In this paper, we explore the application of the Decision Transformer, a decision-making algorithm based on the Generative Pre-trained Transformer (GPT) architecture, to multi-vehicle coordination at unsignalized intersections. We formulate the coordination problem so as to find the optimal trajectories for multiple vehicles at intersections, modeling it as a sequence prediction task to fully leverage the power of GPTs as a sequence model. Through extensive experiments, we compare our approach to a reservation-based intersection management system. Our results show that the Decision Transformer can outperform the training data in terms of total travel time and can be generalized effectively to various scenarios, including noise-induced velocity variations, continuous interaction environments, and different vehicle numbers and road configurations.

翻译：本文探讨了基于生成式预训练Transformer（GPT）架构的决策算法——决策Transformer在无信号交叉口多车协同中的应用。我们将交叉口多车最优轨迹求解的协同问题建模为序列预测任务，以充分发挥GPT作为序列模型的优势。通过大量实验，我们将本方法与基于预约机制的交叉口管理系统进行了对比。结果表明，决策Transformer在总通行时间指标上能够超越训练数据表现，并能有效泛化至多种场景，包括噪声引起的速度波动、连续交互环境以及不同车辆数量与道路配置。