Automated road markings extraction from mobile laser scanning data

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Automated

road

markings

extraction

from

mobile

laser

scanning

data

Pankaj

Kumar

∗

,

Conor

P. McElhinney,

Paul

Lewis,

Timothy

McCarthy

NationalCentreforGeocomputation(NCG),NationalUniversityofIrelandMaynooth(NUIM),Maynooth,Co.Kildare,Ireland

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received18October2013 Accepted20March2014 Availableonline4May2014 Keywords:

Roadmarkings Mobilelaserscanning LiDAR

Automation Extraction

a

b

s

t

r

a

c

t

Roadmarkingsareusedtoprovideguidanceandinstructiontoroadusersforsafeandcomfortabledriving. Enablingrapid,cost-effectiveandcomprehensiveapproachestothemaintenanceofroutenetworkscan begreatlyimprovedwithdetailedinformationaboutlocation,dimensionandconditionofroadmarkings. MobileLaserScanning(MLS)systemsprovidenewopportunitiesintermsofcollectingandprocessingthis information.Laserscanningsystemsenablemultipleattributesoftheilluminatedtargettoberecorded includingintensitydata.Therecordedintensitydatacanbeusedtodistinguishtheroadmarkingsfrom otherroadsurfaceelementsduetotheirhigherretro-reﬂectiveproperty.Inthispaper,wepresentan automatedalgorithmforextractingroadmarkingsfromMLSdata.Wedescribearobustandautomated wayofapplyingarangedependentthresholdingfunctiontotheintensityvaluestoextractroadmarkings. Wemakenoveluseofbinarymorphologicaloperationsandgenericknowledgeofthedimensionsofroad markingstocompletetheirshapesandremoveotherroadsurfaceelementsintroducedthroughtheuse ofthresholding.Wepresentadetailedanalysisofthemostapplicablevaluesrequiredfortheinput parametersinvolvedinouralgorithm.Wetestedouralgorithmondifferentroadsectionsconsistingof multipledistincttypesofroadmarkings.Thesuccessfulextractionoftheseroadmarkingsdemonstrates theeffectivenessofouralgorithm.

1. Introduction

Roadusersafetymaybeaffectedbyexistenceandconditionof safetyinterventionsalongtheroutecorridor.Awelldesignedand maintainedroutecorridorassistsindriversafetyaswellasinthe efficientuseofoverallnetworkintermsofroutenavigation(ETSC, 1997).Roadmarkingsplayanimportantroleinreducingaccident frequencyandseverityastheyprovideguidanceandinstructionto theroadusersforsafeandcomfortabledriving.Theyareintended todirect trafficbyindicating thedirectionof travel,warnroad usersaboutspecificobstaclesorhazardsanddefinethe territo-riallimitfortrafficflows(Gattietal.,2007).Roadmarkingsare retro-reflectivesurfaceshavingan abilitytoreflect mostof the incidentlightbacktoitsoriginatingsource.Thesemarkingsretain theirvisibilitycriteriaindayandnight.Roadmarkingsmay dete-riorateduetointensiveuseorsufferfromreducedvisibilitydueto manyfactorssuchasocclusionsthatarisefromvegetationgrowth. Roadmarkingsarerequiredtobelocated,measured,classifiedand recordedinatimely,costeffectivemannerinordertoschedule maintenanceandensuremaximumsafetyconditionsforroadusers (Kumar,2012).

∗_{Corresponding}_author._Tel.:₊₃₅₃_17086180.

VarioussafetyschemesandstandardssuchastheRoadSafety Audit (RSA), Road Safety Inspection (RSI) and Network Safety Management (NSM) are implemented to qualitatively estimate potentialroadsafetyissuesalongtheroutecorridor.Theaimof theseroadsafetyassessmentmethodologiesistoidentifythe ele-mentsoftheroadthatmaypresentasafetyconcernandexplore thevariousopportunitiestoeliminateidentiﬁedsafetyconcerns (ETSC,1997).Theinformationcollectedthroughthesesurveysis sometimesincompleteandinsufﬁcientforqualitativeestimation of potential road safety issues. It can also be time consuming and expensive toconduct theseinspections ona largescale. A recentresearchcallhighlightedtherequirementforcommon eval-uationtoolsandimplementationstrategiesincarryingoutthese inspections and assessing risk along route corridors (Pecharda etal.,2009).OneresearchprojectEuropeanRoadSafety Inspec-tion(EuRSI)demonstratedthatterrestrialMobileMappingSystems (MMS)couldbeusedtocollectphysicalroutecorridorinformation forrapidsafetyanalysis(McCarthyandMcElhinney,2010).

Mobilemappingreferstoamethodologyofcollecting geospa-tialdatausingmappingandnavigationsensorsthataremounted rigidlyonboardamobileplatform(SchwarzandEl-sheimy,2007; Barberetal.,2008).Multisensorintegratedmappingtechnology hasenabledrapidandcosteffectiveacquisitionofgeoreferenced informationaboutroadnetworkenvironments(LiandChapman, http://dx.doi.org/10.1016/j.jag.2014.03.023

