FASIONAD: Adaptive Uncertainty-Gated Fast–Slow Fusion Framework for Safe Autonomous Driving

Abstract

Previous fast–slow system architectures demonstrated that pairing a reactive E2E planner with a deliberative vision-language model (VLM) can address these long-tail scenarios. However, these dual-system models that query the slow module at fixed intervals are computationally inefficient and introduce unnecessary latency during normal operation. To bridge this gap, we introduce textbf{FASIONAD}, an adaptive fast–slow framework for autonomous driving that selectively integrates E2E planning and VLM reasoning. A lightweight fast planner manages general control, while a slow reasoner is activated only when a Laplace-based uncertainty gate detects changed uncertainty. Rather than overriding control, the VLM provides concise planning states and high-level plans. These inform the planner through an information bottleneck and high-level action guidance, enhancing interpretability and safety. Evaluated on the nuScenes, Bench2Drive, and CARLA Town05 closed-loop benchmarks, FASIONAD lowers the average trajectory error by 6.7% and the collision rate by 28.1% compared with strong E2E baselines, while also markedly reducing computational overhead relative to always-on fast–slow dual systems. These results demonstrate that adaptive fast–slow fusion is a practical route to safer, more reliable, and more efficient autonomous driving.