Introduction:　This　paper　addresses　the　problem　of　detecting　drunk　driving　based　on　classification　of　multivariate　time　series.　Methods:　First,　driving　performance　measures　were　collected　from　a　test　in　a　driving　simulator　located　in　the　Traffic　Research　Center,　Beijing　University　of　Technology.　Lateral　position　and　steering　angle　were　used　to　detect　drunk　driving.　Second,　multivariate　time　series　analysis　was　performed　to　extract　the　features.　A　piece-wise　linear　representation　was　used　to　represent　multivariate　time　series.　A　bottom-up　algorithm　was　then　employed　to　separate　multivariate　time　series.　The　slope　and　time　interval　of　each　segment　were　extracted　as　the　features　for　classification.　Third,　a　support　vector　machine　classifier　was　used　to　classify　driver＇s　state　into　two　classes　(normal　or　drunk)　according　to　the　extracted　features.　Results:　The　proposed　approach　achieved　an　accuracy　of　80.0%.　Conclusions　and　practical　applications:　Drunk　driving　detection　based　on　the　analysis　of　multivariate　time　series　is　feasible　and　effective.　The　approach　has　implications　for　drunk　driving　detection.　(C)　2015　National　Safety　Council　and　Elsevier　Ltd.　All　rights　reserved.