值得信赖的自动驾驶汽车面临的挑战:让我们从生活中学习

Imke Hoppe, Willem Hagemann, Ingo Stierand, Axel Hahn, Andre Bolles
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摘要

尽管信息学和系统工程长期以来一直致力于确保自动驾驶汽车的(技术)可信性,但目前的调查显示,公众和个人对自动驾驶汽车的信任度有限。为了解决这一信任差距,本文探讨了其根本原因。文章阐述了将信任理解为一种社会现象与旨在确保(技术)可信性的研究传统之间的差距。文章讨论了迄今为止,社会科学和人文学科的这些研究传统在多大程度上得到了系统工程研究的认可和反映。根据社会科学和人文科学的研究现状,信任在很大程度上依赖于个人对自动驾驶汽车的能力、仁慈和正直的评估。相比之下,技术可信度被定义为主体间可衡量的技术参数的总和。它们通常根据各自的技术标准和规范来描述系统的某些能力或属性。本文将自主系统的 "可解释性 "置于桥梁地位。可解释性有助于构思一个综合信任层,以传达系统的能力、仁慈和诚信。因此,"可解释性 "应尊重用户的个人需求和情境需求,并因此具有响应性。总之,研究结果表明,"从生活中学习 "需要与邻近研究领域开展广泛的跨学科合作。这种关于可信度的新视角将现有的研究领域结合起来。它深入探讨了 "如何 "的概念,深入研究了错综复杂的问题,并展示了研究现状中(缺失的)相互联系。
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Challenges for trustworthy autonomous vehicles: Let us learn from life
Current surveys indicate limited public and individual trust in autonomous vehicles despite a long tradition to ensure their (technical) trustworthiness in informatics and systems engineering. To address this trust gap, this article explores the underlying reasons. The article elaborates on the gap between trust understood as a social phenomenon and, in contrast, the research tradition aimed at guaranteeing (technical) trustworthiness. It discusses to what extent those research traditions in the social sciences and humanities have been recognized and reflected in systems engineering research to date. Trust, according to the current state of research in the social sciences and humanities, heavily relies on individual assessments of an autonomous vehicle's abilities, benevolence and integrity. By contrast, technical trustworthiness is defined as the sum of intersubjective, measurable, technical parameters. They describe certain abilities or properties of a system, often according to respective technical standards and norms. This article places the “explainability” of autonomous systems in a bridging role. Explainability can help to conceptualize an integrative trust layer to communicate a system's abilities, benevolence and integrity. As such, explainability should respect the individual and situational needs of users, and should therefore be responsive. In conclusion, the results demonstrate that “learning from life” requires extensive interdisciplinary collaboration with neighboring research fields. This novel perspective on trustworthiness aligns existing research areas. It delves deeper into the conceptual “how”, dives into the intricacies and showcases (missing) interconnectedness in the state of research.
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