Scene understanding plays a crucial role in autonomous driving by utilizing sensory data for
contextual information extraction and decision making. Beyond modeling advances, the …

Autonomous driving (AD) has developed tremendously in parallel with the ongoing
development and improvement of deep learning (DL) technology. However, the uptake of …

Neuromorphic event-based image sensors capture only the dynamic motion in a scene,
which is then transferred to computation units for motion recognition. This approach …

Unmanned aerial vehicles (UAVs) equipped with lightweight sensors, such as RGB cameras
and LiDAR, have significant potential in precision agriculture, including object detection …

Event-based cameras are the emerging bioinspired technology in vision sensing. Different
from the traditional standard cameras, the event-based cameras asynchronously record the …

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and
connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger …

Efficient operation of control systems in robotics or autonomous driving targeting real-world
navigation scenarios requires perception methods that allow them to understand and adapt …

Artificial intelligence (AI) is a rapidly growing field of technology, which “will enliven inert
objects, much as electricity did more than a century ago. Everything that we formerly …

The field of Deep Learning (DL) has seen a remarkable series of developments with
increasingly accurate and robust algorithms. However, the increase in performance has …

Vehicle slip angle (VSA) estimation is of paramount importance for connected automated
vehicle dynamic control, especially in critical lateral driving scenarios. In this paper, a novel …