Zobrazeno 1 - 10
of 188
pro vyhledávání: '"Deng Yiqin"'
Collaborative Perception (CP) has shown a promising technique for autonomous driving, where multiple connected and autonomous vehicles (CAVs) share their perception information to enhance the overall perception performance and expand the perception r
Externí odkaz:
http://arxiv.org/abs/2412.12000
Autor:
Fang, Zhengru, Wang, Jingjing, Ma, Yanan, Tao, Yihang, Deng, Yiqin, Chen, Xianhao, Fang, Yuguang
Collaborative perception enhances sensing in multi-robot and vehicular networks by fusing information from multiple agents, improving perception accuracy and sensing range. However, mobility and non-rigid sensor mounts introduce extrinsic calibration
Externí odkaz:
http://arxiv.org/abs/2410.04168
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