Deep Learning for Self-Driving Cars

Explore the cutting-edge applications of deep learning in autonomous driving technology through this conference talk by Christoffer Petersson from Zenseact. Gain insights into the company's mission to develop world-leading autonomous driving software for consumer vehicles, aimed at significantly reducing traffic fatalities and injuries globally. Discover the key challenges in implementing deep learning algorithms for self-driving cars and learn about Zenseact's plans to expand their use. Delve into recent and ongoing deep learning research activities, including self-supervised pretraining and point cloud processing. Understand the software challenges faced in autonomous driving and compare modular and end-to-end approaches. Benefit from Petersson's expertise as he shares his experience in both product development and research, bridging the gap between theoretical physics and practical applications in machine learning and computer vision for autonomous vehicles.

Intro
About the company
The software challenge
Modular approach
Endtoend approach
Recent research projects
Selfsupervised pretraining
Point clouds
Example
Dataset
Conclusion
Questions