In a new comprehensive review, researchers from Tsinghua University, Xiaomi EV, Beijing Institute of Technology, and University of Electronic Science and Technology of China examine how these two AI paradigms are being applied across the development, validation, and operation of autonomous driving systems, discuss their strengths and limitations, and further review their emerging synergy for more robust, interpretable, and trustworthy autonomous driving systems.
The team published their study in Communications in Transportation Research (https://doi.org/10.26599/COMMTR.2026.9640023).
Two complementary AI paradigms for autonomous driving safety
Autonomous vehicles must operate in highly dynamic and uncertain traffic environments. Although recent advances in sensing and learning have improved perception and control, current systems still face major challenges in scene understanding, risk reasoning, rare scenario handling, and explainability.
In the study, the research team examined two important AI approaches with complementary strengths. Knowledge graphs represent structured knowledge such as traffic rules, object relationships, causal chains, and expert driving experience, enabling explicit reasoning and traceable decision support. Large language models, in contrast, are more flexible in semantic understanding, contextual generalization, and reasoning over open-ended situations.
Comparison and synergy of these two AI paradigms for autonomous driving safety
The review shows that knowledge graphs and large language models offer different advantages for autonomous driving safety. Knowledge graphs are well suited for organizing structured knowledge and supporting explicit, interpretable reasoning, especially in tasks involving traffic rules, causal relations, and expert knowledge. Large language models, in contrast, are more flexible in semantic understanding, contextual reasoning, and handling open-ended situations, which gives them strong potential in complex and uncertain traffic environments.
Rather than treating them as competing approaches, the study further highlights their growing synergy. Researchers are increasingly exploring how structured knowledge can improve the transparency and reliability of large language models, while the flexible reasoning ability of large language models can expand how knowledge graphs are queried, understood, and applied. This emerging combination points to a promising direction for building more robust, interpretable, and trustworthy autonomous driving systems.
DOI Link:
https://doi.org/10.26599/COMMTR.2026.9640023
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 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 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
