NEURAL NETWORK REPRESENTATIONS

• A prototypical example of ANN learning is provided by Pomerleau's system ALVINN, which uses a learned ANN to steer an autonomous vehicle driving at normal speeds on public highways.
• The input to the neural network is a 30x32 grid of pixel intensities obtained from a forward-pointed camera mounted on the vehicle.
• The network output is the direction in which the vehicle is steered

Figure: Neural network learning to steer an autonomous vehicle.

• Figure illustrates the neural network representation.
• The network is shown on the left side of the figure, with the input camera image depicted below it.
• Each node (i.e., circle) in the network diagram corresponds to the output of a single network unit, and the lines entering the node from below are its inputs.
• There are four units that receive inputs directly from all of the 30 x 32 pixels in the image. These are called "hidden" units because their output is available only within the network and is not available as part of the global network output. Each of these four hidden units computes a single real-valued output based on a weighted combination of its 960 inputs
• These hidden unit outputs are then used as inputs to a second layer of 30 "output" units.
• Each output unit corresponds to a particular steering direction, and the output values of these units determine which steering direction is recommended most strongly.
• The diagrams on the right side of the figure depict the learned weight values associated with one of the four hidden units in this ANN.
• The large matrix of black and white boxes on the lower right depicts the weights from the 30 x 32 pixel inputs into the hidden unit. Here, a white box indicates a positive weight, a black box a negative weight, and the size of the box indicates the weight magnitude.
• The smaller rectangular diagram directly above the large matrix shows the weights from this hidden unit to each of the 30 output units.