Autonomous Land Vehicle
In a Neural Network (ALVINN)

ALVINN paper

ALVINN is a robot car. It has a camera, and is programmed to follow the road by staying in the middle of its lane.
The control software is a 2-layer feedforward network using backpropagation for learning.
Raw input is a 480 x 512 pixel image 15 times per second. Output is one of 30 discrete steering positions from hard-left to hard-right.
The input color image is preprocessed to obtain a reduced resolution image containing 30 x 32 pixels, where each pixel is an integer from 0 to 255 (i.e., one byte per pixel) representing, approximately, the brightness of the pixel. These 960 pixels values define the input to the 960 input units.
Initially, a human driver controls the vehicle for about 5 minutes while the network learns weights starting from initial random weights. After that one epoch of training using the 200 examples in the buffer pool is performed approximately every 2 seconds.

Google Car