Self-driving cars have made incredible strides, yet navigating unpredictable, high-risk situations remains a challenge. Imagine a car smoothly handling a busy intersection or confidently changing lanes on a packed highway. That's the promise of SafeDrive, a new framework using the power of Large Language Models (LLMs) to boost autonomous vehicle safety. Current self-driving systems often struggle with unexpected events, sometimes reacting too slowly or making risky maneuvers. SafeDrive tackles this by combining real-world driving data with an LLM's ability to reason and learn. Think of it as giving the car a digital driving instructor. This instructor assesses risk from all directions, considering the car, the driver's behavior, and the road conditions, building a 360-degree view of potential hazards. This information is then fed to the LLM, which uses it to make informed decisions, much like a human driver would. But what about learning from past mistakes? SafeDrive includes a memory module that stores successful driving experiences. When the car encounters a similar situation, it can draw on this knowledge to make a safer choice. Furthermore, a 'reflection' module helps the system learn from its errors. If the LLM makes a wrong decision, this module analyzes the mistake and updates the system's knowledge, preventing similar errors in the future. Tests using real-world datasets show promising results. SafeDrive achieved a 100% safety rate in highway and roundabout scenarios and nearly that in complex intersections. It also closely mirrored human driving behavior in over 85% of test cases. While a fully LLM-powered self-driving car isn't here yet, SafeDrive suggests a safer future. By adding an LLM's reasoning power to traditional autonomous driving systems, we're one step closer to cars that can truly handle the complexities of the road.