Imagine a self-driving car navigating a busy city. It needs to react instantly to a pedestrian stepping into the crosswalk, while also planning its route to avoid traffic jams. How can it balance these split-second reactions with more thoughtful decision-making? Researchers are tackling this challenge by drawing inspiration from the human brain. A new AI framework called FASIONAD, short for FAst and Slow FusION Thinking Systems for Human-Like Autonomous Driving with Adaptive Feedback, mimics the “thinking fast and slow” cognitive model. It uses a “fast” system for routine driving, like staying in a lane or adjusting speed, and a “slow” system for complex situations, like navigating tricky intersections or reacting to unexpected obstacles. The fast system relies on tried-and-true path planning algorithms optimized for speed. Meanwhile, the slow system taps into the power of vision-language models (VLMs), which can interpret complex visual scenes and even reason about the intentions of other drivers or pedestrians. What’s really clever is how FASIONAD switches between these two systems. It constantly assesses the uncertainty of the current situation. If things are predictable, the fast system takes the wheel. But if uncertainty is high – say, a pedestrian suddenly appears – the slow system kicks in to carefully analyze the situation and provide feedback to the fast system. This adaptive feedback loop is crucial. The slow system doesn't just react; it teaches the fast system to better handle similar situations in the future. This approach is showing promising results. In tests on the CARLA and nuScenes autonomous driving benchmarks, FASIONAD outperformed existing systems, achieving both higher driving scores and lower collision rates. The research highlights the importance of not just making AI faster, but making it smarter and more adaptable. By mimicking the way humans process information, we can create self-driving systems that are better equipped to handle the complexities of the real world. Future research will focus on expanding FASIONAD's capabilities in more challenging environments like rural roads and incorporating more sensor data, such as from LiDAR and radar. This work paves the way for safer and more efficient autonomous vehicles, potentially revolutionizing transportation as we know it.