ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling
ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling
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DOI：
10.48550/arxiv.2306.12241
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Quanyi Li;Zhenghao Peng;Lan Feng;Zhizheng Liu;Chenda Duan;Wen-An Mo;Bolei Zhou
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文献类型：
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作者：
Quanyi Li;Zhenghao Peng;Lan Feng;Zhizheng Liu;Chenda Duan;Wen-An Mo;Bolei Zhou
Waymo Open Dataset和nuScenes等大规模驾驶数据集大大加速了自动驾驶研究，特别是对于3D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清地图和详细的对象注释，这些注释准确地反映了现实世界中交通行为的复杂性，因此我们可以收获大量复杂的交通场景，并在模拟中重新创建它们的数字孪生模型。与现有模拟器中经常使用的手工制作的场景相比，从真实的世界中收集的数据驱动场景可以促进机器学习和自动驾驶的许多研究机会。在这项工作中，我们提出了ScenarioNet，一个开源的大规模交通场景建模和仿真平台。ScenarioNet定义了统一的场景描述格式，并从各种驾驶数据集中的异构数据中收集了大规模的真实交通场景存储库，包括Waymo，nuScenes，Lyft L5和nuPlan数据集。这些场景可以在多个视图中进一步回放和交互，从鸟瞰图布局到MetaDrive模拟器中的逼真3D渲染。这为在实际部署之前评估自动驾驶堆栈的安全性提供了一个基准。我们进一步展示了ScenarioNet在单智能体和多智能体环境中的大规模场景生成，模仿学习和强化学习方面的优势。代码、演示视频和网站可在https://metadriverse.github.io/scenarionet上获得。
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.