The　proliferation　of　intelligent　connected　vehicles　and　related　applications　with　computation-intensive　tasks　has　boosted　a　pivotal　requirement　for　the　unprecedented　low　latency　and　high　throughput　of　the　Internet-of-Vehicle　(IoV).　Rate-splitting　multiple　access　(RSMA)　has　emerged　as　a　promising　technology　for　achieving　high　spectral　efficiency　and　maintaining　reliable　connectivity　in　mobile　scenes.　Inspired　by　the　idea　of　distributed　computing,　an　RSMA-assisted　distributed　computation　offloading　scheme　(RDCOS)　in　vehicular　networks　is　designed,　where　various　computation　auxiliary　nodes　(CANs)　are　introduced　in　a　stochastic　geometry　approach　to　alleviate　the　computation　dilemma　of　the　task　vehicle.　After　analyzing　the　CAN　pairing　and　the　successful　accessing　probability　(SAP),　the　total　offloaded　tasks　maximization　problem　of　the　task　vehicle　is　investigated.　Then　a　successive　convex　approximation　(SCA)-based　computation　offloading　optimization　algorithm　(SCOOA)　is　developed　to　obtain　the　solution.　The　results　of　the　performance　evaluation　not　only　verify　the　plausibility　of　the　proposed　model　but　also　indicate　that　the　RSMA　can　significantly　promote　the　total　offloaded　tasks　under　moving　conditions,　which　confirms　the　superiority　of　the　proposed　scheme　through　simulations.　　©　1967-2012　IEEE.