The YOLO series has become the most popular framework for real-time object detection due
to its reasonable trade-off between speed and accuracy. However we observe that the …

We introduce a novel visual question answering (VQA) task in the context of autonomous
driving, aiming to answer natural language questions based on street-view clues. Compared …

This research focuses on the discovery and localization of hidden objects in the wild and
serves unmanned systems. Through empirical analysis, infrared and visible image fusion …

Deep learning-based intelligent vehicle perception has been developing prominently in
recent years to provide a reliable source for motion planning and decision making in …

In the past two decades, most research on anomaly detection has focused on improving the
accuracy of the detection, while largely ignoring the explainability of the corresponding …

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 …

Over the past years, YOLOs have emerged as the predominant paradigm in the field of real-
time object detection owing to their effective balance between computational cost and …

Automated online recognition of unexpected conditions is an indispensable component of
autonomous vehicles to ensure safety even in unknown and uncertain situations. In this …

Predictive maintenance in the car industry is an active field of research for machine learning
and anomaly detection. The capability of cars to produce time series data from sensors is …

Road anomaly detection is critical to safe autonomous driving, because current road scene
understanding models are usually trained in a closed-set manner and fail to identify …