用GPT模型优化无信号交叉口多车协同，提升通行效率。
A GPT-based Decision Transformer for Multi-Vehicle Coordination at Unsignalized Intersections
- 将多车协调建模为序列预测问题，利用GPT架构生成最优行驶轨迹。
- 在总通行时间上超越训练数据表现，且在多种场景下具强泛化能力。
- 适合智能交通、自动驾驶领域研究者参考，尤其关注协同决策的场景。
本文探索基于生成式预训练变换器（GPT）架构的决策转换器（Decision Transformer）在无信号交叉口多车协同中的应用。将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，求解多车在交叉口的最优轨迹。通过大量实验，将该方法与基于预约的交叉口管理系统进行对比。结果表明，决策转换器在总通行时间上优于训练数据，并能有效推广至包含速度扰动噪声、连续交互环境以及不同车辆数量和道路配置等多种场景。
原文摘要 · Abstract (English)
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.
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