Optimization of Highway On-Ramp Merging Maneuvers in Pure and Mixed CAV Environments: An Overview

This study provides a comprehensive review of optimization strategies for highway on-ramp merging in the context of connected and automated vehicles (CAVs), addressing both fully automated (pure CAV) and mixed traffic environments involving human-driven vehicles (HDVs). In pure CAV environments, vehicle connectivity and real-time communication enable coordinated control, allowing precise trajectory and speed optimization. Advanced control frameworks, such as hierarchical, predictive, and cooperative models, focus on minimizing travel time, energy consumption, and congestion while ensuring safety. Key optimization variables include vehicle speed, acceleration, and position, which are adjusted dynamically through centralized or distributed control systems. In mixed traffic environments, where CAVs coexist with HDVs, merging becomes more complex due to heterogeneous driving behaviors. Optimization methods here combine automated control for CAVs with behavior models representing HDV dynamics. Such approaches integrate cooperative adaptive cruise control (ACC), rolling-horizon trajectory planning, and hierarchical control frameworks to ensure safe and efficient merging across varying traffic compositions. Findings consistently highlight that higher CAV market penetration improves traffic performance, reducing delays and enhancing flow stability. Overall, the literature demonstrates that optimization of on-ramp merging in both pure and mixed CAV environments relies on fine-tuning speed, position, and acceleration through predictive and cooperative control frameworks. Future research should focus on developing real-time, scalable optimization frameworks that can adapt to varying levels of CAV penetration, account for human behavioral uncertainty, and enhance both traffic efficiency and safety through seamless coordination between automated and human-driven vehicles in complex traffic environments.