  Cooperative on-ramp merging control for connected automated vehicles (CAVs)
has been extensively investigated. However, they did neglect the connected
vehicle identification process, which is a must for CAV cooperations. In this
paper, we introduced a connected vehicle identification system (VIS) into the
on-ramp merging control process for the first time and proposed an evaluation
framework to assess the impacts of VIS on on-ramp merging performance. First,
the mixed-traffic cooperative merging problem was formulated. Then, a
real-world merging trajectory dataset was processed to generate dangerous
merging scenarios. Aiming at resolving the potential collision risks in mixed
traffic where CAVs and traditional human-driven vehicles (THVs) coexist, we
proposed on-ramp merging strategies for CAVs in different mixed traffic
situations considering the connected vehicle identification process. The
performances were evaluated via simulations. Results indicated that while
safety was assured for all cases with CAVs, the cases with VIS had delayed
initiation of cooperation, limiting the range of cooperative merging and
leading to increased fuel consumption and acceleration variations.
