Autonomous vehicles were experiencing rapid development in the past few years. However,
achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic …

Infrastructure-assisted autonomous driving is an emerging paradigm that expects to
significantly improve the driving safety of autonomous vehicles. The key enabling …

Environment perception for autonomous driving traditionally uses sensor fusion to combine
the object detections from various sensors mounted on the car into a single representation of …

Multi-sensor fusion is essential for an accurate and reliable autonomous driving system.
Recent approaches are based on point-level fusion: augmenting the LiDAR point cloud with …

Recently, the advancement of deep learning (DL) in discriminative feature learning from 3-D
LiDAR data has led to rapid development in the field of autonomous driving. However …

Object detection and recognition is a key component of autonomous robotic vehicles, as
evidenced by the continuous efforts made by the robotic community on areas related to …

As camera and LiDAR sensors capture complementary information in autonomous driving,
great efforts have been made to conduct semantic segmentation through multi-modality data …

3D LiDAR scanners are playing an increasingly important role in autonomous driving as
they can generate depth information of the environment. However, creating large 3D LiDAR …

Scene understanding based on LiDAR point cloud is an essential task for autonomous cars
to drive safely, which often employs spherical projection to map 3D point cloud into multi …

Multi-modal fusion is a fundamental task for the perception of an autonomous driving
system, which has recently intrigued many researchers. However, achieving a rather good …