Zobrazeno 1 - 10
of 157
pro vyhledávání: '"Xu, Chenxin"'
Realistic trajectory generation with natural language control is pivotal for advancing autonomous vehicle technology. However, previous methods focus on individual traffic participant trajectory generation, thus failing to account for the complexity
Externí odkaz:
http://arxiv.org/abs/2405.15388
Autor:
Cai, Yuzhu, Yin, Sheng, Wei, Yuxi, Xu, Chenxin, Mao, Weibo, Juefei-Xu, Felix, Chen, Siheng, Wang, Yanfeng
The burgeoning landscape of text-to-image models, exemplified by innovations such as Midjourney and DALLE 3, has revolutionized content creation across diverse sectors. However, these advancements bring forth critical ethical concerns, particularly w
Externí odkaz:
http://arxiv.org/abs/2404.12104
Autor:
Liu, Genjia, Hu, Yue, Xu, Chenxin, Mao, Weibo, Ge, Junhao, Huang, Zhengxiang, Lu, Yifan, Xu, Yinda, Xia, Junkai, Wang, Yafei, Chen, Siheng
Vehicle-to-everything-aided autonomous driving (V2X-AD) has a huge potential to provide a safer driving solution. Despite extensive researches in transportation and communication to support V2X-AD, the actual utilization of these infrastructures and
Externí odkaz:
http://arxiv.org/abs/2404.09496
Decentralized and lifelong-adaptive multi-agent collaborative learning aims to enhance collaboration among multiple agents without a central server, with each agent solving varied tasks over time. To achieve efficient collaboration, agents should: i)
Externí odkaz:
http://arxiv.org/abs/2403.06535
Autor:
Wei, Yuxi, Wang, Zi, Lu, Yifan, Xu, Chenxin, Liu, Changxing, Zhao, Hao, Chen, Siheng, Wang, Yanfeng
Scene simulation in autonomous driving has gained significant attention because of its huge potential for generating customized data. However, existing editable scene simulation approaches face limitations in terms of user interaction efficiency, mul
Externí odkaz:
http://arxiv.org/abs/2402.05746
Learning the dense bird's eye view (BEV) motion flow in a self-supervised manner is an emerging research for robotics and autonomous driving. Current self-supervised methods mainly rely on point correspondences between point clouds, which may introdu
Externí odkaz:
http://arxiv.org/abs/2401.11499
To analyze multivariate time series, most previous methods assume regular subsampling of time series, where the interval between adjacent measurements and the number of samples remain unchanged. Practically, data collection systems could produce irre
Externí odkaz:
http://arxiv.org/abs/2310.11022
Exploring spatial-temporal dependencies from observed motions is one of the core challenges of human motion prediction. Previous methods mainly focus on dedicated network structures to model the spatial and temporal dependencies. This paper considers
Externí odkaz:
http://arxiv.org/abs/2308.08942
Autor:
Xu, Qingyao, Mao, Weibo, Gong, Jingze, Xu, Chenxin, Chen, Siheng, Xie, Weidi, Zhang, Ya, Wang, Yanfeng
Multi-person motion prediction is a challenging problem due to the dependency of motion on both individual past movements and interactions with other people. Transformer-based methods have shown promising results on this task, but they miss the expli
Externí odkaz:
http://arxiv.org/abs/2308.04808
This work considers the category distribution heterogeneity in federated learning. This issue is due to biased labeling preferences at multiple clients and is a typical setting of data heterogeneity. To alleviate this issue, most previous works consi
Externí odkaz:
http://arxiv.org/abs/2305.19229