Mobile　edge　caching　(MEC)　has　grown　substantially　with　the　rapid　development　in　scale　and　complexity　of　data　traffic.　By　exploiting　the　expansive　coverage　of　autonomous　aerial　vehicles　(AAVs),　MEC　enables　services　for　massive　vehicle　users　(VUs)　simultaneously,　which　is　promising　for　enhancing　network　transmission　efficiency.　Nonetheless,　due　to　challenges　arising　from　the　timeliness　and　freshness　of　content　services　caused　by　AAVs＇　limited　endurance　and　airborne　capacity,　caching　strategy　considering　the　real-time　of　content　in　large-scale　dynamic　Internet　of　Vehicles　(IoV)　environments　remains　open.　With　the　above　consideration,　in　this　article,　the　cache　refreshing　cycle　and　content　placement　are　jointly　optimized　in　the　cache-enabled　AAV-assisted　vehicular　integrated　networks　(CAVINs)　to　minimize　the　content　Age　of　Information　(AoI)　and　energy　consumption　of　the　macro　AAV.　Since　the　joint　optimization　problem　is　variational　coupled　with　nonconvex　binary　constraints,　it　is　decoupled　and　solved　by　a　double-iteration　method.　Specifically,　the　optimal　cache　refreshing　cycle　is　derived　in　semi-closed　form　with　the　Karush-Kuhn-Tucker　(KKT)　conditions.　The　locally　optimal　solution　of　the　content　placement　is　obtained　through　successive　convex　approximation　(SCA).　Simulation　results　corroborate　the　effectiveness　and　superiority　of　the　proposed　scheme.　©　2024　IEEE.