This　article　comparatively　analyzed　the　performance　of　K-nearest　neighbor,　support　vector　machine,　and　artificial　neural　network　classifiers　for　driver　drowsiness　detection　with　different　road　geometries　(straight　segments　and　curve　segments)　based　on　a　driving　simulator.　First,　vehicle　performance　measures　(speed,　acceleration,　brake　pedal,　gas　pedal,　steering　angle,　and　lateral　position)　were　collected　through　sensors.　These　measures　were　analyzed,　and　their　correlation　with　drowsiness　on　different　road　segments　was　examined.　The　analysis　was　based　on　data　obtained　from　a　study　that　involved　22　subjects　in　the　driving　simulator　located　in　the　Traffic　Research　Center,　Beijing　University　of　Technology.　Second,　six　classifiers　were　constructed　for　six　curve　segments,　respectively,　while　only　one　classifier　was　constructed　for　all　straight　segments　because　the　waveforms　by　subtracting　the　road　curvature　from　the　steering　angle　in　the　curve　segments　were　different　from　the　waveforms　of　the　straight　segments.　Furthermore,　the　less　the　radius　of　curvature,　the　more　the　difference.　Third,　the　performance　of　K-nearest　neighbor,　support　vector　machine,　and　artificial　neural　network　classifiers　were　compared　and　evaluated.　The　experimental　results　illustrate　that　the　support　vector　machine　classifier　achieved　the　fastest　classification　time　and　the　highest　accuracy　(80.84%).　Support　vector　machine　and　artificial　neural　network　are　effective　classification　methods　for　detecting　drowsy　driving　on　different　road　segments.