This one tripped me up more than I expected.
Start by framing metrics around safety, scalability, and efficiency, since autonomous driving development must balance these pillars. Then, categorize metrics into leading and lagging indicators, and tie them to Waymo's mission of deploying safe, reliable self-driving technology at scale.
Pro tip: Emphasize safety-critical metrics like disengagement rate and collision rate per million miles, but also highlight how you'd use simulation metrics to accelerate development, showing you understand Waymo's simulation-first approach.
Clarify that metrics should align with Waymo's goals: safety, scalability, and operational efficiency. This sets the context for specific metrics.
List key safety metrics such as disengagement rate, collision rate per million miles, and near-miss incidents. These are lagging indicators of safety performance.
Mention metrics like mean time between failures, system uptime, and perception accuracy. These leading indicators help predict safety and reliability.
Discuss simulation metrics such as miles simulated per day, scenario coverage, and regression test pass rate. These accelerate development and validate safety.
Include metrics like cost per mile, rider satisfaction, and fleet utilization to measure commercial viability and user experience.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The interviewer spent a long time on context before even getting to the actual question, so by the time I had to answer I was already a bit frazzled.
Start by clarifying the evaluation criteria and the context of the two approaches, then compare them using appropriate metrics and statistical tests. Consider both quantitative performance and practical trade-offs like safety, cost, and scalability, especially in a safety-critical domain like autonomous driving.
Pro tip: In safety-critical systems, a statistically significant improvement in one metric may be outweighed by a regression in another; always check for guardrail metrics and consider the cost of errors.
Ask what 'better' means in this context: is it higher accuracy, lower latency, better safety, or something else? Identify the primary metric and any guardrail metrics.
Check if the data comes from a controlled experiment (e.g., A/B test) or observational study. Look for biases, sample size, and whether the two sets are directly comparable.
Use appropriate statistical tests (e.g., t-test, bootstrap) to determine if differences are significant. Consider confidence intervals and effect sizes, not just p-values.
Weigh the performance difference against factors like computational cost, safety risks, and scalability. In a safety-critical context, even small improvements may be worth large costs.
Based on the analysis, recommend which approach performs better and explain why, acknowledging any limitations or need for further testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.