As autonomous driving systems being deployed to millions of vehicles, there is a pressing need of improving the system's scalability, safety and reducing the engineering cost. A realistic, scalable, and practical simulator of the driving world is highly desired. In this paper, we present an efficient solution based on generative models which learns the dynamics of the driving scenes. With this model, we can not only simulate the diverse futures of a given driving scenario but also generate a variety of driving scenarios conditioned on various prompts. Our innovative design allows the model to operate in both full-Autoregressive and partial-Autoregressive modes, significantly improving inference and training speed without sacrificing generative capability. This efficiency makes it ideal for being used as an online reactive environment for reinforcement learning, an evaluator for planning policies, and a high-fidelity simulator for testing. We evaluated our model against two real-world datasets: the Waymo motion dataset and the nuPlan dataset. On the simulation realism and scene generation benchmark, our model achieves the state-of-the-art performance. And in the planning benchmarks, our planner outperforms the prior arts. We conclude that the proposed generative model may serve as a foundation for a variety of motion planning tasks, including data generation, simulation, planning, and online training. Source code is public at https://github.com/HorizonRobotics/GUMP/

翻译：随着自动驾驶系统部署至数百万辆汽车，提升系统可扩展性、安全性并降低工程成本的需求日益迫切。业界亟需一个真实、可扩展且实用的驾驶世界模拟器。本文提出一种基于生成模型的高效解决方案，该模型能够学习驾驶场景的动态特性。借助该模型，我们不仅能模拟给定驾驶场景的多样化未来状态，还能根据各类提示生成多种驾驶场景。我们的创新设计使模型可在完全自回归与部分自回归两种模式下运行，在保持生成能力的同时显著提升推理与训练速度。这种高效性使其非常适合作为强化学习的在线反应环境、规划策略的评估器以及高保真测试模拟器。我们在Waymo运动数据集和nuPlan数据集这两个真实世界数据集上评估了模型性能。在模拟真实性与场景生成基准测试中，我们的模型取得了最先进的性能表现；在规划基准测试中，我们的规划器亦优于现有先进方法。研究表明，所提出的生成模型可作为各类运动规划任务（包括数据生成、模拟、规划及在线训练）的基础框架。源代码已公开于https://github.com/HorizonRobotics/GUMP/。