41 citations · 89 across the 25 of their papers we have counts for
9 papers · 1 filter
CP-Guard: Malicious Agent Detection and Defense in Collaborative Bird's Eye View Perception
Senkang Hu, Yihang Tao, Guowen Xu +4
Collaborative Perception (CP) has shown a promising technique for autonomous driving, where multiple connected and autonomous vehicles (CAVs) share their perception information to…
IC3M: In-Car Multimodal Multi-object Monitoring for Abnormal Status of Both Driver and Passengers
Zihan Fang, Zheng Lin, Senkang Hu +4
Recently, in-car monitoring has emerged as a promising technology for detecting early-stage abnormal status of the driver and providing timely alerts to prevent traffic accidents.…
R-ACP: Real-Time Adaptive Collaborative Perception Leveraging Robust Task-Oriented Communications
Zhengru Fang, Jingjing Wang, Yanan Ma +4
Collaborative perception enhances sensing in multirobot and vehicular networks by fusing information from multiple agents, improving perception accuracy and sensing range. However,…
Resources on the Move for Smart City: A Disruptive Perspective on the Grand Convergence of Sensing, Communications, Computing, Storage, and Intelligence
Yuguang Fang, Yiqin Deng, Xianhao Chen
The most commonly seen things on streets in any city are vehicles. However, most of them are used to transport people or goods. What if they also carry resources and capabilities f…
Prioritized Information Bottleneck Theoretic Framework with Distributed Online Learning for Edge Video Analytics
Zhengru Fang, Senkang Hu, Jingjing Wang +3
Collaborative perception systems leverage multiple edge devices, such surveillance cameras or autonomous cars, to enhance sensing quality and eliminate blind spots. Despite their a…
PIB: Prioritized Information Bottleneck Framework for Collaborative Edge Video Analytics
Zhengru Fang, Senkang Hu, Liyan Yang +3
Collaborative edge sensing systems, particularly in collaborative perception systems in autonomous driving, can significantly enhance tracking accuracy and reduce blind spots with…
AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Merging
Senkang Hu, Zhengru Fang, Zihan Fang +4
Ramp merging is one of the bottlenecks in traffic systems, which commonly cause traffic congestion, accidents, and severe carbon emissions. In order to address this essential issue…
AgentsCoDriver: Large Language Model Empowered Collaborative Driving with Lifelong Learning
Senkang Hu, Zhengru Fang, Zihan Fang +3
Connected and autonomous driving is developing rapidly in recent years. However, current autonomous driving systems, which are primarily based on data-driven approaches, exhibit de…
Collaborative Perception for Connected and Autonomous Driving: Challenges, Possible Solutions and Opportunities
Senkang Hu, Zhengru Fang, Yiqin Deng +2
Autonomous driving has attracted significant attention from both academia and industries, which is expected to offer a safer and more efficient driving system. However, current aut…