Avoiding Overly Complex Pipeline Modules During Redesign

According to Machine Learning Yearning, why is adding a dedicated lane-marking detector the recommended redesign choice for the self-driving pipeline?

True or False: Adding a lane-marking detector to the self-driving pipeline is recommended because it provides previously missing lane location data to the path planning module.

When a self-driving pipeline is missing lane-marking information needed by path planning, Ng recommends redesigning it by adding a _____ component.

Why should a lane-marking detector be added to a self-driving pipeline?

Recognizing that a skilled human driver needs lane-marking data suggests redesigning the pipeline to include a lane-marking detector.

Adding a lane-marking detector gets the important, previously missing information about _____ to the path planning module.

Match each pipeline redesign element to its role when adding a lane-marking detector.

Order the reasoning steps Ng uses to justify adding a lane-marking detector to a self-driving pipeline.

Why is a dedicated lane-marking detector preferable to embedding lane-detection into an existing pipeline module?

According to Machine Learning Yearning, the path planning module performs equally well with or without lane-marking location data.

Adding a lane-marking detector is better because it avoids making any particular module overly _____ to build or train.

Match each design principle to the outcome it achieves in the lane-marking detector redesign.

Order the steps for evaluating and implementing a pipeline redesign that adds a lane-marking detector.

Redesigning a Self-Driving Pipeline for Lane Markings

Resolving Path Planning Failures in a Self-Driving Vehicle

Impact of Lane-Marking Data on Path Planning