Automated creation of synthetic traffic scenarios is a key part of validating the safety of autonomous vehicles (AVs). In this paper, we propose Scenario Diffusion, a novel diffusion-based architecture for generating traffic scenarios that enables controllable scenario generation. We combine latent diffusion, object detection and trajectory regression to generate distributions of synthetic agent poses, orientations and trajectories simultaneously. To provide additional control over the generated scenario, this distribution is conditioned on a map and sets of tokens describing the desired scenario. We show that our approach has sufficient expressive capacity to model diverse traffic patterns and generalizes to different geographical regions.

翻译：自动生成合成交通场景是验证自动驾驶车辆（AVs）安全性的关键环节。本文提出场景扩散（Scenario Diffusion），一种新颖的基于扩散的交通场景生成架构，支持可控场景生成。我们将潜在扩散、目标检测与轨迹回归相结合，同步生成合成智能体的位置、朝向及轨迹分布。为增强对生成场景的额外控制，该分布以地图和描述期望场景的标记集为条件。实验表明，本方法具备充分表达能力以建模多样化交通模式，并能泛化至不同地理区域。