The　remarkable　performance　of　Bird＇s　Eye　View　(BEV)　in　perception　tasks　has　led　to　its　gradual　emergence　as　a　focal　point　of　attention　in　both　industry　and　academia.　Environmental　information　perception　technology　represents　a　core　challenge　in　the　field　of　autonomous　driving,　and　traditional　autonomous　driving　perception　algorithms　typically　perform　tasks　such　as　detection,　segmentation,　and　tracking　from　a　frontal　or　specific　viewpoint.　As　the　complexity　of　sensor　parameters　configured　on　vehicles　increases,　it　has　become　crucial　to　integrate　multi-source　information　from　different　sensors　and　present　features　in　a　unified　view.　BEV　perception　is　favored　because　it　is　an　intuitive　and　user-friendly　way　to　fuse　information　about　the　surrounding　environment　and　provide　an　ideal　object　representation　for　subsequent　planning　and　control　modules.　However,　BEV　perception　also　faces　some　key　challenges.　One　such　challenge　is　how　to　convert　from　a　perspective　view　to　a　BEV　view　while　reconstructing　lost　3D　information.　The　question　of　how　to　obtain　accurate　ground　truth　annotations　in　the　BEV　grid　is　of　great　importance.　Similarly,　the　design　of　effective　methods　to　integrate　features　from　different　sources　is　a　crucial　aspect　of　BEV　perception.　In　this　paper,　we　first　discuss　the　inherent　advantages　of　BEV　perception　and　introduce　the　mainstream　datasets　and　performance　evaluation　criteria　for　BEV　perception.　Furthermore,　we　present　a　comprehensive　examination　of　recent　research　on　BEV　perception　from　four　distinct　perspectives,　exploring　a　range　of　solutions,　including　BEV　camera,　BEV　LiDAR,　BEV　fusion,　and　V2V　multi-vehicle　cooperative　BEV　perception.　Finally,　we　identify　prospective　research　directions　and　challenges　in　this　field,　with　the　aim　of　providing　inspiration　to　related　researchers.