Investigation of Merge Strategies at Ramp Area in Connected Vehicle Environment based on Multi-Driver Simulator System

This study aims to investigate the impacts of merge strategies of a ramp CAV on mainline human drivers. Previous studies evaluated CAV merge strategies mostly based on either the simulation or the restricted field testing, which lacks consideration of realistic driving behaviors in the merging scenario. To deal with this research gap, this study developed a multi-driver simulator system and considered realistic driving behaviors in the validation of merge strategies. Four CAV merge strategies were evaluated regarding their impacts on driving safety and comfort of the mainline human drivers. A set of driving safety and comfort metrics was adopted to verify the merge strategies. The results show that these algorithms might not have consistent performance when evaluated by different safety and comfort metrics. In addition, results revealed significant variations of the algorithm influences between the merging and the following periods. Moreover, the AHS and GFM may have some superiority when evaluated at specific dimensions in terms of driving safety and comfort; nevertheless, the AHS may outperform other merge strategies in more scenarios. Findings suggest that the CAV merge strategy should not only ensure the ramp vehicle’s merging task but also consider mainline vehicles’ driving performance.