Negative　emotions　may　induce　dangerous　driving　behaviors　leading　to　extremely　serious　traffic　accidents.　Therefore,　it　is　necessary　to　establish　a　system　that　can　automatically　recognize　driver　emotions　so　that　some　actions　can　be　taken　to　avoid　traffic　accidents.　Existing　studies　on　driver　emotion　recognition　have　mainly　used　facial　data　and　physiological　data.　However,　there　are　fewer　studies　on　multimodal　data　with　contextual　characteristics　of　driving.　In　addition,　fully　fusing　multimodal　data　in　the　feature　fusion　layer　to　improve　the　performance　of　emotion　recognition　is　still　a　challenge.　To　this　end,　we　propose　to　recognize　driver　emotion　using　a　novel　multimodal　fusion　framework　based　on　convolutional　long-short　term　memory　network　(ConvLSTM),　and　hybrid　attention　mechanism　to　fuse　non-invasive　multimodal　data　of　eye,　vehicle,　and　environment.　In　order　to　verify　the　effectiveness　of　the　proposed　method,　extensive　experiments　have　been　carried　out　on　a　dataset　collected　using　an　advanced　driving　simulator.　The　experimental　results　demonstrate　the　effectiveness　of　the　proposed　method.　Finally,　a　preliminary　exploration　on　the　correlation　between　driver　emotion　and　stress　is　performed.　IEEE