In　Internet　of　Vehicles　(IoV),　unmanned　aerial　vehicles　(UAVs)　assisted　mobile　edge　computing　(MEC)　can　improve　the　system　performance　and　communication　range　of　intelligent　transportation　systems　(ITSs).　However,　the　resource　allocation　and　computation　offloading　in　UAVs-assisted　IoV　systems　still　face　huge　challenges　due　to　the　growing　number　of　vehicle　terminals　(VTs),　potential　privacy　leakage,　and　inefficient　problem-solving.　Existing　solutions　cannot　adapt　to　such　dynamic　multi-UAV　scenarios　and　meet　the　real-time　requirements　of　VTs.　To　address　these　challenges,　we　propose　RACOMU,　a　novel　resource　allocation　and　collaborative　offloading　framework　for　multi-UAV-assisted　IoV.　First,　we　introduce　the　convex　optimization　theory　to　decouple　the　original　problem　and　then　obtain　the　near-optimal　allocation　of　transmission　power　and　computing　resources　by　solving　the　Karush-Kuhn-Tucker　(KKT)　condition.　Next,　we　design　a　new　collaborative　offloading　strategy　with　federated　deep　reinforcement　learning　(FDRL),　where　the　offloading　requests　from　VTs　are　processed　in　a　distributed　manner　to　approach　the　global　optimum　while　preserving　data　privacy.　Extensive　experiments　verify　the　effectiveness　of　the　proposed　RACOMU.　Compared　to　benchmark　methods,　RACOMU　achieves　better　performance　in　terms　of　task　processing　latency,　decision-making　time,　and　load　balancing　degree　under　various　scenarios.