Zobrazeno 1 - 10
of 611
pro vyhledávání: '"Shi Haotian"'
Autor:
You, Junwei, Gan, Rui, Tang, Weizhe, Huang, Zilin, Liu, Jiaxi, Jiang, Zhuoyu, Shi, Haotian, Wu, Keshu, Long, Keke, Fu, Sicheng, Chen, Sikai, Ran, Bin
Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS). Although deep learning-based approaches - especially those utilizing transformer-based and generative models - have markedly impr
Externí odkaz:
http://arxiv.org/abs/2411.16747
Autor:
Qin Senbiao, Sun Junqiang, Jiang Jialin, Zhang Yi, Cheng Ming, Yu Linfeng, Wang Kang, Kai Li, Shi Haotian, Huang Qiang
Publikováno v:
Nanophotonics, Vol 10, Iss 11, Pp 2847-2857 (2021)
The strain technology is accelerating the progress on the CMOS compatible Ge-on-Si laser source. Here, we report a monolithically integrated microbridge-based emitting-detecting configuration, equipped with lateral p–i–n junctions, waveguide and
Externí odkaz:
https://doaj.org/article/ee94343a50ef4fa3bda011a5a3b4928b
Autor:
Gan, Rui, Shi, Haotian, Li, Pei, Wu, Keshu, An, Bocheng, Li, Linheng, Ma, Junyi, Ma, Chengyuan, Ran, Bin
Vehicle trajectory prediction plays a vital role in intelligent transportation systems and autonomous driving, as it significantly affects vehicle behavior planning and control, thereby influencing traffic safety and efficiency. Numerous studies have
Externí odkaz:
http://arxiv.org/abs/2409.15182
Linear control models have gained extensive application in vehicle control due to their simplicity, ease of use, and support for stability analysis. However, these models lack adaptability to the changing environment and multi-objective settings. Rei
Externí odkaz:
http://arxiv.org/abs/2409.15595
The intricate nature of real-world driving environments, characterized by dynamic and diverse interactions among multiple vehicles and their possible future states, presents considerable challenges in accurately predicting the motion states of vehicl
Externí odkaz:
http://arxiv.org/abs/2409.11676
The implementation of intelligent transportation systems (ITS) has enhanced data collection in urban transportation through advanced traffic sensing devices. However, the high costs associated with installation and maintenance result in sparse traffi
Externí odkaz:
http://arxiv.org/abs/2409.03906
Autor:
You, Junwei, Shi, Haotian, Jiang, Zhuoyu, Huang, Zilin, Gan, Rui, Wu, Keshu, Cheng, Xi, Li, Xiaopeng, Ran, Bin
Advancements in autonomous driving have increasingly focused on end-to-end (E2E) systems that manage the full spectrum of driving tasks, from environmental perception to vehicle navigation and control. This paper introduces V2X-VLM, an innovative E2E
Externí odkaz:
http://arxiv.org/abs/2408.09251
Motivated by the emergent reasoning capabilities of Vision Language Models (VLMs) and their potential to improve the comprehensibility of autonomous driving systems, this paper introduces a closed-loop autonomous driving controller called VLM-MPC, wh
Externí odkaz:
http://arxiv.org/abs/2408.04821
Given the complexity and nonlinearity inherent in traffic dynamics within vehicular platoons, there exists a critical need for a modeling methodology with high accuracy while concurrently achieving physical analyzability. Currently, there are two pre
Externí odkaz:
http://arxiv.org/abs/2406.14696
Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS), enhancing road safety and traffic efficiency. While traditional methods have laid foundational work, modern deep learning techniq
Externí odkaz:
http://arxiv.org/abs/2406.11941