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126 P.Kumaretal./InternationalJournalofAppliedEarthObservationandGeoinformation32(2014)125–137 2008).Theirinitialdevelopmentwasprimarilydrivenbyadvances

indigitalcamerasandnavigationtechnologies.Later,laserscanning systemswereintegratedwithMMSswhichfacilitatedmore accu-rateanddensecollectionof3Dpointclouddata.Theapplicabilityof mobilelaserscanning(MLS)systemscontinuestoprovetheirworth inroutecorridormappingduetotheirrapid,continuousandcost effective3Ddataacquisitioncapability.MLSsystemsusuallyrecord theintensitydatawhichcanbeusedtodistinguishroadmarkings thatproducehighreflectivityduetotheirretro-reflectiveproperty. Knowledgeofthelocation,dimensionandconditionofroad mark-ingscanbeusefulforroadsafety,routenetworkmaintenanceand driverassistancesystems.InKumaretal.(2013),wepresentedan automatedalgorithmforextractingroadedgesfromMLSdata.The automatedroadedgeextractionalgorithmisappliedtoestimate roadboundariesfromLiDARdata.Theoutputroadboundariesare thenusedtoidentifytheLiDARpointsthatbelongtotheroad sur-face.Knowledgeoftheroadsurfaceareafacilitatesamoreefficient andaccurateextractionofroadmarkings.Inthis paper,wewill presentanautomatedalgorithmforextractingroadmarkingsfrom MLSdata.Thealgorithmisbasedonapplyingarangedependent thresholdingfunctiontotheintensityvaluestoextractroad mark-ingsfromMLSdata.InSection2,wereview variousapproaches developedforextractingroadmarkingsfromLiDARdata.In Sec-tion3,wepresentastepwisedescriptionofourautomatedroad markingextractionalgorithm.InSection4,wepresentour analy-sistofindthemostapplicablevaluesofinputparametersrequired toautomatetheroadmarkingextractionalgorithm.InSection5, wetestouralgorithmonvariousroadsections,demonstratingthe successfulextractionofdifferenttypesofroadmarkings.We dis-cusstheresultsfollowingvalidationoftheroadmarkingextraction processinSection6.Finally,weconcludethepaperinSection7.

2. Literaturereview

MLSsystemsenabletheacquisitionofanaccurately georeferen-cedsetofdenseLiDARpointclouddata.Becauseofthefundamental structureandintrinsicpropertiesofLiDARdata,itenablesmore efficientand accurateroad featureextraction approaches tobe explored.Apartfromfacilitating the collectionof 3Dpositional information,laserscanningsystemsrecordanumberofattributes includingintensity,pulsewidth,rangeandmultipleechowhich canbeusedforreliableand preciseextractionof different spa-tialobjects.Thepulsewidthfromthelaserscanningsystemrefers toarecordedtimedifferencebetweenhalfmaximumamplitudes of the pulse (Kumar, 2012). The pulse width attribute can be usedto classifyterrain objectsasits values varywith the sur-faceroughness(LinandMills,2010).Themethodsdevelopedfor segmentingLiDARdataaremostlybasedontheidentificationof planaror smooth surfacesand the classification ofpoint cloud databasedonitsattributes(Vosselman,2009).Inarelatedarea, severalmethodshave beendevelopedover thepastdecade for extractingurbanbuildingfeatures fromLiDARdata(Hammoudi etal.,2009;Rutzingeretal.,2009;Kabolizadeetal.,2010;Haala andKada,2010).Otherapproacheshavebeenbasedon extract-ingroadanditsenvironmentfromLiDARdata.Clodeetal.(2004)

and Huet al. (2004) segmentedAirborne LaserScanning (ALS) data into road and non-road based on elevation and intensity attributes,whileSamadzadeganetal.(2009)usedﬁrstecho,last echo,rangeandintensityattributestoclassifytheALSpointsinto roadobjects.MumtazandMooney(2009)usedALSelevationand intensityattributestoextractspatialinformationaboutbuildings, trees,roads,polesandwiresintheroutecorridorenvironment.Lam etal.(2010)extractedroadsthroughﬁttingaplaneto3Dterrestrial mobilepointclouddataandthenusedtheextractedinformation todistinguishlampposts,powerlinepostsand powerlinesby

employingcontextbasedconstraints.Puetal.(2011)segmented MLSdataintotrafﬁcsigns,poles,barriers,treesandbuildingwalls basedonspatialcharacteristicsofpointcloudsegmentslikesize, shape,orientation andtopologicalrelationships. Similarly,Zhou andVosselman(2012)usedelevationattribute,whileMcElhinney etal.(2010)andKumaretal.(2013)employedelevation,intensity andpulsewidthattributestoextractroadedgesinmultipleroute corridorenvironmentfromMLSdata.

