Data-driven simulation has become a favorable way to train and test autonomous driving algorithms. The idea of replacing the actual environment with a learned simulator has also been explored in model-based reinforcement learning in the context of world models. In this work, we show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving, and based on TrafficBots we obtain a world model tailored for the planning module of autonomous vehicles. Existing data-driven traffic simulators are lacking configurability and scalability. To generate configurable behaviors, for each agent we introduce a destination as navigational information, and a time-invariant latent personality that specifies the behavioral style. To improve the scalability, we present a new scheme of positional encoding for angles, allowing all agents to share the same vectorized context and the use of an architecture based on dot-product attention. As a result, we can simulate all traffic participants seen in dense urban scenarios. Experiments on the Waymo open motion dataset show TrafficBots can simulate realistic multi-agent behaviors and achieve good performance on the motion prediction task.

翻译：数据驱动的仿真已成为训练和测试自动驾驶算法的有效方法。在基于世界模型的模型强化学习领域，利用学习型仿真器替代真实环境的思想已得到探索。本研究表明，数据驱动的交通仿真可被形式化为一个世界模型。我们提出TrafficBots——一种基于运动预测与端到端驾驶构建的多智能体策略，并在此基础上获得一个专为自动驾驶车辆规划模块定制的世界模型。现有数据驱动的交通仿真器缺乏可配置性与可扩展性。为生成可配置行为，我们为每个智能体引入目的地作为导航信息，并引入一个时不变潜在人格参数以指定行为风格。为提高可扩展性，我们提出一种新的角度位置编码方案，使所有智能体能够共享相同的向量化上下文，并采用基于点积注意力的架构。最终，我们能够模拟密集城市场景中所有交通参与者。在Waymo开放运动数据集上的实验表明，TrafficBots可模拟逼真的多智能体行为，并在运动预测任务上取得良好性能。