In　the　Internet　of　Vehicles　(IoV)　with　network　slicing　functions,　both　the　inter-slice　resource　allocation　and　the　intra-slice　task　scheduling　have　received　extensive　attention.　However,　the　joint　optimization　of　these　two　problems　has　not　been　fully　studied.　This　letter　focuses　on　the　complexity　issue　of　the　joint　resource　allocation　and　task　scheduling　problem　in　the　IoV　with　both　ultra-reliable　low-latency　communication　(URLLC)　and　enhanced　mobile　broadband　(eMBB)　slices.　A　heterogeneous　Markov　decision　process　(HMDP)　which　models　the　resource　allocation　process　and　the　task　scheduling　process　in　two　different　layers　is　proposed.　Benefiting　from　the　two-layer　structure　of　the　HMDP,　the　size　of　action　space　is　significantly　reduced,　and　the　asynchronous　decisions　of　different　sub-MDPs　are　enabled.　The　corresponding　layered　deep　reinforcement　learning　(DRL)　architecture　is　also　designed　to　solve　the　HMDP-based　optimization　problem.　Simulation　results　show　that　the　layered　DRL-based　algorithm　outperforms　other　methods.