Development of Autonomous driving capabilities through machine and deep learning requires training upon huge annotated data. Obtaining such training data requires a lot of efforts, not to mention the large time required to do so. This talk will explore the possibility of accelerating autonomous driving research by training machine and deep learning models upon objects in a rich virtual world. The talk will briefly comment on how models, trained on simulated data, perform when tested with the real world driving data.
Gaurav Kumar Singh is a Machine and Deep Learning Researcher at Research and Advanced Engineering at Ford Motor Company, located in Dearborn, Michigan. He has over 6 years of research experience ranging from Control Systems to Machine Learning and Data Science. His side gigs involve consulting friends in ways to utilize machine learning techniques in their startups. He has served as project reviewer and mentor for Machine Learning and Self Driving Car Nanodegree at Udacity as well. Gaurav graduated with a Masters’ degree in Electrical and Computer Engineering from University of Michigan, Ann Arbor in December 2015. He received his Bachelors of Technology (B.Tech) degree from National Institute of Technology, Trichy, India in 2014.