Autonomous Vehicles aim to understand their surrounding environment by detecting
relevant objects in the scene, which can be performed using a combination of sensors. The …

Multi-vehicle cooperative perception has recently emerged for facilitating long-range and
large-scale perception ability of connected automated vehicles (CAVs). Nonetheless …

The 3-D light detection and ranging (3-D LIDAR) sensors are widely used in autonomous
vehicles; however, their drawback is the significant computation processing requirement …

Developing datasets that cover comprehensive sensors, annotations, and out-of-distribution
data is important for innovating robust multi-sensor multi-task perception systems in …

Autonomous driving requires an accurate and fast 3D perception system that includes 3D
object detection, tracking, and segmentation. Although recent low-cost camera-based …

In this paper, a multi-modal 360° framework for 3D object detection and tracking for
autonomous vehicles is presented. The process is divided into four main stages. First …

Autonomous Vehicles (AVs) use multiple sensors to gather information about their
surroundings. By sharing sensor data between Connected Autonomous Vehicles (CAVs) …

Place recognition is one of the most crucial modules for autonomous vehicles to identify
places that were previously visited in GPS-invalid environments. Sensor fusion is …

To achieve more accurate perception performance, LiDAR and camera are gradually
chosen to improve 3D object detection simultaneously. However, it is still a non-trivial task to …

Employing Vehicle-to-Vehicle communication to enhance perception performance in self-
driving technology has attracted considerable attention recently; however, the absence of a …