20 citations · 93 across the 13 of their papers we have counts for
13 papers · 1 filter
V2X-Real: a Large-Scale Dataset for Vehicle-to-Everything Cooperative Perception
Hao Xiang, Zhaoliang Zheng, Xin Xia +15
Recent advancements in Vehicle-to-Everything (V2X) technologies have enabled autonomous vehicles to share sensing information to see through occlusions, greatly boosting the percep…
Breaking Data Silos: Cross-Domain Learning for Multi-Agent Perception from Independent Private Sources
Jinlong Li, Baolu Li, Xinyu Liu +3
The diverse agents in multi-agent perception systems may be from different companies. Each company might use the identical classic neural network architecture based encoder for fea…
DUSA: Decoupled Unsupervised Sim2Real Adaptation for Vehicle-to-Everything Collaborative Perception
Xianghao Kong, Wentao Jiang, Jinrang Jia +3
Vehicle-to-Everything (V2X) collaborative perception is crucial for autonomous driving. However, achieving high-precision V2X perception requires a significant amount of annotated…
Optimizing the Placement of Roadside LiDARs for Autonomous Driving
Wentao Jiang, Hao Xiang, Xinyu Cai +5
Multi-agent cooperative perception is an increasingly popular topic in the field of autonomous driving, where roadside LiDARs play an essential role. However, how to optimize the p…
Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception
Si Liu, Chen Gao, Yuan Chen +8
Vehicle-to-everything (V2X) autonomous driving opens up a promising direction for developing a new generation of intelligent transportation systems. Collaborative perception (CP) a…
Domain Adaptation based Object Detection for Autonomous Driving in Foggy and Rainy Weather
Jinlong Li, Runsheng Xu, Xinyu Liu +5
Typically, object detection methods for autonomous driving that rely on supervised learning make the assumption of a consistent feature distribution between the training and testin…