Deep　neural　networks　(DNNs)　have　advanced　autonomous　driving,　but　their　lack　of　transparency　remains　a　major　obstacle　to　real-world　application.　Attribution　methods,　which　aim　to　explain　DNN　decisions,　offer　a　potential　solution.　However,　existing　methods,　primarily　designed　for　image　classification　models,　often　suffer　from　performance　degradation　and　require　specialized　algorithmic　adjustments　when　applied　to　the　diverse　models　in　autonomous　driving.　To　address　this　challenge,　we　introduce　a　universally　applicable　representation　of　traffic　scenes,　forming　the　basis　for　our　unified　attribution　method.　Specifically,　we　leverage　the　first-order　Taylor　expansion　at　a　specific　hidden　layer,　i.e.,　the　product　of　gradients　and　feature　maps,　to　represent　abstract　traffic　scene　information.　This　representation　guides　both　the　optimization　of　attribution　path　generation　and　the　attribution　computation,　enabling　consistent　and　effective　attributions　for　both　lane-change　prediction　and　vision-based　control　models.　Experiments　on　two　distinct　autonomous　driving　models　demonstrate　that　our　approach　outperforms　state-of-the-art　methods　in　explanation　accuracy　and　robustness,　advancing　the　interpretability　of　DNN-based　autonomous　driving　models.　©　2025　Institute　of　Electrical　and　Electronics　Engineers　Inc..　All　rights　reserved.