《智能传感器装调与测试（双语）》课件-8.激光雷达工作原理应用

激光雷达工作原理及应用WorkingPrincipleandApplicationofLiDAR暴雨大雾里的安全守望者SafetyWatcherinHeavyRainandFog激光雷达是如何实现三维感知的？不同类型的激光雷达有什么区别？它的核心技术参数又有哪些？HowdoesLiDARachieve3Dperception?WhatarethedifferencesbetweendifferenttypesofLiDARs?Whatarethecoretechnicalparameters?PART01分类方式全景速览PanoramicQuickViewbyClassificationMethod激光雷达的角色与定义RoleandDefinitionofLiDAR主动激光探测Activelaserdetection激光雷达通过发射激光并测量往返时间、相位或频率，计算目标距离、速度、角度，生成三维点云，实现高精度三维感知。LiDARemitslaserlightandmeasurestheround-triptime,phaseorfrequency,calculatesthetargetdistance,speedandangle,generatesa3Dpointcloud,andrealizeshigh-precision3Dperception.全天候优势All-weatheradvantages不受光照和天气影响，白天黑夜、雨雪雾霾均能稳定工作，为自动驾驶系统提供持续可靠的环境建模数据。LiDARisnotaffectedbylightandweather,andcanworkstablydayandnightinweatherofrain,snowandhaze,providingcontinuousandreliableenvironmentalmodelingdatafortheautonomousdrivingsystem.L3+核心传感器L3+coresensor作为L3及以上自动驾驶的关键输入源，激光雷达直接决定车辆对复杂环境的理解精度，是“自动驾驶的三维之眼”。AsakeyinputsourceforL3andaboveautonomousdriving,LiDARdirectlydeterminesthevehicle'sprecisioninunderstandingthecomplexenvironments,anditisthe"3Deyesforautonomousdriving"..按扫描方式划分的三大阵营ThreeMajorCategoriesByScanningMethod1机械式LiDARMechanicalLiDAR360°旋转扫描，点云均匀，测距远，但体积大、成本高，适合早期测试车。Itadopts360°rotationscanning.Itfeaturesuniformpointcloudandlongranging,buthasthedisadvantagesoflargesizeandhighcost.Itissuitableforearlytestvehicles.2固态LiDARSolid-stateLiDAR无旋转部件，芯片级扫描，体积小、可靠性高，成本持续下降，是乘用车量产主流。Ithasnrotatingparts,andfeatureschip-levelscanning,smallsize,highreliability,andcontinuouscostreduction.Itisthemainstreamofpassengercarmassproduction.3混合固态LiDARHybridsolid-stateLiDAR小角度旋转+芯片扫描，兼顾视场与成本，已在中端智能网联汽车规模落地。Itfeaturessmall-anglerotation+chipscanning,andconsidersboththefieldofviewandthecost.Ithasalreadybeenimplementedonalargescaleinmid-rangeintelligentconnectedvehicles.距离与波长的双维细分ClassificationbyDistanceandWavelength短距激光雷达

Short-rangeLiDAR中距激光雷达

Mid-rangeLiDAR长距激光雷达

Long-rangeLiDAR探测距离

Detectionrange0.5-50米

0.5–50m50-150米

50–150m150-300米

150–300m适配范围

Application泊车

Parking覆盖城市场景

Coveringurbanscenarios服务高速提前预警

Earlywarningonexpressways距离分段策略Classificationbydistance距离与波长的双维细分ClassificationbyDistanceandWavelength分类

