For autonomous driving, machine learning can be used to detect cars and pedestrians, and labeled car or pedestrian image data is relatively easy to obtain. By contrast, a pure end-to-end approach would require a large dataset of image and steering-direction pairs that is time-consuming and expensive to collect, making it difficult to train. This data availability supports considering intermediate car and pedestrian detectors in a multi-stage, non-end-to-end pipeline.

Autonomous Driving Data Availability Favors Intermediate Detectors

If a lot of training data is available for intermediate modules in a pipeline, a multi-stage pipeline is worth considering. This structure can be superior because the available data can be used to train those intermediate modules.

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When choosing components for a non-end-to-end pipeline, one important factor is whether data can be collected easily to train each component.

Training Data Availability for Pipeline Component Selection

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

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