Robust Deep Learning for Autonomous Driving

Understand

The last decade's research in artificial intelligence had a significant impact on the advance of autonomous driving.

• Yet, safety remains a major concern when it comes to deploying such systems in high-risk environments.
• The objective of this thesis is to develop methodological tools which provide reliable uncertainty estimates for deep neural networks.
• First, we introduce a new criterion to reliably estimate model confidence: the true class probability (TCP).