Lyft's Kumar Chellapilla twill present Lyft’s Approach to Developing Self-Driving Technology at the ITS Transportation Seminar Jan. 24 at 4 p.m. in 290 Hearst Memorial Mining Building at 4 p.m. See the video.
Abstract
Lyft’s mission is to improve people’s lives with the world’s best transportation. Self driving vehicles have the potential to deliver unprecedented improvements to safety and quality, at a price and convenience that challenges traditional models of vehicle ownership. While the transportation industry is deep in the process of developing fully autonomous vehicles, many technical, regulatory and societal challenges remain. As a relatively late entrant, Level 5 is taking leveraging the second mover advantage as well as some key advantages in development and deployment that our existing rideshare business offers.
In this talk he will cover the why and how of Lyft's Autonomy program including goals, strategy and implementation of our SDS and the supporting infrastructure in both hardware and software. We’ll present the core problems in self-driving and how recent advances in computer vision, robotics, and machine learning are powering this revolution.
Presenter
Kumar Chellapilla leads knowledge efforts for self-driving at Level 5, Lyft’s self driving division. Bringing knowledge to self-driving vehicles is done by using a large scale fleet and modeling the world via a combination of HD maps and large scale scenarios that capture human behavior. Prior to Level 5, he worked at Uber ATG and led teams that worked on offboard perception, machine learning and machine teaching for autonomy & AV maps. He also worked on applying machine learning techniques to improve search, recommendations, and advertising products at LinkedIn, Twitter, and Microsoft. Kumar has a Ph.D from University of California at San Diego wherein he worked on teaching computers to learn by themselves to play games like chess and checkers and control trucks to back up to loading docks, etc. After graduation, he spent five years at Microsoft Research working on computer vision and pattern recognition techniques for OCR, document processing, and text recognition in camera images.