Not All Uncertainty Matters: Simulation-in-the-Loop Fast-Slow Reasoning for Decision-Critical Autonomous Driving System

This research proposes SIGMA, a simulation-in-the-loop framework for fast-slow collaboration in autonomous driving systems. By integrating uncertainty assessment into planning, SIGMA evaluates the impact of resolving semantic and geometric uncertainties on feasible trajectories and planning cost. Experiments in CARLA demonstrate that SIGMA reduces unnecessary cloud interactions by 50%, improves navigation success by over 6%, and reduces finish time by up to 26.2% in dynamic scenarios.