Automated driving systems (ADSs) aim to improve the safety, efficiency and comfort of future
vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This …

This paper explores the role and challenges of Artificial Intelligence (AI) algorithms,
specifically AI-based software elements, in autonomous driving systems. These AI systems …

There are currently around thirty companies testing self-driving cars in San Francisco, CA,
effectively creating a living laboratory. Of these companies, only Waymo is engaged in …

The deployment of Deep Learning models requires attention to certain aspects not typically
considered during the training phase. One of these is identifying and labeling samples from …

Modern machine learning (ML) systems excel in recognising and classifying images with
remarkable accuracy. However, like many computer software systems, they can fail by …

This paper proposes a novel metric learning method for fair face attribute recognition.
Datasets with imbalanced sample distributions can spoil the fair discrimination ability of …

Object recognition is a crucial step in perception systems for autonomous and intelligent
vehicles, as evidenced by the numerous research works in the topic. In this paper, object …

To be able to use machine learning models in practice, it is important to know when their
predictions can be trusted. Confidence estimations can help end users to calibrate their trust …

The detection of tumoural cells from whole slide images is an essential task in medical
diagnosis and research. In this thesis, we propose and analyse a novel approach that …

Time-sensitive machine learning benefits from Sequential Probability Ratio Test (SPRT),
which provides an optimal stopping time for early classification of time series. However, in …