How can Modular Autonomous Vehicles achieve safe docking and undocking on the road?

Modular Autonomous Vehicles (MAVs) can dynamically connect and disconnect en route, enabling flexible and efficient passenger and freight transportation. However, ensuring safety during the docking and undocking process remains a major challenge due to close-proximity operation, perception noise, and system uncertainties. To address these challenges, researchers at the University of Wisconsin–Madison, USA, proposed a Safety Assurance Adaptive Model Predictive Control (SAAMPC) framework. The method combines adaptive control and safety barrier functions to handle real-time disturbances and ensure robust operation. The effectiveness of the approach was validated through both simulations and reduced-scale physical experiments using cars.

They published their study on 3 September 2025, in Communications in Transportation Research.

Modular Autonomous Vehicles (MAVs) have the potential to transform transportation by allowing multiple vehicle units to connect and disconnect dynamically. However, how to safely and reliably control such docking and undocking operations in uncertain environments remains an open challenge.

“Our work is to move beyond theoretical modeling and simulation to implement and verify MAV docking control in a physical testbed,” says Chengyuan Ma, a postdoctoral researcher at the University of Wisconsin–Madison. “We developed a Safety Assurance Adaptive Model Predictive Control (SAAMPC) framework that combines model predictive control with adaptive parameter tuning and safety assurance using control barrier functions. This allows the MAVs to maintain safe operation despite perception noise, control uncertainty, and external disturbances.”

Adaptive safety constraints ensure robust control under uncertainty

“One of the key features of our framework is the use of adaptive Control Barrier Functions (CBFs),” says Chengyuan Ma. “Instead of setting fixed safety limits, our method dynamically adjusts the CBF constraints in real time based on perception reliability and control stability. This ensures the following vehicle does not over-accelerate during docking, even under disturbances or sensor noise.”

Validated through both simulation and reduced-scale physical testing

“To ensure practical applicability, we validated our method in both simulation and real-world conditions,” Chengyuan Ma explains. “We used Simulink to test the SAAMPC framework under various disturbances, and conducted physical experiments with robot vehicles on a circular track. The experimental results confirmed that our method works reliably even in the presence of real-world noise, uncertainty, and limited sensing.”

The results demonstrate that our SAAMPC framework enables smooth, safe, and robust docking and undocking operations. Our work lays a technical foundation for the future deployment of modular vehicle systems in real-world transportation networks

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.