The improvement of traffic efficiency at urban intersections receives strong research interest in the field of automated intersection management. So far, mostly non-learning algorithms like reservation or optimization-based ones were proposed to solve the underlying multi-agent planning problem. At the same time, automated driving functions for a single ego vehicle are increasingly implemented using machine learning methods. In this work, we build upon a previously presented graph-based scene representation and graph neural network to approach the problem using reinforcement learning. The scene representation is improved in key aspects by using edge features in addition to the existing node features for the vehicles. This leads to an increased representation quality that is leveraged by an updated network architecture. The paper provides an in-depth evaluation of the proposed method against baselines that are commonly used in automatic intersection management. Compared to a traditional signalized intersection and an enhanced first-in-first-out scheme, a significant reduction of induced delay is observed at varying traffic densities. Finally, the generalization capability of the graph-based representation is evaluated by testing the policy on intersection layouts not seen during training. The model generalizes virtually without restrictions to smaller intersection layouts and within certain limits to larger ones.
翻译:城市十字路口交通效率的提高在自动化交叉管理领域引起了强烈的研究兴趣,迄今为止,大多数非学习算法,如保留或优化法,都是为了解决基本的多试剂规划问题。与此同时,利用机器学习方法越来越多地实施单一自负车辆的自动驾驶功能。在这项工作中,我们利用以前以图表为基础的场面表现和图形神经网络,利用强化学习来解决这一问题。除了现有车辆节点特征外,通过使用边线功能,实地代表在关键方面得到了改进。这导致通过更新的网络结构来提高代表质量。该文件根据自动交叉管理通常使用的基线,对拟议方法进行了深入的评估。与传统的信号交叉和强化的一出一出一出一出计划相比,在不同的交通密度下观察到引致的延误显著减少。最后,通过测试培训期间看不到的交叉布局政策,对图表代表的通用能力进行了评估。模型几乎没有限制地概括了较小的交叉布局,在一定限度内与更大范围内。