日本フィジカルAI新聞

世界のフィジカルAIを、日本語で。

週刊ニュースレター購読
arXiv:2505.06071

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

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

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著者: Melih Yazgan, Süleyman Tatar, J. Marius Zöllner

分類: cs.RO, cs.SY, eess.SY

原文アブストラクト

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.