Simulation of autonomous vehicle systems requires that simulated traffic participants exhibit diverse and realistic behaviors. The use of prerecorded real-world traffic scenarios in simulation ensures realism but the rarity of safety critical events makes large scale collection of driving scenarios expensive. In this paper, we present DJINN - a diffusion based method of generating traffic scenarios. Our approach jointly diffuses the trajectories of all agents, conditioned on a flexible set of state observations from the past, present, or future. On popular trajectory forecasting datasets, we report state of the art performance on joint trajectory metrics. In addition, we demonstrate how DJINN flexibly enables direct test-time sampling from a variety of valuable conditional distributions including goal-based sampling, behavior-class sampling, and scenario editing.

翻译：自动驾驶系统的仿真需要模拟交通参与者表现出多样且真实的行为。在仿真中使用预录的真实世界交通场景可确保真实性，但安全关键事件的稀有性使得大规模收集驾驶场景成本高昂。本文提出DJINN——一种基于扩散方法的交通场景生成技术。我们的方法对智能体的所有轨迹进行联合扩散，并基于来自过去、现在或未来的灵活状态观测集合进行条件约束。在主流轨迹预测数据集上，我们在联合轨迹指标上取得了最先进的性能。此外，我们展示了DJINN如何灵活地实现从多种有价值的条件分布中进行直接测试时采样，包括基于目标的采样、行为类别采样和场景编辑。