Case context: An engineer tests a self-driving car on a closed track and finds a scenario where the car chooses a noticeably more jarring steering direction than a skilled human driver would. The pipeline consists of detect-cars (A), detect-pedestrians (B), and plan-path (C).

Question: Describe the specific steps the engineer should take to determine whether component A (detect cars) is responsible for this jarring steering scenario, and how they should interpret the result.

Sample answer: The engineer should manually modify A's output for this scenario to be 'perfect,' for example by manually specifying the true positions of the other cars. They should then re-run B (detect pedestrians) as normal and C (plan path), but allow C to use A's newly perfected output. If the pipeline now plans a much smoother, less jarring path, this shows that A's original detection error was responsible for the bad path decision, so the error should be attributed to component A. If the path is still jarring even with a perfect A, component A is not the cause, and the engineer should move on to test component B next using the same approach.

Key points:

• Manually perfect component A's output for the scenario
• Re-run B normally and C using A's perfected output
• A much better path indicates the error is attributable to A
• No improvement means A is not the cause and B should be tested next
• The comparison isolates A's contribution to the overall error

Rubric: Full credit describes manually perfecting A's output, re-running B and C with C using the perfected A output, and correctly interprets both a positive and negative result; partial credit for describing only part of the procedure.