FASIONAD : FAst and Slow FusION Thinking Systems for Human-Like
Autonomous Driving with Adaptive Feedback
FASIONAD : FAst and Slow FusION Thinking Systems for Human-Like
Autonomous Driving with Adaptive Feedback
Ensuring safe, comfortable, and efficient navigation is a critical goal for autonomous driving systems. While end-to-end models trained on large-scale datasets excel in common driving scenarios, they often struggle with rare, long-tail events. Recent progress in large language models (LLMs) has introduced enhanced reasoning capabilities, but their computational demands pose …