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

2025-12-08

AI Summary

Traditional autonomous driving testing—via simulation, closed-course trials, and on-road testing—suffers from low fidelity, limited scenario coverage, and high costs. To address these challenges, this work proposes the first virtual-physical integrated testing platform enabling deep fusion and dynamic collaboration among heterogeneous digital and physical traffic entities. The platform integrates over ten categories of virtual and physical traffic elements and achieves full-scenario coordination—including single-vehicle interaction and multi-vehicle cooperation—through V2X communication. It innovatively incorporates adversarial testing, parallel simulation, and an AI-based expert system to establish a multidimensional dynamic evaluation framework with built-in credibility self-assessment, facilitating algorithmic stress-testing and intelligent defect diagnosis. Experimental validation on real-world roads demonstrates significant improvements in test coverage and defect detection rate. The platform has been deployed in the OnSite autonomous driving public service platform.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Application Domains: Mobility, Driving & Flight
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Integrates diverse virtual and physical elements for realistic traffic simulation
• Supports single and multi-vehicle testing with adversarial methods to find faults
• Uses multidimensional AI evaluation and credibility checks to ensure testing fidelity

Innovation

• Integrates diverse virtual and physical elements for realistic traffic simulation
• Uses adversarial testing and parallel deduction to accelerate fault detection
• Incorporates AI-driven expert systems for comprehensive performance assessment