A Robust Cooperative Vehicle Coordination Framework for Intersection Crossing

--Cooperative vehicle coordination at unsignalized intersections has garnered significant interest from both academia and industry in recent years, highlighting its notable advantages in improving traffic throughput and fuel efficiency. The oversights pose driving risks in the presence of state uncertainty and communication constraint. T o address this gap, we propose a robust and comprehensive intersection coordination framework consisting of a robust cooperative trajectory planner and a context-aware status update scheduler . The trajectory planner directly controls the evolution of the trajectory distributions during frequent vehicle interactions, thereby offering probabilistic safety guarantees. T o further align with coordination safety in practical bandwidth-limited conditions, we propose a context-aware status update scheduler that dynamically prioritizes the state updating order of vehicles based on their driving urgency. Simulation results validate the robustness and effectiveness of the proposed coordination framework, showing that the collision probability can be significantly reduced while maintaining comparable coordination efficiency to state-of-the-art strategies. Moreover, our proposed framework demonstrates superior effectiveness in utilizing wireless resources in practical uncertain and bandwidth-limited conditions. Recent advancements in information and control technologies have shown significant potential to enhance the performance of connected and autonomous vehicles (CA Vs) [1]. Unlike standalone autonomous driving solutions, CA Vs share information via vehicle-to-everything (V2X) communication links and make decisions collaboratively to achieve a common goal. This collectivism has demonstrated its superiority in driving safety and traffic efficiency [2], [3]. In recent years, vehicle coordination at critical areas, especially road intersections, has gained substantial research interest and is considered a key enabler for intelligent transportation systems (ITS) [4]. This work has been supported in part by the Science and Technology Project of Shenzhen under Grant JCYJ20200109113424990, and the Marine Economy Development Project of Guangdong Province under Grant GDNRC [2020]014.