Trajectory Planning and Tracking With Multiobjective Optimization for Connected and Automated Vehicles at Expressway On‐Ramps

Abstract

On‐ramp merging is a common expressway maneuver for connected and automated vehicles (CAVs), where trajectory planning and tracking control are central to avoiding collisions. However, existing studies rarely optimize the selection of merge start and end points and give limited attention to constraints from acceleration‐lane length. This study proposes a structured trajectory planning and tracking method with multiobjective optimization under the CAV’s environment. First, by sampling the starting and ending points of the merging process, the quintic polynomial is used to plan the initial trajectory of the merging vehicles, and trajectory safety is checked with a collision‐avoidance algorithm based on rectangular vehicle geometry. Then, a multiobjective optimization model selects the on‐ramp trajectory by balancing merging urgency, driving safety, traffic efficiency, and comfort. Finally, an integrated tracking strategy combines lateral and longitudinal control: a feedforward LQR for lateral motion and a PID‐based longitudinal controller. To further improve the tracking accuracy, the particle swarm algorithm tunes key parameters of the lateral LQR controller. The numerical result demonstrates that the planner can generate smooth and stable trajectories that could be selected as an optimal reference for the tracking controller. The simulation results show that when the initial speed of the on‐ramp vehicle is 68 km/h, the maximum tracking errors of lateral and longitudinal displacements are less than 0.02 and 0.2 m, respectively.