Abstract
Driver distraction is the primary cause of car accidents among USA teenage drivers. Predicting distractive driver behaviour and adapting the car systems accordingly is one solution to this problem. We use neural networks to find a correlation between driving patterns and car system variables. We conducted an experiment to induce distractive tasks to drivers and collected corresponding data patterns, then used them to train the network. With our triangulation algorithm, we reused the trained network to predict driver behaviour using the data patterns from part 1. Our neural network accurately predicts driver distraction when fed with system variables alone.
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