Automated driving systems (ADS) have gained sizable attention over the last decade, promising safer, cheaper, and more accessible transportation. However, the discourse driving this research has not been thoroughly explored, with scant qualitative work detailing specific cases. To address this gap, this paper explores the motivations for the societal need for ADS research for the 500 most cited publications in the field, investigating explicit motivations (e.g., accident reduction), writing style, assumed likelihood of outcomes, and the text's tone. Qualitative and quantitative techniques are used, as well as the tool ChatGPT to investigate the large number of texts. The results show that the most common motivation is the emergence itself (‘ADS are coming, so they need to be studied’), followed by potential benefits such as accident reduction, congestion mitigation, increased comfort and productivity onboard, and environmental concerns. The tone of the publications is primarily neutral or slightly positive but with some deterministic descriptions like ‘ADS will lead to’ rather than more cautious language like ‘can’ or ‘may’. The results offer a glimpse rather than a comprehensive overview of the discourse on ADS within the research community. While the analysis might not capture the nuanced perspectives that readers encounter in publications focused on 'impacts', the reviewed publications remain the most cited works in the field, likely affecting the discourse to some extent.

Self-driving vehicles have been hailed as a revolutionary force that will benefit society through increased accessibility and reduced accident rates. However, implementing such a new technological system will be complex, requiring immense societal adaptions and changes. In this paper, we analyse the challenges faced in implementing self-driving vehicles based on interviews with onboard operators and stakeholders responsible for the operations of self-driving buses in northwest Stockholm, Sweden. From these interviews, we suggest a framework for understanding which societal changes would be required to implement self-driving vehicles.

We highlight the complexities of self-driving and the sociotechnical perspective needed to understand the challenges of implementing self-driving vehicles, underscoring human drivers' complex and tacit knowledge. Further, we identify a crucial technological gap in the vehicles' understanding of context, suggesting that comprehensive self-driving operation requires a form of general intelligence beyond the current, specialised, driving-only intelligence. The findings presented in this paper pave the way for further research and efforts needed to enable broader adoption of self-driving vehicles and offer insights for policymakers to guide future legislation in self-driving transport. 

In conclusion, several adaptions are required to facilitate self-driving vehicles. However, policymakers are advised to uphold stringent transport requirements, regardless of human or machine drivers, and resist any pressures to unnecessarily relax regulations. Such a cautious approach will ensure the continued prioritisation of safety and efficacy without compromising the human norms and behaviours that are integral and necessary to transport systems. 

Self-driving technology may lead to a paradigm shift for the transport industry with shared cars available to every-one. However, this vision has increasingly been challenged as too optimistic and unsubstantiated. In this study we explore societal impacts of using this technology for both cars and public transport and investigate differences depending on geography and trip purpose. Four scenarios were designed through workshops with 130 transport experts, modelled using a conventional four-step model for Stockholm, Sweden and evaluated in terms of changes to mode choice, number of trips and person kilometres.We find larger increases for non-commuting trips, i.e. service and leisure trips, than for commuting trips, questioning the view of the 'productive work trip' as self-driving technology's main impact on society. As these trips are primarily made outside of rush hours, this may lead to a changed transport system. Geographic differences are substantial and heavily dependent on the cost model for car alternatives, even indicating a reduction in car travel in rural areas if private ownership would be replaced by shared cars. Furthermore, walking and cycling levels decreased in all scenarios while enhancing public transport using self-driving technology had a limited impact on ridership.These results show that the impacts of self-driving technology may have varied societal impacts even within a region and may lead to increased car travel, especially off-peak. These conclusions stress the need for policies that are sensitive to both geography and time.

Numerous studies have studied the impacts of automated driving (AD) technology on e.g. accident rates or CO2 emissions using various frameworks. In this paper we present an overview of previous frameworks used for societal impacts and review their advantages and limitations. Additionally, we introduce the Total Impact Assessment (TIA) framework developed by the Swedish Transport Administration and use this framework to evaluate three scenarios for AD bus services in Stockholm. We conclude that the reviewed frameworks cover different aspects of AD technology, and that e.g. cybersecurity and biodiversity are areas largely neglected. Furthermore, most frameworks assume effects to be homogenous, when there may be large variation in e.g. perceived security. The TIA framework does not manage to include all societal aspects of AD technology, but has great benefits and manages to provide important insights of the societal impacts of AD technology, especially how effects may wary for different actors. 

Researchers struggle to create simple yet nuanced diagrams or short abstracts that are easily comprehendible by non-experts. Research articles may be our primary communication method, but they are generally difficult to understand, making them unsuitable for general communication. This paper investigates a rarely used method in transport research – vignettes – to convey research results and elicit implications for public planners. Historically, these vignettes have been designed using qualitative methods, e.g., the Delphi method or by researchers' review of trends. However, this paper uses two vignettes originating from quantitative results from a simulation model paper (Almlöf et al., 2022), investigating the impacts of self-driving technology. Rewriting the results into two short stories – vignettes – these stories then served as the starting point for semi-structured interviews and a workshop with six public officials in Stockholm, Sweden. The vignettes were used to understand what these results would mean to the participants and how they would combat potential problems connected to sustainability, given the vignettes. Finally, the interviews and the workshop were qualitatively analysed, and policy tools were identified that can steer the transport system towards societal goals. The vignettes were a powerful tool to help the participants envision a future with self-driving transport options and promote critical thinking regarding how each participant would be affected, given such a future. As such, vignettes should be further explored to help researchers investigate abstract subjects which participants may have difficulties understanding.