Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Ling, Neiwen"'
Autor:
Shi, Shuyao, Ling, Neiwen, Jiang, Zhehao, Huang, Xuan, He, Yuze, Zhao, Xiaoguang, Yang, Bufang, Bian, Chen, Xia, Jingfei, Yan, Zhenyu, Yeung, Raymond, Xing, Guoliang
Recently,smart roadside infrastructure (SRI) has demonstrated the potential of achieving fully autonomous driving systems. To explore the potential of infrastructure-assisted autonomous driving, this paper presents the design and deployment of Soar,
Externí odkaz:
http://arxiv.org/abs/2404.13786
Recent advancements in robot control using large language models (LLMs) have demonstrated significant potential, primarily due to LLMs' capabilities to understand natural language commands and generate executable plans in various languages. However,
Externí odkaz:
http://arxiv.org/abs/2312.14950
Autor:
Yang, Bufang, He, Lixing, Ling, Neiwen, Yan, Zhenyu, Xing, Guoliang, Shuai, Xian, Ren, Xiaozhe, Jiang, Xin
Deep Learning (DL) models have been widely deployed on IoT devices with the help of advancements in DL algorithms and chips. However, the limited resources of edge devices make these on-device DL models hard to be generalizable to diverse environment
Externí odkaz:
http://arxiv.org/abs/2311.10986
Fusing Radar and Lidar sensor data can fully utilize their complementary advantages and provide more accurate reconstruction of the surrounding for autonomous driving systems. Surround Radar/Lidar can provide 360-degree view sampling with the minimal
Externí odkaz:
http://arxiv.org/abs/2309.04806
Many applications such as autonomous driving and augmented reality, require the concurrent running of multiple deep neural networks (DNN) that poses different levels of real-time performance requirements. However, coordinating multiple DNN tasks with
Externí odkaz:
http://arxiv.org/abs/2307.04339
Achieving efficient execution of machine learning models has attracted significant attention recently. To generate tensor programs efficiently, a key component of DNN compilers is the cost model that can predict the performance of each configuration
Externí odkaz:
http://arxiv.org/abs/2201.05752