Driverless Car Coding | Visionhack

This presentation is the result of a 48 hour hackathon called Visionhack which took place at NUST MISIS in September 2017. The team included, Mitchel Mueller, Maggie Clarke, Scott Thom and Nick Richards. My roles were to help problem solve with the programming team and create this visual presentation to share our process for judging. We combined my image processing knowledge with the team's coding expertise to help cameras read the road and control vehicles. Coders, designers and computer specialists were chosen from the University of Wisconsin Stout to compete against schools from around the globe in this hackathon. Other teams hailed from Russia, China, MIT, ASU, Cambridge, Catalonia Poly and more. Search Visionhack 2017 to learn more. Special thank you to the University of Wisconsin Stout, Jen Astwood, Holly Yuan and Dean Maria Alm.

This presentation was the culmination of a hackathon that took place during September 2017 at NUST MISIS in Moscow. The objective was to develop efficient machine vision for driverless cars. 
Ideally we would build a neural network to teach computers about reading visuals in order to keep anomalies to a minimum. This system our team programmed is however a fairly reliable way to build the system's level of confidence in the road ahead and can be seen as an asset to a neural network. No neural network was implemented because of computing restrictions put on by the competition. 
Additional thank you to AMD for the presentation award and recognition. 

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