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安全性的关键环节。本文提出了一种新颖的基于扩散架构的方法——情景扩散，用于生成可实现可控场景的交通场景。我们结合了潜在扩散、目标检测和轨迹回归技术，以同步生成合成代理的姿态、朝向和轨迹的概率分布。为增强对生成场景的额外控制能力，该分布以地图及描述期望场景的标记集为条件。实验表明，该方法具备充足的表达能力，能够建模多样化的交通模式，并泛化至不同地理区域。