LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic Simulation

arXiv:2403.17601v1 Announce Type: cross
Abstract: 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 …

abstract arxiv behavior challenges cs.ai cs.lg driving engineering flow however human imitation learning insights long-term role simulation simulator traffic transportation type