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：数据驱动模拟已成为培训和测试自主驱动算法的有利方式。在以模型为基础的世界模型强化学习中,也探索了以学习模拟器取代实际环境的想法。在这项工作中,我们展示了数据驱动的交通模拟可以作为一种世界模型。我们展示了以运动预测和端到端驱动为基础的多试剂政策ThalbBots,这是基于运动预测和端到端驱动的多试剂政策,并且基于ThalfBots,我们获得了为自主车辆规划模块量身定制的世界模型。现有的数据驱动交通模拟器缺乏可调和性能。为了产生可配置的行为,我们为每个代理器引入一个导航信息目的地,以及一个规定行为风格的时间变化潜伏性个性,我们展示了一个世界模型。为了改进可变性,我们提出了一个新的角度定位编码计划,允许所有代理器共享相同的矢量环境,并使用基于点产品关注的架构。因此,我们可以模拟在密集的城市假设中看到的所有交通参与者。在Wawaymo 开放运动数据模型上进行实验,从而实现良好的动态预测。</s>