In this paper, we propose SceNDD: a scenario-based naturalistic driving dataset that is built upon data collected from an instrumented vehicle in downtown Indianapolis. The data collection was completed in 68 driving sessions with different drivers, where each session lasted about 20--40 minutes. The main goal of creating this dataset is to provide the research community with real driving scenarios that have diverse trajectories and driving behaviors. The dataset contains ego-vehicle's waypoints, velocity, yaw angle, as well as non-ego actor's waypoints, velocity, yaw angle, entry-time, and exit-time. Certain flexibility is provided to users so that actors, sensors, lanes, roads, and obstacles can be added to the existing scenarios. We used a Joint Probabilistic Data Association (JPDA) tracker to detect non-ego vehicles on the road. We present some preliminary results of the proposed dataset and a few applications associated with it. The complete dataset is expected to be released by early 2023.
翻译:在本文中,我们提议SceNDD:一个基于假想的自然驾驶数据集,该数据集以印第安纳波利斯市中心一个仪器车辆收集的数据为基础。数据收集工作在68个由不同驾驶员驾驶的驾驶班中完成,每次历时约20-40分钟。创建该数据集的主要目的是为研究界提供真实的驾驶场景,这些驾驶场景具有不同的轨迹和驾驶行为。该数据集包含自动飞行器的航道点、速度、极角,以及非潜水员的航道点、速度、极角、入场时间和退出时间。为用户提供了一定的灵活性,以使行为者、传感器、航道、道路和障碍能够加入现有的航道。我们使用了联合概率数据协会的追踪器来探测道路上的非潜水车。我们提供了拟议的数据集的一些初步结果和一些相关的应用。完整的数据集预计将在2023年初发布。