Understanding　decision-making　in　autonomous　driving　models　is　essential　for　real-world　applications.　Attribution　explanation　is　a　primary　research　direction　for　interpreting　neural　network　decisions.　However,　in　the　context　of　autonomous　driving,　numerical　attributions　fail　to　interpret　the　complex　semantic　information　and　often　result　in　explanations　that　are　difficult　to　understand.　This　paper　introduces　a　novel　semantic　attribution　approach　that　both　identifies　where　important　features　appear　and　provides　intuitive　information　about　what　they　represent.　To　establish　the　semantic　correspondences　for　attributions,　we　propose　an　interpreting　framework　that　integrates　unsupervised　differentiable　semantic　representations　with　the　attribution　computational　model.　To　further　enhance　the　accuracy　of　the　attribution　computation　while　ensuring　strong　semantic　correspondence,　we　design　a　Semantic-Informed　Aumann-Shapley　(SIAS)　method,　which　defines　a　novel　integration　path　solution　using　constraints　from　semantic　scores　and　discrete　gradients.　Extensive　experiments　confirm　that　our　method　outperforms　state-of-the-art　explanation　techniques　both　qualitatively　and　quantitatively　in　autonomous　driving　scenarios.