Autonomous　driving　is　a　promising　way　to　future　safe,　efficient,　and　low-carbon　transportation.　Real-time　accurate　target　detection　is　an　essential　precondition　for　the　generation　of　proper　following　decision　and　control　signals.　However,　considering　the　complex　practical　scenarios,　accurate　recognition　of　occluded　targets　is　a　major　challenge　of　target　detection　for　autonomous　driving　with　limited　computational　capability.　To　reveal　the　overlap　and　difference　between　various　occluded　object　detection　by　sharing　the　same　available　sensors,　this　paper　presents　a　review　of　detection　methods　for　occluded　objects　in　complex　real-driving　scenarios.　Considering　the　rapid　development　of　autonomous　driving　technologies,　the　research　analyzed　in　this　study　is　limited　to　the　recent　five　years.　The　study　of　occluded　object　detection　is　divided　into　three　parts,　namely　occluded　vehicles,　pedestrians　and　traffic　signs.　This　paper　provided　a　detailed　summary　of　the　target　detection　methods　used　in　these　three　parts　according　to　the　differences　in　detection　methods　and　ideas,　which　is　followed　by　the　comparison　of　advantages　and　disadvantages　of　different　detection　methods　for　the　same　object.　Finally,　the　shortcomings　and　limitations　of　the　existing　detection　methods　are　summarized,　and　the　challenges　and　future　development　prospects　in　this　field　are　discussed.　©　2023　The　Author(s)