Design autonomous-driving experience metrics
Design Autonomous-Driving Experience Metrics
You are tasked with evaluating the rider experience for an autonomous-driving ride service. You have access to trip-level data with the following fields:
trip_id: Unique identifier for each trip.city: City where the trip took place.route_type: Type of route (e.g., highway, urban, suburban).distance_km: Distance covered during the trip in kilometers.planned_eta_min: Estimated time of arrival in minutes, provided by a map provider.actual_ride_time_min: Actual duration of the ride in minutes.hard_brake_count: Number of hard braking events during the trip.max_jerk: Maximum jerk experienced during the trip (rate of change of acceleration).takeover_count: Number of times a human driver had to take over control from the autonomous system.safety_event_flag: A flag indicating if a safety-critical event occurred.rider_rating: Rating provided by the rider (e.g., 1-5 stars).complaint_flag: A flag indicating if a rider complaint was filed for the trip.trip_completed_flag: A flag indicating if the trip was completed successfully.traffic_level: Level of traffic experienced during the trip.weather: Weather conditions during the trip.time_of_day: Time of day when the trip occurred.
Part 1: Metric Framework Proposal
- Propose a comprehensive metric framework for measuring the overall rider experience of the autonomous-driving product.
- Identify a single "north-star" metric that best encapsulates the product's success from a rider experience perspective.
- Alongside the north-star metric, define specific "guardrail" metrics to track for safety, comfort, reliability, and efficiency.
- Discuss how to manage potential trade-offs between these various metrics and how to avoid a misleading single-number summary.
Part 2: Investigating trip_time_ratio Discrepancies
Consider a candidate metric: trip_time_ratio = actual_ride_time_min / planned_eta_min. You observe that this ratio is significantly higher than expected, suggesting a potential performance issue with the autonomous driving product.
Describe a detailed investigation plan to determine whether this high ratio reflects a genuine product problem or is merely an artifact of other factors. Your investigation should explicitly consider and address:
- Metric definition issues.
- Route mix biases.
- Map-ETA provider biases.
- Selection biases.
- Data quality problems.
Be explicit about the segmentations, comparisons, and follow-up analyses you would perform.
AI interview prep
Core dilemma
Designing a robust, actionable, and unbiased metric system that accurately reflects complex, multi-faceted user experience in a safety-critical domain, while navigating noisy data and potential misinterpretations to drive correct engineering and product decisions.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design autonomous-driving experience metrics
Design Autonomous-Driving Experience Metrics
You are tasked with evaluating the rider experience for an autonomous-driving ride service. You have access to trip-level data with the following fields:
trip_id: Unique identifier for each trip.city: City where the trip took place.route_type: Type of route (e.g., highway, urban, suburban).distance_km: Distance covered during the trip in kilometers.planned_eta_min: Estimated time of arrival in minutes, provided by a map provider.actual_ride_time_min: Actual duration of the ride in minutes.hard_brake_count: Number of hard braking events during the trip.max_jerk: Maximum jerk experienced during the trip (rate of change of acceleration).takeover_count: Number of times a human driver had to take over control from the autonomous system.safety_event_flag: A flag indicating if a safety-critical event occurred.rider_rating: Rating provided by the rider (e.g., 1-5 stars).complaint_flag: A flag indicating if a rider complaint was filed for the trip.trip_completed_flag: A flag indicating if the trip was completed successfully.traffic_level: Level of traffic experienced during the trip.weather: Weather conditions during the trip.time_of_day: Time of day when the trip occurred.
Part 1: Metric Framework Proposal
- Propose a comprehensive metric framework for measuring the overall rider experience of the autonomous-driving product.
- Identify a single "north-star" metric that best encapsulates the product's success from a rider experience perspective.
- Alongside the north-star metric, define specific "guardrail" metrics to track for safety, comfort, reliability, and efficiency.
- Discuss how to manage potential trade-offs between these various metrics and how to avoid a misleading single-number summary.
Part 2: Investigating trip_time_ratio Discrepancies
Consider a candidate metric: trip_time_ratio = actual_ride_time_min / planned_eta_min. You observe that this ratio is significantly higher than expected, suggesting a potential performance issue with the autonomous driving product.
Describe a detailed investigation plan to determine whether this high ratio reflects a genuine product problem or is merely an artifact of other factors. Your investigation should explicitly consider and address:
- Metric definition issues.
- Route mix biases.
- Map-ETA provider biases.
- Selection biases.
- Data quality problems.
Be explicit about the segmentations, comparisons, and follow-up analyses you would perform.