On-Ramp Merging Strategy Integrating Dynamic Lane-Changing Decision and Quasi-Uniform B-Spline Trajectory Optimization

Coordination of vehicles in on-ramp merging scenarios is crucial for preventing traffic bottlenecks, congestion, and accidents. Connected and automated vehicles (CAVs) with vehicle-to-everything (V2X) communication have the potential to improve vehicle coordination and enhance the safety and efficiency of on-ramp merging. While the existing research mainly concentrates on single-lane mainline scenarios, and its applicability to multi-lane on-ramp merging scenario is limited. To address this gap, a multi-lane on-ramp merging strategy is proposed, integrating dynamic lane-changing decision-making and quasi-uniform B-spline trajectory optimization. Initially, a multi-lane dynamic lane-changing decision model is developed, leveraging lane-changing benefits and traffic flow assisted decision-making. This approach comprehensively considers vehicle surroundings, combining global road section information to enhance accuracy of lane-changing decision and overall traffic efficiency. Subsequently, a trajectory optimization model based on quasi-uniform B-splines is designed, formulating the optimization problem as a Quadratic Programming (QP) problem to balance trajectory feasibility and computational load. Simulation results reveal that the proposed lane-changing decision model enhances lane-change accuracy and reduces travel delay in different mainline traffic flow proportions and mainline-ramp traffic demands, outperforming the existing model. Moreover, the proposed trajectory method surpasses other methods in safety and comfort during the merging process, with a noticeable improvement in traffic efficiency compared to SUMO's built-in algorithm.