Autonomous driving and assistance systems rely on annotated data from traffic and road scenarios to model and learn the various object relations in complex real-world scenarios. Preparation and training of deploy-able deep learning architectures require the models to be suited to different traffic scenarios and adapt to different situations. Currently, existing datasets, while large-scale, lack such diversities and are geographically biased towards mainly developed cities. An unstructured and complex driving layout found in several developing countries such as India poses a challenge to these models due to the sheer degree of variations in the object types, densities, and locations. To facilitate better research toward accommodating such scenarios, we build a new dataset, IDD-3D, which consists of multi-modal data from multiple cameras and LiDAR sensors with 12k annotated driving LiDAR frames across various traffic scenarios. We discuss the need for this dataset through statistical comparisons with existing datasets and highlight benchmarks on standard 3D object detection and tracking tasks in complex layouts. Code and data available at https://github.com/shubham1810/idd3d_kit.git
翻译:自主驾驶和援助系统依靠交通和道路情景的附加说明的数据,在复杂的现实世界情景中建模和学习各种物体关系。准备和培训可部署的深层学习结构要求模型适合不同的交通情景并适应不同情况。目前,现有数据集虽然规模庞大,但缺乏这种多样性,在地理上偏向主要发达城市。在诸如印度等几个发展中国家发现的不结构的复杂驾驶布局对这些模型构成了挑战,因为物体类型、密度和位置的差异程度不同。为了更好地研究如何适应这些情景,我们建立了一套新的数据集,IDD-3D,其中包括多摄像头和LIDAR传感器的多式数据,12公里的驱动激光雷达框架,跨越各种交通情景。我们通过与现有数据集的统计比较来讨论这一数据集的必要性,并突出在复杂布局中标准3D物体探测和跟踪任务的基准。我们可在https://github.com/shubham1810idd_kit.git查阅的代码和数据。