Self-driving cars have a problem: they struggle with unusual situations. Imagine a car encountering a sudden detour or a pedestrian jaywalking—situations not explicitly programmed into its system. Researchers are now exploring a new approach to make self-driving cars more adaptable using Large Language Models (LLMs), the technology behind AI chatbots. LLMs are trained on vast amounts of text data, giving them a form of 'common sense' that traditional self-driving systems lack. But how do you translate the complex sensory inputs of a car, like video feeds and maps, into something an LLM can understand? Researchers have introduced a new framework called TOKEN. It works by breaking down the world around the car into object-level tokens—think of them as digital labels for everything from pedestrians to traffic cones. These tokens are then fed to the LLM, allowing it to reason about the scene and make decisions accordingly. The results are promising. In simulated tests, cars using TOKEN navigated complex scenarios like three-point turns, construction zones, and overtaking parked cars with greater accuracy and safety than traditional methods. By thinking in terms of objects and their relationships, much like a human driver would, these AI-powered vehicles demonstrate an improved ability to handle the unexpected. This research doesn't mean we'll see LLM-powered cars on the road tomorrow. Challenges remain, including integrating real-time information and refining the motion planning process. But TOKEN demonstrates an exciting new direction for autonomous driving, hinting at a future where self-driving cars can truly handle the complexities of the real world.