Imagine a world where self-driving cars not only navigate roads but also explain their every move, predicting potential hazards in real-time. This isn't science fiction but the focus of exciting new research exploring how Large Language Models (LLMs), the brains behind AI chatbots, can revolutionize autonomous driving. Researchers are experimenting with deploying LLMs directly onto edge devices—local processing units like roadside units—creating a network of intelligent observers. These edge-based LLMs analyze real-time data from cameras and sensors to provide human-like descriptions of driving behaviors. For example, they can articulate observations like, "The car is slowing down because a pedestrian is crossing the street," adding a layer of transparency and understanding to autonomous actions. More than just describing, these LLMs can reason and predict. If a car makes a sudden lane change, the system can analyze the surrounding environment and determine the likely cause, such as avoiding a pedestrian or reacting to a sudden stop by the vehicle ahead. This capability can be a game-changer for safety, enabling the system to anticipate and broadcast warnings about potential hazards to other connected vehicles. This integration of LLMs with edge computing offers crucial speed advantages. Edge processing allows for near-instantaneous analysis of the driving scene, avoiding the delays associated with sending data to the cloud. This real-time processing is critical for autonomous systems requiring split-second decision-making. The research also shows how prompting LLMs with specific instructions about environment, agents, and motion—like weather conditions, nearby vehicles, or sudden braking—significantly improves their ability to describe and reason about driving behaviors. Though still in its early stages, this research points towards a future where autonomous driving becomes more transparent, predictable, and ultimately safer. The ability to generate real-time narratives about driving behavior could be instrumental in building trust in self-driving technology and unlocking its full potential.