Decentralized On-Ramp Merging Control of Connected and Automated Vehicles in the Mixed Traffic Using Control Barrier Functions

The cooperative control of the connected and automated vehicle (CAV) is recognized as an effective approach to alleviate traffic congestion and improve traffic safety, especially for on-ramp bottlenecks. However, in the mixed traffic, the uncertainty of human-driven vehicles (HDVs) makes the on-ramp merging control for CAVs more challenging. This paper proposes a decentralized optimal control method to address the merging control problem of CAVs at highway on-ramps in the mixed traffic. We first formulate the optimal merging control problem, which includes the constraints of safety and vehicle dynamics, with the objectives of minimizing travel time and energy consumption. Then, a control framework, combining control barrier functions (CBFs) and control Lyapunov functions (CLFs) is proposed. CBFs render the system subject to safety-critical constraints, while CLFs stabilize the system to the objectives. In addition, to enable effective control of CAVs in the mixed traffic, a recursive merging control framework is proposed, where HDVs are regarded as disturbances, and CAVs collect surrounding vehicles' states repeatedly and update their trajectories recursively to satisfy strict merging requirements. Finally, the merging problem is reformulated as a quadratic programming problem, which allows for real-time application. Simulation results show that the proposed on-ramp merging control method is robust in resisting disturbance from the HDV with traffic efficiency improvement.