Autonomous vehicle platooning, where multiple vehicles travel cooperatively with short inter-vehicle distances, has attracted increasing attention due to its potential to improve energy efficiency, reduce aerodynamic resistance, and enhance road capacity. However, deploying vehicle platooning in real-world environments remains challenging because of sensor uncertainties, imperfect vehicle models, and nonlinear actuator responses.
A research team from Southeast University and Monash University developed a physics-informed platooning control framework that combines traditional vehicle control methods with learning-based optimization techniques. The study was published in Communications in Transportation Research.
The proposed framework first introduces a multi-source state fusion method that integrates wheel odometry, inertial measurement unit (IMU), and global positioning system (GPS) measurements. By combining continuous motion information with global position corrections, the method provides more reliable vehicle state estimation for subsequent control decisions.
Based on the estimated vehicle states, the researchers developed a model-based controller combining Model Predictive Control (MPC) and Lateral Feedforward-Feedback Control (LFFC). The controller separates longitudinal and lateral control tasks, allowing efficient optimization of vehicle speed and trajectory tracking while maintaining real-time performance.
To further improve adaptability under changing driving conditions, the research team introduced a data-driven improvement module. An offline Conservative Q-Learning (CQL) algorithm was used to automatically calibrate key controller parameters from historical driving data. Meanwhile, a neural residual learning model was designed to compensate for discrepancies between theoretical control commands and actual vehicle responses caused by actuator delays and unmodeled dynamics.
The proposed framework was evaluated through both simulation experiments and real-world vehicle tests using a ROS-based two-vehicle platform equipped with multiple sensors. Experimental results demonstrated that the method improved vehicle platooning stability and tracking accuracy under realistic operating conditions. The learning-based compensation module effectively reduced control errors caused by hardware uncertainties, showing the potential of combining physical models with artificial intelligence for autonomous driving applications.
"Our work provides a practical approach to integrating physics-based control and data-driven learning for real-world autonomous vehicle systems," the researchers noted. The study highlights how intelligent algorithms can enhance traditional control frameworks while maintaining interpretability and safety.
The research provides new insights into the development of robust autonomous vehicle platooning systems. Future studies may further investigate multi-vehicle platooning with heterogeneous vehicle dynamics, communication delays, and more complex road environments to improve the scalability and reliability of cooperative autonomous driving technologies.
The team published their study in Communications in Transportation Research ( https://doi.org/10.26599/COMMTR.2026.9640049 ).
DOI Link:
https://doi.org/10.26599/COMMTR.2026.9640049
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
