Abstract
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.
Abstract (translated)
本文介绍了UniGen，一种通过仿真生成新的交通场景以评估和改进自动驾驶软件的新方法。我们的方法将所有驾驶场景元素建模为一个统一的模型：新代理的位置、它们的初始状态和未来运动轨迹。通过从共享全局场景嵌入中预测这些变量的分布，我们确保生成的场景完全依赖于现有场景中所有可用上下文的条件。我们统一的建模方法与自回归代理器的注入相结合，条件所有新代理的位置和运动轨迹，从而实现具有低碰撞率的现实场景。我们的实验结果表明，UniGen在Waymo Open Motion Dataset上超越了前人的水平。
URL
https://arxiv.org/abs/2405.03807
