Road　intersection　is　one　of　the　most　complex　and　accident-prone　traffic　scenarios,so　it＇s　challenging　for　autonomous　vehicles(AVs)to　make　safe　and　efficient　decisions　at　the　intersections.Most　of　the　related　studies　focus　on　the　solution　to　a　single　scenario　or　only　guarantee　safety　without　considering　driving　efficiency.To　address　these　problems,this　study　proposed　a　deep　reinforcement　learning　enabled　decision-making　framework　for　AVs　to　drive　through　intersections　automatically,safely　and　efficiently.The　mapping　relationship　between　traffic　images　and　vehicle　operations　was　obtained　by　an　end-to-end　decision-making　framework　established　by　convolutional　neural　networks.Traffic　images　collected　at　two　timesteps　were　used　to　calculate　the　relative　velocity　between　vehicles.Markov　decision　process　was　employed　to　model　the　interaction　between　AVs　and　other　vehicles,and　the　deep　Q-network　algorithm　was　utilized　to　obtain　the　optimal　driving　policy　regarding　safety　and　efficiency.To　verify　the　effectiveness　of　the　proposed　decision-making　framework,the　top　three　accident-prone　crossing　path　crash　scenarios　at　intersections　were　simulated,when　different　initial　vehicle　states　were　adopted　for　better　generalization　capability.The　results　showed　that　the　developed　method　could　make　AVs　drive　safely　and　efficiently　through　intersections　in　all　of　the　tested　scenarios.