no code implementations • 7 Sep 2020 • Hao Chen, Gang Guo, Bangbei Tang, Guo Hu, Xiaolin Tang, Teng Liu
For specification, many source driving cycles are utilized for training the parameters of deep network based on PPO.
no code implementations • 26 Aug 2020 • Hao Chen, Xiaolin Tang, Teng Liu
Decision-making strategy for autonomous vehicles de-scribes a sequence of driving maneuvers to achieve a certain navigational mission.
no code implementations • 14 Aug 2020 • Feng Wang, Dongjie Shi, Teng Liu, Xiaolin Tang
Decision-making module enables autonomous vehicles to reach appropriate maneuvers in the complex urban environments, especially the intersection situations.
no code implementations • 4 Aug 2020 • Teng Liu, Yuyou Yang, Wenxuan Xiao, Xiaolin Tang, Mingzhu Yin
Deep reinforcement learning (DRL) has emerged as a pervasive and potent methodology for addressing artificial intelligence challenges.
no code implementations • 24 Jul 2020 • Teng Liu, Wenhao Tan, Xiaolin Tang, Jiaxin Chen, Dongpu Cao
This article proposes a transfer reinforcement learning (RL) based adaptive energy managing approach for a hybrid electric vehicle (HEV) with parallel topology.
no code implementations • 24 Jul 2020 • Teng Liu, Bo wang, Dongpu Cao, Xiaolin Tang, Yalian Yang
As the core of this study, model predictive control and reinforcement learning are combined to improve the powertrain mobility and fuel economy for a group of automated vehicles.
no code implementations • 21 Jul 2020 • Teng Liu, Xing Yang, Hong Wang, Xiaolin Tang, Long Chen, Huilong Yu, Fei-Yue Wang
The three virtual vehicles (descriptive, predictive, and prescriptive) dynamically interact with the real one in order to enhance the safety and performance of the real vehicle.
no code implementations • 16 Jul 2020 • Teng Liu, Wenhao Tan, Xiaolin Tang, Jinwei Zhang, Yang Xing, Dongpu Cao
This paper focusing on helping the relevant researchers realize the state-of-the-art of HEVs energy management field and also recognize its future development direction.
no code implementations • 16 Jul 2020 • Jiangdong Liao, Teng Liu, Xiaolin Tang, Xingyu Mu, Bing Huang, Dongpu Cao
The advantages of the proposed framework in convergence rate and control performance are illuminated.
no code implementations • 16 Jul 2020 • Teng Liu, Xingyu Mu, Xiaolin Tang, Bing Huang, Hong Wang, Dongpu Cao
This work optimizes the highway decision making strategy of autonomous vehicles by using deep reinforcement learning (DRL).
no code implementations • 16 Jul 2020 • Hao Chen, Xiaolin Tang, Guo Hu, Teng Liu
With online and real-time requirements in mind, this article presents a human-like energy management framework for hybrid electric vehicles according to deep reinforcement learning methods and collected historical driving data.
no code implementations • 16 Jul 2020 • Xiaowei Guo, Teng Liu, Bangbei Tang, Xiaolin Tang, Jinwei Zhang, Wenhao Tan, Shufeng Jin
This paper proposes an adaptive energy management strategy for hybrid electric vehicles by combining deep reinforcement learning (DRL) and transfer learning (TL).
no code implementations • 16 Jul 2020 • Teng Liu, Xiaolin Tang, Jiaxin Chen, Hong Wang, Wenhao Tan, Yalian Yang
Energy management strategies (EMSs) are the most significant components in hybrid electric vehicles (HEVs) because they decide the potential of energy conservation and emission reduction.
no code implementations • 16 Jul 2020 • Teng Liu, Xiaolin Tang, Jinwei Zhang, Wenbo Li, Zejian Deng, Yalian Yang
As a typical vehicle-cyber-physical-system (V-CPS), connected automated vehicles attracted more and more attention in recent years.
no code implementations • 29 Apr 2013 • Xiaolin Tang, Chunhua Yang, Xiaojun Zhou, Weihua Gui
In this paper, an efficient discrete state transition algorithm (DSTA) for GTSP is proposed, where a new local search operator named \textit{K-circle}, directed by neighborhood information in space, has been introduced to DSTA to shrink search space and strengthen search ability.