Vehicular　Edge　Computing　(VEC)　is　a　feasible　solution　for　autonomous　driving　as　it　can　offload　latency-sensitive　and　computation-intensive　tasks　from　vehicle　terminals　to　roadside　units　(RSUs)　for　real-time　processing.　Due　to　the　high　computational　resource　the　workflow　applications　(i.e.,　autonomous　driving)　required,　the　small　vehicle-to-RSUs　communication　range,　and　the　scarce　available　resources　for　each　vehicle,　it　might　be　difficult　to　accomplish　complex　workflow　applications　through　the　single-hop　offloading　paradigm　in　the　Internet　of　Vehicles　(IoV).　In　this　paper,　we　propose　a　reinforcement　learning　(RL)　based　multi-hop　computation　offloading　scheme　for　workflow　applications　to　reduce　their　execution　latency　in　the　VEC　networks,　which　considers　the　data　dependency　in　workflow　applications　as　well　as　the　trust　relationship　and　communication　interference　among　RSUs.　Firstly,　a　preprocessing　method　is　employed　to　merge　the　cut-edges　in　a　workflow　to　reduce　the　offloading　scale　of　tasks　and　compress　the　encoding　dimension　of　offloading　solutions.　Then,　a　Deep　Q-network　algorithm　using　Phase-optimal　State　update　(DQPS)　is　proposed　to　update　the　offloading　policy　distribution,　in　which　the　deviation　of　the　phase-optimal　state　from　the　current　state　is　estimated　as　the　reward　of　RL　to　promote　the　algorithm＇s　convergence　to　adapt　the　dynamic　VEC　networks.　Simulation　results　show　that　DQPS　has　the　best　performance　compared　to　other　benchmark　schemes.　Moreover,　the　latency　of　the　identical　applications　can　be　reduced　by　2.5%-25.3%　through　our　offloading　scheme　with　a　multi-hop　model　compared　to　that　with　the　single-hop　paradigm　in　IoV.