The rapid iteration of autonomous driving algorithms has created a growing demand for high-fidelity, replayable, and diagnosable testing data. However, many public datasets lack real vehicle dynamics feedback and closed-loop interaction with surrounding traffic and road infrastructure, limiting their ability to reflect deployment readiness. To address this gap, we present OVPD (OnSite Virtual-Physical Dataset), a virtual-physical fusion testing dataset released from the 2025 OnSite Autonomous Driving Challenge. Centered on real-vehicle-in-the-loop testing, OVPD integrates virtual background traffic with vehicle-infrastructure perception to build controllable and interactive closed-loop test environments on a proving ground. The dataset contains 20 testing clips from 20 teams over a scenario chain of 15 atomic scenarios, totaling nearly 3 hours of multi-modal data, including vehicle trajectories and states, control commands, and digital-twin-rendered surround-view observations. OVPD supports long-tail planning and decision-making validation, open-loop or platform-enabled closed-loop evaluation, and comprehensive assessment across safety, efficiency, comfort, rule compliance, and traffic impact, providing actionable evidence for failure diagnosis and iterative improvement. The dataset is available via: https://huggingface.co/datasets/Yuhang253820/Onsite_OPVD

翻译：自动驾驶算法的快速迭代对高保真度、可回放及可诊断的测试数据需求日益增长。然而，许多公开数据集缺乏真实车辆动力学反馈以及与周围交通和道路基础设施的闭环交互能力，限制了其反映部署就绪状态的有效性。为填补这一空白，我们提出OVPD（实车虚实融合数据集）——自2025年实车自动驾驶挑战赛发布的虚实融合测试数据集。该数据集以真车在环测试为核心，通过将虚拟背景交通与车路感知相融合，在试验场构建可控制、可交互的闭环测试环境。数据集包含来自20支参赛队伍在15个原子场景构成的场景链中的20个测试片段，总计近3小时的多模态数据，涵盖车辆轨迹与状态、控制指令以及数字孪生渲染的全景环视观测。OVPD支持长尾规划与决策验证、开环或平台使能的闭环评估，以及安全性、效率、舒适性、规则合规性和交通影响的全方位评估，为故障诊断与迭代优化提供可操作的依据。该数据集可通过https://huggingface.co/datasets/Yuhang253820/Onsite_OPVD获取。