LiDARdataprovidesanintensityattributewhichdependsupon therange,incidenceangleofthelaserbeamandsurface charac-teristics.Theintensityvaluesarerequiredtobenormalisedwith respecttothesefactorspriortothethresholdimplementationor theuseofarangedependentthresholdapproachisrecommended forsegmentation orfeatureextraction (Vosselman,2009).Most oftheapproachesusedfornormalisingtheirvaluesarebasedon usingmodelsanddatadrivenmethods(Pfeiferetal.,2008;Jutzi andGross,2009;Oh,2010)whileotherapproachesarebasedon usingexternalreferencetargetsofknownreflectivitybehaviour (Kaasalainenetal.,2009;Vainetal.,2009).Theintensityattribute canbeusedtodistinguishroadmarkingsfromtheroadsurface. Pre-ciseextractionofroadmarkingsfromLiDARdatahasdrawnlimited attentionfromtheresearchcommunity.Jaakkolaetal.(2008) esti-matedroadmarkingsbyfirstperformingaradiometriccorrection oftheLiDARintensitydatausingasecondordercurvefitting func-tion.Finally,roadmarkingswereestimatedbyapplyingathreshold andmorphologicalfilteringmethods.Tothetal.(2007,2008)used roadmarkingsasgroundcontrolforassessingthepositioning qual-ityofALSdata.Thesearchwindowforfindingtheroadmarkingsin theLiDARdatawasreducedbymakinguseoftheGlobal Position-ingSystem(GPS)surveydatacollectedoverthepavement.Theroad markingswereextractedbythresholdingtheLiDARintensity val-ues.Later,extractedroadmarkingswerecomparedwiththeGPS surveydatatoassessthequalityoftheLiDARpoints.Vosselman (2009)describedtheuseof arangedependentthresholdingfor extracting roadmarkings fromMLS data.The thresholdedroad markingpointsweregroupedusingconnectedcomponent anal-ysisandthenfulloutliningsoftheroadmarkingswereobtained byfittingpredefinedshapestothegroupedsegments.Chenetal. (2009)developedamethodforextractinglanemarkingsfromthe MLSdata.Theroadsurfacewasdetectedbydiscardingthenon-road pointsbasedonthestandarddeviationvaluesofLiDARelevation attributeandthencandidatelanemarkingpointswerelocalisedby applyingathresholdtotheintensityvaluesofroadsurfacepoints. Thelanemarkings wereclusteredbyapplyingtheHough trans-formto2Dbinaryimagegeneratedfromcandidatepointsandwere furtherrefinedusingtrajectoryandgeometrycheckconstraints.

Smadjaetal.(2010)developedanalgorithmforextractingroads fromMLSdatabasedonthedetectionofslopebreakpoints cou-pledwiththeRANdomSAmpleConsensus(RANSAC)algorithm.The estimatedroadinformationwasusedtoextractroadmarkingsby applyingathresholdapproachtotheLiDARintensitydata.Butler (2011)developedanautomatedapproachtoextractroad mark-ingsfromMLSsystem.Theroadmarkingpointswereextractedby thresholdingtheLiDARintensityattributeandthenoutputpoints were filtered based ontheir neighbourhood within a specified thresholddistance.Theroadmarkingpointswereclusteredand convexhullswerefittedtothem.Yangetal.(2012)describedan automatedapproachforextractingroadmarkingsfromMLSdata. Intheirapproach,2DimagewasgeneratedfromLiDARpointcloud dataandthenroadmarkingswerefilteredbyapplyingthreshold totheLiDARintensityandelevationvalues.Finally,theoutlines ofroadmarkingswereextractedbasedonprioriknowledgeofthe shapesandarrangementoftheroadmarkings.

Themajorityofmethodsdevelopedforextractingroadmarkings arebasedonapplyingathresholdtotheLiDARintensityvalues. Thedevelopmentofarobustthresholdapproach willprovidea

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Fig.1. Roadmarkingextractionalgorithm.

morepreciseextractionofroadmarkings.Thefactorsthataffect thevaluesofintensityattributearerequiredtobeaddressedprior totheiruseforroadmarkingsextraction.Athresholdappliedto theintensityvaluesoftenintroducessomeoftheotherroad sur-faceelements,whichneedstoberemoved.Aprioriknowledgeof theroadboundariesanditssurfacewillfacilitateamoreefﬁcient extractionofroadmarkings.Inthenextsection,wepresentour automatedroadmarkingextractionalgorithm.

3. Algorithm

InKumaretal.(2013),wepresentedanautomatedalgorithm forextractingroadedgesfromMLSdata.Weinputnnumberof 30m×10m×5mLiDARand nnumberof 10mnavigationdata sectionsintheroad edgeextraction algorithm. Thedimensions of input data sections were selected based on empirical tests astheyimpacttheprocessefﬁciencyinterms ofcomputational cost (Kumar, 2012;Kumar et al.,2013).The algorithm outputs road boundarywhich is used toidentifytheLiDARpoints that belongtotheroadsurface.Weapplyourautomatedroad mak-ing extraction algorithm to the estimated road surface LiDAR points which facilitates a more efﬁcient and accurate extrac-tion ofroad markings. Our automated roadmarking extraction algorithm is based on the assumption that the intensity val-uesofthelaserreturnsfromtheroadmarkings arehigherthan those fromother road surface elements. We expect to extract

theseroadmarkingsbyapplyingarangedependentthresholding totheintensityvalues.WeconverttheLiDARintensityandrange attributesinto2Drastersurfaceswhichallowustoapply mor-phologicaloperationstothem.Aworkﬂow oftheroadmarking extractionalgorithmisshowninFig.1.Inthefollowingsections, wedescribethevariousprocessingstepsinvolvedinouralgorithm.

3.1. Roadsurfaceestimation

WeinputtheLiDARdataandtheroadboundaryestimatedusing ourautomatedroadedgeextractionalgorithm. InStep1of our roadmarkingextractionalgorithm,weusetheroadboundaryto identifytheLiDARpointsthatbelongtotheroadsurface.Aroad boundaryislaidovertheLiDARdatasuchthatthepointsoutside theroadboundaryareremoved,whiletheinnerpointsareretained toestimatetheroadsurface.

3.2. 2Drastersurfacegeneration

WeuseLiDARintensityandrangeattributesinouralgorithm toextracttheroadmarkings.InStep2ofouralgorithm,we gen-erate2DintensityandrangerastersurfacesfromtheLiDARdata usingacellsize,cparameter.Anoptimalvalueofcparameteris selectedbasedonadetailedanalysisprovidedinSection4.1.The valueofeachcellintherastersurfaceisestimatedastheaverageof theintensityandrangevaluesoftheLiDARpointsthatfallwithin

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128 P.Kumaretal./InternationalJournalofAppliedEarthObservationandGeoinformation32(2014)125–137

Fig.2. Navigationdataisusedtoselectarangevaluetoapplymultiplethresholdvaluestotheintensityrastersurface.

the2Dboundaryofthecell.Theintensityandrangerastersurface valuesarenormalisedwithrespecttotheirglobalminimumand maximum,andconvertedintoan8-bitdatatype.Thiswillallow fortheuseofonesetofvaluesforallroadsections.

