Collision-Free Merging Control Via Trajectory Optimization for Connected and Autonomous Vehicles

Abstract

On-ramp merging zones have long been accident-prone spots as well as major triggers of traffic congestion in freeway networks. This paper proposes a collision-free merging control for connected and autonomous vehicles based on trajectory optimization to facilitate traffic safety and efficiency. The optimization is embedded in a model predictive control algorithm with the aim of maximizing the average travel speeds of all vehicles in the planning horizon. To guarantee collision-free merging, the merging control allows only one vehicle to pass the merging zone at a time. Safety constraints, including minimum time gaps and minimum distances between vehicles, are also considered. A rolling horizon control framework is established to enable dynamic traffic control; specifically, generating optimal acceleration decisions for each time step. The optimization problem is formulated as mixed-integer linear programming to enable efficient computation. Numerical studies and sensitivity analysis were conducted to validate the effectiveness and applicability of the proposed method. The results show that the introduced safety constraints can guarantee collision-free merging. Compared to the traditional method, Ih is built on the intelligent driver model and the minimizing overall braking induced by lane-change model, the proposed optimization-based approach results in 5.0%–22.2% improvements with respect to average travel speeds during the merging process. The safety and efficiency improvements are more significant when dealing with higher demand levels. Notably, compared to the difference in initial speeds between the mainline and on-ramp vehicles, the length of the acceleration lane has a greater impact on the control performance.