In　the　engineering　control　practice　of　High-Speed　Train　(HST),　the　traditional　automatic　driving　method　increases　the　energy　consumption　and　impairs　the　intelligence　of　train　operation.　Different　from　previous　studies,　we　propose　the　intelligent　driving　methods　(IDMs),　including　expert　knowledge　system　and　online　optimization　algorithms,　to　achieve　the　multi-objective　(safety,　punctuality,　energy　efficient,　passengers’　riding　comfort,　and　so　on)　control　of　HST.　First,　we　establish　the　expert　knowledge　system　based　on　the　driving　data　and　control　rules　of　excellent　drivers.　Then,　in　order　to　enhance　the　adaptability　and　real-time　performance　of　proposed　IDMs,　two　online　optimization　algorithms,　including　exact　online　programming　driving　(EOPD)　and　inexact　online　programming　driving　(IOPD),　are　developed　by　improved　gradient　descent　and　stochastic　meta-decent　method　to　update　the　controller＇s　output　online.　Finally,　using　the　field　data　collected　from　Beijing-Shanghai　High-Speed　Railway,　the　proposed　IDMs　are　verified　under　the　real　speed-limit　conditions.　The　simulation　results　show　that　EOPD　and　IOPD　can　achieve　better　performances　than　automatic　driving　method　based　on　ATO,　Fuzzy　PID　controller　and　traditional　multi-objective　optimization　method,　especially　in　passengers’　riding　comfort　and　energy-consumption.　Furthermore,　as　the　step　size　is　selected　with　wide　randomness　in　the　updating　process,　IOPD　has　more　operating　mode　switching　times　than　EOPD　but　its　punctuality　is　better.　©　2017　Elsevier　Ltd