3.3. Rangedependentthresholding

Roadmarkingsaregenerallymorereflectivethantheroad sur-face.Thisresultsintheintensityvaluesofthelaserreturnsfromthe roadmarkingsbeinghigherthanthebackgroundsurface.However, apartfromthereflectivityoftheilluminatedsurface,therearetwo otherfactorsaffectingtheintensityvalues,therangeandthe inci-denceangleofthelaserbeam.Thesefactorsneedtobeaddressedin anyalgorithmextractingroadmarkings.InStep4ofouralgorithm, weapplyrangedependentthresholdingtotheintensityraster sur-face.Weusethenavigationdatatoselectarangevaluethatisused toapplymultiplethresholdvalues.InmostMLSsystems,thelaser scannerismountedonamobilevanatsomehorizontaland verti-calinclinedpositioninordertoproducerich3Dinformation.Letus supposethatalaserscannerismountedonthebackofthemobile vanatainclinationanglefromboththehorizontalandvertical axesofthevehicle.Thisinclinedpositionmodifiestheinitial scan-ningpointfromdirectlybelowthescannertotheposition,O,shown inFig.2.RistherangeofalaserbeamfromthenavigationpointN

totheinitialscanningpointO.Thetransverserangefromthepoint

NtoO isestimatedasRcos.Foreachlaserreturn,wereplace theirrangevaluewiththenewtransverserangevalueusingthe inclinationangle.Weusetheestimatedtransverserangevalue

Rcostodividetheintensityrastersurfaceintodifferentblocks whichallowsustothresholdtheintensityvaluesbasedontheir rangefromthescanner.

Weapplyadifferentthresholdvaluetoeachblockofthe inten-sityrastersurfacetodealwiththerangeandincidenceanglefactors thataffectintensityvalues.Theroadsurfaceisusuallyconstructed withanon-planarshape,showninFig.3.Thissurfaceisengineered toallowrainwater torunoff theroadsurfacetoreducewater poolingwhichcandamagetheroadsurfaceovertime.Thistypeof roadsurfacemightinﬂuencetheincidenceangleofthelaserbeam, resultinginachangeinintensityreturnsastherangeincreases.We applythethresholdvaluesTI1,TI2,TI3andTI4todataintheblocks B1,B2,B3andB4,respectively,toextractroadmarkings.Weselect

asingleoptimalvalueofthreshold,TIempiricallyanduseitto

esti-matethevaluesofTI1,TI2,TI3 andTI4.Adetailedanalysisonthe

selectionoftherangedependentthresholdvaluesisprovidedin Section4.2.

3.4. Morphologicaloperations

TheextractedroadmarkingsfromStep3maybeincompleteand containotherroadsurfaceelementsthatareintroducedthrough theuseofthresholding.Toovercomethis,wemakeanoveluseof binarymorphologicaloperations(HaralickandShapiro,1992)and prioriknowledgeofthedimensionsofroadmarkingsinStep4of ouralgorithm.Theirimplementationinvolvesthreeprocesses.In theﬁrstprocess,thethresholdedrastersurfaceisconvertedinto abinaryimageandthenweapplythedilationoperation tothe

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Fig.3.Sideviewofthenon-planarroadsurface.

binaryimageinwhichastructuringelementisplacedoverthecells oftheimage.Thepurposeofdilationistousethestructuring ele-menttogrowcellswithavalueof1inordertoﬁllinanyholes. Astructuringelementconsistsofabinarymatrixthatrepresents theselectedshapeandsize.Examplesofstructuringelementswith differentshapesandsizesareshowninFig.4.Acentralelement ofthematrixrepresentsanoriginandtheelementswithavalue of1describeaneighbourhoodofthestructuringelement.The ori-ginofthestructuringelementispositionedovereachcellinthe binaryrastersurfacetodilatethatcellalongtheneighbourhoodof thestructuringelement.

Alinearshapedstructuringelementisusedtodilatethecells ofeachroadmarking.Wechoosealinearshapeforthestructuring elementsduetothegenerallinearpatternsofroadmarkings.The linearshapedstructuringelementisusedwitha_angle_that_is

cal-culatedfromtheaverageheadingofthemobilevanalongtheroad sectionunderinvestigation.Thisangleisusedinordertodilatethe roadmarkingsalongthelongitudinaldirection.Aprocessof calcu-latingthe _angle_from_the_average_heading_angle_of_the_mobile

van,,isdescribedinFig.5.Theangleprovidesthedirectionof themobilevan’strajectorywithrespecttothenorthdirectionand the_angle_is_measured_in_the_{anticlockwise}_direction_from_the

horizontalaxis.Ifthevalueofangleliesinbetween0◦and90◦, thenthe_angle_is_estimated_as₉₀◦₋_in_the_{anticlockwise}

direc-tionasshowninFig.5(a).Similarly,the_angle_can_be_estimated

forotherpossiblevaluesoftheangleasshowninFig.5(b)–(d). Thel,lengthofthelinearshapedstructuringelementisselected empirically.We usethesamevalueineachroadsection,which allowsustoautomatethemorphologicaloperation inour algo-rithm.AnexampleoftheinputbinaryimageisshowninFig.6(a). Theinputbinaryimageisdilatedusingalinearshapedstructuring

elementwithl=9and₌_38.37◦_as_shown_in_Fig.₆_(b)._The_use_of

dilationoperationﬁllstheholesandcompletestheshapesofroad markingsintheinputbinaryimage.

