Abstract
Realistic and interactive traffic simulation is essential for training and evaluating autonomous driving systems. However, most existing data-driven simulation methods rely on static initialization or log-replay data, limiting their ability to model dynamic, long-horizon scenarios with evolving agent populations. We propose InfGen, a scenario generation framework that outputs agent states and trajectories in an autoregressive manner. InfGen represents the entire scene as a sequence of tokens, including traffic light signals, agent states, and motion vectors, and uses a transformer model to simulate traffic over time. This design enables InfGen to continuously insert new agents into traffic, supporting infinite scene generation. Experiments demonstrate that InfGen produces realistic, diverse, and adaptive traffic behaviors. Furthermore, reinforcement learning policies trained in InfGen-generated scenarios achieve superior robustness and generalization, validating its utility as a high-fidelity simulation environment for autonomous driving. More information is available at this https URL.
Abstract (translated)
现实而互动的交通模拟对于自动驾驶系统的训练和评估至关重要。然而，大多数现有的数据驱动仿真方法依赖于静态初始化或日志回放数据，这限制了它们在长时间范围内建模动态变化且不断发展的代理群体的能力。我们提出了InfGen这一场景生成框架，它能够以自回归的方式输出代理的状态和轨迹。InfGen将整个场景表示为一个包括交通信号灯、代理状态和运动向量的标记序列，并采用Transformer模型来模拟随着时间推移的变化中的交通状况。这种设计使得InfGen能够在交通中持续插入新的代理，支持无限场景生成。实验表明，InfGen能够产生现实性强、多样化且适应性好的交通行为。此外，在InfGen生成的情景中训练的强化学习策略在鲁棒性和泛化能力上表现更佳，验证了它作为自动驾驶高保真模拟环境的有效性。 更多信息可在此网址获取：[请在此处插入链接]
URL
https://arxiv.org/abs/2506.23316
