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What Makes Autonomous Vehicles Autonomous? Posted on Jul 09 - 2018

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An artificial neural network is trained by showing it a driving situation and telling it the desired response. It then adjusts each node so the response of the neural network mimics the desired response.

To properly understand the legal issues presented by the burgeoning field of autonomous vehicles, we must first understand how they work. In a word: magic.

Unlike most other devices which operate by a defined set of computer-encoded rules, it is impossible to determine how an autonomous vehicle makes a decision. It may as well be magic.

We cannot burrow into an autonomous vehicle’s computer code and see a traditional “If-then” statement.  There is no code that says, “If the car in front slows, then apply the brakes.” That is because an autonomous vehicle decides what to do based on its learning, as opposed to the knowledge of a smart computer programmer.

And its learning is embedded into a black box known as a neural network.

What is a neural network?  First, a neural network is short for an artificial neural network. Artificial neural networks are computers that simulate a real neural network – a human brain. Like we did at age 16, neural networks learn to drive based on the same input: what they are taught.

They then encode that learning in multiple layers of highly interconnected nodes.  Each of these nodes functions somewhat like a neuron in your brain. They sum up various inputs from other nodes, perform a simple function and send an output to all nodes in the next layer.

An artificial neural network is trained by showing it a driving situation and telling it the desired response. It then adjusts each node so the response of the neural network mimics the desired response.

In this neural network, the inputs to the input layer nodes may be the brightness of three pixels of an image. The output layer node is the decision that the neural network has been trained to make based on the inputs. The hidden layers represent the training of the neural network.

Each node in the hidden layers is connected to each of the input nodes and to the output node. During the training process, the neural network assigns a weighting to each input of each node in the hidden layers. In operation, each node simply totals each input with its respective weighting.

If the sum exceeds a certain threshold, then that node exports a 1 or a 0 to each node in the next layer. Of course, the neural network in an autonomous vehicle would be more complex and receive many more inputs, but it functions in the same fashion. View More

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