Cooperative Platoon Control for a Mixed Traffic Flow Including Human-Driven Vehicles and Connected and Autonomous Vehicles

This study seeks to develop a cooperative platoon control for a platoon mixed with connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs), aiming to ensure system level traffic flow smoothness and stability as well as individual vehicles’ mobility and safety. Specifically, the authors consider a sample mixed flow platoon including a HDV platoon sandwiched by two CAV platoons. The study contributes the following technical approaches. First, the car-following behavior of human-driven vehicles is modeled by well-accepted Newell car-following models. Accordingly, an online curve matching algorithm is integrated to anticipate the aggregated response delay of the human-driven vehicles using real-time trajectory data. Built upon that, constrained One- or P-step MPC models are developed to control the movement of the two CAV platoons downstream or upstream of the HDV platoon so that the authors can ensure both transient traffic smoothness and asymptotic stability of this sample mixed flow platoon, leveraging the communication and computation technologies equipped on CAVs. Considering the lack of the centralized computation facilities and frequent changes of the platoon topology, this study develops a distributed algorithm to solve the MPCs according to the properties of the optimizers, such as solution uniqueness, sequentially feasibility, and nonempty interior point of the solution space. The convergence of the distributed algorithm as well as the stability of the MPC control is proved by both the theoretical analysis and the experimental study. The numerical experiments based on the field data validated the effectiveness and efficiency of the algorithms and the cooperative platoon control scheme.

• Supplemental Notes:
  • This paper was sponsored by TRB committee AHB45 Standing Committee on Traffic Flow Theory and Characteristics.
• Corporate Authors:

  Transportation Research Board

  ,    
• Authors:
  • Gong, Siyuan
  • Du, Lili
• Conference:
  • Transportation Research Board 98th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2019-1-13 to 2019-1-17
• Date: 2019

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: 22p

Subject/Index Terms

• TRT Terms: Algorithms; Autonomous vehicles; Car following; Connected vehicles; Traffic control centers; Traffic flow; Traffic platooning; Vehicle mix
• Candidate Terms: Human-driven vehicles
• Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01698037
• Record Type: Publication
• Report/Paper Numbers: 19-03607
• Files: TRIS, TRB, ATRI
• Created Date: Mar 1 2019 3:51PM