6.S094: Deep Learning for Self-Driving Cars (mit.edu)

See the discussion here:
https://news.ycombinator.com/item?id=13365492

This comment has a lot of truth in it, and in OSSDC.org we should follow this approach:

Animats 16 hours ago [-]

Trying to do self-driving entirely by deep learning in a reactive system seems like a terrible idea. Deep learning to classify objects (bicycles, pedestrians, cars, traffic lights, telephone poles, cops) is fine. But map building, planning, and obstacle avoidance needs to be more reliable than a purely reactive system can do.
Look at the videos from Urmson's talk at SXSW. That shows the worldview of a Google self-driving car. It's about 80% geometry and 20% classification.
Yes, you can get a pure deep learning system to drive on a freeway. But how does it do in a more cluttered environment? A system that builds local maps and profiles terrain with LIDAR can deal with clutter.

Join us in Open Source Self Driving Car Initiative at:

http://ossdc.org/