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：摘要：自动驾驶系统的仿真要求模拟的交通参与者展现出多样且逼真的行为。在仿真中使用预录的真实世界交通场景能够确保真实性，但安全关键事件的稀缺性使得大规模收集驾驶场景成本高昂。本文提出DJINN——一种基于扩散的交通场景生成方法。我们的方法联合扩散所有智能体的轨迹，并基于来自过去、现在或未来的灵活状态观测条件进行约束。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优性能。此外，我们展示了DJINN如何灵活地支持直接从测试阶段采样多种有价值的条件分布，包括基于目标的采样、行为类别采样和场景编辑。