Challenges & Directions for Applying Machine Learning in Autonomous Vehicles
The rapid growth in the field of self-driving cars and connected vehicles has been fairly recent yet phenomenal. We are witnessing a greater collaboration between the industries, academia and also the open source community to set foot into the higher levels of autonomy. While it is exciting to see the continuously increasing usage of AI and deep machine learning algorithms in this domain, one needs to be aware of the challenges that come along with these intelligent modules under various circumstances. There are challenges from functionality to security, online to offline processing and even in communicating the autonomous capabilities and limitations to the end consumer. This talk will try to throw light on some of these problems and possible solutions. This talk will also present some of our in house R&D and how we see the future of autonomous vehicles.
Pratik Prabhanjan Brahma is a machine learning research engineer at the Volkswagen Electronics Research Labarotary (ERL) in Belmont, California. His current research is focused on developing deep learning technologies for autonomous driving. He received his Ph.D. from the University of Florida where he worked on reliable subspace representation for machine learning. He received his Bachelor’s and Master’s from the Indian Institute of Technology, Kharagpur. He was also one of the top 30 selected candidates in the Indian National Mathematics Olympiad (INMO) of 2005.