Explaining How a Deep Neural Network Trained with End-to-End Learning Steers aCar

How a Car Learns to Steer by Watching People

Imagine a car that learn to steer by watching people drive.
NVIDIA built a system named PilotNet that studies video from a front camera and copies the steering the human did, so it can drive on its own.
The system looks at many road images from real trips and picks up what matters without a human telling it every rule.
That means it finds lane edges, other cars, but also surprising cues like bushes or odd little cars, things engineers might not think to program.
The team also made a way to show which parts of a picture the car used to decide.
So you can see why it turned left or right.
On real roads it kept the car in the lane, even when markings were gone.
Simple idea: watch humans, learn the task, do the job.
It's not magic; it's pattern learning, sometimes it gets it wrong, but many times it does pretty well.

Read article comprehensive review in Paperium.net:
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers aCar

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