TY  - JOUR
AU  - Liu, Jiaxin
AU  - Peng, Liang
AU  - Yan, Xiangyu
AU  - Zhang, Lingjun
AU  - Yang, Chenye
AU  - Tao, Yueming
AU  - Tan, Ashton Yu Xuan
AU  - Xu, Tianli
AU  - Guo, Sai
AU  - Wang, Hong
AU  - Li, Jun
PY  - 2026
TI  - Enhancing autonomous vehicle safety with knowledge graphs and large language models: Comprehensive review
JO  - Communications in Transportation Research
SN  - 2097-5023
SP  - 9640023
VL  - 6
IS  - 2
AB  - As autonomous vehicle technology continues to evolve, ensuring its safety in complex and dynamic environments has become a critical challenge. Knowledge graphs (KGs) and large language models (LLMs), as two cutting-edge approaches representing the forefront of knowledge-driven and connectionist paradigms in modern artificial intelligence, are emerging as powerful tools for enhancing autonomous vehicle safety. In this study, we provide a comprehensive review of the applications of KGs and LLMs in enhancing the safety of autonomous driving systems. Building upon a brief introduction to the fundamental concepts and underlying technologies of KGs and LLMs, we then present their respective applications in autonomous vehicle safety from complementary perspectives. We further compare the advantages and limitations of KGs and LLMs in terms of knowledge representation, inference capability, scalability, and real-time performance. To leverage the complementary strengths of structured knowledge and language based reasoning, we review existing research efforts that integrate KGs and LLMs in the context of autonomous vehicle (AV) safety enhancement. Based on this analysis, we propose a hybrid safety-enhancement framework that combines explicit knowledge structures of KGs with the flexible reasoning capabilities of LLMs, offering a promising direction toward more robust, interpretable, and adaptive autonomous driving systems.
UR  - https://doi.org/10.26599/COMMTR.2026.9640023
DO  - 10.26599/COMMTR.2026.9640023