Self-driving cars rely heavily on understanding their surroundings. But even the most advanced AI systems can struggle with the nuances of real-world driving. Imagine a self-driving car approaching an intersection with a cyclist nearby and oncoming traffic. Without a deep understanding of the scene, the car might make a dangerous decision. New research introduces “hints” to boost how AI perceives these complex scenarios. Researchers explored how providing subtle clues about instance-level relationships (like recognizing that different parts of a cyclist belong together), high-level semantic information (like identifying cars, pedestrians, and traffic signals), and question-specific context (like focusing on areas relevant to the current situation) could drastically improve an AI’s visual reasoning. These hints are fed into the AI’s visual processing system, enabling it to grasp the subtleties of the scene more accurately. The results are impressive: equipped with these hints, AI systems show a marked improvement in correctly interpreting complex driving situations. In our example, the AI now correctly identifies the cyclist and oncoming cars, making the safe decision to wait. This research is a significant step towards building truly reliable and safe self-driving systems. While the technology holds immense promise, challenges remain. Fine-tuning these hints for specific driving scenarios and ensuring they function efficiently in real-time are crucial next steps. But with continued progress, we can expect self-driving cars to navigate our roads with greater confidence and safety than ever before.