With　the　development　of　autonomous　driving　systems,　the　detection　of　three-dimensional　objects　in　road　scenes　has　garnered　widespread　attention.　However,　most　single-sensor　or　multi-sensor　fusion-based　object　detection　methods　do　not　consider　the　synchronized　rotation　of　the　captured　scene　owing　to　vehicle　movement　in　real　road　scenes,　which　impairs　object　detection　performance.　To　address　such　problems,　this　study　proposes　a　multi-level　global　rotational　equivariant　object　detection　network　framework　based　on　multi-sensor　fusion　to　alleviate　the　difficulty　of　object　detection　caused　by　scene　rotation　and　thereby　improve　object　detection　performance.　First,　the　interior　of　the　voxels　is　encoded　by　the　distance　between　each　point　to　enhance　the　local　point　cloud　geometric　information　and　extract　the　global　rotational　equivariant　features　of　the　voxels.　Second,　the　semantic　information　of　the　image　is　introduced,　and　global　rotational　equivariant　features　are　extracted　to　further　improve　the　network　performance.　Finally,　the　point　cloud　and　image　information,　all　with　rotational　equivariants,　are　fused　on　a　Bird＇s-Eye　View　(BEV)　and　embedded　in　a　group　equivariant　network　to　extract　the　global　rotational　equivariant　features　on　the　fused　BEV　level.　Experimental　results　on　the　nuScenes　validation　set　show　that　the　network　architecture　achieves　a　mean　Average　Precision　(mAP)　of　68.7%　and　a　nuScenes　Detection　Score　(NDS)　of　71.7.　Moreover,　the　mean　Average　Orientation　Error　(mAOE)　decreases　to　0.288.　Compared　with　mainstream　object　detection　methods,　the　proposed　method　realizes　the　rotational　equivariance　of　the　network　architecture　and　improves　performance.　In　addition,　each　component　plays　an　important　role　in　improving　the　object　detection　performance　of　the　overall　network　architecture.　©　(Publication　Year),　(publisher　Name).　All　rights　reserved