Deep learning applied to driving environments

Abstract

This presentation was aimed to explore the growing opportunities to merge two booming fields: deep learning and autonomous vehicles, from a technical point of view. It addressed some Intelligent Systems Laboratory (Universidad Carlos III de Madrid, Spain) developments in this line of research, such as an obstacle detection framework using convolutional neural networks (CNNs). Furthermore, it presented a large number of challenging driving-related tasks that were expected to become tractable through this new approach, with the focus on the strong requirements posed by the upcoming self-driving systems.</p>

This presentation was part of the 6th LSI Ph.D. Meeting, which was held on 14 Jun 2016 at the Escuela Politécnica Superior of the Universidad Carlos III de Madrid. It was published on Zenodo as an exercise within the THOR Bootcamp on Open Data, organized on 16 Nov 2016.

Original document

The different versions of the original document can be found in:

• https://figshare.com/articles/Deep_learning_applied_to_driving_environments/6306844

• https://zenodo.org/record/167820,

http://dx.doi.org/10.5281/zenodo.167820 under the license https://creativecommons.org/licenses/by-sa/4.0