Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Fu, Daocheng"'
Autor:
Yang, Xuemeng, Wen, Licheng, Ma, Yukai, Mei, Jianbiao, Li, Xin, Wei, Tiantian, Lei, Wenjie, Fu, Daocheng, Cai, Pinlong, Dou, Min, Shi, Botian, He, Liang, Liu, Yong, Qiao, Yu
This paper presented DriveArena, the first high-fidelity closed-loop simulation system designed for driving agents navigating in real scenarios. DriveArena features a flexible, modular architecture, allowing for the seamless interchange of its core c
Externí odkaz:
http://arxiv.org/abs/2408.00415
Autor:
Jiang, Kemou, Cai, Xuan, Cui, Zhiyong, Li, Aoyong, Ren, Yilong, Yu, Haiyang, Yang, Hao, Fu, Daocheng, Wen, Licheng, Cai, Pinlong
Large language models (LLMs) as autonomous agents offer a novel avenue for tackling real-world challenges through a knowledge-driven manner. These LLM-enhanced methodologies excel in generalization and interpretability. However, the complexity of dri
Externí odkaz:
http://arxiv.org/abs/2407.14239
Autor:
Xia, Renqiu, Mao, Song, Yan, Xiangchao, Zhou, Hongbin, Zhang, Bo, Peng, Haoyang, Pi, Jiahao, Fu, Daocheng, Wu, Wenjie, Ye, Hancheng, Feng, Shiyang, Wang, Bin, Xu, Chao, He, Conghui, Cai, Pinlong, Dou, Min, Shi, Botian, Zhou, Sheng, Wang, Yongwei, Yan, Junchi, Wu, Fei, Qiao, Yu
Scientific documents record research findings and valuable human knowledge, comprising a vast corpus of high-quality data. Leveraging multi-modality data extracted from these documents and assessing large models' abilities to handle scientific docume
Externí odkaz:
http://arxiv.org/abs/2406.11633
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:
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:
Fu, Daocheng, Lei, Wenjie, Wen, Licheng, Cai, Pinlong, Mao, Song, Dou, Min, Shi, Botian, Qiao, Yu
The emergence of Multimodal Large Language Models ((M)LLMs) has ushered in new avenues in artificial intelligence, particularly for autonomous driving by offering enhanced understanding and reasoning capabilities. This paper introduces LimSim++, an e
Externí odkaz:
http://arxiv.org/abs/2402.01246
Autor:
Wang, Lening, Ren, Yilong, Jiang, Han, Cai, Pinlong, Fu, Daocheng, Wang, Tianqi, Cui, Zhiyong, Yu, Haiyang, Wang, Xuesong, Zhou, Hanchu, Huang, Helai, Wang, Yinhai
Traffic accidents, being a significant contributor to both human casualties and property damage, have long been a focal point of research for many scholars in the field of traffic safety. However, previous studies, whether focusing on static environm
Externí odkaz:
http://arxiv.org/abs/2312.13156
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:
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
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