  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.
