VP-AutoTest: A Virtual-Physical Fusion Autonomous Driving Testing Platform

• URL: http://arxiv.org/abs/2512.07507v1
• Date: Mon, 08 Dec 2025 12:43:33 GMT
• Title: VP-AutoTest: A Virtual-Physical Fusion Autonomous Driving Testing Platform
• Authors: Yiming Cui, Shiyu Fang, Jiarui Zhang, Yan Huang, Chengkai Xu, Bing Zhu, Hao Zhang, Peng Hang, Jian Sun,
• Abstract summary: We propose the Virtual-Physical Testing Platform for Autonomous Vehicles (VP-AutoTest) to replicate the diversity of real-world traffic participants.<n>VP-AutoTest incorporates a multidimensional evaluation framework and AI-driven expert systems to conduct comprehensive performance assessment and defect diagnosis.
• Score: 47.03629760732478
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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