Inthesecondprocess,wegroupcellsintoobjectsinthedilated imageusingconnectivity.Ifacellhasavalueof1thenitis con-nectedtothecellswhosevaluesare1andaredirectlyabove,below, leftorrightofthatcell.Wecalculatethelengthandaveragewidth valuesofeachobjectinthedilatedimage.Objectswhoselength andaveragewidthvaluesarelessthanlengththreshold,TL and

widththreshold,TWareconsideredasotherroadsurfaceelements

andareremovedfromtheimage.Anexampleofroadsurfacecells removedfromthedilatedimageisshowninFig.6(c).

Inthethirdprocess,weapplyanerosionoperationtothedilated imageinordertoretaintheoriginalboundaryshapeoftheroad markings.Inanerosionoperation,cellsareremovedfromtheroad markingcellsusingastructuringelement.Thelinearshaped struc-turingelementusedfordilationisalsoappliedtoerodetheroad markings.Anexampleofthedilatedroadmarkingserodedusing thelinear shaped structuringelement withl=9 and ₌_38.37◦

angleisshowninFig.6(d).Inthisway,thecombineduseof morpho-logicaloperationsandprioriknowledgeofthedimensionsofroad markingsisabletocompletetheirshapesandtoremoveotherroad surfaceelements.

3.5. 3Droadmarkings

InStep5ofouralgorithm,weextractthe3Droadmarkingsusing the2Doutput.Theoriginal3DLiDARpointswhicharecontained withinthe2Droadmarkingcellboundariesareextracted.Inthe nextsection,wepresentouranalysisoftheinputparametersused toautomateourroadmarkingextractionalgorithm.

Fig.4.Structuringelements:(a)diamondshapedwithradius,r=1,(b)linearshapedwithlength,l=3andangle,₌₄₅◦_and_(c)_linear_shaped_with_length,_l₌₅_and_angle,

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Fig.5.Estimationof_angle_of_the_linear_shaped_structuring_element_from_the_average_heading_angle_of_the_mobile_van,_,_along_the_road_section_that_can_lie_in_between_(a) 0◦_and₉₀◦_,_(b)₉₀◦_and₁₈₀◦_,_(c)₁₈₀◦_and₂₇₀◦_and_(d)₂₇₀◦_and₃₆₀◦_.

4. Parameteranalysis

Our road marking extraction algorithm requires two input parameters,thecellsizevalueforconvertingtheLiDARdatainto 2Drastersurfacesandtherangedependentthresholdvalue.To implementanautomatedalgorithm,weneedthemost applica-ble value for each of these. In the following sections, we will

detailourrecommendedvaluesandshowtheeffectofchanging these.

4.1. Optimalcellsizeparameter

WeconverttheLiDARdatainto2Drastersurfacesasdescribedin Section3.2.Eachcellintherastersurfacehasaphysicaldimension.

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Fig.7. Roadmarkingsextractedwith(a)0.01m2_,_(b)_0.04_m2_,_(c)_0.06_m2_,_(d)_0.08_m2_and_(e)_0.1_m2_cell_size_in_the_ﬁve_test_cases_of_the_optimal_cell_size_analysis.

Thecellsizeinﬂuencestheaverageintensityandrangevaluesof thecellwhichinturnaffectstheoutputresultintermsofaccuracy and computationalcost.Our roadmarkingextraction algorithm isprimarilybasedonrangedependentthresholdingwhichisnot computationallyexpensive.Therefore,weconsideredaccuracyas themaincriteriaforcellsizeselection.Toﬁnditsoptimalvalue, weanalysedtheperformanceofourroadmarkingextraction algo-rithmin raster surfacesgenerated withdifferentcell sizes. We selectedone10msectionofruralroadwhichcontainedbroken andcontinuouslinemarkingsoveritssurface.Toprocessthisroad section,weusedn=1numberof30m×10m×5msectionofLiDAR dataandn=1numberof10msectionofnavigationdata.Thedata werecollectedusingtheeXperimentalPlatform(XP-1)MMSwhich hasbeendesignedanddevelopedatNationalUniversityofIreland Maynooth(Kumaretal.,2010,2011).

Theautomatedroadedgeextractionalgorithmwasappliedto the selected road section toobtain output road boundary. We thenappliedourautomatedroadmarkingextractionalgorithmby consideringﬁvetestcasesinwhichrastersurfacesweregenerated fromtheLiDARintensityandrangeattributeswithdifferentcell sizes.Theparametersusedinthealgorithmintheﬁvetestcases areshowninTable1.Thecellsizeswereselectedwithdecreasing andincreasingvaluesbasedonanaveragepointspacingof0.08m2

intheLiDARpointswhilethevalueoflparameterwaschosenin accordancewiththerespectivecellsizeselectedineachcase.The

_angle_was_calculated_as₉₀◦₋_while_the_values_of_T_L _and_T_W

wereselectedbasedonminimumpossible0.5mlengthand0.1m

Table1

Theparametersusedintheﬁvetestcasesoftheoptimalcellsizeparameteranalysis.

