Zobrazeno 1 - 10
of 171
pro vyhledávání: '"Yin Yuming"'
Autor:
Yang, Shuo, Wang, Caojun, Zhang, Yuanjian, Yin, Yuming, Huang, Yanjun, Li, Shengbo Eben, Chen, Hong
Adversarial scenario generation is crucial for autonomous driving testing because it can efficiently simulate various challenge and complex traffic conditions. However, it is difficult to control current existing methods to generate desired scenarios
Externí odkaz:
http://arxiv.org/abs/2408.14000
Autonomous driving at intersections is one of the most complicated and accident-prone traffic scenarios, especially with mixed traffic participants such as vehicles, bicycles and pedestrians. The driving policy should make safe decisions to handle th
Externí odkaz:
http://arxiv.org/abs/2108.13038
Autor:
Tang, Kaiming, Li, Shengbo Eben, Yin, Yuming, Guan, Yang, Duan, Jingliang, Cao, Wenhan, Li, Jie
State estimation is critical to control systems, especially when the states cannot be directly measured. This paper presents an approximate optimal filter, which enables to use policy iteration technique to obtain the steady-state gain in linear Gaus
Externí odkaz:
http://arxiv.org/abs/2103.05505
Autor:
Liu, Zhengyu, Duan, Jingliang, Wang, Wenxuan, Li, Shengbo Eben, Yin, Yuming, Lin, Ziyu, Sun, Qi, Cheng, Bo
This paper proposes an off-line algorithm, called Recurrent Model Predictive Control (RMPC), to solve general nonlinear finite-horizon optimal control problems. Unlike traditional Model Predictive Control (MPC) algorithms, it can make full use of the
Externí odkaz:
http://arxiv.org/abs/2102.11736
Autor:
Liu, Zhengyu, Duan, Jingliang, Wang, Wenxuan, Li, Shengbo Eben, Yin, Yuming, Lin, Ziyu, Cheng, Bo
Publikováno v:
IEEE Transactions on Industrial Electronics, 2022
This paper proposes an offline control algorithm, called Recurrent Model Predictive Control (RMPC), to solve large-scale nonlinear finite-horizon optimal control problems. It can be regarded as an explicit solver of traditional Model Predictive Contr
Externí odkaz:
http://arxiv.org/abs/2102.10289
Safety is essential for reinforcement learning (RL) applied in real-world situations. Chance constraints are suitable to represent the safety requirements in stochastic systems. Previous chance-constrained RL methods usually have a low convergence ra
Externí odkaz:
http://arxiv.org/abs/2012.10716
The uncertainties in plant dynamics remain a challenge for nonlinear control problems. This paper develops a ternary policy iteration (TPI) algorithm for solving nonlinear robust control problems with bounded uncertainties. The controller and uncerta
Externí odkaz:
http://arxiv.org/abs/2007.06810
The design of an automated vehicle controller can be generally formulated into an optimal control problem. This paper proposes a continuous-time finite-horizon approximate dynamicprogramming (ADP) method, which can synthesis off-line near-optimal con
Externí odkaz:
http://arxiv.org/abs/2007.02070
Autor:
Zhu, Jie, Yin, Yuming, Lu, Jingshan, Warner, Timothy A., Xu, Xinwen, Lyu, Mingyu, Wang, Xue, Guo, Caili, Cheng, Tao, Zhu, Yan, Cao, Weixing, Yao, Xia, Zhang, Yongguang, Liu, Liangyun
Publikováno v:
In Remote Sensing of Environment 1 December 2023 298
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.