In　the　control　of　the　self-driving　vehicles,　PID　controllers　are　widely　used　due　to　their　simple　structure　and　good　stability.　However,　in　complex　self-driving　scenarios　such　as　curvature　curves,　car　following,　overtaking,　etc.,　it　is　necessary　to　ensure　the　stable　control　accuracy　of　the　vehicles.　Some　researchers　used　fuzzy　PID　to　dynamically　change　the　parameters　of　PID　to　ensure　that　the　vehicle　control　remains　in　a　stable　state.　It　is　difficult　to　ensure　the　control　effect　of　the　fuzzy　controller　when　the　size　of　the　domain　is　not　selected　properly.　This　paper　designs　a　variable-domain　fuzzy　PID　intelligent　control　method　based　on　Q-Learning　to　make　the　system　robust　and　adaptable,　which　is　dynamically　changed　the　size　of　the　domain　to　further　ensure　the　control　effect　of　the　vehicle.　The　variable-domain　fuzzy　PID　algorithm　based　on　Q-Learning　takes　the　error　and　the　error　rate　of　change　as　input　and　uses　the　Q-Learning　method　to　learn　the　scaling　factor　online　so　as　to　achieve　online　PID　parameters　adjustment.　The　proposed　method　is　verified　on　the　Panosim　simulation　platform.The　experiment　shows　that　the　accuracy　is　improved　by　15%　compared　with　the　traditional　fuzzy　PID,　which　reflects　the　effectiveness　of　the　algorithm.　©　2023　the　Author(s).