A　method　for　drunk　driving　detection　using　Feature　Selection　based　on　the　Random　Forest　was　proposed.　First,　driving　behavior　data　were　collected　using　a　driving　simulator　at　Beijing　University　of　Technology.　Second,　the　features　were　selected　according　to　the　Feature　Importance　in　the　random　forest.　Third,　a　dummy　variable　was　introduced　to　encode　the　geometric　characteristics　of　different　roads　so　that　drunk　driving　under　different　road　conditions　can　be　detected　with　the　same　classifier　based　on　the　random　forest.　Finally,　the　linear　discriminant　analysis,　support　vector　machine,　and　AdaBoost　classifiers　were　used　and　compared　with　the　random　forest.　The　accuracy,　F1　score,　receiver　operating　characteristic　curve,　and　area　under　the　curve　value　were　used　to　evaluate　the　performance　of　the　classifiers.　The　results　show　that　Accelerator　Depth,　Speed,　Distance　to　the　Center　of　the　Lane,　Acceleration,　Engine　Revolution,　Brake　Depth,　and　Steering　Angle　have　important　influences　on　identifying　the　drivers＇　states　and　can　be　used　to　detect　drunk　driving.　Specifically,　the　classifiers　with　Accelerator　Depth　outperformed　the　other　classifiers　without　Accelerator　Depth.　This　means　that　Accelerator　Depth　is　an　important　feature.　Both　the　AdaBoost　and　random　forest　classifiers　have　an　accuracy　of　81.48%,　which　verified　the　effectiveness　of　the　proposed　method.