The　rapid　growth　of　intelligent　connected　vehicles　fosters　the　development　of　Internet-of-Vehicle　(IoV)　applications.　Due　to　the　complex　traffic　environments,　integrated　sensing,　communication　and　computation　(ISCC)　technology　would　be　seen　as　an　efficient　paradigm　to　support　a　plethora　of　IoV　applications,　which　causes　such　a　surge　of　computation-intensive　tasks　that　the　task　computation　with　a　single　server　node　cannot　keep　energy-efficient　under　the　strict　delay　constraints.　Inspired　by　the　idea　of　distributed　computing,　a　scheme　of　distributed　edge　computing-based　task　offloading　for　ISCC　is　designed　and　various　assisted　edge　nodes　(AENs)　are　introduced　in　a　stochastic　geometry　approach　to　alleviate　the　dilemma　of　energy　consumption.　After　analyzing　the　proposed　joint　optimization　problem　for　energy　minimization,　a　double-iteration　joint　optimization　algorithm　(DIJOA)　is　developed　to　derive　the　solution.　The　results　of　the　performance　evaluation　not　only　verify　the　plausibility　of　the　proposed　model,　but　also　indicate　that　the　introduction　of　AENs　can　significantly　reduce　system　energy　consumption　and　the　proposed　algorithm　outperforms　other　schemes　by　over　2.5%　in　energy　consumption,　which　corroborates　the　superiority　of　the　proposed　scheme　through　numerical　simulations.