In　various　time-slots,　the　real-time　reasoning　tasks　generated　by　autonomous　vehicles　are　scheduled　within　tolerance　time,　which　is　the　key　problem　to　be　solved　in　autonomous　driving.　Tasks　are　traditionally　scheduled　on　the　on-board　unit　(OBU),　which　leads　to　long　completion　time　of　tasks.　Heuristic　algorithms　are　widely　used　in　task　scheduling　problems,　which　usually　causes　to　premature　convergence.　Scheduling　tasks　in　edge　environment　can　effectively　reduce　completion　time　of　tasks.　In　this　paper,　a　workflow　scheduling　strategy　was　designed　in　edge　environment　according　to　the　difference　of　reasoning　tasks　and　the　changes　of　edge　nodes　in　various　time-slots.　Firstly,　the　model　of　Markov　decision　process　(MDP)　was　built　to　describe　the　problem　scenario,　and　the　completion　time　of　reasoning　tasks　was　calculated　by　the　workflow　scheduling　algorithm.　Secondly,　the　Q-learning　algorithm　based　on　simulated　anealing　(SA-QL)　was　proposed　to　optimize　the　completion　time　of　reasoning　tasks.　Finally,　the　performance　of　reinforcement　learning　algorithms　based　on　simulated　annealing　(SA-RL)　and　particle　swarm　optimization　(PSO)　algorithm　were　comprehensively　displayed　from　four　perspectives:　effectiveness,　feasibility,　exploration　and　convergence.　The　experimental　results　show　that　both　SA-RL　algorithms　and　PSO　algorithm　have　good　performance　in　feasibility　and　effectiveness.　TD(0)　algorithms　have　better　performance　of　exploration　and　TD()　algorithms　have　that　of　convergence.　©　2019　IEEE.