Mind the gap: Developments in autonomous driving research and the sustainability challenge

Abstract

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.

Emotional framing of NGO press releases: Reformative versus radical NGOs

“…The WordStat Sentiment Dictionary is partially based on several individual sentiment lists, including the ‘Harvard IV’ dictionary as well as the ‘Linguistic and Word Count’ dictionary (Tausczik & Pennebaker, 2010). It is a general‐purpose dictionary applied in various contexts, including corporate sustainability reporting (Lock & Seele, 2016), academic articles (Mora, Wu, & Panori, 2020) or policy documents (van Alstine & Barkemeyer, 2014). It provides individual scores for positive and negative sentiment, each calculated based on terms comprising several thousand‐word patterns, respectively, and is therefore well‐suited for our analysis.…”

The Great Reconfiguration

“…To monitor these environments, essential technologies include cameras (to recognise and 'read' traffic signs, traffic lights, road lanes, etc. ), laser sensors such as lidar (which measure distances to objects by sending laser light and measuring the returning reflection), radar sensors (which send out and receive radio waves that bounce off objects), and ultra-sonic sensors (which measure distance using ultrasonic waves) (Mora et al, 2020). To assess the car's location within environments, important technologies include GPS, digital road maps, cloud servers, and big-data-based vehicular networks (Soteropoulos et al, 2019;Sperling, 2018).…”

Do not put the blame on me: Asymmetric responses to service outcome with autonomous vehicles versus human agents

“…Finally, we did not consider other important aspects deriving from the adoption of AVs, such as sustainability implications, that, as noted by Mora et al (2020), are still almost unexplored. Therefore, we encourage further research to expand our view of AVs by bringing in sustainable issues, especially from an environmental perspective.…”