Testcase c(m2₎ _T I l (◦) TL(m) TW(m) 1 0.01 70 51 24.23 1 0.1 2 0.04 70 13 24.23 1 0.1 3 0.06 70 9 24.23 1.02 0.1 4 0.08 70 7 24.23 1.04 0.1 5 0.1 70 5 24.23 0.9 0.1

widthoftheroadmarkingsusedintherealworldroad environ-ment(Transport,2010).ThevalueofTIwasmodiﬁedasafunction ofrangeandappliedtofourblocksoftheintensityrastersurface. WeusedasimilarvalueofTWineachtestcaseasthecellswerenot dilatedalongthetransversedirection.Theroadmarkingsextracted intheﬁvetestcasesareshowninFig.7.

Inordertocarryoutacomparativeanalysis,wecalculatedthe lengthandaveragewidthofthefinalextractedroadmarkingsin thefivecases.We consideredfourbrokenlinemarkings named asD1,D2,D3andD4andonecontinuouslinemarkingnamedasC, showninFig.7.Thecalculatedlength,Landaveragewidth,Wvalues arelistedinTable2.Wefoundastandardlengthandwidthofthe fiveroadmarkingsfromthetrafficsignsmanual(Transport,2010). WecomparedtheextractedLandWvaluesoftheroadmarkings withtheexpectedstandarddimensions.Forcellsizes0.08m2_and

0.1m2_,_the_W_values_of_the_road_markings_were_generally_found_to

bemorethantheirstandardvalues.Forthe0.06m2_cell_size,_the_L

Table2

Lengthandaveragewidthvaluesoftheextractedroadmarkingsintheﬁvetestcasesoftheoptimalcellsizeanalysis.

Roadmarkings(standardL×W) 0.01m2 _0.04_m2 _0.06_m2 _0.08_m2 _0.1_m2

L W L W L W L W L W D1(2m×0.15m) 1.75m 0.12m 1.68m 0.12m 1.68m 0.12m 1.68m 0.16m 1.70m 0.13m D2(2m×0.15m) 1.84m 0.07m 1.84m 0.07m 1.80m 0.08m 1.84m 0.08m 1.80m 0.07m D3(2m×0.15m) 1.71m 0.002m 1.80m 0.10m 1.80m 0.13m 1.84m 0.17m 1.80m 0.20m D4(2m×0.15m) 1.78m 0.002m 1.80m 0.08m 1.80m 0.14m 1.84m 0.18m 1.80m 0.16m C(8.6m×0.15m) 7.80m 0.034m 7.84m 0.145m 7.86m 0.141m 7.92m 0.17m 7.90m 0.16m

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Fig.8. Rangedependentthresholdingappliedtotheintensityrastersurfaceintheroadsectionwith(a)narrowerand(b)greaterwidth.

andWvaluesoftheroadmarkingswereclosesttotheirstandard values.Thus,weselected0.06m2_cell_size_as_the_most_applicable

valuetogenerate2Dintensityandrangerastersurfacesfromthe LiDARdata.

4.2. Rangedependentthresholdparameter

Afterselectingtheoptimalcellsize,theﬁnalparameter requir-ingselection istherange dependentthreshold.Theaimof this analysiswastodetermineamethodforautomaticallyselecting thisthresholdvalueirrespectiveoftheroaddimension.Weuse thenavigationdatatoselecttherangevaluethatisusedtodivide theintensityrastersurfaceintodifferentblocks.Inaroadsection witha narrowerwidth,theintensityrastersurface wasdivided intofourblocks,showninFig.8(a).Inaroadsectionwithagreater width,theintensityrastersurfacewasdividedintosevenblocks, shown inFig.8(b). We selecteda singlethreshold valueTI=70 empiricallyandmodiﬁeditasafunctionoftherange.Weapplieda

TI+mathresholdtoeachblockoftheintensityrastersurface,where a=10andmrepresentsablocknumber.WeappliedtheTI+ma−nb

thresholdtotheblocksafterthecentreoftheroad,whereb=5and

n=1,2,4,8,...representinganintegerforthethird,fourth,ﬁfth, sixth,etc.blocks,respectively.Thethresholdvaluesappliedbefore thecentreoftheroadwereconsecutivelyincreasedwiththema

term,astheroadsurfaceinthoseblocksbeginstoorienttowards thelaserscanner.Thethresholdvaluesappliedafterthecentreof

Table3

Theparametersusedintheroadmarkingextractionalgorithm.

Parameter Value c 0.06m2 TI 70 l 9 TL 1.02m TW 0.1m

theroadwereincreasedwiththetermmaandthendecreasedwith thenbtermtoaccountfortheincreasedrangeandthechangein surfaceorientation.Thepurposeofﬁndingthesevariableswasto automatetheprocessofapplyingtherangedependent threshold-ingtotheintensityrastersurface.

Todemonstratetheimportanceoftheempiricallyselectedvalue ofTI,weapplieditslower,optimalandhighervaluetotheintensity rastersurfaceas55,70and90.Eachvaluewasmodiﬁedasa func-tionoftherangeinaccordancewiththeaforementionedformulae andusedtoextracttheroadmarkings,showninFig.9(a)–(c).The useofalowerthresholdvalueledtotheextractionofroad mark-ingswithlargeareasofotherroadsurfaceelements,whileitshigher valueremovedthoseelementsattheexpenseofextractingallthe roadmarkings.Thus,theselectionofanoptimalthresholdvalue isessentialfortherobustextractionofroadmarkings.Inthenext section,wetestourautomatedroadmarkingextractionalgorithm onvariousroadsections.

