Imagine an AI taking the wheel – not just controlling the car, but understanding the rules of the road like a human driver. Researchers are exploring this very idea, testing whether Large Language Models (LLMs), the brains behind AI chatbots, can actually pass driving theory tests. A recent study put several leading LLMs, including OpenAI's GPT models, Baidu's Ernie, and Alibaba's Qwen, through a rigorous exam based on the official UK driving theory test. These AI models were quizzed on hundreds of multiple-choice questions covering road rules, traffic signs, and safe driving practices. The results were intriguing. While some LLMs like GPT-3.5 struggled, barely reaching 80% accuracy, the more advanced GPT-4 aced the exam with a 95% score. This highlights how quickly LLMs are evolving and learning complex real-world information. Even more impressive was GPT-4o, a multimodal model capable of processing both text and images. It scored an outstanding 96% on questions involving traffic scenes and visual challenges. But not all LLMs performed equally. Some open-source and less resource-intensive models fell short of the passing mark, emphasizing the link between model size, training data, and performance. This research isn’t just about robot drivers; it has implications for how LLMs could assist human drivers in the future. Imagine an AI co-pilot that understands traffic laws and can offer real-time advice in tricky situations. While the dream of fully autonomous vehicles is still down the road, this research shows that AI can grasp the theoretical side of driving. The next step is to see how these models handle the practical aspects, including hazard perception and real-time decision-making in complex driving environments. The real challenge is not just passing a test, but navigating the complexities of the open road.