A Merging Strategy Framework for Connected and Automated Vehicles in Multi-Lane Mixed Traffic Scenarios

Abstract

Frequent merging, overtaking, and lane-changing behaviors at freeway on-ramp areas usually cause traffic bottlenecks with low efficiency and significant safety concerns. Fortunately, the development of connected and autonomous vehicles (CAVs) presents a promising technology for improving merging efficiency and safety. Current research on CAV merging strategies mainly focuses on single-lane scenarios and relies heavily on oversimplified simulation tests. To this end, this study proposes a merging strategy framework for the merging CAV in complex multi-lane mixed traffic scenarios, taking into account the potential interference caused by other vehicles’ lane-changing behaviors. First, a merging gap selection method assists the merging CAV in choosing a more suitable merging gap. Then, a lateral speed control strategy provides the CAV with highly efficient lateral guidance by giving optimal lateral speed control parameters. Furthermore, a pre-merging safety preparation method controls the CAV to adjust its speed longitudinally to avoid potential conflicts. Finally, the merging execution part is proposed to guarantee the CAV an effective, safe, and comfortable merging experience. The proposed model is tested in merging scenarios extracted from the Delft freeway trajectory dataset. Results indicate that the proposed merging strategy can significantly improve the merging efficiency by 45%, while offering a safe and comfortable merging trajectory for CAVs in multi-lane mixed traffic scenarios.

Keywords

• Freeway
• multi-lane
• merge
• CAV
• trajectory optimization