Ramp Merging Strategy Based on Dual-layer Optimization Model in Non-connected Scenarios

The ramp merging task is a major challenge in the development of autonomous driving technology. In scenarios where all other vehicles in the merging area are manually driven, the ego driving vehicle must rely on its own sensors and communication with the roadside unit to obtain environmental information, make decisions, and complete the merging task in a non-cooperative setting. However, the current research on ramp merging lacks the capability to dynamically adjust merging gaps and longitudinal speeds, and also exhibits limited interaction intelligence with mainline vehicles. To address this problem, this paper introduces a staged maneuvering model based on optimal control. Simultaneously, a merging strategy grounded on the dual-layer optimization model predictive control is proposed, empowering ego driving vehicles to make more intelligent and efficient merging decisions. Firstly, a python-based simulation platform is established to verify the ramp merge decision. After that, a staged maneuvering model is developed based on the optimal control method, and both the proposed merging strategy based on the dual-layer optimization model predictive control and the rule-based baseline control strategy are introduced. The merging process is then simulated under two scenarios using the baseline strategy and the proposed strategy, respectively. The results are analyzed, demonstrating that the proposed algorithm enables safer and smarter decisions. The proposed strategy achieves dynamic selection of the merging gap, thereby enhancing the intelligence of the ramp merging process, achieving both efficiency and safety, and providing a reliable method for vehicle ramp merging in non-cooperative environments.