Vehicle tracking, motion estimation, and collision prediction are fundamental components of traffic safety and management in Intelligent Transportation Systems (ITS). Many recent approaches rely on computationally intensive prediction models, which limits their practical deployment on resource-constrained edge devices. This paper presents a lightweight digital-twin-based framework for vehicle tracking and spatiotemporal collision prediction that relies solely on object detection, without requiring complex trajectory prediction networks. The framework is implemented and evaluated in Quanser Interactive Labs (QLabs), a high-fidelity digital twin of an urban traffic environment that enables controlled and repeatable scenario generation. A YOLO-based detector is deployed on simulated edge cameras to localize vehicles and extract frame-level centroid trajectories. Offline path maps are constructed from multiple traversals and indexed using K-D trees to support efficient online association between detected vehicles and road segments. During runtime, consistent vehicle identifiers are maintained, vehicle speed and direction are estimated from the temporal evolution of path indices, and future positions are predicted accordingly. Potential collisions are identified by analyzing both spatial proximity and temporal overlap of predicted future trajectories. Our experimental results across diverse simulated urban scenarios show that the proposed framework predicts approximately 88% of collision events prior to occurrence while maintaining low computational overhead suitable for edge deployment. Rather than introducing a computationally intensive prediction model, this work introduces a lightweight digital-twin-based solution for vehicle tracking and collision prediction, tailored for real-time edge deployment in ITS.

翻译：车辆追踪、运动估计与碰撞预测是智能交通系统中交通安全与管理的基础组成部分。当前许多方法依赖于计算密集的预测模型，这限制了其在资源受限边缘设备上的实际部署。本文提出一种基于数字孪生的轻量化车辆追踪与时空碰撞预测框架，该框架仅依赖目标检测技术，无需复杂的轨迹预测网络。该框架在Quanser交互实验室中实现并评估——这是一个高保真的城市交通环境数字孪生系统，支持可控且可重复的场景生成。基于YOLO的检测器部署在模拟边缘摄像头上，用于定位车辆并提取帧级质心轨迹。通过多次路径遍历构建离线路径地图，并采用K-D树进行索引，以支持检测车辆与道路路段之间的高效在线关联。在运行过程中，系统保持一致的车辆标识符，根据路径索引的时间演化估计车辆速度与方向，并据此预测未来位置。通过分析预测轨迹的空间邻近性与时间重叠性来识别潜在碰撞。在多样化模拟城市场景中的实验结果表明，所提框架能在碰撞发生前预测约88%的碰撞事件，同时保持适用于边缘部署的低计算开销。本研究未引入计算密集的预测模型，而是提出了一种基于数字孪生的轻量化解决方案，专为智能交通系统中的实时边缘部署而设计。