6.S094: Deep Learning for Self-Driving Cars

Re:6.S094: Deep Learning for Self-Driving Cars by Animats on Tuesday January 10, 2017 @05:30PM (#13369797)

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.

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Re:6.S094: Deep Learning for Self-Driving Cars by tabeth on Tuesday January 10, 2017 @08:18PM (#13370956)

Why exactly can't you use deep learning to drive in a "cluttered environment?" You didn't mention why it's a terrible idea (I have no background in deep learning, just curious)

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Re:6.S094: Deep Learning for Self-Driving Cars by visarga on Wednesday January 11, 2017 @05:04AM (#13372891)

Both model-based and model-free reinforcement learning methods are useful and sometimes are combined in an agent.

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Re:6.S094: Deep Learning for Self-Driving Cars by nojvek on Tuesday January 10, 2017 @08:10PM (#13370911)

Totally agree on this. Making neural networks build a debuggable scene graph is a pretty hard challenge. With the scene graph things become quite easy.

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