Waymo Open Motion Dataset Dataset

As autonomous driving systems mature, motion forecasting has received increasing attention as a critical requirement for planning. Of particular importance are interactive situations such as merges, unprotected turns, etc., where predicting individual object motion is not sufficient. Joint predictions of multiple objects are required for effective route planning. There has been a critical need for high-quality motion data that is rich in both interactions and annotation to develop motion planning models. In this work, we introduce the most diverse interactive motion dataset to our knowledge, and provide specific labels for interacting objects suitable for developing joint prediction models. With over 100,000 scenes, each 20 seconds long at 10 Hz, our new dataset contains more than 570 hours of unique data over 1750 km of roadways. It was collected by mining for interesting interactions between vehicles, pedestrians, and cyclists across six cities within the United States. We use a high-accuracy 3D auto-labeling system to generate high quality 3D bounding boxes for each road agent, and provide corresponding high definition 3D maps for each scene. Furthermore, we introduce a new set of metrics that provides a comprehensive evaluation of both single agent and joint agent interaction motion forecasting models. Finally, we provide strong baseline models for individual-agent prediction and joint-prediction. We hope that this new large-scale interactive motion dataset will provide new opportunities for advancing motion forecasting models.

随着自动驾驶系统的日益成熟，运动预测因其对路径规划的关键性需求而备受关注。特别是在交互式情境，如合并、未受保护的转弯等情况下，预测单个对象的运动已不足以满足需求。对于有效的路径规划，需要联合预测多个对象。针对富含交互和注释的高质量运动数据的需求变得尤为迫切。在本研究中，我们引介了据我们所知最为多样化的交互式运动数据集，并为适合开发联合预测模型的交互对象提供了特定的标签。该数据集包含超过 10 万个场景，每个场景时长 20 秒，采样频率为 10 Hz，总计超过 570 小时，覆盖了 1750 公里的道路。数据通过挖掘美国六个城市中车辆、行人和自行车之间的有趣交互而收集。我们采用高精度三维自动标注系统为每个道路参与者生成高质量的 3D 边界框，并为每个场景提供相应的高清 3D 地图。此外，我们引入了一套新的度量标准，该标准能够对单智能体和联合智能体交互运动预测模型进行全面的评估。最后，我们为个体预测和联合预测提供了强大的基线模型。我们期望这个新的大规模交互式运动数据集将为推进运动预测模型提供新的机遇。