Hierarchical Decision-Making and Control for Connected and Autonomous Vehicle Platoons at Unsignalized Intersections

Abstract

Efficient and safe coordination of connected and automated vehicle (CAV) platoons at unsignalized intersections remains a critical challenge for smart city logistics. Existing hierarchical frameworks often suffer from sequential scheduling inefficiencies, non-convergent low-level control loops, stop-line singular deadlocks, and ambiguous experimental validation. To address these deficiencies, this paper proposes a tractable bilevel architecture that strictly decouples strategic scheduling from tactical trajectory regulation. The strategic layer employs an event-triggered mixed-integer linear programming scheduler with a batch execution bonus to compute conflict-free crossing time windows. The tactical layer integrates a fuel-optimal quintic polynomial planner with real-time spatiotemporal projection feedback to track the assigned windows smoothly. Meanwhile, internal platoon members execute a localized proportionalderivative-plus-integral (PD+I) controller, augmented with antiwindup saturation clamps and brick-wall kinematic safety filters. Extensive simulations are conducted in the SUMO platform using a realistic multi-lane intersection topology under hyper-saturated traffic demand profiles. Benchmarked against conventional firstcome-first-served and actuated signal control, the proposed model achieves a peak macroscopic throughput of 4493 veh/h, a 100% non-stop passage rate, an average reduction of 41.2% in control delays, and savings of up to 47.1% in cumulative fuel consumption and CO2 emissions, while maintaining zero operational deadlocks across all high-volume traffic gradients. These results highlight the proposed framework's effectiveness in enhancing intersection throughput, energy efficiency, and operational robustness for CAV platoons.