[ilyaeck]

With just 72 hours of training data, this is an extremely impressive result, scientifically speaking. However, in terms of quality control/best practices in automotive, it's simply unacceptable to trust human lives with a monolithic black-box system. The reason to have modular building blocks in a production system is not necessarily better performance, but the must-have ability to test, troubleshoot, debug, fix and replace things by reducing the degrees of freedom - it's the ABC of engineering even in much less sensitive industries. So while N2N is going to be very useful for rapid prototyping, and perhaps setting the performance bar for other method, I doubt it will ever be used in production.

[EugeneOZ]

Can be used as additional system for taking decisions.

[ksml]

This is a great point. Reminds me of how airplanes have redundant flight computers and compare the outputs of the computers to determine if one might be faulty. https://en.wikipedia.org/wiki/Fly-by-wire#Redundancy The output of different self driving models could be compared to handle more difficult driving situations -- I never thought of that.

[iamaaditya]

This reminds me of ensemble. Most machine learning techniques benefit in accuracy and precision by ensembling different methods. More different the methods are (variance) better improvement you get.