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Research in Cooperative Intersection Management (CIM), utilizingVehicle-to-Everything (V2X) communication among Connected and/or AutonomousVehicles (CAVs), is crucial for enhancing intersection safety and drivingexperience. CAVs can transceive basic and/or advanced safety information,thereby improving situational awareness at intersections. The focus of thisstudy is on unsignalized intersections, particularly StopControlled-Intersections (SC-Is), where one of the main reasons involvingcrashes is the ambiguity among CAVs in SC-I crossing priority upon arriving atsimilar time intervals. Numerous studies have been performed on CIM forunsignalized intersections based on centralized and distributed systems in thepresence and absence of Road-Side Unit (RSU), respectively. However, most ofthese studies are focused towards replacing SC-I where the scheduler providesspatio-temporal or sequence-based reservation to CAVs, or where it controlsCAVs via kinematic commands. These methods cause CAVs to arrive at theintersection at non-conflicting times and cross without stopping. This logic isseverely limited in real-world mixed traffic comprising human drivers wherekinematic commands and other reservations cannot be implemented as intended.Thus, given the existence of SC-Is and mixed traffic, it is significant todevelop CIM systems incorporating SC-I rules while assigning crossingpriorities and resolving the related ambiguity. In this regard, we propose adistributed Automated Right-of-Way (AROW) algorithm for CIM to assign explicitSC-I crossing turns to CAVs and mitigate hazardous scenarios due to ambiguitytowards crossing priority. The algorithm is validated with extensiveexperiments for its functionality, scalability, and robustness towards CAVnon-compliance, and it outperforms the current solutions.

AROW: V2X-based Automated Right-of-Way Algorithm for Cooperative Intersection Management