Affordable Modular Autonomous Vehicle Development Platform
This addresses the lack of affordable self-driving technology for experimentation in resource-constrained regions like Africa, though it is incremental as it adapts existing methods to a new context.
The paper tackled the problem of high road fatalities, especially in Africa, by designing RollE, an affordable modular autonomous vehicle development platform that enables remote-controlled data collection and autonomous driving using a convolutional neural network, aimed at students and researchers.
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 [1]. Research shows that three out of five road accidents are caused by driver-related behavioral factors [2]. Self-driving technology has the potential of saving lives lost to these preventable road accidents. 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. This paper describes the design of RollE, an affordable modular autonomous vehicle development platform. It is capable of driving via remote control for data collection and also capable of autonomous driving using a convolutional neural network. This system is aimed at providing students and researchers with an affordable autonomous vehicle to develop and test self-driving car technology.