Autonomous vehicles rely extensively on onboard sensors to perceive their surrounding
environments for motion planning and vehicle control. Despite recent advancements …

3D object detection is a significant aspect of the perception module in autonomous driving;
however, with current technology, data sharing between vehicles and cloud servers for …

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 …

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 …

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

Cooperative perception offers several benefits for enhancing the capabilities of autonomous
vehicles and improving road safety. Using roadside sensors in addition to onboard sensors …

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 …

3D-LiDAR-based cooperative perception has been generating significant interest for its
ability to tackle challenges such as occlusion, sparse point clouds, and out-of-range issues …

Occlusion is a major challenge for LiDAR-based object detection methods as it renders
regions of interest unobservable to the ego vehicle. A proposed solution to this problem …

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 …