Imagine a self-driving car cruising down the street when suddenly, its sensors get tricked. A stop sign turns into a speed limit sign in the car's "eyes," or a pedestrian vanishes from view. These aren’t glitches, but potential attacks on the car's perception, and they are a big roadblock in our journey towards autonomous driving. Recent research reveals how vulnerable current self-driving systems are to such attacks. They often make unsafe decisions, like speeding through stop signs or colliding with unseen objects. But don't hit the brakes on AI-powered driving just yet. Researchers are working on a new system called HUDSON, an AI "guardian angel" designed to protect against these attacks. Traditional self-driving cars use fixed rules that can't handle such manipulation, essentially trusting their sensors blindly. However, HUDSON utilizes large language models (LLMs), like those powering chatbots, in a unique way. It collects real-time data from the car's sensors, and translates that data into natural language to form a "story” of the driving scene. Then, with some clever instructions (called prompts), it asks the LLM to reason through the situation, identify inconsistencies in the scene description, and make a safe decision. Think of it like a detective using logic and contextual awareness to determine if what they’re being told adds up. For instance, if a pedestrian's position changes unrealistically between moments, the LLM would recognize this as an inconsistency. The results have been promising. When tested with the powerful GPT-4 LLM, HUDSON detects over 83% of attacks and successfully navigates around 86% of them—a big leap forward from current LLM-based driving systems. The AI effectively uses "causal reasoning," analyzing temporal (changes over time), spatial (object dynamics), and contextual (driving environment) information to determine if the perceived scene aligns with reality. While this research is a big step, challenges remain. One is speed—generating and processing these LLM queries takes time, a luxury you don't have in traffic. Another is broadening HUDSON’s abilities to combat wider-ranging attacks against sensors and networks. But the promise of safer autonomous driving is closer than you might think. By merging LLM’s reasoning power with the vigilance of a skilled human driver, we are approaching an exciting new era of autonomous vehicles. It is not simply about “seeing” the road; it is about “understanding” the road. And in that understanding, the journey ahead will be much safer.