Autonomous self-driving vehicle: Perception, supervised learning, control.
|dc.description||Applied Thesis 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.subject||autonomous vehicle development platform||en_US|
|dc.subject||road data collection||en_US|
|dc.title||Autonomous self-driving vehicle: Perception, supervised learning, control.||en_US|