18 citations · 80 across the 16 of their papers we have counts for
17 papers
Driving Tasks Transfer in Deep Reinforcement Learning for Decision-making of Autonomous Vehicles
Hong Shu, Teng Liu, Xingyu Mu +1
Knowledge transfer is a promising concept to achieve real-time decision-making for autonomous vehicles. This paper constructs a transfer deep reinforcement learning framework to tr…
Data-Driven Transferred Energy Management Strategy for Hybrid Electric Vehicles via Deep Reinforcement Learning
Hao Chen, Gang Guo, Bangbei Tang +3
Real-time applications of energy management strategies (EMSs) in hybrid electric vehicles (HEVs) are the harshest requirements for researchers and engineers. Inspired by the excell…
Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon
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. This paper utilizes the deep reinforcement le…
Decision-making at Unsignalized Intersection for Autonomous Vehicles: Left-turn Maneuver with Deep Reinforcement Learning
Feng Wang, Dongjie Shi, Teng Liu +1
Decision-making module enables autonomous vehicles to reach appropriate maneuvers in the complex urban environments, especially the intersection situations. This work proposes a de…
A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles
Teng Liu, Yuyou Yang, Wenxuan Xiao +2
Deep reinforcement learning (DRL) has emerged as a pervasive and potent methodology for addressing artificial intelligence challenges. Due to its substantial potential for autonomo…
Driving Conditions-Driven Energy Management for Hybrid Electric Vehicles: A Review
Teng Liu, Wenhao Tan, Xiaolin Tang +3
Motivated by the concerns on transported fuel consumption and global air pollution, industrial engineers, and academic researchers have made many efforts to construct more efficien…
Defining Digital Quadruplets in the Cyber-Physical-Social Space for Parallel Driving
Teng Liu, Yang Xing, Long Chen +2
Parallel driving is a novel framework to synthesize vehicle intelligence and transport automation. This article aims to define digital quadruplets in parallel driving. In the cyber…
Integrated Longitudinal Speed Decision-Making and Energy Efficiency Control for Connected Electrified Vehicles
Teng Liu, Bo Wang, Dongpu Cao +2
To improve the driving mobility and energy efficiency of connected autonomous electrified vehicles, this paper presents an integrated longitudinal speed decision-making and energy…
Adaptive Energy Management for Real Driving Conditions via Transfer Reinforcement Learning
Teng Liu, Wenhao Tan, Xiaolin Tang +2
This article proposes a transfer reinforcement learning (RL) based adaptive energy managing approach for a hybrid electric vehicle (HEV) with parallel topology. This approach is bi…
Digital Quadruplets for Cyber-Physical-Social Systems based Parallel Driving: From Concept to Applications
Teng Liu, Xing Yang, Hong Wang +4
Digital quadruplets aiming to improve road safety, traffic efficiency, and driving cooperation for future connected automated vehicles are proposed with the enlightenment of ACP ba…
Comparison of Different Methods for Time Sequence Prediction in Autonomous Vehicles
Teng Liu, Bin Tian, Yunfeng Ai +3
As a combination of various kinds of technologies, autonomous vehicles could complete a series of driving tasks by itself, such as perception, decision-making, planning, and contro…
Reinforcement Learning-Enabled Decision-Making Strategies for a Vehicle-Cyber-Physical-System in Connected Environment
Teng Liu, Xiaolin Tang, Jinwei Zhang +3
As a typical vehicle-cyber-physical-system (V-CPS), connected automated vehicles attracted more and more attention in recent years. This paper focuses on discussing the decision-ma…
Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning
Jiangdong Liao, Teng Liu, Xiaolin Tang +3
Autonomous driving is a promising technology to reduce traffic accidents and improve driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision-making p…
Transfer Deep Reinforcement Learning-enabled Energy Management Strategy for Hybrid Tracked Vehicle
Xiaowei Guo, Teng Liu, Bangbei Tang +4
This paper proposes an adaptive energy management strategy for hybrid electric vehicles by combining deep reinforcement learning (DRL) and transfer learning (TL). This work aims to…
Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study
Teng Liu, Xingyu Mu, Xiaolin Tang +3
This work optimizes the highway decision making strategy of autonomous vehicles by using deep reinforcement learning (DRL). First, the highway driving environment is built, wherein…
Transferred Energy Management Strategies for Hybrid Electric Vehicles Based on Driving Conditions Recognition
Teng Liu, Xiaolin Tang, Jiaxin Chen +3
Energy management strategies (EMSs) are the most significant components in hybrid electric vehicles (HEVs) because they decide the potential of energy conservation and emission red…
Human-like Energy Management Based on Deep Reinforcement Learning and Historical Driving Experiences
Hao Chen, Xiaolin Tang, Guo Hu +1
Development of hybrid electric vehicles depends on an advanced and efficient energy management strategy (EMS). With online and real-time requirements in mind, this article presents…