Environmental　perception　for　self-driving　has　become　a　hot　topic　in　the　domain　of　computer　vision.　The　field　has　made　great　progress　in　the　past　decade,　and　many　deep　learning　frameworks　based　on　single-modal　data　for　self-driving　perception　tasks　have　emerged.　However,　single-modal　information-based　perception　systems　still　have　limitations.　Recently,　researchers　have　improved　this　problem　using　point　clouds　and　images　cross-modal　fusion　and　obtained　more　satisfactory　results.　Therefore,　it　is　necessary　to　develop　a　comprehensive　review　of　recent　research　work.　In　this　paper,　we　summarize　different　fusion　methods,　cross-modal　datasets,　and　evaluation　metrics　oriented　to　three　phases　for　self-driving　perception　tasks.　Then,　the　point　cloud　and　image　fusion　challenges　are　analyzed　and　prospected.　According　to　the　above　observations,　we　hope　that　this　paper　can　provide　research　ideas　for　self-driving　point　cloud　and　image　cross-modal　fusion.　©　2022　IEEE.