To study naturalistic in-cabin emotion we developed SKYNIVI, a modified open source driving simulator, with scenarios designed to elicit startle and frustration. We target generating these emotions because we believe that by detecting these it will be possible for autonomous vehicles to learn to drive better. We show how to use SKYNIVI to develop datasets that capture naturalistic emotions in drivers and passengers for algorithmic development. We recruited 51 participants as dyads and single drivers to participate in two different scenarios. We show that we were able to evoke hundreds of instances of our target emotions in this cohort and present an analysis of factors we found to impact emotional expression including: scenario design $\mathbf{(\mathrm{p} < 0.01)}$ , demographic factors, personality and baseline affect $\mathbf{(\mathrm{p} < 0.05)}$ . We find that having a second person in the vehicle impacts observed expressions of emotion $\mathbf{(\mathrm{p} < 0.05},\mathbf{\mathrm{p} < 0.01)}$ even when no difference in baseline affect is reported.