Yougang Bian

Prof. Dr. Yougang Bian

orcid

Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 May 2019 in Transportation Research Part C: Emerging Technologies
Verified
Journal Article
Closed access
YBYougang Bian
Yougang Bian
YZYang Zheng
Yang Zheng
WRWei Ren
Wei Ren
SLShengbo Li
Shengbo Li
JWJianqiang Wang
Jianqiang Wang
KLKeqiang Li
Keqiang Li

27 December 2019 in IEEE Intelligent Transportation Systems Magazine
Verified
Journal Article
Closed access
ZWZiran Wang
Ziran Wang
YBYougang Bian
Yougang Bian
SSSteven E. Shladover
Steven E. Shladover
GWGuoyuan Wu
Guoyuan Wu
SLShengbo Li
Shengbo Li
MBMatthew J. Barth
Matthew J. Barth

9 July 2018 in IEEE Transactions on Intelligent Transportation Systems
Verified
Journal Article
Closed access
BXBiao Xu
Biao Xu
XBXuegang Jeff Ban
Xuegang Jeff Ban
YBYougang Bian
Yougang Bian
WLWan Li
Wan Li
JWJianqiang Wang
Jianqiang Wang
KLKeqiang Li
Keqiang Li
Signalized intersections play an important role in transportation efficiency and vehicle fuel economy in urban areas. This paper proposes a cooperative method of traffic signal control and vehicle speed optimization for connected automated vehicles, which optimizes the traffic signal timing and vehicles' speed trajectories at the same time. The method consists of two levels, i.e., roadside traffic signal optimization and onboard vehicle speed control. The former calculates the optimal traffic signal timing and vehicles' arrival time to minimize the total travel time of all vehicles; the latter optimizes the engine power and brake force to minimize the fuel consumption of individual vehicles. The enumeration method and the pseudospectral method are applied in roadside and onboard optimization, respectively. Simulation studies are conducted to compare the proposed method with benchmark methods. The results show significant improvement of transportation efficiency and fuel economy by the cooperation method.

1 August 2018 in Transportation Research Part C: Emerging Technologies
Verified
Journal Article
Closed access
BXBiao Xu
Biao Xu
SLShengbo Eben Li
Shengbo Eben Li
YBYougang Bian
Yougang Bian
SLShen Li
Shen Li
XBXuegang Jeff Ban
Xuegang Jeff Ban
KLKeqiang Li
Keqiang Li

20 June 2022 in IEEE Transactions on Intelligent Transportation Systems
Verified
Journal Article
Closed access
MHManjiang Hu
Manjiang Hu
CLChongkang Li
Chongkang Li
YBYougang Bian
Yougang Bian
FZFeifei Zhang
Feifei Zhang
ZQZhaobo Qin
Zhaobo Qin
BXBiao Xu
Biao Xu
Vehicle platoon control based on vehicle-to-vehicle (V2V) communication is one of the promising technologies to improve the performance of transportation systems. This paper presents a distributed controller to optimize a vehicle platoon’s fuel consumption by combining the switching feedback control and economic model predictive control (EMPC) methods. The closed-loop dynamics involving switching feedback gains with the constant time headway (CTH) policy are established firstly, and the multiple-predecessor following (MPF) communication topology is considered. In order to obtain the economy optimal feedback gain, we design a local optimal control problem for each vehicle, based on which the average dwell time is defined and the distributed EMPC algorithm is designed. Based on linear matrix inequalities (LMIs) and the Lyapunov theorem, the feedback gain selection method and the lower bound for average dwell time that guarantees asymptotic stability are analyzed rigorously. Then a modified algorithm that can ensure string stability is designed. Numerical simulations show a maximum of 6.84% fuel benefit compared with pure tracking-oriented methods.

24 December 2019 in IEEE Transactions on Industrial Electronics
Verified
Journal Article
Closed access
YBYougang Bian
Yougang Bian
SLShengbo Li
Shengbo Li
WRWei Ren
Wei Ren
JWJianqiang Wang
Jianqiang Wang
KLKeqiang Li
Keqiang Li
HLHenry X. Liu
Henry X. Liu
Cooperation of connected vehicles is a promising approach for autonomous intersection control. This article presents a systematic approach to the cooperation of connected vehicles at unsignalized intersections without global coordination. A task-area partition framework is proposed to decompose the mission of cooperative passing into three main tasks, i.e., vehicle state observation, arriving time optimization, and trajectory tracking control. To accomplish these tasks, a distributed observation algorithm is introduced to achieve fixed-time observation of other vehicles' states for passing sequence determination, a distributed optimization algorithm is introduced to schedule conflict-free arriving times for trajectory planning, and a distributed control algorithm is proposed to address parameter mismatches and acceleration saturation for fixed-time trajectory tracking control. Numerical simulations demonstrate that the proposed method can achieve cooperative passing of vehicles without global coordination at the cost of a growth of 8.8-18.1% average travel times in low and medium traffic volumes.