Cooperative perception in the field of connected autonomous vehicles (CAVs) aims to
overcome the inherent limitations of single-vehicle perception systems, including long-range …

In autonomous driving, cooperative perception makes use of multi-view cameras from both
vehicles and infrastructure, providing a global vantage point with rich semantic context of …

Deep learning has been widely used in intelligent vehicle driving perception systems, such
as 3D object detection. One promising technique is Cooperative Perception, which …

Cooperative perception for connected and automated vehicles is traditionally achieved
through the fusion of feature maps from two or more vehicles. However, the absence of …

Utilizing the latest advances in Artificial Intelligence (AI), the computer vision community is
now witnessing an unprecedented evolution in all kinds of perception tasks, particularly in …

Autonomous vehicles may make wrong decisions due to inaccurate detection and
recognition. Therefore, an intelligent vehicle can combine its own data with that of other …

Accurately detecting objects in 3D point clouds is critical for achieving precise scene
understanding in autonomous driving systems. Cooperative perception, through information …

Sharing collective perception messages (CPM) between vehicles is investigated to
decrease occlusions so as to improve the perception accuracy and safety of autonomous …

Accurate detection of objects in 3D point clouds is a key problem in autonomous driving
systems. Collaborative perception can incorporate information from spatially diverse sensors …

Roadside perception is a fundamental task for vehicle-to-road cooperative perception and
traffic scheduling. However, most existing roadside perception strategies prefer to deploy …