Widely adopted motion forecasting datasets substitute the observed sensory inputs with higher-level abstractions such as 3D boxes and polylines. These sparse shapes are inferred through annotating the original scenes with perception systems' predictions. Such intermediate representations tie the quality of the motion forecasting models to the performance of computer vision models. Moreover, the human-designed explicit interfaces between perception and motion forecasting typically pass only a subset of the semantic information present in the original sensory input. To study the effect of these modular approaches, design new paradigms that mitigate these limitations, and accelerate the development of end-to-end motion forecasting models, we augment the Waymo Open Motion Dataset (WOMD) with large-scale, high-quality, diverse LiDAR data for the motion forecasting task. The new augmented dataset WOMD-LiDAR consists of over 100,000 scenes that each spans 20 seconds, consisting of well-synchronized and calibrated high quality LiDAR point clouds captured across a range of urban and suburban geographies (https://waymo.com/open/data/motion/). Compared to Waymo Open Dataset (WOD), WOMD-LiDAR dataset contains 100x more scenes. Furthermore, we integrate the LiDAR data into the motion forecasting model training and provide a strong baseline. Experiments show that the LiDAR data brings improvement in the motion forecasting task. We hope that WOMD-LiDAR will provide new opportunities for boosting end-to-end motion forecasting models.

翻译：广泛采用的运动预测数据集通常将原始感官输入替换为更高层次的抽象表示，如3D框和折线。这些稀疏形状是通过借助感知系统的预测对原始场景进行标注而推导得出的。这种中间表示将运动预测模型的质量与计算机视觉模型的性能紧密关联。此外，感知与运动预测之间的人工显式接口通常仅传递原始感官输入中存在的部分语义信息。为研究此类模块化方法的影响、设计缓解这些局限性的新范式，并加速端到端运动预测模型的发展，我们为Waymo开放运动数据集（WOMD）增补了大规模、高质量、多样化的LiDAR数据，专门用于运动预测任务。新构建的增广数据集WOMD-LiDAR包含超过10万个场景，每个场景持续20秒，由横跨多种城市与郊区地理区域的高质量、经过良好同步与标定的LiDAR点云组成（https://waymo.com/open/data/motion/）。与Waymo开放数据集（WOD）相比，WOMD-LiDAR数据集包含的场景数量增加了100倍。此外，我们将LiDAR数据集成至运动预测模型训练中，并提供了一个强基准。实验表明，LiDAR数据能够提升运动预测任务的性能。我们期待WOMD-LiDAR为推动端到端运动预测模型的发展提供新机遇。