{"ID":2831678,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.07507","arxiv_id":"2512.07507","title":"VP-AutoTest: A Virtual-Physical Fusion Autonomous Driving Testing Platform","abstract":"The rapid development of autonomous vehicles has led to a surge in testing demand. Traditional testing methods, such as virtual simulation, closed-course, and public road testing, face several challenges, including unrealistic vehicle states, limited testing capabilities, and high costs. These issues have prompted increasing interest in virtual-physical fusion testing. However, despite its potential, virtual-physical fusion testing still faces challenges, such as limited element types, narrow testing scope, and fixed evaluation metrics. To address these challenges, we propose the Virtual-Physical Testing Platform for Autonomous Vehicles (VP-AutoTest), which integrates over ten types of virtual and physical elements, including vehicles, pedestrians, and roadside infrastructure, to replicate the diversity of real-world traffic participants. The platform also supports both single-vehicle interaction and multi-vehicle cooperation testing, employing adversarial testing and parallel deduction to accelerate fault detection and explore algorithmic limits, while OBU and Redis communication enable seamless vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) cooperation across all levels of cooperative automation. Furthermore, VP-AutoTest incorporates a multidimensional evaluation framework and AI-driven expert systems to conduct comprehensive performance assessment and defect diagnosis. Finally, by comparing virtual-physical fusion test results with real-world experiments, the platform performs credibility self-evaluation to ensure both the fidelity and efficiency of autonomous driving testing. Please refer to the website for the full testing functionalities on the autonomous driving public service platform OnSite:https://www.onsite.com.cn.","short_abstract":"The rapid development of autonomous vehicles has led to a surge in testing demand. Traditional testing methods, such as virtual simulation, closed-course, and public road testing, face several challenges, including unrealistic vehicle states, limited testing capabilities, and high costs. These issues have prompted incr...","url_abs":"https://arxiv.org/abs/2512.07507","url_pdf":"https://arxiv.org/pdf/2512.07507v1","authors":"[\"Yiming Cui\",\"Shiyu Fang\",\"Jiarui Zhang\",\"Yan Huang\",\"Chengkai Xu\",\"Bing Zhu\",\"Hao Zhang\",\"Peng Hang\",\"Jian Sun\"]","published":"2025-12-08T12:43:33Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.SE\"]","methods":"[]","project_urls":"[\"https://www.onsite.com.cn\"]","has_code":false}