Scenarios provide a fundamental link between driving simulators and real-world conditions, shaping the extent to which the findings of a user study can be applied to public roads. However, compared to other aspects of study design, scenario development in human–vehicle interaction research tends to receive less deliberate attention. To encourage more methodical scenario generation, this work introduces a mixed methods approach for extracting representative scenarios from an integration of three real-world data sources: aggregated crash statistics, interviews with experienced drivers, and naturalistic driving data. Through a case study on winter driving, we outline the derivation of a nighttime, two-lane road scenario from these data sources and conduct an initial driving simulator pilot study to assess its realism. We hope that this demonstration of scenario generation from quantitative and qualitative data inspires researchers to consider more rigorous methods for scenario design in future work.
