The development of safety-oriented research and applications requires fine-grain vehicle trajectories that not only have high accuracy, but also capture substantial safety-critical events. However, it would be challenging to satisfy both these requirements using the available vehicle trajectory datasets do not have the capacity to satisfy both.This paper introduces the CitySim dataset that has the core objective of facilitating safety-oriented research and applications. CitySim has vehicle trajectories extracted from 1140 minutes of drone videos recorded at 12 locations. It covers a variety of road geometries including freeway basic segments, signalized intersections, stop-controlled intersections, and control-free intersections. CitySim was generated through a five-step procedure that ensured trajectory accuracy. The five-step procedure included video stabilization, object filtering, multi-video stitching, object detection and tracking, and enhanced error filtering. Furthermore, CitySim provides the rotated bounding box information of a vehicle, which was demonstrated to improve safety evaluations. Compared with other video-based critical events, including cut-in, merge, and diverge events, which were validated by distributions of both minimum time-to-collision and minimum post-encroachment time. In addition, CitySim had the capability to facilitate digital-twin-related research by providing relevant assets, such as the recording locations' three-dimensional base maps and signal timings.

翻译：安全导向的研究与应用开发需要精细化的车辆轨迹数据，这些数据不仅需具备高精度，还需捕获大量关键安全事件。然而，现有车辆轨迹数据集难以同时满足这两项要求。本文推出的CitySim数据集，其核心目标是促进安全导向的研究与应用。该数据集从12个地点采集的1140分钟无人机视频中提取车辆轨迹，涵盖多种道路几何结构，包括高速公路基本路段、信号控制交叉口、停车让行交叉口以及无控制交叉口。CitySim通过五步流程生成以确保轨迹精度：视频稳定、目标过滤、多视频拼接、目标检测与跟踪，以及增强型误差过滤。此外，该数据集提供车辆的旋转边界框信息，经证明可提升安全评估效果。相较于其他基于视频的关键事件（包含切入、合流与分流事件），CitySim通过最小碰撞时间与最小后侵占时间的分布验证了有效性。同时，CitySim通过提供记录地点的三维底图及信号配时等相关资源，具备支撑数字孪生相关研究的能力。