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.

翻译：本文提出UniGen，一种通过仿真生成新交通场景以评估和提升自动驾驶软件性能的创新方法。该方法将所有驾驶场景要素纳入统一模型进行建模：新智能体的位置、初始状态及其未来运动轨迹。通过从共享的全局场景嵌入中预测所有变量的分布，我们确保最终生成的场景完全依赖于现有场景中的全部可用上下文。这种统一建模方法结合自回归智能体注入机制，使每个新智能体的放置位置和运动轨迹均受所有现有智能体及其轨迹的约束，从而生成碰撞率低且符合真实性的场景。实验结果表明，UniGen在Waymo开放运动数据集上优于现有最先进方法。