Researchers have developed a method to generate customizable virtual road networks with adjustable complexity, which cuts required test mileage by over 30% while preserving real-world road features. This enables faster, more efficient identification of functional defects and performance boundaries in autonomous vehicles.
The team published their study in Communications in Transportation Research ( https://doi.org/10.26599/COMMTR.2026.9640024 ).
The "Mileage Compression" Breakthrough
Traditional testing relies on driving millions of miles on real roads. This new method analyzes road complexity and creates a "greatest hits" version of a city's streets.
Researchers utilized cartographic data from Xi'an to validate their model. They found that by intelligently assembling road elements—such as merge, diverge and complex intersections—the generated networks achieved a 32.4% compression in test mileage.
“The infeasibility of on-road testing that requires millions of kilometers has been a proven bottleneck. Our method integrates real-world cartographic data with optimization algorithms to generate networks that preserve real-world road features while eliminating redundant mileage.” says Yu Zhu, a researcher at the School of Information Engineering at Chang’an University.
Pinpointing Safety Limits
The research didn't just save time; it proved effective at finding flaws. When tested against a leading autonomous driving algorithm, the virtual networks successfully identified functional defects that might be missed in standard testing.
Key findings include:
“The experiment results demonstrated that the test scenarios constructed using generated networks could successfully capture both the collision avoidance performance limits of the algorithm and its functional defects under lateral approach conditions.” explains Professor Zhigang Xu, the project’s supervisor.
Methodology and Future Outlook
The team utilized a combination of Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms to stitch together road segments without overlaps. This "non-intersecting" assembly allows for continuous scenario testing, where a car drives through a series of complex maneuvers without stopping.
While the current model focuses on standard intersections, the authors note future work will expand the library to include roundabouts and highway interchanges
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 Xiaopeng (Shaw) Li from University of Wisconsin–Madison. 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 PhaseⅡ”. 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 2026, its 2025 IF was announced as 12.7, maintaining the Top1 position (1/66, Q1) in the same category.
From Volume 6 (2026), Communications in Transportation Research will be published by Tsinghua University Press on the SciOpen platform with the official journal website at https://www.sciopen.com/journal/2097-5023 . We kindly request that all new manuscript submissions be made through the journal’s submission system at https://mc03.manuscriptcentral.com/commtr . For any submission-related inquiries, please contact the Editorial Office at commtr_e@mail.tsinghua.edu.cn.
Communications in Transportation Research
Complexity Controllable Road Network Generation for Virtual Testing of Autonomous Driving
30-Sep-2026