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Fig.10.Digitalimageof(a)ﬁrst,(b)second,(c)third,(d)fourth,(e)ﬁfthand(f)sixthroadsection(geographiclocations:(a)53◦₃₄_28.07_N₇◦₁₀_13.76_W,_(b)₅₃◦₃₆_33.43_N 7◦₅_46.96_W,_(c)₅₃◦₃₄_50.546_N₇◦₈_57.62_W,_(d)₅₃◦₃₉_19.225_N₇◦₃₁_9.792_W,_(e)₅₃◦₃₃_49.878_N₇◦₂₁_24.148_W_and_(f)₅₃◦₃₅_30.558_N₇◦₂₂_28.428_W).

5. Experimentation

Weselectedsixsectionsofroadtotestourroadmarking extrac-tionalgorithm.Thesesixsectionscovered140mofrural,urban andnationalprimaryroads,whichcontainedsixdistincttypesof roadmarkingstodemonstratetheeffectivenessofouralgorithm. TheprocesseddatawerecollectedwiththeXP-1MMSalongthese roadsections.Theﬁrst50msectionofruralroadandsecond50m sectionofurbanroadconsistedofbrokenandcontinuousline mark-ings,showninFig.10(a)and(b).Thethird10msectionofruralroad consistedofword,brokenand continuouslinemarkings,fourth 10msectionofurbanroadconsistedofzig-zagmarkings,ﬁfth10m sectionof nationalprimaryroadconsisted ofhatchand broken linemarkings whilesixth10msectionofnationalprimaryroad consistedof arrowand broken linemarkings ascan beseenin

Fig.10(c)–(f),respectively.Toprocesseach50mroadsection,we usedn=6numberof30m×10m×5msectionsofLiDARdataand

n=6numberof10msectionofnavigationdataandtoprocesseach 10mroadsection,weusedn=1numberof30m×10m×5m sec-tionsofLiDARdataandn=1numberof10msectionofnavigation data.

Weappliedourautomatedroadmarkingextractionalgorithm toeachroadsectionusingparametersasshowninTable3.The

anglewascalculatedas90◦−ineachnavigationsectionoftheﬁrst andsecondroadsectionswhile180◦−+270◦ineachnavigation sectionofthethird,fourth,ﬁfthandsixthroadsections.Thevalueof

angleineachnavigationsectionoftheroadsectionsareshownin

Table4.TheoriginalLiDARdataandextractedroadmarkingsofthe

Table5

Validationofextractedroadmarkingtestresults.

Roadmarkings Manualinspection Extractedresult

Objects Points Objects Points

Continuousline 3 5280 3 5007 Brokenline 71 8953 64 7718 Words 8 948 8 786 Zig-zag 3 1816 2 1299 Hatch 1 1077 1 1065 Arrow 2 1076 2 1060 Total 88 19150 80 16935

sixroadsectionsareshowninFigs.11–13.Inthenextsection,we validatetheroadmarkingexperimentalresultsanddiscussthem.

6. Results&discussion

We validated the extracted road marking results through a quantitativeandqualitativeassessmentbasedonmanual inspec-tion.Ourerrorassessmentwasfocusedonthenumberandshape detection of the extracted road marking objects. We manually counteda numberofroadmarkingobjectsandanumberof3D LiDARpointsineachobjectasshowninTable5.Wefound88 num-berofroadmarkingobjectsand19150numberofroadmarking pointsin thetestedroadsections. Ourroad markingextraction algorithmwasabletocorrectlyextractatotal of80road mark-ingsbutfailedtodetect8roadmarkings.Similarly,thealgorithm extracted16935numberofroadmarkingpointswhile7458points Table4

Averageheadingangle,,ineachnavigationsectionofthesixroadsections.

Navigationsection Firstroadsection Secondroadsection Thirdroadsection Fourthroadsection Fifthroadsection Sixthroadsection

1 65.59◦ _51.90◦ _106.48◦ _137.07◦ _147.56◦ _153.65◦ 2 65.77◦ _51.59◦ 3 65.97◦ _51.62◦ 4 66.24◦ 51.64◦ 5 66.46◦ 51.34◦ 6 66.85◦ 50.08◦

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Fig.11.3D(a)LiDARdataand(b)extractedbrokenandcontinuouslinemarkingsofthe50mﬁrstroadsection&3D(a)LiDARdataand(b)extractedbrokenandcontinuous linemarkingsofthe50msecondroadsection.

Fig.12.3D(a)LiDARdataand(b)extractedword,brokenandcontinuouslinemarkingsofthe10mthirdroadsection&3D(a)LiDARdataand(b)extractedzig-zagmarkings ofthe10mfourthroadsection.

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Fig.13.3D(a)LiDARdataand(b)extractedhatchandbrokenlinemarkingsofthe10mﬁfthroadsection&3D(a)LiDARdataand(b)extractedarrowandbrokenline markingsofthe10msixthroadsection.

Fig.14.Theroadboundariesextractedusingautomatedroadedgeextractionalgorithminthe(a)third,(b)ﬁfthand(c)sixthroadsectionarerepresentedwithredwhile thenavigationpointsarerepresentedwithyellow.(Forinterpretationofthereferencestocolorinthisﬁgurelegend,thereaderisreferredtothewebversionofthearticle.)

weremissed.Thus,wewereabletodetect90.91%oftheroad mark-ingobjectsand88.43%oftheroadmarkingpoints.Weidentiﬁed10 groupsofLiDARpointswhichwereincorrectlylabelledasextracted roadmarkingsonthebasisoftheirlengthandwidth.

