  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). We show that TCP offers better properties for failure prediction than
current uncertainty measures. Since the true class is by essence unknown at
test time, we propose to learn TCP criterion from data with an auxiliary model,
introducing a specific learning scheme adapted to this context. The relevance
of the proposed approach is validated on image classification and semantic
segmentation datasets. Then, we extend our learned confidence approach to the
task of domain adaptation where it improves the selection of pseudo-labels in
self-training methods. Finally, we tackle the challenge of jointly detecting
misclassification and out-of-distributions samples by introducing a new
uncertainty measure based on evidential models and defined on the simplex.
