Case context: You are building a self-driving car pipeline. The 'Plan path' component receives the location of pedestrians and other cars from earlier components. However, the path planning is still erratic and unsafe. You evaluate the planner and determine the 'Plan path' algorithm is mathematically optimal for the inputs it currently receives.

Question: Based on the concept of missing information, what is likely the cause of this failure, and what specific question should you ask to guide your pipeline redesign?

Sample answer: The likely cause is that the inputs (locations of pedestrians and cars) do not contain enough information to plan a safe path. For example, it might be missing lane markings or traffic light states. To guide the redesign, I should ask: 'What other information, other than the outputs from the earlier components, would a skilled human driver need to plan a safe path?'

Key points:

• The current inputs are insufficient for the task.
• The algorithm is not the problem.
• Ask what a skilled human driver would need.
• Identify potential missing inputs (e.g., lane markings).

Rubric: The answer must correctly diagnose the issue as missing input information and state the key question regarding what a skilled human driver would need.