Given two sets of experimental data from autonomous driving systems (e.g., two experimental groups each producing latency or performance metrics for a planning module or overall AV system), analyze and determine which solution/approach is better. The candidate must define and discuss key progress metrics for autonomous driving, then apply a structured analytical framework to compare the two datasets and justify a conclusion. The interviewer typically spends significant time explaining the autonomous driving technical stack and background before the actual question begins.
Common mistakes: The interviewer spending 30 minutes reading the problem and explaining the AV tech stack left the candidate with almost no time to actually answer — one candidate had a sinking feeling just from looking at how little time remained after the setup.; Candidates consistently reported this round is nearly impossible to prepare for — 'this is something you really can't prepare for, it just comes down to live performance' — and those who felt they performed well in other rounds were still rejected when data fluency was the weak link.
Interviewer hints: The interviewer explicitly spent a very long time (reported as ~30 minutes in a single round) explaining the autonomous driving technical stack before posing the actual question, even when the candidate already had relevant AV experience.; One interviewer told the candidate he would submit feedback quickly after the data fluency round — candidates interpreted this as a positive signal, but it did not predict the outcome.; The simulation-vs-real-road-data imbalance question was raised mid-round as a follow-up, not pre-announced — candidates should expect it to be embedded in a broader system/analysis discussion.
What passers do: Candidates who engaged conversationally with the interviewer on autonomous driving progress metrics (rather than going silent) seemed to get positive in-room signals — one candidate reported the interviewer said he would submit feedback quickly, suggesting a good impression was made during the discussion.; Applying a statistical testing framework when comparing two groups of experimental results was the approach both candidates who faced the latency comparison question reached for.
Alternative approaches: Qualitative/domain-driven metric discussion (Easier to structure without deep stats knowledge; can demonstrate AV domain expertise, but may not satisfy a data-fluency interviewer who expects quantitative rigor.); A/B testing framework (Familiar structure for many data-oriented engineers; applicable when sample sizes are known, but may be harder to apply when simulation vs. real-world data imbalance is significant.)