This paper introduces UniGen, a novel approach to generating new traffic
scenarios for evaluating and improving autonomous driving software through
simulation. Our approach models all driving scenario elements in a unified
model: the position of new agents, their initial state, and their future motion
trajectories. By predicting the distributions of all these variables from a
shared global scenario embedding, we ensure that the final generated scenario
is fully conditioned on all available context in the existing scene. Our
unified modeling approach, combined with autoregressive agent injection,
conditions the placement and motion trajectory of every new agent on all
existing agents and their trajectories, leading to realistic scenarios with low
collision rates. Our experimental results show that UniGen outperforms prior
state of the art on the Waymo Open Motion Dataset.

UniGen 是一种用于通过模拟生成新交通场景以评估和改进自动驾驶软件的新方法，通过统一建模来模拟所有驾驶场景元素，包括新代理的位置、初始状态和未来运动轨迹，以实现对现有场景中所有上下文的完全条件。该方法结合自回归代理注入，使每个新代理的位置和运动轨迹都依赖于所有已有代理及其轨迹，从而产生具有低碰撞率的逼真场景。在 Waymo Open Motion 数据集上的实验证明 UniGen 优于现有前沿技术。