  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.
