Autonomous Driving Pipeline Encodes Simpler Subtasks

Why can an autonomous-driving pipeline perform far below human level even when each of its components individually operates near human level?

True or False: In an autonomous-driving pipeline, every component—including path planning—must use a machine-learned model.

Adding a _____ detector to an autonomous-driving pipeline can supply missing road-context information to the path planner without requiring the planner to consume raw camera images.

What are the three components in the simple autonomous driving pipeline described in Machine Learning Yearning?

True or False: Every component in a machine learning pipeline must be a learned model.

When an autonomous driving pipeline underperforms overall despite each component being near human-level, adding a _____ detector can supply the missing information.

Match each autonomous driving pipeline component to its role in the pipeline.

Order the reasoning steps for diagnosing why an autonomous driving pipeline underperforms humans despite each sub-component being individually strong.

If each sub-component of an autonomous driving pipeline is near human-level given its inputs but the full system underperforms humans, what is the most likely cause?

True or False: According to Machine Learning Yearning, obtaining training data for car and pedestrian detectors is relatively easy due to existing datasets and crowdsourcing.

Machine Learning Yearning states that _____ (such as Amazon Mechanical Turk) can be used to obtain larger labeled datasets for training car and pedestrian detectors.

Match each pipeline diagnosis concept to its correct description in the autonomous driving example.

Order the steps for fixing an information gap in the autonomous driving pipeline by adding a lane-marking detector.

Explain how to address an information gap in an autonomous driving pipeline.

Diagnose pipeline failure in an autonomous vehicle system.

Explain the benefit of using a lane-marking detector in a pipeline.