Large-scale driving datasets such as Waymo Open Dataset and nuScenes substantially accelerate autonomous driving research, especially for perception tasks such as 3D detection and trajectory forecasting. Since the driving logs in these datasets contain HD maps and detailed object annotations which accurately reflect the real-world complexity of traffic behaviors, we can harvest a massive number of complex traffic scenarios and recreate their digital twins in simulation. Compared to the hand-crafted scenarios often used in existing simulators, data-driven scenarios collected from the real world can facilitate many research opportunities in machine learning and autonomous driving. In this work, we present ScenarioNet, an open-source platform for large-scale traffic scenario modeling and simulation. ScenarioNet defines a unified scenario description format and collects a large-scale repository of real-world traffic scenarios from the heterogeneous data in various driving datasets including Waymo, nuScenes, Lyft L5, and nuPlan datasets. These scenarios can be further replayed and interacted with in multiple views from Bird-Eye-View layout to realistic 3D rendering in MetaDrive simulator. This provides a benchmark for evaluating the safety of autonomous driving stacks in simulation before their real-world deployment. We further demonstrate the strengths of ScenarioNet on large-scale scenario generation, imitation learning, and reinforcement learning in both single-agent and multi-agent settings. Code, demo videos, and website are available at https://metadriverse.github.io/scenarionet.

翻译：大型驾驶数据集（如Waymo Open Dataset和nuScenes）极大加速了自动驾驶研究，尤其是针对三维检测和轨迹预测等感知任务。由于这些数据集中的驾驶日志包含高精地图和详细物体标注，能够准确反映真实世界中交通行为的复杂性，因此我们可以从中采集海量复杂交通场景，并在仿真环境中重建其数字孪生。与现有仿真器中常用的人工构建场景相比，从真实世界采集的数据驱动场景可促进机器学习和自动驾驶领域的诸多研究机遇。本文提出ScenarioNet——一个面向大规模交通场景建模与仿真的开源平台。ScenarioNet定义了统一的场景描述格式，并从Waymo、nuScenes、Lyft L5和nuPlan等多个驾驶数据集的异构数据中收集了大规模真实交通场景库。这些场景可在MetaDrive仿真器中通过从鸟瞰图布局到逼真三维渲染的多视图进行回放和交互，为自动驾驶系统在实际部署前提供仿真安全评估基准。本文进一步展示了ScenarioNet在单智能体与多智能体设置下的大规模场景生成、模仿学习及强化学习中的优势。代码、演示视频和网站见https://metadriverse.github.io/scenarionet。