Zobrazeno 1 - 10
of 435
pro vyhledávání: '"xu, Chengzhong"'
Autor:
Lei, Chenyang, Zhang, Meiying, Peng, Weiyuan, Hao, Qi, Xu, Chengzhong, Ji, Chunlin, Zhou, Guang
Most autonomous driving (AD) datasets incur substantial costs for collection and labeling, inevitably yielding a plethora of low-quality and redundant data instances, thereby compromising performance and efficiency. Many applications in AD systems ne
Externí odkaz:
http://arxiv.org/abs/2412.18870
Autor:
Xie, Zhixuan, Chen, Jianjun, Li, Guoliang, Wang, Shuai, Ye, Kejiang, Eldar, Yonina C., Xu, Chengzhong
Occlusion is a key factor leading to detection failures. This paper proposes a motion-assisted detection (MAD) method that actively plans an executable path, for the robot to observe the target at a new viewpoint with potentially reduced occlusion. I
Externí odkaz:
http://arxiv.org/abs/2412.18453
Autor:
Wang, Chengyue, Liao, Haicheng, Wang, Bonan, Guan, Yanchen, Rao, Bin, Pu, Ziyuan, Cui, Zhiyong, Xu, Chengzhong, Li, Zhenning
Accurate trajectory prediction is essential for the safety and efficiency of autonomous driving. Traditional models often struggle with real-time processing, capturing non-linearity and uncertainty in traffic environments, efficiency in dense traffic
Externí odkaz:
http://arxiv.org/abs/2412.11682
Publikováno v:
Software: Practice and Experience 2024
Microservices architecture has become the dominant architecture in cloud computing paradigm with its advantages of facilitating development, deployment, modularity and scalability. The workflow of microservices architecture is transparent to the user
Externí odkaz:
http://arxiv.org/abs/2411.11493
Autor:
Sun, Xingwu, Chen, Yanfeng, Huang, Yiqing, Xie, Ruobing, Zhu, Jiaqi, Zhang, Kai, Li, Shuaipeng, Yang, Zhen, Han, Jonny, Shu, Xiaobo, Bu, Jiahao, Chen, Zhongzhi, Huang, Xuemeng, Lian, Fengzong, Yang, Saiyong, Yan, Jianfeng, Zeng, Yuyuan, Ren, Xiaoqin, Yu, Chao, Wu, Lulu, Mao, Yue, Xia, Jun, Yang, Tao, Zheng, Suncong, Wu, Kan, Jiao, Dian, Xue, Jinbao, Zhang, Xipeng, Wu, Decheng, Liu, Kai, Wu, Dengpeng, Xu, Guanghui, Chen, Shaohua, Chen, Shuang, Feng, Xiao, Hong, Yigeng, Zheng, Junqiang, Xu, Chengcheng, Li, Zongwei, Kuang, Xiong, Hu, Jianglu, Chen, Yiqi, Deng, Yuchi, Li, Guiyang, Liu, Ao, Zhang, Chenchen, Hu, Shihui, Zhao, Zilong, Wu, Zifan, Ding, Yao, Wang, Weichao, Liu, Han, Wang, Roberts, Fei, Hao, Yu, Peijie, Zhao, Ze, Cao, Xun, Wang, Hai, Xiang, Fusheng, Huang, Mengyuan, Xiong, Zhiyuan, Hu, Bin, Hou, Xuebin, Jiang, Lei, Ma, Jianqiang, Wu, Jiajia, Deng, Yaping, Shen, Yi, Wang, Qian, Liu, Weijie, Liu, Jie, Chen, Meng, Dong, Liang, Jia, Weiwen, Chen, Hu, Liu, Feifei, Yuan, Rui, Xu, Huilin, Yan, Zhenxiang, Cao, Tengfei, Hu, Zhichao, Feng, Xinhua, Du, Dong, Yu, Tinghao, Tao, Yangyu, Zhang, Feng, Zhu, Jianchen, Xu, Chengzhong, Li, Xirui, Zha, Chong, Ouyang, Wen, Xia, Yinben, Li, Xiang, He, Zekun, Chen, Rongpeng, Song, Jiawei, Chen, Ruibin, Jiang, Fan, Zhao, Chongqing, Wang, Bo, Gong, Hao, Gan, Rong, Hu, Winston, Kang, Zhanhui, Yang, Yong, Liu, Yuhong, Wang, Di, Jiang, Jie
In this paper, we introduce Hunyuan-Large, which is currently the largest open-source Transformer-based mixture of experts model, with a total of 389 billion parameters and 52 billion activation parameters, capable of handling up to 256K tokens. We c
Externí odkaz:
http://arxiv.org/abs/2411.02265
Autonomous cooperative planning (ACP) is a promising technique to improve the efficiency and safety of multi-vehicle interactions for future intelligent transportation systems. However, realizing robust ACP is a challenge due to the aggregation of pe
Externí odkaz:
http://arxiv.org/abs/2411.00413
Federated Learning (FL) enables multiple devices to collaboratively train a shared model while preserving data privacy. Ever-increasing model complexity coupled with limited memory resources on the participating devices severely bottlenecks the deplo
Externí odkaz:
http://arxiv.org/abs/2410.11577
In the context of Machine Learning as a Service (MLaaS) clouds, the extensive use of Large Language Models (LLMs) often requires efficient management of significant query loads. When providing real-time inference services, several challenges arise. F
Externí odkaz:
http://arxiv.org/abs/2409.14961
Cloud-native applications are increasingly becoming popular in modern software design. Employing a microservice-based architecture into these applications is a prevalent strategy that enhances system availability and flexibility. However, cloud-nativ
Externí odkaz:
http://arxiv.org/abs/2409.05093
Autor:
Liao, Haicheng, Li, Yongkang, Wang, Chengyue, Lai, Songning, Li, Zhenning, Bian, Zilin, Lee, Jaeyoung, Cui, Zhiyong, Zhang, Guohui, Xu, Chengzhong
The primary goal of traffic accident anticipation is to foresee potential accidents in real time using dashcam videos, a task that is pivotal for enhancing the safety and reliability of autonomous driving technologies. In this study, we introduce an
Externí odkaz:
http://arxiv.org/abs/2409.01256