Distractive driver behaviour detection with spiked neural networks
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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu |
Master's thesis
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Authors
Date
2022-11-01
Department
Major/Subject
Data Science
Mcode
SCI13115
Degree programme
Master's Programme in ICT Innovation
Language
en
Pages
33+5
Series
Abstract
Spiking neural networks are biologically plausible neural networks, capable of utilizing the concept of time throughout their simulations. They offer unique opportunities for the automotive industry because once they are deployed they require lower computational power and thus have decreased power consumption compared to other artificial neural networks. Theoretically, spiking neural network models can be just as powerful as the current state-of-the-art models, but in practice the technology is not matured enough to over-perform standard neural networks in terms of accuracy. The thesis examines a possible application of spiking neural networks to a real-life image classification problem, distractive drive behaviour detection. Detecting the distractive behaviours of drivers could prevent many accidents, thus saving lives. As the automotive industry moves towards even more automation to increase driving safety the need for low-power neural networks will significantly increase in the near future.Description
Supervisor
Jung, AlexanderThesis advisor
Tarsoly, MateKeywords
driver monitoring, distraction detection, machine learning, spiked neural networks