Microscopic traffic simulation plays a crucial role in transportation engineering by providing insights into individual vehicle behavior and overall traffic flow. However, creating a realistic simulator that accurately replicates human driving behaviors in various traffic conditions presents significant challenges. Traditional simulators relying on heuristic models often fail to deliver accurate simulations due to the complexity of real-world traffic environments. Due to the covariate shift issue, existing imitation learning-based simulators often fail to generate stable long-term simulations. In this paper, we propose a novel approach called learner-aware supervised imitation learning to address the covariate shift problem in multi-agent imitation learning. By leveraging a variational autoencoder simultaneously modeling the expert and learner state distribution, our approach augments expert states such that the augmented state is aware of learner state distribution. Our method, applied to urban traffic simulation, demonstrates significant improvements over existing state-of-the-art baselines in both short-term microscopic and long-term macroscopic realism when evaluated on the real-world dataset pNEUMA.

中文翻译：

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LASIL：用于长期微观交通模拟的学习者感知监督模仿学习

微观交通仿真通过提供对个体车辆行为和整体交通流量的洞察，在交通工程中发挥着至关重要的作用。然而，创建一个能够准确复制人类在各种交通条件下驾驶行为的真实模拟器面临着巨大的挑战。由于现实交通环境的复杂性，依赖启发式模型的传统模拟器通常无法提供准确的模拟。由于协变量偏移问题，现有的基于模仿学习的模拟器通常无法生成稳定的长期模拟。在本文中，我们提出了一种称为学习者感知监督模仿学习的新方法，以解决多智能体模仿学习中的协变量转移问题。通过利用变分自动编码器同​​时对专家和学习者状态分布进行建模，我们的方法增强了专家状态，使得增强状态能够了解学习者状态分布。我们的方法应用于城市交通模拟，在现实世界数据集 pNEUMA 上进行评估时，在短期微观和长期宏观现实主义方面比现有最先进的基线有了显着改进。