Given　the　critical　need　for　more　reliable　autonomous　driving　systems,　explainability　has　become　a　key　focus　within　the　research　community.　In　autonomous　driving　models,　even　minor　perception　differences　can　significantly　influence　the　decision-making　process,　and　this　impact　often　diverges　markedly　from　human　cognition.　However,　understanding　the　specific　reasons　why　a　model　decides　to　stop　or　keep　forward　remains　a　significant　challenge.　This　paper　presents　an　attribution-guided　visualization　method　aimed　at　exploring　the　triggers　behind　decision　shifts,　providing　clear　insights　into　the　underlying　＂why＂　and　＂why　not＂　of　such　decisions.　We　propose　the　cumulative　layer　fusion　attribution　method　that　identifies　the　parameters　most　critical　to　decision-making.　These　attributions　are　then　used　to　inform　the　visualization　optimization　by　applying　attribution-guided　weights　to　crucial　generation　parameters,　ensuring　that　decision　changes　are　driven　only　by　modifications　to　critical　information.　Furthermore,　we　develop　an　indirect　regularization　method　that　increases　visualization　quality　without　necessitating　additional　hyperparameters.　Experiments　on　large　datasets　demonstrate　that　our　method　produces　insightful　visualization　explanations　and　outperforms　state-of-the-art　methods　in　both　qualitative　and　quantitative　evaluations.