Socially compliant automated vehicles: new conceptual framework paves the way for safer mixed-traffic environments

Automated vehicles (AVs) hold promise for revolutionizing transportation by improving road safety, traffic efficiency, and overall mobility. Despite the steady advancement in high-level AVs in recent years, the transition to full automation entails a period of mixed traffic, where AVs of varying automation levels coexist with human-driven vehicles (HDVs). Making AVs socially compliant and understood by human drivers is expected to improve the safety and efficiency of mixed traffic. Thus, ensuring AVs’ compatibility with HDVs and social acceptance is crucial for their successful and seamless integration into mixed traffic.

 

Researchers at Delft University of Technology (Netherlands) and RWTH Aachen University (Germany) carried out a study with a comprehensive scoping review to assess the current state of the art in developing socially compliant automated vehicles (SCAVs), identifying key concepts, methodological approaches, and research gaps. They conducted an informal expert interview to discuss the literature review results and identify critical research challenges and expectations towards SCAVs. Based on the scoping review and expert interview input, they designed a conceptual framework for the development of SCAVs and evaluated it using an online survey targeting researchers, technicians, policymakers, and other relevant professionals worldwide. The framework outlines the key capability elements necessary for SCAVs and incorporates crucial considerations across technical, social, and cultural dimensions, effectively bridging theoretical insights with practical applications to achieve socially compliant automation. The conceptual framework provides actionable insights into developing and embedding social compliance in AV systems, enabling scalable and context-sensitive deployment. It can also foster collaboration among academia, industry, and policymakers, ensuring technical innovation aligns with societal needs and regulatory standards, accelerating the path toward SCAV and further towards safe and socially inclusive automated mobility solutions.

 

The study was published on 8 September 2025, in Communications in Transportation Research.

 

“We conducted the first comprehensive scoping review to identify key concepts, methodologies, and research gaps in the emerging field of socially compliant automated driving in mixed traffic,” says Yongqi Dong, lead author and researcher at Delft University of Technology and RWTH Aachen University. “By combining this review with expert interviews and a worldwide survey, we have distilled core requirements for AVs to coexist seamlessly and predictably with human drivers through a conceptual framework.” (The conceptual framework is shown in the figure.)

 

Five methodological pillars

In reviewing 68 pivotal studies on SCAVs, the team clustered existing approaches into five main categories:

 

• Imitation learning to clone human social driving norms
• Reinforcement learning with utility-based models
• Model‑based (e.g., game theory, social‑force models, and driving risk field models) generation of human-like behaviors
• Socially‑aware trajectory prediction with social factors and machine learning
• Optimization of social driving parameters balancing safety, comfort, and courtesy

 

Expert insights reveal critical gaps

Through informal interviews with ten AV experts across academia, industry, and government, the authors uncovered key limitations in today’s AVs:

• Excessive conservatism, leading to inefficient traffic flow
• Poor interpretation of implicit human cues, from hand gestures to assertive lane changes
• Inflexibility to diverse driving cultures and styles
• Lack of bidirectional adaptation, where AVs and human drivers dynamically adjust to each other

 

A novel conceptual framework

To address these gaps, the author team proposes a novel conceptual framework for SCAV (see Image) comprising:

• Sensing & perception module, fusing multi-modal sensor data
• Socially‑compliant decision‑making module, embedding social components (including culture, norms, and cues), different driving styles (e.g., aggressive, cautious, pro-social), and bidirectional behavioral adaptation mechanisms
• Safety constraints module, a real‑time safeguard layer
• Utility trade‑off mechanisms, balancing individual vehicles’ benefits with network‑level traffic performance
• Bidirectional behavioral adaptation, enabling AVs to learn from and respond to human drivers’ adaptations (there will be bidirectional iterative adaptations between AVs and human drivers)
• Spatial‑temporal memory module, continuously updating AV models with past interaction data to facilitate the long- and short-term updating of knowledge and driving rules and contribute to the implementation of bidirectional behavioral adaptation.

 

Global survey validation

An online questionnaire gathered insights from 90 experts across 29 countries. Respondents overwhelmingly endorsed the framework’s key modules, particularly the need for mutual bidirectional adaptation between AVs and human drivers, and highlighted priorities such as anticipation capability, multi-objective optimization, and spatial-temporal memory buffer integration.

 

“Our framework lays a robust foundation for both academia and industry,” concludes Bart van Arem, co‑author from Delft University of Technology. “By integrating social dynamics into AV design, we can pave the way toward mixed‑traffic environments that are safer, more efficient, socially beneficial, and widely accepted.”

 

This research marks a significant step toward realizing the promise of automated vehicles in everyday traffic, ensuring they not only drive safely but also drive in harmony with human behavior and societal expectations.

 

 

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 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 2025, its 2024 IF was announced as 14.5, maintaining the Top1 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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