This paper explores the emerging knowledge-driven autonomous driving technologies. Our
investigation highlights the limitations of current autonomous driving systems, in particular …

Sensor fusion has become a popular topic in robotics. However, conventional fusion
methods encounter many difficulties, such as data representation differences, sensor …

Calibrating the relative positional posture of the roadside sensor with respect to the road is
crucial for target tracking and sensor fusion. However, most previous studies rely on prior …

The development of intelligent Vehicles (IVs) requires extensive standard datasets for
training, benchmarking, and improvement. Autonomous Navigation and Mapping (ANM), as …

Properly-calibrated sensors are the prerequisite for a dependable autonomous driving
system. However, most prior methods focus on extrinsic calibration between sensors, and …

Prior point cloud provides 3D environmental context, which enhances the capabilities of
monocular camera in downstream vision tasks, such as 3D object detection, via data fusion …

Autonomous surface vessels (ASVs) are growing rapidly due to their ability to execute
hazardous and time-consuming missions over water surfaces. Recently, camera-based …

High-precision vehicle localization with commercial setups is a crucial technique for high-
level autonomous driving tasks. Localization with a monocular camera in LiDAR map is a …

In order to address the problems associated with low detection accuracy, weak detection
ability of small targets, insufficiently obvious differentiation of colors, and inability to …

Currently, the demand for automobiles is increasing, and daily travel is increasingly reliant
on cars. However, accompanying this trend are escalating traffic safety issues. Surveys …