Learnability in Automated Driving (LiAD): Concepts for Applying Learnability Engineering (CALE) Based on Long-Term Learning Effects

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Naomi Y. Mbelekani, Klaus Bengler
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Abstract

Learnability in Automated Driving (LiAD) is a neglected research topic, especially when considering the unpredictable and intricate ways humans learn to interact and use automated driving systems (ADS) over the sequence of time. Moreover, there is a scarcity of publications dedicated to LiAD (specifically extended learnability methods) to guide the scientific paradigm. As a result, this generates scientific discord and, thus, leaves many facets of long-term learning effects associated with automated driving in dire need of significant research courtesy. This, we believe, is a constraint to knowledge discovery on quality interaction design phenomena. In a sense, it is imperative to abstract knowledge on how long-term effects and learning effects may affect (negatively and positively) users’ learning and mental models. As well as induce changeable behavioural configurations and performances. In view of that, it may be imperative to examine operational concepts that may help researchers envision future scenarios with automation by assessing users’ learning ability, how they learn and what they learn over the sequence of time. As well as constructing a theory of effects (from micro, meso and macro perspectives), which may help profile ergonomic quality design aspects that stand the test of time. As a result, we reviewed the literature on learnability, which we mined for LiAD knowledge discovery from the experience perspective of long-term learning effects. Therefore, the paper offers the reader the resulting discussion points formulated under the Learnability Engineering Life Cycle. For instance, firstly, contextualisation of LiAD with emphasis on extended LiAD. Secondly, conceptualisation and operationalisation of the operational mechanics of LiAD as a concept in ergonomic quality engineering (with an introduction of Concepts for Applying Learnability Engineering (CALE) research based on LiAD knowledge discovery). Thirdly, the systemisation of implementable long-term research strategies towards comprehending behaviour modification associated with extended LiAD. As the vehicle industry revolutionises at a rapid pace towards automation and artificially intelligent (AI) systems, this knowledge is useful for illuminating and instructing quality interaction strategies and Quality Automated Driving (QAD).
自动驾驶中的可学习性:基于长期学习效应的可学习性工程应用概念
自动驾驶的易学性(LiAD)是一个被忽视的研究课题,特别是考虑到人类学习交互和使用自动驾驶系统(ADS)的不可预测和复杂的方式。此外,还缺乏专门用于指导科学范式的LiAD(特别是扩展可学习性方法)的出版物。因此,这产生了科学上的分歧,因此,与自动驾驶相关的长期学习影响的许多方面迫切需要进行重要的研究。我们认为,这是对高质量交互设计现象的知识发现的约束。从某种意义上说,有必要抽象出长期效果和学习效果如何影响用户的学习和心理模型(消极和积极)的知识。以及诱导变化的行为配置和表现。鉴于此,可能有必要检查操作概念,通过评估用户的学习能力,他们如何学习以及他们在时间序列中学习什么,来帮助研究人员设想自动化的未来场景。以及建立一个理论的效果(从微观,中观和宏观的角度),这可能有助于轮廓符合人体工程学的质量设计方面经得起时间的考验。因此,我们回顾了关于可学习性的文献,从长期学习效应的经验角度挖掘了LiAD知识发现。因此,本文向读者提供了在可学习性工程生命周期下制定的讨论要点。例如,首先,LiAD的上下文化,重点是扩展LiAD。其次,将LiAD的操作机制概念化和可操作化,作为人体工程学质量工程中的一个概念(介绍了基于LiAD知识发现的应用可学习性工程(CALE)研究的概念)。第三,系统化可实施的长期研究策略,以理解与扩展LiAD相关的行为改变。随着汽车行业朝着自动化和人工智能(AI)系统的快速发展,这些知识对于阐明和指导高质量的交互策略和高质量的自动驾驶(QAD)非常有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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