Autonomous　driving　technology　represents　a　pivotal　direction　in　the　future　of　intelligent　transportation.Despite　significant　advancements　in　recent　years,　this　technology　faces　numerous　challenges,　particularly　in　making　robust　and　trustworthy　intelligent　decisions　amidst　highly　uncertain　environments.This　paper　addresses　the　issue　of　uncertainty　quantification　in　autonomous　driving　decision-making,　focusing　on　the　roles　of　aleatoric　uncertainty　and　epistemic　uncertainty.It　analyzes　the　impact　of　different　uncertainty　thresholds　on　the　accuracy,　safety,　and　reliability　of　driving　decisions,　ultimately　determining　significant　threshold　ranges　of　uncertainty　affecting　decision　performance　under　varying　conditions.Experimental　results　underscore　the　critical　importance　of　considering　uncertainty　in　autonomous　driving　systems.The　findings　provide　theoretical　insights　and　parameter　guidance　for　developing　more　robust　and　trustworthy　autonomous　driving　systems.　©　2024　IEEE.