To　obtain　the　information　of　drivable　area　and　obstacles　for　a　driverless　car,　unique　characteristics　of　the　road　edge　data　points　were　summarized　and　concluded　by　analyzing　a　large　number　of　scanning　lidar　data,　and　a　road　edge　detection　algorithm　was　proposed　based　on　the　features　of　road　edge　data　points　and　multi-layer　fusion　technology.　DSmT　was　applied　to　establish　a　grid　map　for　the　road　environment　in　front　of　the　unmanned　vehicle.　The　DSmT　conflict　coefficient　was　used　to　detect　dynamic　obstacles.　Finally,　the　clustering　and　information　extraction　of　dynamic　obstacles　was　completed　by　the　expansion　algorithm,　erosion　algorithm,　and　the　improved　eight　neighborhood　labeling　algorithm.　Results　show　that　the　algorithm　can　stably　and　accurately　perceive　the　environment　information　around　driverless　vehicle.　©,　2014,　Beijing　University　of　Technology.　All　right　reserved.