Computer Vision Meetup: Scaling Autonomous Vehicles with End2end

Computer Vision has been primarily used as a way to perform scene understanding when deploying autonomous vehicles. But recently, more and more research has been into how we can leverage Deep Learning and neural networks to create learned mappings from raw data to control outputs. In this talk, Sri will introduce a method to train self-driving cars completely on camera, through a technique known as end2end learning. He will go into how we can create internal mapping spaces to go from camera images to control, how we can interpret these “black-box networks”, and how we can use end2end learning to solve self-driving. Additionally, he’ll dive into some of the most promising research in the space and how we can create end2end systems that scale faster than any other method.

Sri Anumakonda is an autonomous vehicle developer focusing on creating Computer Vision software to help push the boundaries of self-driving cars. He's built more than 20+ projects in the Computer Vision space, ranging from lane detection all the way to synthetic data generation for autonomous vehicle training, and is an Associate Member at the Masason Foundation - a grant program to fund ambitious people focusing on solving some of the biggest problems of our generation.

Read a summary of this presentation in the recap blog post:

Join the Computer Vision Meetup closest to you:

This video was recorded on Nov 10, 2022 at the virtual Computer Vision Meetup.