Case context: You are working on a self-driving car application with a pipeline consisting of a car detection algorithm, a pedestrian detection algorithm, and a path planning module. You want to debug the pipeline and improve the overall system performance without following a rigorous formal procedure.

Question: How should you informally apply error analysis by parts to decide which component of the autonomous driving pipeline to prioritize for improvement? What specific comparisons should you make?

Sample answer: To informally apply error analysis by parts, I should ask how far each individual component is from human-level performance. Specifically, I need to compare the car detection algorithm's performance to human-level car detection, and the pedestrian detection algorithm's performance to human-level pedestrian detection. Finally, I should evaluate how far the overall self-driving system's performance is from human-level driving performance. By making these comparisons, I can identify which component is furthest from human capability and prioritize improving it.

Key points:

• Compare the car detection component to human-level performance at detecting cars.
• Compare the pedestrian detection component to human-level performance at detecting pedestrians.
• Compare the overall self-driving system's performance to human-level performance.
• Use these comparisons to determine which component requires the most effort to improve.

Rubric: The answer must identify the specific informal questions to ask, focusing on comparing the individual components (car and pedestrian detection) and the overall system to human-level performance.