  With the rapid development of connected and automated vehicles (CAVs) and
intelligent transportation infrastructure, CAVs and connected human-driven
vehicles (CHVs) will coexist on the roads in the future for a long time. This
paper comprehensively considers the different traffic characteristics of CHVs
and CAVs, and systemically investigates the unsignalized intersection
management strategy from upper decision-making level to lower execution level.
Combined with the designed vehicle planning and control algorithm, the
unsignalized intersection management strategy consists of two parts: the
heuristic priority queues based right of way allocation (HPQ) algorithm, and
the vehicle planning and control algorithm. In the HPQ algorithm, a vehicle
priority management model considering the difference between CAVs and CHVs is
built to design the right of way management for CAVs and CHVs, respectively. In
the lower level for vehicle planning and control algorithm, different control
modes of CAVs are designed according to the upper level decision made by the
HPQ algorithm. Moreover, the vehicle control execution is realized by the model
predictive controller combined with the geographical environment constraints
and the unsignalized intersection management strategy. The proposed strategy is
evaluated by simulations, which show that the proposed intersection management
strategy can effectively reduce travel time and improve traffic efficiency. The
intersection management strategy captures the real-world balance between
efficiency and safety for potential future intelligent traffic systems.
