Self-driving　mainly　includes　four　parts:　perception,　planning,　decision-making　and　control.　The　decision　system　is　used　to　output　safe　and　reasonable　driving　behaviors　according　to　vehicle＇s　state　and　surrounding　environment,　so　as　to　autonomously　control　the　vehicle　to　driving,　which　is　the　key　part　and　core　of　self-driving.　With　the　development　of　machine　learning　and　AI,　the　self-driving　technology　gets　further　improvement.　Using　the　deep　reinforcement　learning　(DRL)　algorithms　to　solve　the　behavior　decision-making　problems　of　self-driving　vehicles　has　become　one　of　the　most　concerned　research　hot　spot.　Traditional　rule-based　method　lacks　the　ability　to　adapt　the　dynamic　environments,　DRL　can　overcome　this　drawback　very　well,　it　has　unique　advantage　for　processing　those　problems.　In　this　paper,　we　summarize　recent　years　research　on　DRL　for　decision　making　in　the　field　of　self-driving　and　provide　a　classification　of　driving　tasks　using　these　methods.　And　analyze　in　detail　the　advantages　and　disadvantages　of　DRL　methods　proposed　by　researchers　for　solving　these　driving　tasks,　and　finally　summarize　some　of　the　challenges　and　difficulties　in　applying　DRL　to　complete　decision-making　problems　and　look　forward　to　future　research　works　and　directions.　The　study　contributes　to　a　deeper　understanding　of　the　theories　and　methods　related　to　DRL　and　decision-making　of　self-driving,　and　provides　a　reference　for　future　research.　©　2023　IEEE.