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.

自动驾驶车辆的安全验证方面，合成交通场景的自动创建是关键部分。本文提出了一种新颖的基于扩散的场景生成架构 ——Scenario Diffusion，可实现可控的场景生成。我们结合了潜在扩散、目标检测和轨迹回归，同时生成合成代理人的姿势、方向和轨迹的分布。为了对生成的场景进行额外控制，此分布以描述所需场景的地图和令牌组为条件。我们证明我们的方法具有足够的表达能力，能够建模多样的交通模式，并且适用于不同的地理区域。