Mora Luca, Wu Xinyi, Panori Anastasia
The Business School, Edinburgh Napier University, Edinburgh, EH14 1DJ, United Kingdom.
School of Social and Political Science, The University of Edinburgh, Edinburgh, EH8 9LD, United Kingdom.
J Clean Prod. 2020 Dec 1;275:124087. doi: 10.1016/j.jclepro.2020.124087. Epub 2020 Sep 11.
Scientific knowledge on autonomous-driving technology is expanding at a faster-than-ever pace. As a result, the likelihood of incurring information overload is particularly notable for researchers, who can struggle to overcome the gap between information processing requirements and information processing capacity. We address this issue by adopting a multi-granulation approach to latent knowledge discovery and synthesis in large-scale research domains. The proposed methodology combines citation-based community detection methods and topic modelling techniques to give a concise but comprehensive overview of how the autonomous vehicle (AV) research field is conceptually structured. Thirteen core thematic areas are extracted and presented by mining the large data-rich environments resulting from 50 years of AV research. The analysis demonstrates that this research field is strongly oriented towards examining the technological developments needed to enable the widespread rollout of AVs, whereas it largely overlooks the wide-ranging sustainability implications of this sociotechnical transition. On account of these findings, we call for a broader engagement of AV researchers with the sustainability concept and we invite them to increase their commitment to conducting systematic investigations into the sustainability of AV deployment. Sustainability research is urgently required to produce an evidence-based understanding of what new sociotechnical arrangements are needed to ensure that the systemic technological change introduced by AV-based transport systems can fulfill societal functions while meeting the urgent need for more sustainable transport solutions.
关于自动驾驶技术的科学知识正以前所未有的速度扩展。因此，对于研究人员来说，产生信息过载的可能性尤为显著，他们可能难以克服信息处理需求与信息处理能力之间的差距。我们通过采用多粒度方法来解决大规模研究领域中的潜在知识发现与合成问题。所提出的方法结合了基于引用的社区检测方法和主题建模技术，以简洁而全面地概述自动驾驶汽车（AV）研究领域的概念结构。通过挖掘50年自动驾驶研究产生的大量丰富数据环境，提取并呈现了13个核心主题领域。分析表明，该研究领域强烈倾向于研究实现自动驾驶汽车广泛推广所需的技术发展，而很大程度上忽视了这种社会技术转型所带来的广泛的可持续性影响。基于这些发现，我们呼吁自动驾驶研究人员更广泛地参与可持续性概念，并邀请他们加大力度对自动驾驶汽车部署的可持续性进行系统调查。迫切需要开展可持续性研究，以便基于证据理解需要哪些新的社会技术安排，以确保基于自动驾驶汽车的交通系统所带来的系统性技术变革能够在满足对更可持续交通解决方案的迫切需求的同时履行社会功能。
