2 citations · 4 across the 8 of their papers we have counts for
11 papers · 1 filter
Unsupervised Multi-agent and Single-agent Perception from Cooperative Views
Haochen Yang, Baolu Li, Lei Li +5
The LiDAR-based multi-agent and single-agent perception has shown promising performance in environmental understanding for robots and automated vehicles. However, there is no exist…
DarkDriving: A Real-World Day and Night Aligned Dataset for Autonomous Driving in the Dark Environment
Wuqi Wang, Haochen Yang, Baolu Li +7
The low-light conditions are challenging to the vision-centric perception systems for autonomous driving in the dark environment. In this paper, we propose a new benchmark dataset…
Advancing from Automated to Autonomous Beamline by Leveraging Computer Vision
Baolu Li, Hongkai Yu, Huiming Sun +4
The synchrotron light source, a cutting-edge large-scale user facility, requires autonomous synchrotron beamline operations, a crucial technique that should enable experiments to b…
V2X-DG: Domain Generalization for Vehicle-to-Everything Cooperative Perception
Baolu Li, Zongzhe Xu, Jinlong Li +4
LiDAR-based Vehicle-to-Everything (V2X) cooperative perception has demonstrated its impact on the safety and effectiveness of autonomous driving. Since current cooperative percepti…
CoMamba: Real-time Cooperative Perception Unlocked with State Space Models
Jinlong Li, Xinyu Liu, Baolu Li +4
Cooperative perception systems play a vital role in enhancing the safety and efficiency of vehicular autonomy. Although recent studies have highlighted the efficacy of vehicle-to-e…
Light the Night: A Multi-Condition Diffusion Framework for Unpaired Low-Light Enhancement in Autonomous Driving
Jinlong Li, Baolu Li, Zhengzhong Tu +5
Vision-centric perception systems for autonomous driving have gained considerable attention recently due to their cost-effectiveness and scalability, especially compared to LiDAR-b…