LLM-Based Autonomous Vehicle Self-Protection Mechanisms: A Comprehensive Review

As autonomous driving technology advances, traditional safety mechanisms face limitations in dynamic threat recognition and real-time decision-making under mechanical stress constraints. This paper synthesizes current research on integrating LLM capabilities with autonomous vehicle safety systems, analyzing how natural language processing and reasoning can enhance threat detection, improve algorithmic decision-making, and optimize the balance between safety imperatives and mechanical structure preservation. Our findings reveal that while LLM integration offers significant potential for improving threat recognition accuracy and decision-making sophistication, critical gaps remain in real-time processing capabilities, mechanical stress modeling, and safety-performance trade-offs. The paper concludes with recommendations for future research directions, emphasizing the need for comprehensive frameworks that seamlessly integrate LLM reasoning with vehicle dynamics and mechanical constraints to achieve optimal safety outcomes in autonomous transportation systems.