(2025)
This paper presents InfGen, a long-term traffic simulator that integrates autoregressive motion and scenario generation in a unified framework. InfGen is designed to address the limitations of existing simulators that fail to maintain realism in long-term traffic simulations by dynamically generating agents based on the scene layout as the ego vehicle navigates. Using a tokenizer, the authors convert real-world driving logs into a sequence of discrete tokens that capture both motion and scenario generation, enabling stable long-term rollouts. Through extensive experimentation, the authors demonstrate that InfGen significantly outperforms state-of-the-art models in both motion and scenario realism, particularly in a 30-second rollout scenario, while also proving adaptable to shorter simulation tasks. The paper discusses various methodologies related to motion simulation and scenario generation, as well as the tokenization process involved in preparing data for the model. Finally, the authors acknowledge the limitations of their study, including challenges related to real trip-level evaluation and potential overfitting due to supervised training on historical data, and propose future directions for improving traffic simulation realism.
This paper employs the following methods:
The following datasets were used in this research: