Optimizing Vehicle Trajectories at a Signalized Intersection in Mixed Traffic

Abstract

With the advancement of connected and automated vehicles (CAVs), achieving accurate vehicle trajectory prediction and optimal control has become a critical challenge for improving the efficiency and safety of mixed traffic flow. However, due to the complex dynamic interactions between CAVs and human-driven vehicles (HVs) and the nonlinear nature of signal coordination, existing studies lack comprehensive consideration of CAV position adjustments within the platoon and their guidance effects on trailing HVs. This paper proposes a data-driven method for CAV state prediction and trajectory optimization. Employing an application-specific improved Informer model, our method accurately predicts CAV arrival states at a signalized intersection in mixed traffic. Additionally, Bayesian optimization (BO) is utilized to achieve automated and rapid tuning of CAV model predictive control (MPC) parameters through learning human driving characteristics. Experimental results demonstrate that our proposed method significantly enhances overall traffic efficiency and optimization when CAVs operate within mixed traffic, showing strong feasibility and adaptability.