Robustness is key to engineering, automation, and science as a whole. However, the
property of robustness is often underpinned by costly requirements such as over …

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 …

As deep learning continues to dominate all state-of-the-art computer vision tasks, it is
increasingly becoming an essential building block for robotic perception. This raises …

Neural-network classifiers achieve high accuracy when predicting the class of an input that
they were trained to identify. Maintaining this accuracy in dynamic environments, where …

We consider a category-level perception problem, where one is given 3D sensor data
picturing an object of a given category (eg a car), and has to reconstruct the pose and shape …

Object detection plays a critical role in autonomous driving, but current state-of-the-art object
detectors will inevitably fail in many driving scenes, which is unacceptable for safety-critical …

Neural-network classifiers achieve high accuracy when predicting the class of an input that
they were trained to identify. Maintaining this accuracy in dynamic environments, where …

Environment perception is a fundamental part of the dynamic driving task executed by
Autonomous Driving Systems (ADS). Artificial Intelligence (AI)-based approaches have …

Machine learning (ML) provides no guarantee of safe operation in safety-critical systems
such as autonomous vehicles. ML decisions are based on data that tends to represent a …

Uncertainty estimation for 3D object detectors plays a critical role in autonomous driving.
This is because current state-of-the-art 3D object detectors can make severe errors in their …