Zobrazeno 1 - 10
of 776
pro vyhledávání: '"Fang, Yuguang"'
Autor:
An, Haonan, Fang, Zhengru, Zhang, Yuang, Hu, Senkang, Chen, Xianhao, Xu, Guowen, Fang, Yuguang
Connected and autonomous vehicles (CAVs) have garnered significant attention due to their extended perception range and enhanced sensing coverage. To address challenges such as blind spots and obstructions, CAVs employ vehicle-to-vehicle (V2V) commun
Externí odkaz:
http://arxiv.org/abs/2410.04320
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
Autor:
Yuan, Shuai, Li, Hongwei, Han, Xingshuo, Xu, Guowen, Jiang, Wenbo, Ni, Tao, Zhao, Qingchuan, Fang, Yuguang
Physical adversarial patches have emerged as a key adversarial attack to cause misclassification of traffic sign recognition (TSR) systems in the real world. However, existing adversarial patches have poor stealthiness and attack all vehicles indiscr
Externí odkaz:
http://arxiv.org/abs/2409.12394
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 (CP) leverages visual data from connected and autonomous vehicles (CAV) to enhance an ego vehicle's field of view (FoV). Despite recent progress, current CP methods expand the ego vehicle's 360-degree perceptual range almost
Externí odkaz:
http://arxiv.org/abs/2409.08840
Traffic sign recognition systems play a crucial role in assisting drivers to make informed decisions while driving. However, due to the heavy reliance on deep learning technologies, particularly for future connected and autonomous driving, these syst
Externí odkaz:
http://arxiv.org/abs/2409.04133
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