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 …

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 …

Reliable detection of various objects and road users in the surrounding environment is
crucial for the safe operation of automated driving systems (ADS). Despite recent progresses …

Despite significant advances in machine learning and perception over the past few decades,
perception algorithms can still be unreliable when deployed in challenging time-varying …

This paper discusses ongoing work in demonstrating research in mobile autonomy in
challenging driving scenarios. In our approach, we address fundamental technical issues to …

This paper explores the idea of predicting the likely performance of a robot's perception
system based on past experience in the same workspace. In particular, we propose to build …

Modern perception systems are notoriously complex, featuring dozens of interacting
parameters that must be tuned to achieve good performance. Conventional tuning …

This paper addresses the problem of detecting people and vehicles on a surface mine by
presenting an architecture that combines the complementary strengths of deep …

This paper presents a learned, place-dependent terrain-assessment classifier that improves
over time. Whereas typical methods aim to assess all of the terrain in a given environment …