Vehicular Ad Hoc Networks (VANETs) are a key component of Intelligent Transportation Systems, enabling cooperative communication among vehicles and between vehicles and roadside infrastructure. However, their highly dynamic topology makes them vulnerable to network fragmentation, particularly in highway scenarios, low-density traffic conditions, localized accident zones, and communication-stressed environments. Although Unmanned Aerial Vehicles (UAVs) have been increasingly investigated as temporary aerial relays for improving VANET connectivity, reusable, future-labeled, and reproducible datasets designed to support short-term fragmentation risk analysis remain limited. This paper proposes a reproducible UAV-assisted VANET dataset generator for short-term fragmentation risk prediction. The proposed framework simulates a two-lane highway scenario in which vehicles move in opposite directions while UAVs operate as aerial support nodes. It incorporates multiple data collection profiles, including free-flow traffic, localized accidents, sparse extended topologies, dense bursty traffic, and mixed stress conditions. During each simulation episode, the generator periodically extracts mobility, topology, UAV coverage, and communication-window features, then assigns each sample a future fragmentation label based on the network state observed after a configurable prediction horizon. An illustrative generated dataset is descriptively characterized in terms of scenario balance, UAV policy balance, future-label distribution, scenario-specific label behavior, and representative feature ranges. By providing a modular, extensible, and reproducible ns-3-based data-generation framework, this work offers a practical basis for future supervised learning studies and connectivity management strategies in UAV-assisted VANETs.

翻译：车载自组织网络（VANETs）作为智能交通系统的重要组成，支持车辆间及车辆与路边基础设施间的协作通信。然而，其高度动态的拓扑结构使其易受网络碎片化问题影响，尤其在高速公路场景、低密度交通状况、局部事故区域及通信压力环境下。尽管无人机（UAVs）作为临时空中中继以改善VANET连通性的研究日益深入，但支持短期碎片化风险分析的可复现、带未来标签的标准化数据集仍较为匮乏。本文提出一种面向短期碎片化风险预测的可复现无人机辅助VANET数据集生成器。该框架模拟双向双车道高速公路场景，其中车辆沿相反方向行驶，无人机作为空中支持节点运行。它集成了多种数据采集配置，包括自由流交通、局部事故、稀疏扩展拓扑、密集突发交通及混合压力条件。在每个仿真周期中，生成器周期性提取移动性、拓扑、无人机覆盖及通信窗口特征，并根据可配置预测时域后的网络状态为每个样本赋予未来碎片化标签。本文通过场景平衡性、无人机策略平衡性、未来标签分布、场景特异性标签行为及代表性特征取值范围等维度对生成的示例数据集进行了描述性表征。通过提供基于ns-3的模块化、可扩展及可复现的数据生成框架，本研究为未来无人机辅助VANET的监督学习研究及连通性管理策略提供了实用基础。