交通管理車(chē)輛檢測(cè)技術(shù)英文翻譯Abstract1.IntroductionTheglobalpopulationlivingincitiesisexpectedtoreach68%by2050,accordingtotheUnitedNations.Thisrapidurbanizationhasledtoasurgeinmotorvehicleownership,resultinginseveretrafficcongestion,frequentaccidents,andincreasedairpollution.Forexample,trafficcongestioncoststheUnitedStatesapproximately$124billionannuallyinlostproductivityandfuel,asreportedbytheTexasA&MTransportationInstitute.Toaddresstheseissues,IntelligentTransportationSystems(ITS)haveemergedasakeysolution,andvehicledetectiontechnologyisthecoreofITS.Thisarticleaimsto:(1)systematicallyreviewtheprinciplesandcharacteristicsofvariousvehicledetectiontechnologies;(2)analyzetheirapplicationsintrafficmanagement;(3)discusscurrentchallengesandfuturedirections.2.MainstreamVehicleDetectionTechnologies2.1Sensor-BasedTechnologies2.1.1RadarRadarworksbytransmittingelectromagneticwavesandreceivingthereflectedsignalsfromvehicles.TheDopplereffectisusedtocalculatethevehicle’sspeed,whilethetimedelaybetweentransmissionandreceptionisusedtodeterminethedistance.Radarcanbedividedintomillimeter-wave(mmWave)radar(24–77GHz)andultrawideband(UWB)radar(3.1–10.6GHz).Advantages:不受光線andweatherconditions(e.g.,rain,fog,night)影響;longdetectionrange(uptoseveralhundredmeters);highaccuracyinmeasuringspeedanddistance.Limitations:Lowresolution(difficulttodistinguishsmallobjectsormultiplevehicles);vulnerabletointerferencefromotherelectromagneticdevices.ApplicableScenarios:Trafficflowmonitoringonhighways;speeddetection;collisionwarning.2.1.2LidarLidaremitslaserpulsesandmeasuresthetimeittakesforthepulsestoreflectoffavehicletocalculatedistance.Itgeneratesa3Dpointcloudoftheenvironment,whichcanbeusedtodeterminethevehicle’sposition,size,andshape.Lidarisclassifiedintomechanicallidar(withrotatingparts)andsolid-statelidar(withoutrotatingparts).Advantages:Highresolution(candistinguishpedestrians,cyclists,andvehicles);accurate3Dpositioning;unaffectedbyambientlight.Limitations:Highcost(mechanicallidar);shortrangeinadverseweather(e.g.,heavyrain,fog);susceptibilitytodirtanddebrisonthelens.ApplicableScenarios:Advanceddriverassistancesystems(ADAS);autonomousvehicles;high-precisiontrafficmonitoring.2.1.3UltrasonicSensorsUltrasonicsensorsemithigh-frequencysoundwaves(20–100kHz)anddetectthereflectedwavesfromvehicles.Thetimedelayisusedtocalculatethedistance.Advantages:Lowcost;easytoinstall;suitableforshort-rangedetection(upto5meters).Limitations:Shortrange;affectedbytemperatureandhumidity;unabletodetectfast-movingvehicles.ApplicableScenarios:Parkingassistance;low-speedtrafficmonitoringinparkinglots.2.1.4InductiveLoopsInductiveloopsareembeddedintheroadsurfaceanddetectvehiclesbymeasuringchangesintheelectromagneticfieldcausedbythevehicle’smetalbody.Advantages:Highreliability;lowmaintenancecost;suitableforhigh-trafficareas.Limitations:Destructiveinstallation(requirescuttingtheroad);unabletodetectnon-metalvehicles;difficulttodistinguishmultiplevehicles.ApplicableScenarios:Trafficflowmonitoringatintersections;tollcollection.BackgroundSubtraction:Separatesforeground(vehicles)frombackground(road)bymodelingthebackground.FeatureExtraction:UsesfeaturessuchasHistogramofOrientedGradients(HOG)orScale-InvariantFeatureTransform(SIFT)torepresentvehicles,followedbyclassificationusingSupportVectorMachines(SVM)orAdaboost.Limitations:Pooradaptabilitytoenvironmentalchanges(e.g.,light,weather);difficultyindetectingoccludedorsmallvehicles.2.2.2DeepLearning-BasedMethodsYOLO(YouOnlyLookOnce):Real-timedetectionwithhighspeed(upto100FPS)butslightlyloweraccuracy.FasterR-CNN:Highaccuracy(upto90%)butslowerspeed(around5FPS).EfficientDet:Balancesspeedandaccuracyusingaweightedbi-directionalfeaturepyramidnetwork(BiFPN).ApplicableScenarios:Traffic違法monitoring(e.g.,闖紅燈,逆行);vehicleclassification;pedestriandetection.2.3.1RFIDRFIDusesradiofrequencysignalstoidentifyvehiclesequippedwithRFIDtags.Thesystemconsistsoftags(attachedtovehicles),readers(installedonroadsorgantries),andabackenddatabase.Advantages:Highrecognitionspeed;unaffectedbylightandweather;suitableforstaticorlow-speedvehicles.Limitations:Shortrange(upto10metersforpassivetags);highcostforactivetags;requiresvehiclestobeequippedwithtags.ApplicableScenarios:Parkingmanagement(e.g.,automaticentry/exit);tollcollection;fleetmanagement.2.3.2V2XAdvantages:Real-timedataexchange;providespredictiveinformation(e.g.,vehicleintent);supportsvehicle-to-infrastructure協(xié)同.Limitations:Highdeploymentcost;requireswidespreadadoptionofon-boardunits(OBUs)androadsideunits(RSUs);dataprivacyconcerns.ApplicableScenarios:Intelligentsignalcontrol;collisionwarning;platooning.2.4Multi-SourceFusionTechnologiesApplicableScenarios:Autonomousdriving;high-precisiontrafficmonitoring;severeweatherconditions.3.ApplicationsofVehicleDetectionTechnologiesinTrafficManagementVehicledetectiontechnologiesarewidelyusedintrafficmanagementtoaddressvariouschallenges.Belowarethekeyapplicationscenarios:3.1TrafficFlowMonitoringTrafficflowmonitoringisthefoundationoftrafficmanagement.Itinvolvescollectingdataonvehiclevolume,speed,occupancy,anddensitytoanalyzetrafficconditionsandoptimizetrafficmanagementstrategies.UseCases:Highways:MmWaveradarisusedtomonitorreal-timetrafficflow,speed,andoccupancy.Thedataistransmittedtothetrafficmanagementcentertopredictcongestionandadjust限速.Benefits:Reducescongestion;improvesroadutilization;savesfuelandreducesemissions.3.2AccidentDetectionandResponseAccidentdetectioniscriticaltoreducingsecondaryaccidentsandimprovingemergencyresponseefficiency.Vehicledetectiontechnologiescanquicklyidentifyaccidents(e.g.,collisions,rollovers)andalertthetrafficmanagementcenter.UseCases:Highways:Lidarisusedtodetectvehiclecollisionsbyanalyzing3Dpointclouddata.Onceanaccidentisdetected,thesystemautomaticallyalertsthetrafficmanagementcenterandsetsup警示標(biāo)志inthe后方道路.UrbanRoads:Cameraswithdeeplearningalgorithmsareusedtodetectpedestriansbeinghitorvehiclesbreakingdown.Thesystemautomaticallycallsemergencyservicesandprovidesreal-time監(jiān)控錄像tohelp警方調(diào)查.Benefits:Shortensemergencyresponsetime;reducessecondaryaccidents;improvesroadsafety.3.3Traffic違法MonitoringTraffic違法monitoringusesvehicledetectiontechnologiestoidentifyandrecord違法行為(e.g.,闖紅燈,逆行,超速)forlawenforcement.UseCases:Speeding:Radarisusedtomeasurevehiclespeed.Ifavehicleexceedsthespeedlimit,thecameracapturesaphotoofthevehicle’s號(hào)牌.Benefits:Improves執(zhí)法效率;reducesmanualpatrols;deters違法行為.3.4ParkingManagementParkingmanagementinvolvesdetectingvehicleentry/exitandmonitoring車(chē)位occupancytoimproveparkingefficiency.UseCases:ResidentialParking:RFIDtagsareinstalledon業(yè)主vehicles.Thesystemautomatically識(shí)別業(yè)主車(chē)輛and抬桿放行.Benefits:Reducesparkingsearchtime;improvesparkinglotutilization;increasesrevenueforparkingoperators.3.5IntelligentSignalControlIntelligentsignalcontrolusesreal-timetrafficdatatoadjust信號(hào)燈配時(shí)dynamically,reducingvehiclewaitingtimeandimprovingintersection通行效率.UseCases:AdaptiveSignalControl:Camerasorradarmonitorreal-timetrafficflowatintersections.Thesignalcontrolleradjusts綠燈timebasedonthetrafficvolume(e.g.,increasing綠燈timeforthedirectionwithmorevehicles).Vehicle-to-Infrastructure(V2I)協(xié)同:Vehiclestransmittheirpositionandspeedtoroadsideunits(RSUs).Thesignalcontrollerusesthisinformationtooptimize信號(hào)燈配時(shí)(e.g.,givingprioritytoemergencyvehicles).Benefits:Reducesvehiclewaitingtime;improvesintersectionthroughput;reducesfuelconsumption.4.ChallengesandFutureDirectionsDespitethesignificantprogressinvehicledetectiontechnologies,therearestillseveralchallengesthatneedtobeaddressed.Additionally,emergingtechnologiesoffernewopportunitiesforfuturedevelopment.4.1CurrentChallenges4.1.1EnvironmentalAdaptabilityManyvehicledetectiontechnologiesareaffectedbyenvironmentalconditions:Camera:Rain,fog,andnightreducevisibility,makingitdifficulttodetectvehicles.Lidar:Heavyrainorsnowscatterslaserpulses,reducingdetectionrangeandaccuracy.Radar:Interferencefromotherelectromagneticdevices(e.g.,mobilephones)affectsperformance.4.1.2HighCostSomeadvancedtechnologiesaretooexpensiveforlarge-scaledeployment:Lidar:Mechanicallidarcostsseveralthousanddollars,whilesolid-statelidarisstillrelativelyexpensive.V2X:DeployingRSUsandOBUsrequiressignificantinvestment.4.1.3DataPrivacyandSecurity4.1.4AlgorithmEfficiency4.2FutureDirections4.2.1DevelopmentofNewSensors77GHzMmWaveRadar:77GHzmmWaveradarhashigherresolutionthantraditional24GHzradar,makingitsuitablefordetectingsmallobjects(e.g.,pedestrians).ThermalCameras:Thermalcamerasdetectheatemittedbyvehicles,makingthemunaffectedbylightandweather.Theyareparticularlyusefulfornightandfoggyconditions.4.2.2OptimizationofDeepLearningAlgor