Can combined virtual-real testing speed up autonomous vehicle testing? Findings from AEB field experiments

Researchers at Chang’an University have developed a novel combined virtual-real testing (CVRT) platform for validating autonomous vehicles. This innovative approach utilizes digital twin technology to simulate realistic scenarios and conduct parallel AEB tests across various conditions. The results indicate that CVRT closely replicates real-world performance while significantly reducing test time by up to 70%. This breakthrough offers a safer, more efficient method for validating autonomous systems, with implications for scalable testing and regulation in the autonomous vehicle industry.

 

They published their study on 16 October 2025, in Communications in Transportation Research.

 

“To overcome the cost and inefficiency of traditional field experiments, we developed a combined virtual-real testing (CVRT) platform based on digital twin technology,” explains Ying Gao, assistant professor and one of the study’s authors. “We systematically compared CVRT with real-world tests for autonomous emergency braking (AEB), using rigorous metrics to ensure the method’s reliability.”

 

Reliable performance across scenarios and conditions

The team conducted parallel AEB experiments in four challenging scenarios, covering both car-to-car and car-to-pedestrian hazards, as specified by C-NCAP 2024. Each test, which ran at multiple speeds and repeated more than 15 times, combined high-fidelity virtual scenarios with real-world vehicles, sensors, and conditions.

 

Quantitative results showed that the CVRT platform can closely replicate the speed, trajectory, and acceleration patterns observed in traditional proving ground tests. In particular, the Fréchet distance, a standard metric for measuring similarity between two time series, demonstrated that discrepancies between CVRT and real-world trials were negligible and not statistically significant. “Trigger times for emergency braking were nearly identical, and sensor fusion in the CVRT system proved robust across all tested scenarios,” says Meng Zhang, lead author and PhD candidate.

 

Efficiency, flexibility, and safer testing

While high fidelity is crucial, CVRT’s biggest advantage may be efficiency. The study found that scenario preparation and test time for a single experiment could be reduced by 40%–70% compared with physical-only experiments, especially as scenarios grow more complex. Automated scenario resets and virtual scene injection accelerate testing while minimizing risk to vehicles and people.

 

This approach not only expands the variety of hazardous scenarios that can be safely tested, but also reduces costs and logistical hurdles. “With CVRT, we can rapidly iterate, expand scenario coverage, and focus on rare but critical corner cases,” says co-author Jiatong Xu. “It’s a path towards scalable, repeatable, and safer validation of AVs.”

 

A foundation for the future of AV development

Importantly, the researchers also confirmed that communication delays inherent to the virtual-real integration remained well below the threshold that would impact system safety or performance, a crucial finding for real-world deployment.

 

“CVRT is poised to redefine how the automotive industry validates autonomy. Our results demonstrate that CVRT can provide the reliability of proving-ground tests, but with much greater speed and flexibility,” explains Professor Zhigang Xu, the project’s supervisor.

 

While the current study focused on standard AEB scenarios and controlled conditions, the team plans to extend the framework to more diverse driving environments in future work. The open-source test data can be accessed at ETS-Data.

 

The above research is published in Communications in Transportation Research (COMMTR), which is a fully open access journal co-published by Tsinghua University Press and Elsevier. COMMTR publishes peer-reviewed high-quality research representing important advances of significance to emerging transport systems. COMMTR is also among the first transportation journals to make the Replication Package mandatory to facilitate researchers, practitioners, and the general public in understanding and advancing existing knowledge. At its discretion, Tsinghua University Press will pay the open access fee for all published papers in 2025.

 

About Communications in Transportation Research

Communications in Transportation Research was launched in 2021, with academic support provided by Tsinghua University and China Intelligent Transportation Systems Association. The Editors-in-Chief are Professor Xiaobo Qu, a member of the Academia Europaea from Tsinghua University and Professor Shuai’an Wang from Hong Kong Polytechnic University. The journal mainly publishes high-quality, original research and review articles that are of significant importance to emerging transportation systems, aiming to serve as an international platform for showcasing and exchanging innovative achievements in transportation and related fields, fostering academic exchange and development between China and the global community.

It has been indexed in SCIE, SSCI, Ei Compendex, Scopus, CSTPCD, CSCD, OAJ, DOAJ, TRID and other databases. It was selected as Q1 Top Journal in the Engineering and Technology category of the Chinese Academy of Sciences (CAS) Journal Ranking List. In 2022, it was selected as a High-Starting-Point new journal project of the “China Science and Technology Journal Excellence Action Plan”. In 2024, it was selected as the Support the Development Project of “High-Level International Scientific and Technological Journals”. The same year, it was also chosen as an English Journal Tier Project of the “China Science and Technology Journal Excellence Action Plan Phase Ⅱ”. In 2024, it received the first impact factor (2023 IF) of 12.5, ranking Top1 (1/58, Q1) among all journals in “TRANSPORTATION” category. In 2025, its 2024 IF was announced as 14.5, maintaining the Top 1 position (1/61, Q1) in the same category. Tsinghua University Press will cover the open access fee for all published papers in 2025.

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