Imagine self-driving cars that not only navigate roads but also anticipate the actions of pedestrians, cyclists, and other drivers. This isn't science fiction, but the focus of cutting-edge research using Knowledge Graphs (KGs) and Large Language Models (LLMs). Why is predicting road user behavior so crucial for autonomous driving? Consider the complexity of a busy intersection: pedestrians stepping off the curb, cyclists weaving through traffic, and cars making split-second decisions. Accurately predicting these actions is paramount for ensuring safety and building trust in self-driving technology. Traditional prediction methods often rely solely on kinematic information (like speed and direction). However, human behavior is far more nuanced, influenced by context, social cues, and even cultural norms. This is where KGs and LLMs come into play. KGs excel at representing complex relationships between different entities. In this case, the entities could be pedestrians, vehicles, traffic lights, crosswalks, and even the surrounding environment. The relationships capture how these entities interact, enabling a more holistic understanding of the traffic scene. LLMs, known for their language processing prowess, add another layer of intelligence. They can incorporate human knowledge, such as traffic rules and common sense reasoning, into the prediction process. Imagine an LLM that understands that a pedestrian looking at their phone is less likely to notice an approaching car. This integration of structured knowledge (KGs) and human-like reasoning (LLMs) allows for more accurate and explainable predictions. The system can provide insights into *why* a certain prediction was made, increasing transparency and trust. For example, the system might explain that a car is likely to change lanes because it's approaching a slower vehicle and the adjacent lane is clear. Researchers have tested this approach on real-world datasets, demonstrating its superior performance compared to traditional methods. The results show significant improvements in predicting pedestrian crossing intentions and vehicle lane changes, even several seconds in advance. While promising, challenges remain. Gathering data from diverse cultural settings is crucial for ensuring the system's adaptability. Furthermore, integrating this prediction system with the decision-making modules of autonomous vehicles is a complex task. The future of autonomous driving hinges on the ability of AI to understand and predict human behavior. KGs and LLMs, by combining structured knowledge with human-like reasoning, offer a powerful approach to achieving this goal. As research progresses, we can expect even more sophisticated prediction systems that pave the way for safer and more reliable self-driving cars.