Imagine teaching a self-driving car not just to follow rules, but to understand the world around it like a human driver. That's the exciting premise behind SimpleLLM4AD, a groundbreaking new approach to autonomous driving. Traditional self-driving systems rely on complex code to handle every possible scenario, which can lead to rigid and sometimes unpredictable behavior. SimpleLLM4AD flips the script, using the power of large language models (LLMs), the same technology behind AI chatbots, to make driving decisions in a more human-like way. The system essentially asks itself a series of questions, just like we do when navigating traffic: "What are the important objects around me?", "How are they likely to move?", and "What's the best course of action?" These questions and answers form a connected "graph" of reasoning, allowing the AI to understand the nuances of different situations. The key innovation is combining this reasoning power with visual input. The system processes images from the car's cameras, translating the visual scene into language that the LLM can understand. This visual-language integration is like giving the AI the ability to "see" and "think" simultaneously. Early tests show that SimpleLLM4AD can handle complex scenarios more robustly than traditional methods, demonstrating the potential for more flexible and reliable self-driving technology. While still in its early stages, this research opens a fascinating window into a future where AI-powered cars can truly understand the road and navigate it with the same kind of adaptable intelligence that humans possess. There are still challenges to overcome, like making even more accurate predictions and refining real-time decision-making. But with ongoing development, this approach could be a major stepping stone toward a future where self-driving cars become a seamless part of our daily lives.