Three-dimensional perception from multi-view cameras is a crucial component in
autonomous driving systems, which involves multiple tasks like 3D object detection and …

Masked Autoencoders (MAE) play a pivotal role in learning potent representations,
delivering outstanding results across various 3D perception tasks essential for autonomous …

Autonomous driving technology faces significant safety challenges due to the lack of a
global perspective and the limitations of long-range perception capabilities. It is widely …

Concurrent perception datasets for autonomous driving are mainly limited to frontal view
with sensors mounted on the vehicle. None of them is designed for the overlooked roadside …

Recently, 2D detection in images has made significant progress owing to the emergence of
a convolutional neural network (CNN), which can extract high-level features from the …

Concurrent perception datasets for autonomous driving are mainly limited to frontal view
with sensors mounted on the vehicle. None of them is designed for the overlooked roadside …

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

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 …

In autonomous driving, 3D object detection based on multi-modal data has become an
indispensable perceptual approach when facing complex environments around the vehicle …