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The paper introduces OVPD, a virtual-physical fusion dataset for autonomous driving testing, designed to address the limitations of existing datasets by incorporating real vehicle dynamics and closed-loop interaction. OVPD integrates virtual background traffic with vehicle-infrastructure perception on a proving ground, enabling controllable and interactive test environments. The dataset comprises 20 testing clips with multi-modal data, facilitating comprehensive assessment and failure diagnosis for autonomous driving algorithms.
Autonomous driving validation gets a shot in the arm: OVPD offers a proving ground dataset that fuses real vehicle dynamics with controllable virtual environments, enabling more realistic and diagnosable testing.
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
