Road　obstacle　detection　is　an　important　problem　for　vehicle　driving　safety.　In　this　paper,　we　aim　to　obtain　robust　road　obstacle　detection　based　on　spatio-temporal　context　modeling.　Firstly,　a　data-driven　spatial　context　model　of　the　driving　scene　is　constructed　with　the　layouts　of　the　training　data.　Then,　obstacles　in　the　input　image　are　detected　via　the　state-of-the-art　object　detection　algorithms,　and　the　results　are　combined　with　the　generated　scene　layout.　In　addition,　to　further　improve　the　performance　and　robustness,　temporal　information　in　the　image　sequence　is　taken　into　consideration,　and　the　optical　flow　is　obtained　in　the　vicinity　of　the　detected　objects　to　track　the　obstacles　across　neighboring　frames.　Qualitative　and　quantitative　experiments　were　conducted　on　the　Small　Obstacle　Detection　(SOD)　dataset　and　the　Lost　and　Found　dataset.　The　results　indicate　that　our　method　with　spatio-temporal　context　modeling　is　superior　to　existing　methods　for　road　obstacle　detection.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.