期刊
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
卷 127, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trc.2021.103138
关键词
Connected and Automated Vehicle; Signalized intersection; Mixed traffic system; Optimal control
资金
- National Key Research and Development Program of China [2018YFE0204302]
- National Natural Science Foundation of China [52072212]
- KeyArea Research and Development Program of Guangdong Province, China [2019B090912001]
- Institute of China Intelligent and Connect Vehicles (CICV), China and Connect Vehicles (CICV)
- Tsinghua University-Didi Joint Research Center for Future Mobility, China
This study introduces a concept of 1 + n mixed platoon control, optimizing CAV control at a signalized intersection for improved global traffic efficiency and reduced fuel consumption.
The emergence of Connected and Automated Vehicles (CAVs) promises better traffic mobility for future transportation systems. Existing research mostly focused on fully-autonomous scenarios, while the potential of CAV control at a mixed traffic intersection where human-driven vehicles (HDVs) also exist has been less explored. This paper proposes a notion of 1 + n mixed platoon, consisting of one leading CAV and n following HDVs, and formulates a platoon-based optimal control framework for CAV control at a signalized intersection. Based on the linearized dynamics model of the 1 + n mixed platoon, fundamental properties including stability and controllability are under rigorous theoretical analysis. Then, a constrained optimal control framework is established, aiming at improving the global traffic efficiency and fuel consumption at the intersection via direct control of the CAV. A hierarchical event-triggered algorithm is also designed for practical implementation of the optimal control method between adjacent mixed platoons when approaching the intersection. Extensive numerical simulations at multiple traffic volumes and market penetration rates validate the greater benefits of the mixed platoon based method, compared with traditional trajectory optimization methods for one single CAV.
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