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at normal speeds on public highways The input to the neural network is a 30 x 32 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 The ANN is trained to mimic the observed steering commands of a human driving the vehicle for approximately 5 minutes ALVINN has used its learned networks to successfully drive at speeds up to 70 miles per hour and for distances of 90 miles on public highways (driving in the left lane of a divided public highway, with other vehicles present) Figure 41 illustrates the neural network representation used in one version of the ALVINN system, and illustrates the kind of representation typical of many ANN systems The network is shown on the left side of the figure, with the input camera image depicted below it Each node (ie, 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 As can be seen, 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 The network structure of ALYINN is typical of many ANNs Here the individual units are interconnected in layers that form a directed acyclic graph In general, ANNs can be graphs with many types of structures-acyclic or cyclic, directed or undirected This chapter will focus on the most common and practical ANN approaches, which are based on the BACKPROPAGATION algorithm The BACKPROPAGATION algorithm assumes the network is a fixed structure that corresponds to a directed graph, possibly containing cycles Learning corresponds to choosing a weight value for each edge in the graph Although certain types of cycles are allowed, the vast majority of practical applications involve acyclic feed-forward networks, similar to the network structure used by ALVINN.

The /= operator divides the variable on the left side of the operator by the value on the right side The result is then assigned to the variable Here is an example:

In this example, the variable mymoney is divided by the value of the variable cutpayby, which is 2 The result of 500 is then assigned to the mymoney variable

ANN learning is well-suited to problems in which the training data corresponds to noisy, complex sensor data, such as inputs from cameras and microphones

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