Zobrazeno 1 - 10
of 429
pro vyhledávání: '"CAI Xinyu"'
Autor:
Mei, Jianbiao, Ma, Yukai, Yang, Xuemeng, Wen, Licheng, Cai, Xinyu, Li, Xin, Fu, Daocheng, Zhang, Bo, Cai, Pinlong, Dou, Min, Shi, Botian, He, Liang, Liu, Yong, Qiao, Yu
Autonomous driving has advanced significantly due to sensors, machine learning, and artificial intelligence improvements. However, prevailing methods struggle with intricate scenarios and causal relationships, hindering adaptability and interpretabil
Externí odkaz:
http://arxiv.org/abs/2405.15324
Autor:
Zhang, Wentao, Zhao, Lingxuan, Xia, Haochong, Sun, Shuo, Sun, Jiaze, Qin, Molei, Li, Xinyi, Zhao, Yuqing, Zhao, Yilei, Cai, Xinyu, Zheng, Longtao, Wang, Xinrun, An, Bo
Financial trading is a crucial component of the markets, informed by a multimodal information landscape encompassing news, prices, and Kline charts, and encompasses diverse tasks such as quantitative trading and high-frequency trading with various as
Externí odkaz:
http://arxiv.org/abs/2402.18485
Autor:
Yan, Guohang, Pi, Jiahao, Guo, Jianfei, Luo, Zhaotong, Dou, Min, Deng, Nianchen, Huang, Qiusheng, Fu, Daocheng, Wen, Licheng, Cai, Pinlong, Gao, Xing, Cai, Xinyu, Zhang, Bo, Yang, Xuemeng, Bai, Yeqi, Zhou, Hongbin, Shi, Botian
With deep learning and computer vision technology development, autonomous driving provides new solutions to improve traffic safety and efficiency. The importance of building high-quality datasets is self-evident, especially with the rise of end-to-en
Externí odkaz:
http://arxiv.org/abs/2402.03830
Autor:
Yang, Donglin, Liu, Zhenfeng, Jiang, Wentao, Yan, Guohang, Gao, Xing, Shi, Botian, Liu, Si, Cai, Xinyu
Employing data augmentation methods to enhance perception performance in adverse weather has attracted considerable attention recently. Most of the LiDAR augmentation methods post-process the existing dataset by physics-based models or machine-learni
Externí odkaz:
http://arxiv.org/abs/2312.12772
Autor:
Li, Xin, Bai, Yeqi, Cai, Pinlong, Wen, Licheng, Fu, Daocheng, Zhang, Bo, Yang, Xuemeng, Cai, Xinyu, Ma, Tao, Guo, Jianfei, Gao, Xing, Dou, Min, Li, Yikang, Shi, Botian, Liu, Yong, He, Liang, Qiao, Yu
This paper explores the emerging knowledge-driven autonomous driving technologies. Our investigation highlights the limitations of current autonomous driving systems, in particular their sensitivity to data bias, difficulty in handling long-tail scen
Externí odkaz:
http://arxiv.org/abs/2312.04316
Autor:
Liu, Jiuming, Liao, Liyang, Rong, Bin, Wu, Yuyang, Zhang, Yu, Ruan, Hanzhi, Zhi, Zhenghang, Huang, Puyang, Yao, Shan, Cai, Xinyu, Tang, Chenjia, Yao, Qi, Sun, Lu, Yang, Yumeng, Yu, Guoqiang, Che, Renchao, Kou, Xufeng
Magnetoresistance effects are crucial for understanding the charge/spin transport as well as propelling the advancement of spintronic applications. Here we report the coexistence of magnetic moment-dependent (MD) and magnetic field-driven (FD) unidir
Externí odkaz:
http://arxiv.org/abs/2311.11843
Autor:
Wen, Licheng, Yang, Xuemeng, Fu, Daocheng, Wang, Xiaofeng, Cai, Pinlong, Li, Xin, Ma, Tao, Li, Yingxuan, Xu, Linran, Shang, Dengke, Zhu, Zheng, Sun, Shaoyan, Bai, Yeqi, Cai, Xinyu, Dou, Min, Hu, Shuanglu, Shi, Botian
The pursuit of autonomous driving technology hinges on the sophisticated integration of perception, decision-making, and control systems. Traditional approaches, both data-driven and rule-based, have been hindered by their inability to grasp the nuan
Externí odkaz:
http://arxiv.org/abs/2311.05332
Learning representations purely from observations concerns the problem of learning a low-dimensional, compact representation which is beneficial to prediction models. Under the hypothesis that the intrinsic latent factors follow some casual generativ
Externí odkaz:
http://arxiv.org/abs/2310.13892
Autor:
Jiang, Wentao, Xiang, Hao, Cai, Xinyu, Xu, Runsheng, Ma, Jiaqi, Li, Yikang, Lee, Gim Hee, Liu, Si
Multi-agent cooperative perception is an increasingly popular topic in the field of autonomous driving, where roadside LiDARs play an essential role. However, how to optimize the placement of roadside LiDARs is a crucial but often overlooked problem.
Externí odkaz:
http://arxiv.org/abs/2310.07247
Autor:
Wen, Licheng, Fu, Daocheng, Li, Xin, Cai, Xinyu, Ma, Tao, Cai, Pinlong, Dou, Min, Shi, Botian, He, Liang, Qiao, Yu
Recent advancements in autonomous driving have relied on data-driven approaches, which are widely adopted but face challenges including dataset bias, overfitting, and uninterpretability. Drawing inspiration from the knowledge-driven nature of human d
Externí odkaz:
http://arxiv.org/abs/2309.16292