构建真实车辆闭环测试数据集，用于自动驾驶系统验证与故障诊断。
OVPD: A Virtual-Physical Fusion Testing Dataset of OnSite Auton-omous Driving Challenge
- 融合虚拟交通与车路感知，实现真实车辆在封闭场地的闭环测试。
- 包含20支队伍、15个场景、近3小时多模态数据，支持全维度评估。
- 适合自动驾驶算法迭代、安全验证及长尾问题研究者使用。
自动驾驶算法的快速迭代催生了对高保真、可回放、可诊断测试数据的迫切需求。然而，许多公开数据集缺乏真实车辆动力学反馈以及与周边交通和道路基础设施的闭环交互，难以反映实际部署的成熟度。为填补这一空白，我们提出OVPD（OnSite Virtual-Physical Dataset），源自2025年现场自动驾驶挑战赛。以真实车辆在环测试为核心，OVPD整合虚拟背景交通与车路感知，构建可在实测场地上可控且互动的闭环测试环境。数据集包含20支参赛团队在15个原子场景构成的场景链中采集的20段测试片段，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环视观测。OVPD支持长尾规划与决策验证、开环或平台驱动的闭环评估，以及安全、效率、舒适性、规则符合性与交通影响等多维度综合评价，为故障诊断和迭代优化提供可行动证据。数据集可通过 https://huggingface.co/datasets/Yuhang253820/Onsite_OPVD 获取。
原文摘要 · Abstract (English)
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
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