In　this　paper,　we　propose　a　vehicle　detection　method　based　on　AdaBoost.　We　focus　on　the　detection　of　front-view　car　and　bus　with　occlusions　on　highway.　Samples　with　different　occlusion　situations　are　selected　into　the　training　set.　By　using　basic　and　rotated　Haar-like　features　extracted　from　the　samples　in　the　set,　we　train　an　AdaBoost-based　cascade　vehicle　detector.　The　performance　tests　on　static　images　and　short　time　videos　show　that　(1)our　approach　detects　cars　more　effectively　than　buses　(2)the　real-time　detection　of　our　method　on　video　proceeds　at　30　frames　per　second.　©2009　IEEE.