Left-turn　intersections　without　signal　lights　are　among　the　most　dangerous　scenes　in　autonomous　driving,　and　achieving　efficient　and　safe　left-turn　decision-making　is　highly　challenging　in　autonomous　driving.　The　Deep　Reinforcement　Learning(DRL)　algorithm　has　broad　prospects　in　autonomous　driving　decision-making.　However,　its　sample　efficiency　is　low　and　it　cannot　be　used　to　easily　design　reward　functions　in　autonomous　driving.　Therefore,　a　DRL　algorithm　based　on　expert　priors,　abbreviated　as　CBAM-BC　SAC,　is　proposed　to　solve　the　aforementioned　problems.　First,　a　Scalable　Multiagent　RL　Training　School(SMARTS)　simulation　platform　is　used　to　obtain　expert　prior　knowledge.　Subsequently,　a　Convolutional　Block　Attention　Module(CBAM)　is　used　to　improve　Behavior　Cloning(BC),　which　pretrains　and　imitates　expert　strategies　based　on　the　prior　knowledge　of　experts.　Finally,　the　learning　process　of　the　DRL　algorithm　is　guided　by　an　imitation　expert　strategy　and　verified　in　a　left-turn　decision-making　at　intersection　without　traffic　lights.　Experimental　results　indicate　that　the　DRL　algorithm　based　on　expert　prior　is　more　advantageous　than　conventional　DRL　algorithms.　It　not　only　eliminates　the　workload　of　manually　setting　reward　functions,　but　also　significantly　improves　sample　efficiency　and　achieves　better　performance.　In　left-turn　scene　at　intersection　without　traffic　lights,　the　CBAM-BC　SAC　algorithm　improves　the　average　traffic　success　rate　by　14.2　and　2.2　percentage　points,　respectively,　compared　with　the　conventional　DRL　algorithm　SAC　and　the　DRL　algorithm　BC　SAC　based　on　classic　BC.　©　2024,　Jisuanji　Gongcheng.　All　rights　reserved.