Engineering Research Strategies for Investigating Long-Term Automation Effects, Behavioural Adaptation and Change Processes: Experts’ Views

The rapid resurgence of automated vehicles poses on-road and in-traffic concerns over the sequence of time. We must assess different factors that may contribute to how humans may respond to automation in time and space. We consider the impact of long-term automation exposure on user behavioural modification and transfiguration. Arguably, a major source of difficulty is defining how long a period is enough to contemplate the potential impacts. Thus, the core objective of this paper is to promote an expert evidence-based culture of considering strategies and actual application practices. We consider what constitutes long-term to prolifically draw knowledge benchmarks for empirical evaluation strategies on behavioural adaptation and change processes. The aim is to outline requirements for long-term research standards, by engineering long-term research strategies. Moreover, derive prolific insights for future development of long-term data computation strategies using artificial intelligent mainframes for engineering quality research that predicts the behavioural system minds of users. Furthermore, considers their thinking and unthinking effects. Thus, N = 20 experts contributed their knowledge. The lessons learned are useful for considering research computing strategies.