OVPD: A Virtual-Physical Fusion Testing Dataset of OnSite Auton-omous Driving Challenge
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911614600478720 |
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| author | Zhang, Yuhang Zhang, Jiarui Jian, Bowen Zhou, Xin Lv, Zhichao Hang, Peng Yu, Rongjie Tian, Ye Sun, Jian |
| author_facet | Zhang, Yuhang Zhang, Jiarui Jian, Bowen Zhou, Xin Lv, Zhichao Hang, Peng Yu, Rongjie Tian, Ye Sun, Jian |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20423 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OVPD: A Virtual-Physical Fusion Testing Dataset of OnSite Auton-omous Driving Challenge Zhang, Yuhang Zhang, Jiarui Jian, Bowen Zhou, Xin Lv, Zhichao Hang, Peng Yu, Rongjie Tian, Ye Sun, Jian Robotics 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 |
| title | OVPD: A Virtual-Physical Fusion Testing Dataset of OnSite Auton-omous Driving Challenge |
| topic | Robotics |
| url | https://arxiv.org/abs/2604.20423 |