboratory and aviation simulation experiments that involve performing collaborative tasks with automated teammates that will communicate with human participants in various ways - i.e. traded communication versus haptic shared control communication.

凭借充足的研发投资，预计到 2035 年，英国将在自动化和机器人技术的推动下，在一系列行业实现显着的经济增长。相关应用领域包括未来联网自动驾驶车辆的设计和安全、远程监控远程操作以及涉及手术、护理、制造、施工和维护的人机交互。获得用户对自动化的足够信任对于在英国实现这一目标至关重要。衡量对自动化的信任对于确定自动化的接受度及其正确使用至关重要。此外，决定信任的行为是基于分析和情感决定的混合。这种复杂性意味着广泛使用的基于问卷的信任测量方法是不够的。他们不仅难以衡量信任的全部复杂性，而且无法衡量信任随着自动化体验而实时发生的变化。测量与自动化交互过程中的神经反应是实时衡量信任的潜在客观手段-时间。我们自己发表的研究提倡使用功能性近红外光谱（fNIRS）来识别与情绪信任判断相关的大脑区域。拥有情感信任的实时标记非常重要，因为它可以实现与分析决策过程脱节的信任度量；涉及一系列认知过程的过程，包括脑力负荷。该提案的主要目标是确定自动化信任的独特神经测量。我们将通过在实验期间并行测量和分析信任的神经相关性（fNIRS）和心理负荷的生理相关性（心率变异性、瞳孔测量、皮肤电反应）来应对这一挑战，其中参与者与不同可靠性的自动化队友互动。展示自动化信任的独特神经标记的应用，我们将研究信任如何随着人类与自动化之间的通信方法而变化。传统上，人类和自动化之间的责任作为一个整体从一者“交换”到另一者。例如，自适应巡航控制在现代汽车中的工作方式。驾驶员可以将全部责任转移给汽车，通常通过按下按钮来启动。同样，在自动紧急制动中，转换是快速且完整的，并且当检测到即将发生碰撞时由自动化系统启动。这些转变通常被称为“坎坷”，并且涉及安全问题。一种有前途的替代通信方法是“触觉共享控制”。它通过系统的控制输入（例如方向盘、油门踏板）对自动化行为进行连续的力反馈通信，从而提供更大的透明度。这意味着用户可以更好地“了解情况”，支持“平滑”的权力转移，以响应自动化引起的故障。然而，尚未进行研究来比较交易控制和触觉共享控制之间的信任。因此，当前的提案不仅旨在提供自动化信任的神经测量应用的演示，而且还解决了围绕触觉共享控制和信任的知识的根本缺乏。为了实现这项雄心勃勃的研究，我们将专注于启动考文垂大学和代尔夫特理工大学之间的学术合作关系，两者都是操作员生理监测和触觉共享控制领域的世界专家。我们将共同在一系列实验室和航空模拟实验中建立自动化信任的神经标记，这些实验涉及与自动化队友执行协作任务，这些队友将以各种方式与人类参与者进行通信，即交易通信与触觉共享控制通信。