论文标题
Scendd：一个基于场景的自然主义驾驶数据集
SceNDD: A Scenario-based Naturalistic Driving Dataset
论文作者
论文摘要
在本文中，我们提出了Scendd：基于场景的自然主义驾驶数据集，该数据集建立在印第安纳波利斯市中心的仪器车辆收集的数据上。数据收集在68次驾驶会议上与不同的驱动程序完成，每个会话持续约20--40分钟。创建此数据集的主要目标是为研究社区提供具有多种轨迹和驾驶行为的实际驾驶场景。该数据集包含自我车辆的航路点，速度，偏航角以及非EGO演员的路点，速度，偏航角，入口时间和退出时间。为用户提供了一定的灵活性，以便可以将演员，传感器，车道，道路和障碍物添加到现有场景中。我们使用联合概率数据关联（JPDA）跟踪器来检测道路上的非EGO车辆。我们提供了提出的数据集的一些初步结果以及与之相关的一些应用程序。完整的数据集预计将在2023年初发布。
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.
