This　paper　presented　a　novel　method　for　solid　lanes　extraction　from　Mobile　laser　scanning　(MLS)　point　clouds.　The　proposed　method　firstly　removed　the　off-ground　point　clouds　and　then　calculated　the　scanning　distance　between　the　points　of　road　surface　and　sensors.　Then,　the　reflective　intensity　data　of　road　surface　were　transformed　into　relative　values　to　overcome　the　influence　of　the　scanning　distance,　the　points＇　density,　abrasion　and　roughness　of　road　surface　block　by　block.　After　the　intensity　enhancement,　road　markings　were　separated　from　the　road　surface　based　on　the　k-means　clustering　and　connected　component.　In　order　to　deal　with　the　problem　of　under-segmentation　and　over-segmentation　caused　by　the　adhesion　of　solid　lines　and　stop　lines　or　other　entrance　markings,　some　features　of　geometric　shape　and　the　spatial　distribution　were　then　used　to　refine　the　results　of　intensity　segmentation　by　the　Normalized　Cuts.　Finally,　the　semantic　structure　information　of　road　markings　was　explored　to　separate　the　solid　lines　from　other　road　markings　like　zebra　crossings,　dashed　lines.　Experiments　were　undertaken　to　evaluate　the　validities　of　the　proposed　method　with　four　test　data　sets　acquired　from　different　MLS　systems.　Quantitative　evaluations　on　four　MLS　data　sets　indicated　that　the　proposed　method　achieved　a　Precision,　Recall　and　F1-Measure　of　95.98%,　91.87%　and　95.55%,　respectively,　which　validated　that　the　proposed　method　has　achieved　promising　performance.　©　2019,　Surveying　and　Mapping　Press.　All　right　reserved.