轻量级基于数字孪生的边缘辅助车辆跟踪与碰撞预测框架
A Lightweight Digital-Twin-Based Framework for Edge-Assisted Vehicle Tracking and Collision Prediction
AI总结：
本文提出一种基于数字孪生的轻量级框架，用于边缘计算环境下的车辆跟踪与碰撞预测，通过目标检测实现高效且低开销的实时应用。
摘要：
车辆跟踪、运动估计和碰撞预测是智能交通系统（ITS）中交通安全和管理的基本组成部分。许多近期方法依赖于计算强度大的预测模型，这限制了它们在资源受限的边缘设备上的实际部署。本文提出了一种轻量级基于数字孪生的框架，用于车辆跟踪和时空碰撞预测，该框架仅依赖于目标检测，而不需要复杂的轨迹预测网络。该框架在Quanser Interactive Labs（QLabs）中实现和评估，QLabs是一个高保真的城市交通环境数字孪生，能够实现受控且可重复的场景生成。一个基于YOLO的检测器被部署在模拟的边缘摄像头上，用于定位车辆并提取帧级质心轨迹。从多个遍历中构建离线路径地图，并使用K-D树进行索引，以支持检测车辆与道路段之间的高效在线关联。在运行时，保持一致的车辆标识符，从路径索引的时序演变中估计车辆速度和方向，并相应地预测未来位置。通过分析预测未来轨迹的空间接近性和时间重叠来识别潜在碰撞。我们的实验结果在多样化的模拟城市场景中显示，所提出的框架在碰撞事件发生前预测了大约88%的碰撞事件，同时保持了低计算开销，适合边缘部署。而不是引入计算强度大的预测模型，这项工作引入了一种轻量级基于数字孪生的解决方案，专门用于实时边缘部署的ITS车辆跟踪和碰撞预测。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
