Zobrazeno 1 - 10
of 190
pro vyhledávání: '"Deng, Yiqin"'
Autor:
Fang, Zihan, Lin, Zheng, Hu, Senkang, Cao, Hangcheng, Deng, Yiqin, Chen, Xianhao, Fang, Yuguang
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. Although training models with multimodal data enhances the reliability
Externí odkaz:
http://arxiv.org/abs/2410.02592
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 for sensing, communications, computing, storage, and intelligence (SCCS
Externí odkaz:
http://arxiv.org/abs/2409.09417
Collaborative perception systems leverage multiple edge devices, such surveillance cameras or autonomous cars, to enhance sensing quality and eliminate blind spots. Despite their advantages, challenges such as limited channel capacity and data redund
Externí odkaz:
http://arxiv.org/abs/2409.00146
Collaborative edge sensing systems, particularly in collaborative perception systems in autonomous driving, can significantly enhance tracking accuracy and reduce blind spots with multi-view sensing capabilities. However, their limited channel capaci
Externí odkaz:
http://arxiv.org/abs/2408.17047
Autor:
Hu, Senkang, Fang, Zhengru, Fang, Zihan, Deng, Yiqin, Chen, Xianhao, Fang, Yuguang, Kwong, Sam
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 and enhance the safety and efficiency of connected and autonomous veh
Externí odkaz:
http://arxiv.org/abs/2408.03624
Connected and autonomous driving is developing rapidly in recent years. However, current autonomous driving systems, which are primarily based on data-driven approaches, exhibit deficiencies in interpretability, generalization, and continuing learnin
Externí odkaz:
http://arxiv.org/abs/2404.06345
Federated learning (FL) allows multiple parties (distributed devices) to train a machine learning model without sharing raw data. How to effectively and efficiently utilize the resources on devices and the central server is a highly interesting yet c
Externí odkaz:
http://arxiv.org/abs/2402.15903
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 autonomous driving systems are mostly based on a single vehicle, which ha
Externí odkaz:
http://arxiv.org/abs/2401.01544
Semantic communication has been identified as a core technology for the sixth generation (6G) of wireless networks. Recently, task-oriented semantic communications have been proposed for low-latency inference with limited bandwidth. Although transmit
Externí odkaz:
http://arxiv.org/abs/2312.03252
Autor:
Chen, Xianhao, Deng, Yiqin, Ding, Haichuan, Qu, Guanqiao, Zhang, Haixia, Li, Pan, Fang, Yuguang
Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, s
Externí odkaz:
http://arxiv.org/abs/2304.11397