Sensor fusion is crucial for an accurate and robust perception system on autonomous
vehicles. Most existing datasets and perception solutions focus on fusing cameras and …

Autonomous driving requires an accurate and fast 3D perception system that includes 3D
object detection, tracking, and segmentation. Although recent low-cost camera-based …

The fusion of multi-modal perception in autonomous driving plays a pivotal role in vehicle
behavior decision-making. However, much of the previous research has predominantly …

Employing Vehicle-to-Vehicle communication to enhance perception performance in self-
driving technology has attracted considerable attention recently; however, the absence of a …

In the context of autonomous driving environment perception, multi-modal fusion plays a
pivotal role in enhancing robustness, completeness, and accuracy, thereby extending the …

As a critical component to realizing widespread autonomous driving, 3D perception systems
have come to be heavily studied in the community. However, many solutions are solely …

Low-cost, vision-centric 3D perception systems for autonomous driving have made
significant progress in recent years, narrowing the gap to expensive LiDAR-based methods …

Radar has stronger adaptability in adverse scenarios for autonomous driving environmental
perception compared to widely adopted cameras and LiDARs. Compared with commonly …

Multi-modal sensor fusion techniques have promoted the development of autonomous
driving, while perception in the complex environment remains a challenging problem. In …

Robust environmental sensing and accurate object detection are crucial in enabling
autonomous driving in urban environments. To achieve this goal, autonomous mobile …