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<p>The merging problems are widely recognized as one of the major reasons for the significant degeneration of traffic efficiency. All the vehicles’ interests involved in the merging process should be appropriately guaranteed. How to utilize all the multi-lane road spaces to merge with the premise of all vehicles’ interests remains open to discussion. Fortunately, using connected and autonomous vehicles (CAVs) is a powerful tool for creating control nodes to complete cooperative merging maneuvers and balance the interests of all vehicles. Hence, this paper proposes a hierarchical cooperative merging constrained control (HCMCC) algorithm-based decentralized framework under the mixed traffic scenario. The multi-lane merging problem is solved in the Tactical layer using a Mixed Integer Linear Programming (MILP) optimization model, which computes the programmed trajectories of the CAVs involved. The Laguerre-function-based Continuous-time Model Predictive Control with the Prescribed-Time Observer (LCMPC-PTO) is built into the Operational layer to execute the computed trajectories. The entire system can be optimized to regenerate the trajectories when an emergency occurs due to unexpected behaviors from human-driven vehicles (HVs). Numerical studies in a realistic traffic simulation environment show that the congestion caused by the multi-lane merging problem is improved along with the reduction of the average travel time and the increment of the actual traffic flow.</p>

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Hierarchical cooperative constrained control for multi-lane merging process under mixed traffic scenario

  • 期刊论文

  • 发表日期：

    2025年4月15日

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  • Xiaoyu Liu,
  • Min Zhao,
  • Liuping Wang,
  • Dihua Sun

摘要

The merging problems are widely recognized as one of the major reasons for the significant degeneration of traffic efficiency. All the vehicles’ interests involved in the merging process should be appropriately guaranteed. How to utilize all the multi-lane road spaces to merge with the premise of all vehicles’ interests remains open to discussion. Fortunately, using connected and autonomous vehicles (CAVs) is a powerful tool for creating control nodes to complete cooperative merging maneuvers and balance the interests of all vehicles. Hence, this paper proposes a hierarchical cooperative merging constrained control (HCMCC) algorithm-based decentralized framework under the mixed traffic scenario. The multi-lane merging problem is solved in the Tactical layer using a Mixed Integer Linear Programming (MILP) optimization model, which computes the programmed trajectories of the CAVs involved. The Laguerre-function-based Continuous-time Model Predictive Control with the Prescribed-Time Observer (LCMPC-PTO) is built into the Operational layer to execute the computed trajectories. The entire system can be optimized to regenerate the trajectories when an emergency occurs due to unexpected behaviors from human-driven vehicles (HVs). Numerical studies in a realistic traffic simulation environment show that the congestion caused by the multi-lane merging problem is improved along with the reduction of the average travel time and the increment of the actual traffic flow.