The Operational Design Domain (ODD) of urbanoriented Level 4 (L4) autonomous driving, especially for autonomous robotaxis, confronts formidable challenges in complex urban mixed traffic environments. These challenges stem mainly from the high density of Vulnerable Road Users (VRUs) and their highly uncertain and unpredictable interaction behaviors. However, existing open-source datasets predominantly focus on structured scenarios such as highways or regulated intersections, leaving a critical gap in data representing chaotic, unstructured urban environments. To address this, this paper proposes an efficient, high-precision method for constructing drone-based datasets and establishes the Vehicle-Vulnerable Road User Interaction Dataset (VRUD), as illustrated in Figure 1. Distinct from prior works, VRUD is collected from typical "Urban Villages" in Shenzhen, characterized by loose traffic supervision and extreme occlusion. The dataset comprises 4 hours of 4K/30Hz recording, containing 11,479 VRU trajectories and 1,939 vehicle trajectories. A key characteristic of VRUD is its composition: VRUs account for about 87% of all traffic participants, significantly exceeding the proportions in existing benchmarks. Furthermore, unlike datasets that only provide raw trajectories, we extracted 4,002 multi-agent interaction scenarios based on a novel Vector Time to Collision (VTTC) threshold, supported by standard OpenDRIVE HD maps. This study provides valuable, rare edge-case resources for enhancing the safety performance of ADS in complex, unstructured urban environments. To facilitate further research, we have made the VRUD dataset open-source at: https://zzi4.github.io/VRUD/.

翻译：摘要：面向城市环境的L4级自动驾驶（特别是自动驾驶出租车）的运行设计域（ODD）在复杂的城市混合交通环境中面临着严峻挑战。这些挑战主要源于弱势道路使用者（VRU）的高密度及其高度不确定、不可预测的交互行为。然而，现有开源数据集主要聚焦于结构化场景（如高速公路或规控交叉口），导致表征混乱、非结构化城市环境的数据存在关键缺口。为解决这一问题，本文提出了一种高效高精度的无人机数据集构建方法，并建立了车辆-弱势道路使用者交互数据集（VRUD），如图1所示。与先前工作不同，VRUD采集自深圳典型“城中村”区域，其特点为松散交通监管与极端遮挡。该数据集包含4小时4K/30Hz录制内容，涵盖11,479条VRU轨迹和1,939条车辆轨迹。VRUD的核心特征在于其构成：VRU约占所有交通参与者的87%，显著超过现有基准数据集的比例。此外，区别于仅提供原始轨迹的数据集，我们基于新型矢量碰撞时间（VTTC）阈值提取了4,002个多智能体交互场景，并辅以标准OpenDRIVE高精地图。本研究为提升自动驾驶系统（ADS）在复杂非结构化城市环境中的安全性能提供了珍贵的稀缺边缘案例资源。为促进后续研究，VRUD数据集已在https://zzi4.github.io/VRUD/ 开源发布。