Autonomous　intersection　management　systems　aim　to　efficiently　control　connected　and　autonomous　vehicles　at　urban　intersections.　However,　current　driving　behavior　models　face　challenges　in　accurately　capturing　the　distinctive　human　driver　characteristics　specific　to　intersection　interactions.　This　article　introduces　a　human-like　driving　behavior　model　based　on　the　driver＇s　risk　field　(DRF)　for　intersection　scenarios.　The　DRF　represents　the　driver＇s　belief　regarding　the　likelihood　of　an　event　occurring,　and　the　associated　cost　function　is　determined　by　the　consequences　of　said　event.　A　driving　simulation　experiment　was　conducted　at　a　signalized　intersection　to　evaluate　the　model,　and　the　results　were　compared　with　a　human-like　driving　behavior　model.　The　results　show　that　the　proposed　model　has　a　high　degree　of　fit.　Furthermore,　a　statistical　analysis　of　the　data　distribution　demonstrates　that　the　predictions　generated　by　the　driver　model　align　closely　with　the　driving　behavior　observed　in　the　signalized　intersection　experiment.