Connected and autonomous vehicles (CAVs) are emerging as a potential solution to merging safety problems. However, in mixed traffic scenarios where CAVs coexist with human-driven vehicles (HVs), challenges arise due to the lack of proactive cooperation and the limited length of the acceleration lane, complicating the merging processes of CAVs. In these cases, CAVs should actively seize the transit opportunity and perform safe and efficient merging. Failure to do so can lead to decreased traffic efficiency, increased fuel consumption and emissions, compromised self-merging capacities, and heightened crash risk. Therefore, this article employs the roadside unit and proposes a two-level hierarchical self-merging strategy for CAVs to increase the merging efficiency while ensuring high safety. Since the surrogate safety measures (SSMs) can formulate reliable safety assessment and identify the merging conflict risk by setting appropriate threshold, the upper level uses a novel SSM-based method (i.e., the minimum acceleration rate, MIAR) to determine the merging sequence (MS). A theoretical model for merging safety (TMMS) is developed to estimate the MIAR value, and the MIAR threshold is determined using signal detection theory (SDT). At the lower level, the strategy recommends the optimized merging maneuvers, pregenerated using sequential quadratic programming-model predictive control (SQP-MPC), based on the determined MS and the CAV's velocity. A case study at a real-world freeway merging area demonstrates the effectiveness of the MIAR in measuring merging conflict risk. Besides, numerous simulations are conducted, and the results demonstrate that the proposed strategy significantly improves merging success rate and overall traffic efficiency.
