A hybrid connected and autonomous vehicles trajectory optimization for on-ramp freeway merging in mixed vehicular traffic environment

Efficient highway merging is essential for traffic safety and efficiency. The rise of connected and automated vehicles (CAVs) offers new opportunities to enhance merging operations. This study introduces a novel control algorithm that optimizes CAV trajectories within a hierarchical hybrid control framework consisting of three layers: strategic, tactical, and operational. At the top layer, the system identifies vehicles most affected by merging and relays this data to the tactical layer. There, a multiphase kinematic model and a multi-input multi-output (MIMO) model predictive control (MPC) scheme generate optimal trajectories. The operational layer uses these trajectories to guide vehicle maneuvers: longitudinal movement is controlled by a Proportional-Integral (PI) controller, while lateral movement is managed using a Proportional-Integral-Derivative (PID) controller, both are tailored to the specific dynamics of each vehicle. Simulations across various scenarios demonstrate the algorithm’s reliability, robustness, and accuracy under diverse initial conditions. The paper also proposes a centralized control technique that integrates all three layers into a unified, continuous control process. When compared, the hybrid control approach offers superior computational efficiency and resilience to model uncertainties and communication disturbances, while the centralized control achieves better stability, fuel efficiency, and passenger comfort.