Spatio-Temporal Coordinated Decision-Making in Heterogeneous Platooning via Interflow Hub-and-Spoke Graph Reinforcement Learning

In mixed vehicular platooning, heterogeneity from varying vehicle types and control systems leads to unpredictable traffic behaviors and frequent inconsistencies. These variances worsen the spatio-temporal discord within platoons, leading to persistent virtual bottlenecks that substantially impede traffic flow and increase energy consumption. To address these challenges, a centralized decision-making framework utilizing interflow hub-and-spoke graph reinforcement learning (InterHub-GRL) is implemented. This strategy enhances the precision of spatio-temporal interaction models among heterogeneous elements within the platooning, promoting collaborative decision-making, ensuring energy efficiency, and alleviating congestion. The proposed framework captures dynamic interactions within and between platoons in non-Euclidean spaces. Integrating spatio-temporal weighted graphs with a multi-head attention mechanism significantly improves the model’s ability to process both local and global data. Comparative algorithm experiments, generalizability assessments, and permeability ablation studies using the I-24 dataset demonstrate that this strategy achieves a 10% increase in throughput and a 9% reduction in energy consumption compared to baseline metrics. Notably, increasing the penetration rate of connected and autonomous vehicles (CAVs) markedly enhances traffic throughput while managing energy consumption.