The recent advancements in autonomous driving research have seen a significant shift towards integrating advanced language models and multimodal frameworks to enhance decision-making, perception, and control systems. A notable trend is the use of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) to address complex scenarios such as traffic control, personalized motion control, and real-time traffic sign recognition. These models are being fine-tuned to provide context-aware, real-time solutions that improve safety and efficiency, even under adverse weather conditions. Additionally, there is a growing focus on memory-augmented models for tasks like surgical visual question answering, which enhance scene understanding and reasoning capabilities. The integration of vision-language models (VLMs) for personalized driving experiences and the development of benchmarks to evaluate spatial understanding in autonomous driving are also key areas of innovation. Notably, the introduction of novel frameworks like Hints of Prompt and LaVida Drive demonstrates efforts to optimize computational efficiency while maintaining high-resolution inputs for detailed visual perception. These developments collectively underscore the potential of advanced AI models to revolutionize autonomous driving systems, making them more adaptable, efficient, and user-centric.