Avoiding Overly Complex Pipeline Modules During Redesign
Feeding the raw camera image directly into the path-planning component could provide missing information, but it would violate task simplicity by making path planning input a raw image and solve a much more complex task. Adding an intermediate lane-marking detector avoids making any module overly complex to build or train.
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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
Learn After
Why is adding a lane-marking detector preferred over feeding the raw camera image directly into the path-planning module?
True or False: Feeding the raw camera image directly into the path-planning module is the recommended fix because it supplies missing lane information without adding pipeline components.
Adding an intermediate _____ component passes the missing lane information to path planning while keeping every module's task appropriately simple.
Why does Machine Learning Yearning advise against feeding the raw camera image directly into the path-planning module?
Feeding the raw camera image directly into path planning is the recommended fix when lane-marking information is missing from the pipeline.
Feeding the raw camera image into path planning violates the design principle of '_____ simplicity' described in Machine Learning Yearning.
Match each pipeline design choice or concept to its correct consequence or definition from Machine Learning Yearning.
Order the reasoning steps used in Machine Learning Yearning to fix a missing-information problem without violating task simplicity.
What is the primary benefit of adding a lane-marking detector rather than routing raw camera images to path planning?
The task simplicity principle holds that each pipeline module should handle a focused, well-scoped task rather than a highly complex one.
Adding an intermediate _____ component gives path planning the lane information it needs without making any module overly complex.
Match each module or concept to its role in the redesigned self-driving pipeline from Machine Learning Yearning.
Order the data-flow steps in the correctly redesigned self-driving pipeline that avoids violating task simplicity.
Explain the tradeoff between feeding raw camera images directly to path-planning and adding a lane-marking detector.
Redesigning a Self-Driving Pipeline to Support Missing Lane Information
Identify the primary design principle violated when feeding raw images into a path-planner.