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.

To successfully implement the platoon control of connected and automated vehicles, it is necessary to address motion control issues to achieve longitudinal and lateral collaborative control. However, due to traffic capacity limitations and the complex traffic environment in which autonomous and human-driven vehicles coexist, autonomous platoon faces significant risks and challenges. This paper investigates longitudinal and lateral control issues from the perspective of a single vehicle up to a platoon, simulating the performance and suitability of various controllers. First, a longitudinal controller based on fuzzy logic and PID control is employed for speed tracking control of a single vehicle, followed by the adoption of an MPC controller based on the vehicle kinematics model to realize the lateral motion of a single vehicle. Second, the communication methods of the autonomous platoon are discussed, and the longitudinal controller that considers the platoon's various communication topologies is developed. Thirdly, a framework for robust integrated motion control is established, which combines the robust H-infinity longitudinal controller and the APF-based MPC lateral controller. Simulation results validate the effectiveness of the aforementioned controllers and reveal their limitations.