Autonomous self-driving vehicle: Perception, supervised learning, control.

dc.contributor.author Quartey, Benedict
dc.date.accessioned 2019-08-02T10:56:09Z
dc.date.available 2019-08-02T10:56:09Z
dc.date.issued 2018-04
dc.description Applied project submitted to the Department of Computer Science, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2018 en_US
dc.description.abstract Road accidents are estimated to be the ninth leading cause of death across all age groups globally. 1.25 million people die annually from road accidents and Africa has the highest rate of road fatalities (WHO, 2015). Self-driving technology has the potential of saving over a million lives lost to preventable road accidents worldwide. Africa accounts for the majority of road fatalities and as such would benefit immensely from this technology. However, financial constraints prevent viable experimentation and research into self-driving technology in Africa. In this applied project I designed and implemented RollE to bridge this gap. RollE is an affordable modular autonomous vehicle development platform. It is capable of road data collection and autonomous driving using a convolutional neural network. This system is aimed at providing students and researchers with an affordable autonomous vehicle to develop self-driving car technology. en_US
dc.identifier.uri http://hdl.handle.net/20.500.11988/443
dc.language.iso en_US en_US
dc.publisher Ashesi University en_US
dc.subject Road accidents en_US
dc.subject road fatalities en_US
dc.subject Self-driving technology en_US
dc.subject preventable road accidents en_US
dc.subject RollE en_US
dc.subject modular autonomous vehicle en_US
dc.subject road data collection en_US
dc.subject autonomous driving en_US
dc.subject Convolutional Neural Network (CNN) en_US
dc.title Autonomous self-driving vehicle: Perception, supervised learning, control. en_US
dc.type Applied project en_US
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