Many　advancements　are　being　made　in　vehicular　networks,　such　as　self-driving,　dynamic　route　scheduling,　real-time　traffic　condition　monitoring,　and　on-board　infotainment　services.　However,　these　services　require　high　computation　power　and　precision　and　can　be　met　using　mobile　edge　computing　(MEC)　mechanisms　for　vehicular　networks.　MEC　operates　through　the　edge　servers　available　at　the　roadside,　also　known　as　roadside　units　(RSU).　MEC　is　very　useful　for　vehicular　networks　because　it　has　extremely　low　latency　and　supports　operations　that　require　near-real-time　access　to　rapidly　changing　data.　This　paper　proposes　an　efficient　computational　offloading,　smart　division　of　tasks,　and　cooperation　among　RSUs　to　increase　service　performance　and　decrease　the　delay　in　a　vehicular　network　via　MEC.　The　computational　delay　is　further　reduced　by　parallel　processing.　In　the　division　of　tasks,　each　task　is　divided　into　two　sub-components　which　are　fed　to　a　deep　neural　network　(DNN)　for　training.　Consequently,　this　reduces　the　overall　time　delay　and　overhead.　We　also　adopt　an　efficient　routing　policy　to　deliver　the　results　through　the　shortest　path　to　improve　service　reliability.　The　offloading,　computing,　division,　and　routing　policies　are　formulated,　and　a　model-based　DNN　approach　is　used　to　obtain　an　optimal　solution.　Simulation　results　prove　that　our　proposed　approach　is　suitable　in　a　dynamic　environment.　We　also　compare　our　results　with　the　existing　state-of-the-art,　showing　that　our　proposed　approach　outperforms　the　existing　schemes.　IEEE