Component Far from Human-Level Performance as Improvement Priority

Flawed Pipeline Despite Human-Level Components

When comparing the Plan path component to human-level performance in a self-driving pipeline, what inputs should the human evaluator receive?

ML Yearning describes comparing pipeline components to human-level performance as a rigorous, procedure-based debugging technique.

In informal pipeline debugging, you ask how _____ each component is from human-level performance.

Match each self-driving pipeline component from ML Yearning to its primary output or function.

Order the informal debugging questions ML Yearning recommends asking when evaluating a self-driving car pipeline.

Why does ML Yearning require the human evaluator for path planning to use only the detection components' outputs rather than camera images?

In ML Yearning's self-driving example, both the Detect cars and Detect pedestrians components feed their outputs directly into the Plan path component.

To compare the Plan path component fairly to a human, the human must plan the route using only the _____ of the detection components.

Match each informal debugging question to the pipeline element it targets in ML Yearning's self-driving car example.

Arrange the reasoning steps a practitioner follows when diagnosing a self-driving pipeline problem using human-level comparison.

Analyzing Pipeline Component Inputs for Human-Level Performance Comparison

Evaluating a Self-Driving Path Planning Component

Input Constraint in Human-Level Comparison for Path Planning