Three-dimensional object detection is crucial for autonomous driving to understand the
driving environment. Since the pooling operation causes information loss in the standard …

Vehicle-Infrastructure Cooperative (VIC) 3D object detection is a challenging task for
balancing communication bandwidth and detection performance. Intermediate fusion is …

Recognition of surrounding objects is crucial for ensuring the safety of automated driving
systems. In the realm of 3D object recognition through deep learning, several methods …

Existing Vehicle-to-Everything (V2X) cooperative perception methods rely on accurate multi-
agent 3D annotations. Nevertheless, it is time-consuming and expensive to collect and …

Autonomous driving faces great safety challenges for a lack of global perspective and the
limitation of long-range perception capabilities. It has been widely agreed that vehicle …

Vehicle-to-Vehicle technologies have enabled autonomous vehicles to share information to
see through occlusions, greatly enhancing perception performance. Nevertheless, existing …

3D object detection serves as the core basis of the perception tasks in autonomous driving.
Recent years have seen the rapid progress of multi-modal fusion strategies for more robust …

Autonomous driving requires accurate, robust, and fast decision-making perception systems
to understand the driving environment. Object detection is critical in allowing the perception …

Multi-modality three-dimensional (3D) object detection is a crucial technology for the safe
and effective operation of environment perception systems in autonomous driving. In this …

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