Introduction
The unexpected spike in disengagements during left turns at four-way intersections for Cruise this week presents a critical issue for our autonomous vehicle technology. To address this problem, I'll employ a systematic approach to identify, validate, and resolve the root cause while considering both immediate and long-term implications for our product and users.
Framework overview
This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.
Step 1
Clarifying Questions (3 minutes)
Why it matters: This helps us determine if the issue is specific to left turns or indicative of a broader problem. Expected answer: The issue is primarily observed in left turns at four-way intersections. Impact on approach: If isolated, we'll focus on left turn algorithms; if widespread, we'll investigate system-wide changes.
Why it matters: Recent updates could be directly related to the spike in disengagements. Expected answer: A minor update to the perception system was deployed last week. Impact on approach: If confirmed, we'll prioritize investigating the recent update and its potential unintended consequences.
Why it matters: Understanding the scale helps prioritize the issue and determine the urgency of our response. Expected answer: The disengagement rate has doubled from 1% to 2% for left turns at four-way intersections. Impact on approach: A significant increase would warrant immediate action, while a smaller spike might allow for a more measured response.
Why it matters: Safety is paramount, and user feedback can provide valuable insights into the real-world impact of the issue. Expected answer: No serious safety incidents reported, but passengers have noted increased hesitation during left turns. Impact on approach: This would help us balance the urgency of the fix with the current safety implications.
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