Birds-Eye-View (BEV) perception can naturally represent natural scenes, which is conducive
to multimodal data processing and fusion. BEV data contain rich semantics and integrate the …

Autonomous driving related researches require the analysis and usage of massive amounts
of driving scenario data. Compared to raw data collected by sensors, scenario data provide …

Camera calibration involves estimating camera parameters to infer geometric features from
captured sequences, which is crucial for computer vision and robotics. However …

A system with light detection and ranging (LiDAR) and camera (named as LiDAR-camera
system) plays the essential role in intelligent vehicle (IV), for it provides 3D spatial and 2D …

Extrinsic parameter calibration is the foundation and prerequisite for LiDAR and camera
data fusion of the autonomous system. This technology is widely used in fields such as …

LiDAR-Camera alignment (LCA) is an important preprocessing procedure for fusing LiDAR
and camera data. For it, one key issue is to extract unified cross-modality representation for …

Lidar and cameras serve as essential sensors for automated vehicles and intelligent robots,
and they are frequently fused in complicated tasks. Precise extrinsic calibration is the …

Multi-sensor collaborative perception has been a significant trend in self-driving and robot
navigation. The precondition for multi-sensor fusion is the accurate calibration between …

Environmental perception is a key technology for autonomous driving, enabling vehicles to
analyze and interpret their surroundings in real time to ensure safe navigation and decision …

Recent advancements in sensor technologies, coupled with signal processing and machine
learning, have enabled real-time traffic control systems to effectively adapt to changing traffic …