Camera and LIDAR are both important sensor modalities for real-world applications,
especially autonomous driving. The sensors provide complementary information and make …

Autonomous driving perception has made significant strides in recent years, but accurately
sensing the environment using a single sensor remains a daunting task. This review offers a …

Autonomous driving has been widely applied in commercial and industrial applications,
along with the upgrade of environmental awareness systems. Tasks such as path planning …

In autonomous cars accurate and reliable detection of objects in the proximity of the vehicle
is necessary in order to perform further safety critical actions which depend upon it. Many …

Abstract 3D vehicle detection is a significant step for traffic scene understanding. Several
recent works have achieved state-of-the-art performance with only LiDAR point clouds …

Both images and point clouds are beneficial for object detection in a visual navigation
module for autonomous driving. The spatial relationships between different objects at …

Autonomous driving perception relies on standard sensors such as color cameras and
LiDAR, but each sensor has limitations when sensing complex and diverse environments …

Three-dimensional object detection is the core of an autonomous driving perception system,
which detects and analyzes targets around the vehicle to obtain their sizes, shapes, and …

Robust road detection is a key challenge in safe autonomous driving. Recently, with the
rapid development of 3D sensors, more and more researchers are trying to fuse information …

3D vehicle detectors based on point clouds generally have higher detection performance
than detectors based on multi-sensors. However, with the lack of texture information, point …