Imagine a world where cars could predict accidents before they happen, not just react to them. This isn’t science fiction, but the reality explored by groundbreaking new research that uses Large Language Models (LLMs) to anticipate and even pinpoint the location of potential accidents. Traditionally, accident prediction systems have relied on dashcam footage to estimate *when* a collision might occur. This new research goes further, asking not just *when*, but *where* and *what*—identifying which vehicles or objects are most likely to be involved in a potential accident. The researchers achieved this by creating a three-stage model. First, they extract and combine visual features from dashcam video. Then, they use this information to anticipate accidents and pinpoint involved objects. Finally, and most impressively, they use LLMs to translate these predictions into clear, verbal warnings for passengers. This last stage is a game-changer for human-AI interaction in autonomous vehicles, making AI's predictions more understandable and actionable. This research marks a leap forward in accident prevention technology. By accurately localizing risks and providing timely, intuitive alerts, it could transform the future of autonomous driving and pave the way for safer roads for everyone. Testing this model on real-world dashcam datasets showed impressive results. The model not only outperformed existing methods in predicting *when* accidents would happen but also successfully pinpointed *where* in the scene the danger lay, with almost 90% accuracy. While this technology holds immense promise, challenges remain. The research paper acknowledges the need for further development and validation in more diverse and complex driving scenarios. However, the implications of this work are profound. By integrating LLMs into accident prediction, we're not just improving safety; we're also building trust between humans and AI, making the future of autonomous driving not just safer, but more human-centered.