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

dc.contributor.authorQuartey, Benedict
dc.date.accessioned2019-08-02T10:56:09Z
dc.date.available2019-08-02T10:56:09Z
dc.date.issued2018-04
dc.descriptionApplied project submitted to the Department of Computer Science, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2018en_US
dc.description.abstractRoad 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.urihttp://hdl.handle.net/20.500.11988/443
dc.language.isoen_USen_US
dc.publisherAshesi Universityen_US
dc.subjectRoad accidentsen_US
dc.subjectroad fatalitiesen_US
dc.subjectSelf-driving technologyen_US
dc.subjectpreventable road accidentsen_US
dc.subjectRollEen_US
dc.subjectmodular autonomous vehicleen_US
dc.subjectroad data collectionen_US
dc.subjectautonomous drivingen_US
dc.subjectConvolutional Neural Network (CNN)en_US
dc.titleAutonomous self-driving vehicle: Perception, supervised learning, control.en_US
dc.typeApplied projecten_US
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