用实时信号数据优化车队行驶，省油超四成。
Centralized Decision-Making for Platooning By Using SPaT-Driven Reference Speeds
- 中心化控制结合V2X与信号相位数据规划车速
- 仿真显示最高省油41.2%，停驶次数减少
- 适合智能交通与城市车队管理研究者
本文提出一种基于实时车联网（V2X）通信和信号相位与配时（SPaT）数据的集中式城市车队节能方案。采用非线性模型预测控制（MPC）算法优化车队领头车轨迹，使用非对称代价函数降低燃油密集型加速行为。跟随车辆采用基于间距与速度的控制策略，并通过车队控制消息（PCM）和车队感知消息（PAM）传递动态分组逻辑。在CARLA环境中的仿真结果表明，该方法可实现最高达41.2%的燃油节省，同时带来更平滑的交通流、更少的车辆停驶以及更高的交叉口通行效率。
原文摘要 · Abstract (English)
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.
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