Inthefirstandsecondroadsections,ouralgorithmwasableto extractthebrokenandcontinuouslinemarkings.Somebrokenline markingsalongtheleftsideofbothroadsectionswerenotdetected duetolowerintensityvaluesofthelaserreturnfromthem.The extractedmarkingsinthefirstroadsectioncontainedsomesurface elementsalongtheleftedge,asseeninFig.11(a).Thiswas pri-marilyduetotheroadboundariesextractedusingourautomated roadedgeextractionalgorithmwhichwereextendedincorrectly intoagrassandsoilarea.Theroadboundariesextractedinthefirst 50mruralandthesecond50murbanroadsectioncanbereferred inKumaretal.(2013).TheLiDARpointsbelongingtograssand soilsurfaceinthefirstroadsectionproducedhighintensityvalues whichwerenotremovedbyourroadmarkingextractionalgorithm duetotheirlargephysicaldimension.Thebrokenmarkingsthat werenotdetectedalongtherightsideofthesecondroadsection

wereattributedtoalowerpointdensityoftheLiDARdataalong therightside.Thiswasdue totheuseofa singlelaser scanner intheXP-1systemduringthedataacquisitionprocesswhichled totheacquisitionofLiDARdatawithalowerpointdensityalong therightsideoftheroadsectionthanalongitsleftside(Cahalane etal.,2011,2012).Theuseofmorethanonelaserscannerora doublepassapproachinwhichthevehicleisdrivenbackandforth ontheroadcanbeemployedtoacquireuniformanddensepoint cloudalongboth sidesoftheroad section(Kumaretal.,2013). Ourautomatedroadmarkingextractionalgorithmwillprovidean improvedextractionofroadmarkingsusingsuchLiDARdata.

Inthethirdroadsection,thealgorithmsuccessfullyextracted themajorityoftheword,brokenandcontinuouslinemarkings. The extracted road markings contained some surface elements alongtherightedge.Thiswasduetotheuseofroadboundaries, extendingincorrectlyintograssandsoilareaatsomepoints,as showninFig.14(a).Someoftheextractedwordsalongtheright side were incomplete which was due to a lower LiDAR point density along that side of the road section.In thefourth road

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section,ouralgorithmwasabletoextracttwozig-zagmarkings butfailedtodetectthethirdonealongtherightsideoftheroad section.ThiswasagainduetothelowerpointdensityoftheLiDAR dataalongtherightsideoftheroadsection.

Ouralgorithmwasabletoextractthehatchandbrokenline markingsintheﬁfthroadsection.Surfaceelementspresentalong theleftside of theroadsectionwas duetothe extractedroad boundariesextendingincorrectlyintoagrassandsoilarea,ascan beseeninFig.14(b).Inthesixthroadsection,theroadmarking extractionalgorithmwasabletoextractthearrowandbrokenline markings.Thebrokenlinemarkingsalongtherightsideoftheroad sectionwerenotextractedduetoalowerpointdensityoftheLiDAR dataalongthatside.Surfaceelementsintheformofacontinuous linealongtheleftsideoftheroadsectionwasduetotheextracted roadboundariesextendingintothegrassandsoilarea,ascanbe seeninFig.14(c).Inthenextsection,weconcludetheresearch workpresentedinthispaper.

7. Conclusion

Wepresentedanautomatedalgorithmforextractingroad mark-ings from MLS data. The presented algorithm is based on the assumptionthattheLiDARintensityreturnvaluesfromtheroad markingsarehigherthanthosefromotherroadsurfaceelements. Thesuccessfulextractionofdistinctroadmarkingsfromthe multi-pletestedroadsectionsvalidatesourautomatedalgorithm.These researchﬁndingscouldcontributetoamorerapid,cost-effective andcomprehensiveapproachtothemaintenanceofroadnetworks andensuremaximumsafetyconditionsforroadusers.

We developed a range dependent thresholding function to extracttheroadmarkingsfromtheintensityattribute.Weselect asingleoptimalthresholdvalueanduseittoautomatically esti-matethemultiplerangedependentthresholdvalues.Theuseof propernormalisedvaluesofintensityattributewithrespecttothe rangeandincidenceangleofthelaserbeamwillallowustoselect asingleandrobustthresholdvalueforextractingroadmarkings. Thiswillremove therequirement ofmultiple rangedependent thresholdvaluesinouralgorithm.We madenoveluseofpriori knowledgeofthedimensionsofroadmarkingsandbinary mor-phological operationsin order tocomplete theirshapesand to removeotherroadsurfaceelementsintroducedthroughtheuseof thresholding.Themorphologicaloperationsareappliedusingthe linearshapedstructuringelements.Furtherresearchisrequiredto investigatealternativestructuringelementswhichcouldbe use-fulinremovingsurfaceelementsintroducedthroughtheuseof extractedroadboundaries,extendingincorrectlyintothegrassand soilarea.Theoutputofourroadmarkingextractionalgorithmisa setofLiDARpointsrepresentingroadmarkingobjects.Theshape, dimensionandpositionontheroadoftheseobjectscanbe exam-inedtoconstructmoreeffectivealgorithmsthatcouldrecognise andclassifyeachroadmarkingtype.

Acknowledgements

Research presented in this paper was funded by the Irish ResearchCouncil(IRC)EnterprisePartnershipscheme,Pavement ManagementServices(PMS)Ltd.,StrategicResearchClusterof Sci-enceFoundationIreland(SFI)undertheNationalDevelopmentPlan andNationalRoadAuthority(NRA)researchfellowshipprogram. Theauthorsgratefullyacknowledgethissupport.

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