Due　to　the　complexity　of　the　driving　environment　and　the　dynamics　of　the　behavior　of　traffic　participants,　self-driving　in　dense　traffic　flow　is　very　challenging.　Traditional　methods　usually　rely　on　predefined　rules,　which　are　difficult　to　adapt　to　various　driving　scenarios.　Deep　reinforcement　learning　(DRL)　shows　advantages　over　rule-based　methods　in　complex　self-driving　environments,　demonstrating　the　great　potential　of　intelligent　decision-making.　However,　one　of　the　problems　of　DRL　is　the　inefficiency　of　exploration;　typically,　it　requires　a　lot　of　trial　and　error　to　learn　the　optimal　policy,　which　leads　to　its　slow　learning　rate　and　makes　it　difficult　for　the　agent　to　learn　well-performing　decision-making　policies　in　self-driving　scenarios.　Inspired　by　the　outstanding　performance　of　supervised　learning　in　classification　tasks,　we　propose　a　self-driving　intelligent　control　method　that　combines　human　driving　experience　and　adaptive　sampling　supervised　actor-critic　algorithm.　Unlike　traditional　DRL,　we　modified　the　learning　process　of　the　policy　network　by　combining　supervised　learning　and　DRL　and　adding　human　driving　experience　to　the　learning　samples　to　better　guide　the　self-driving　vehicle　to　learn　the　optimal　policy　through　human　driving　experience　and　real-time　human　guidance.　In　addition,　in　order　to　make　the　agent　learn　more　efficiently,　we　introduced　real-time　human　guidance　in　its　learning　process,　and　an　adaptive　balanced　sampling　method　was　designed　for　improving　the　sampling　performance.　We　also　designed　the　reward　function　in　detail　for　different　evaluation　indexes　such　as　traffic　efficiency,　which　further　guides　the　agent　to　learn　the　self-driving　intelligent　control　policy　in　a　better　way.　The　experimental　results　show　that　the　method　is　able　to　control　vehicles　in　complex　traffic　environments　for　self-driving　tasks　and　exhibits　better　performance　than　other　DRL　methods.　©　2024　the　Author(s),　licensee　AIMS　Press.　This　is　an　open　access　article　distributed　under　the　terms　of　the　Creative　Commons　Attribution　License　(http://creativecommons.org/licenses/by/4.0)