Self-driving cars have always seemed just around the corner, yet truly autonomous vehicles remain elusive. Current approaches, whether modular (breaking down driving into separate tasks like perception and planning) or end-to-end (training a single model to directly map sensor data to control signals), face limitations. Modular systems struggle to integrate the different parts seamlessly, while end-to-end approaches have difficulty handling the unpredictable nature of real-world driving. Enter Large Language Models (LLMs), the AI powerhouses behind tools like ChatGPT. Researchers are exploring whether LLMs, with their reasoning and knowledge capabilities, can solve the autonomous driving puzzle. LLMs are being tested across the board, from enhancing perception by understanding complex scenes to improving prediction by anticipating the actions of other road users. They're even being experimented with for planning routes and making driving decisions. Imagine an LLM that not only navigates but also explains its choices in plain English, increasing transparency and trust. Early studies show promise, suggesting that LLMs could improve safety and efficiency in autonomous driving, potentially tackling challenges like navigating unexpected obstacles and edge cases. They could even analyze complex interactions between drivers, pedestrians, and the environment, leading to more human-like driving behavior. However, LLMs are not a silver bullet. They are prone to "hallucinations," generating incorrect or nonsensical outputs. Their response times can be slow, a crucial factor in real-time driving scenarios. Moreover, LLMs currently lack a true understanding of the physical world, relying on text-based training rather than real-world interaction. The future of autonomous driving likely involves a blend of data-driven models and knowledge-based systems, perhaps with LLMs at the helm. While a fully LLM-powered self-driving car is still a work in progress, the potential for these models to revolutionize transportation is undeniable.