Learning from Humans – Building for Humans

向人类学习 - 为人类而建筑

• 批准号: 433524510
• 负责人: Professor Dr. Klaus Bengler
• 金额: --
• 依托单位: Lehrstuhl für Ergonomie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/433524510?language=en
• 关键词: Learning; Humans; Building

Cyber-physical systems are increasing in their technical potentials and their applicability for different usage scenarios. Still the interaction concept between human and technical system is a crucial success factor for safe, efficient and acceptable usage of these systems. To enable cooperation in this constellation this project will investigate the interrelation between human perceptive capabilities, mental workload and human trust into the CPS. Especially Human Cyber-Physical Systems (HCPS) for traffic applications are of high societial relevance and dynamic in their development. Following a basic cooperation principle an HCPS should use the strength of both human and machine, and compensates their individual limitations. Users with visual limitations put high expectations into the capabilities of automated driving systems. Automated driving systems still try to mimic human strategies in complex traffic situations.Learning from Humans in this project means that experiments with participants successfully driving under visual impairments should provide knowledge how to compensate incomplete technical sensing. Additionally we will learn how cognitive load is related to human decision making and how increasing load is compensated by drivers. The third aspect focuses how these factors influence trust into an automated driving assistance system. Building for Humans means that based on this knowledge a suitable HMI concept shall be implemented that provides information from the technical system towards the user decreasing workload, increasing trust and leading to efficient decision behaviour. The goal is an HCPS adapted to human requirements regarding perception, workload and trust. For this reason the research partners in this project will conduct highly synchronized driving simulation experiments and share their specific measurement expertise. This includes simulation practice, eye-tracking, fMRI measurement, automated driving implementation and interaction prototyping. The driving scenarios of different complexity will be defined in a joint effort, implemented by one partner and distributed to the other partners. This work plan leads to an interdisciplinary cooperation between human factors, cognitive science, neuroscience, and computer science.The data gathered in these distributed experiments will be used to specify modules dedicated to perception, cognitive load and trust. These modules will be integrated into a joint model using Dynamic Bayesian Networks describing the interrelations and their influences on decision processes.

信息物理系统的技术潜力及其对不同使用场景的适用性正在增加。人与技术系统之间的交互概念仍然是安全，有效和可接受地使用这些系统的关键成功因素。为了在这个星座中实现合作，该项目将调查人类感知能力，心理工作量和人类对CPS的信任之间的相互关系。尤其是交通领域的人的信息物理系统（HCPS）具有高度的社会相关性和动态性。HCPS遵循一个基本的合作原则，应该利用人和机器的力量，并补偿他们各自的局限性。视觉受限的用户对自动驾驶系统的功能寄予厚望。自动驾驶系统仍然试图在复杂的交通情况下模仿人类的策略。在这个项目中，向人类学习意味着参与者在视觉障碍下成功驾驶的实验应该提供如何补偿不完整的技术感知的知识。此外，我们将学习认知负荷如何与人类决策有关，以及驾驶员如何补偿增加的负荷。第三个方面关注这些因素如何影响自动驾驶辅助系统的信任。为人类构建意味着基于这些知识，应实施合适的HMI概念，从技术系统向用户提供信息，减少工作量，增加信任并导致有效的决策行为。我们的目标是一个HCPS适应人类的需求，有关的看法，工作量和信任。因此，该项目的研究合作伙伴将进行高度同步的驾驶模拟实验，并分享他们的特定测量专业知识。这包括模拟实践，眼动跟踪，fMRI测量，自动驾驶实施和交互原型。不同复杂性的驱动方案将共同定义，由一个合作伙伴实施，并分发给其他合作伙伴。这项工作计划将导致人的因素，认知科学，神经科学和计算机科学之间的跨学科合作。在这些分布式实验中收集的数据将用于指定专门用于感知，认知负荷和信任的模块。这些模块将被整合到一个联合模型中，使用动态贝叶斯网络描述相互关系及其对决策过程的影响。