As vehicles become increasingly automated, the role of human drivers is changing. Instead of continuously controlling the vehicle, drivers are often expected to supervise automated systems and intervene when necessary. However, this transition has created a new challenge: drivers frequently struggle to understand why a vehicle behaves in a certain way, while automated systems have limited awareness of the driver’s changing cognitive state, emotions, and preferences.
To address this challenge, researchers from McGill University, The Hong Kong University of Science and Technology (Guangzhou), and the University of Luxembourg have developed a new human-vehicle collaboration framework called Mediator-in-the-Loop-Driving (MILD). Their study proposes an intelligent agent that acts as a mediator between the driver and the automated driving system, helping both sides maintain a shared understanding of the driving situation.
The team published their study in Communications in Transportation Research ( https://doi.org/10.26599/COMMTR.2026.9640038 ).
“Current automated vehicles largely rely on a supervision paradigm, where humans monitor automation but automation has only limited understanding of the human,” said Jiangbo Yu, corresponding author of the study. “We envision a future where intelligent agents help drivers and automated systems collaborate as teammates rather than operate as separate entities.”
From supervision to collaboration
Most existing automated driving systems focus primarily on understanding the surrounding traffic environment. While driver monitoring systems can detect fatigue or distraction, they are often isolated warning systems that do not actively contribute to collaborative decision-making.
The MILD framework introduces a new paradigm in which an AI-powered mediator continuously interprets both the driver’s state and the external driving environment. Instead of treating drivers as passive supervisors, the system helps them become active managers of the driving process by providing personalized recommendations, explanations, and assistance strategies.
The researchers argue that this bidirectional understanding is essential for future intelligent vehicles, especially in partially automated driving scenarios where both humans and automation share responsibility for safety.
“Our goal is not to replace human drivers,” explained Jiyao Wang, the first author of the study. “Instead, we want to create a system that helps drivers and automated vehicles understand each other and make better decisions together.”
Combining driver understanding with traffic awareness
A key innovation of MILD is its ability to jointly analyze information from both inside and outside the vehicle.
The system integrates driver-related information, such as attention, emotional state, and driving behavior, with external traffic information including surrounding vehicles, road conditions, and potential hazards. This joint perception enables the AI agent to develop a more comprehensive understanding of the driving situation than conventional vehicle-centric systems.
Based on this understanding, a lightweight large language model generates high-level recommendations, including driving suggestions, safety warnings, human-machine interaction strategies, and personalized cockpit adjustments. Importantly, the system does not directly control steering, braking, or acceleration, ensuring that the driver remains an active participant in the decision-making process.
Ensuring trustworthy and explainable AI decisions
One of the major concerns surrounding AI-assisted driving is whether the generated recommendations are safe, reliable, and aligned with human expectations.
To address this issue, the research team developed a new alignment framework called Evidence- and Constraint-weighted Policy Optimization (ECPO). The method evaluates AI-generated recommendations against multiple layers of constraints, including traffic regulations, vehicle capabilities, driver preferences, and contextual evidence from the driving environment.
This mechanism helps ensure that recommendations are not only accurate but also explainable, auditable, and consistent with real-world driving requirements.
“Future intelligent vehicles will need more than automation,” said Jiyao Wang. “They will require transparent communication and mutual understanding between humans and machines. We see MILD as an important step toward that vision.”
Toward next-generation intelligent cockpits
The researchers evaluated MILD using multiple public datasets covering driver monitoring, traffic-scene understanding, and advanced driver assistance applications. Experimental results demonstrated improvements in both perception performance and strategy quality, while human evaluators rated the generated recommendations as safer, more comfortable, and more explainable than existing baseline approaches.
The team believes that intelligent agents will become a key component of future intelligent cockpits, helping bridge the communication gap between humans and automated systems. Beyond automated driving, the proposed framework could also contribute to broader human-AI collaboration scenarios where trust, transparency, and shared decision-making are essential.
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
MILD: Mediator Agent System with Bidirectional Perception and Multi-Layered Alignment for Human-Vehicle Collaboration
30-Sep-2026