Why does data availability favor intermediate detectors over a pure end-to-end approach for autonomous driving?

True or False: Collecting (Image, Steering Direction) pairs for end-to-end autonomous driving is just as easy as collecting labeled car and pedestrian images.

For a pure end-to-end autonomous driving system, each training example must be an (Image, _____) pair.

Why is it easier to collect training data for a car or pedestrian detector than for a pure end-to-end autonomous driving system?

A pure end-to-end autonomous driving system is easy to train because (Image, Steering Direction) pairs are inexpensive to collect.

To train a pure end-to-end autonomous driving system, you need a large dataset of _____ pairs.

Match each pipeline-related concept to its data characteristic in the autonomous driving context from Machine Learning Yearning.

Order the reasoning steps for deciding whether to use a multi-stage pipeline over a pure end-to-end approach in autonomous driving.

According to Machine Learning Yearning, which outcome does the scarcity of (Image, Steering Direction) pairs most directly support?

Machine Learning Yearning states that it is difficult to find labeled car and pedestrian images for training intermediate detectors.

Machine Learning Yearning advises that if there is a lot of data for training _____ of a pipeline, you might consider using a pipeline with multiple stages.

Match each data-related concept to its pipeline design implication in Machine Learning Yearning's autonomous driving example.

Order the steps that build Machine Learning Yearning's data-availability argument for preferring a multi-stage pipeline in autonomous driving.

Analyze how data collection costs influence autonomous driving pipeline design choices

Evaluating pipeline architecture for a new self-driving car startup

Explain the training data constraint of end-to-end autonomous driving systems