Centralized Decision-Making for Platooning By Using SPaT-Driven Reference Speeds

TL;DR

This work tackles the challenge of fuel-efficient urban mobility by combining SPaT-driven V2X data with a centralized platoon management framework. A nonlinear MPC controls the platoon leader while followers use a gap-based CACC, and dynamic platoon splitting is allowed to maximize green-window passage. In CARLA simulations, the approach yields substantial fuel savings (up to 41.2% for the platoon) and smoother traffic flows with improved intersection throughput. The results demonstrate the practical potential of SPaT-enhanced centralized platooning for urban environments and set the stage for open-source deployment and further research.

Abstract

This paper introduces a centralized approach for fuel-efficient urban platooning by leveraging real-time Vehicle- to-Everything (V2X) communication and Signal Phase and Timing (SPaT) data. A nonlinear Model Predictive Control (MPC) algorithm optimizes the trajectories of platoon leader vehicles, employing an asymmetric cost function to minimize fuel-intensive acceleration. Following vehicles utilize a gap- and velocity-based control strategy, complemented by dynamic platoon splitting logic communicated through Platoon Control Messages (PCM) and Platoon Awareness Messages (PAM). Simulation results obtained from the CARLA environment demonstrate substantial fuel savings of up to 41.2%, along with smoother traffic flows, fewer vehicle stops, and improved intersection throughput.

Figures (6)

• Figure 1: System architecture of the CARLA platooning framework, showing vehicle control, traffic signal integration, and V2X messaging for coordinated platoon management.
• Figure 2: Simulation environment in CARLA showing the test route with three signalized intersections.The colored paths represent different traffic corridors: Corridor 1 (red), Corridor 2 (green), and Corridor 3 (blue).
• Figure 3: Vehicle trajectories illustrating platoon synchronization and dynamic platoon splitting decisions based on SPaT-driven reference velocities. Colored lines represent individual vehicle paths, while horizontal lines indicate intersection locations and corresponding signal phases.
• Figure 4: Detailed analysis of platoon splitting behavior at intersections under SPaT-driven velocity control. Key events include the initial accordion effect (5–10 s), first platoon split (30 s), and second platoon split (60–70 s). The trajectories highlight adaptive platoon formation, robust vehicle coordination, and effective handling of temporary communication losses (DSRC range limitations).
• Figure 5: Vehicle trajectories without coordinated platooning and SPaT-based green window advisory. The irregular and abrupt changes in slope illustrate frequent stops and accelerations at intersections, highlighting inefficiencies caused by independent vehicle decisions and lack of predictive velocity control.