Unbalanced classification is an essential machine learning task, which has attracted
widespread attention from both the academic and industrial communities due mainly to its …

Predicting the motion of a driver's vehicle is crucial for advanced driving systems, enabling
detection of potential risks towards shared control between the driver and automation …

One of the fundamental challenges in the design of perception systems for autonomous
vehicles is validating the performance of each algorithm under a comprehensive variety of …

While deep neural network (DNN) models have become extremely popular for object
detection in automated driving systems (ADS), the dynamic and varied nature of the road …

Open Set Recognition (OSR) is about dealing with unknown situations that were not learned
by the models during training. In this paper, we provide a survey of existing works about …

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 …

Two scenarios have shaped the discussion on the deployment of automated vehicles (AVs).
The first is the revolutionary or disruptive scenario, in which a competitor would reach fully …

As autonomous vehicles (AVs) become ubiquitous, users' trust will be critical for the
successful adoption of such systems. Prior works have shown that the driving styles of AVs …

Despite the unchallenged performance, deep neural network (DNN) based object detectors
(OD) for computer vision have inherent, hard-to-verify limitations like brittleness, opacity, and …

Monitoring the integrity of object detection for errors within the perception module of
automated driving systems (ADS) is paramount for ensuring safety. Despite recent …