Category优势

Advantages劣势

Disadvantages905nm激光雷达

905nmLiDAR技术成熟、成本低

Maturetechnologyandlowcost探测距离和抗干扰能力有限

Limiteddetectionrangeandanti-interferenceability1550nm激光雷达

1,550nmLiDAR探测距离远、抗干扰能力强

Longdetectiondistanceandstronganti-interferenceability技术门槛高、成本较高

Hightechnicalthresholdandhighcost波长选型权衡ClassificationbywavelengthPART02核心技术参数拆解DetailedDescriptionofCoreTechnicalParameters探测距离与点云密度DetectionRangeandPointCloudDensity探测距离Detectionrange点云密度Pointclouddensity高速场景需≥250米提前预警，距离越远，留给决策与制动的时间越充裕，直接关联功能安全。High-speedscenariosrequireanadvancewarningforadistance≥250m.Afartherdistanceleavesmoretimefordecision-makingandbraking.Detectionrangeisdirectlyrelatedtofunctionalsafety.百万点/秒起步，测试车可达千万级，密度越高，模型越精细，但对算力与存储提出指数级需求。Thedensityisatleastonemillionpointspersecond,andthatofthetestvehiclecanreachtensofmillionspointspersecond.Ahigherdensityleadstoamorerefinedmodel,butplacesexponentialdemandsoncomputingpowerandstorage.扫描频率与测角范围ScanningFrequencyandAngleMeasurementRange激光雷达扫描

LiDARscanning扫描频率Scanningfrequency测角范围Anglemeasurementrange10-20Hz为主流，频率越高，对切入车辆捕捉越及时，减少漏检风险。Themainstreamfrequencyis10–20Hz.Ahigherfrequencyleadstoamoretimelycaptureofacut-invehicleandreducestheriskofmisseddetection.激光雷达能探测的角度范围，水平测角范围通常为360°或120°-150°，垂直测角范围为±15°-±30°，测角范围越广，盲区越小。TheanglerangethattheLiDARcandetectisusually360°or120°to150°horizontallyand±15°to±30°vertically.Awideranglemeasurementrangeleadstoasmallerblindspot;测距精度和抗干扰能力RangingprecisionandAnti-interferenceAbility测距精度Rangingprecision测量目标距离的误差范围，主流激光雷达的测距精度为±2cm-±5cm，精度越高，对近距离障碍物的判断越准确。Fortheerrorrangeinmeasuringtargetdistance,therangingprecisionofmainstreamLiDARis±2cmto±5cm.Ahigherprecisionleadstoamoreaccuratejudgmentofshort-rangeobstacles.抗干扰能力Anti-interferenceability指在强光、雨天、大雾等环境下的工作稳定性，1550nm激光雷达的抗干扰能力优于905nm，能在恶劣天气下保持性能稳定。Itreferstotheworkingstabilityinstronglight,rainydays,heavyfogandotherenvironments.Theanti-interferenceabilityof1,550nmLiDARisbetterthanthatof905nm,whichcanmaintainstableperformanceinbadweather.PART03典型应用场景TypicalApplicationScenarios自动驾驶环境建模核心CoreofAutonomousDrivingEnvironmentModeling激光雷达点云LiDARPointCloud自动驾驶环境建模Autonomousdrivingenvironmentmodeling激光雷达通过生成高精度三维点云地图，精准识别车道线、交通标志、行人、车辆、护栏等目标的位置和形状，为决策系统提供“环境全景图”。LiDARgenerateshigh-precision3Dpointcloudmapstoaccuratelyidentifythelocationandshapeoflanelines,trafficsigns,pedestrians,vehicles,guardrailsandothertargets,providingan"environmentalpanorama"forthedecision-makingsystem.自动驾驶环境建模核心CoreofAutonomousDrivingEnvironmentModeling激光雷达点云LiDARPointCloud高级辅助驾驶系统升级Advanceddriverassistantsystemupgrade为L2+级辅助驾驶系统提供更精准的感知数据，如增强型自适应巡航控制系统的跟车距离控制、车道保持辅助系统的车道线识别精度，减少因视觉传感器或毫米波雷达误判导致的故障。ItprovidesmoreaccurateperceptiondataforL2+driverassistantsystems,suchasthefollowingdistancecontrolofenhancedadaptivecruisecontrolsystemandthelanelinerecognitionprecisionoflanekeepingassistsystem,soastoreducefaultscausedbymisjudgmentofvisionsensorsormillimeterwaveradars.自动驾驶环境建模核心CoreofAutonomousDrivingEnvironmentModeling激光雷达点云LiDARPointCloud自动泊车与低速行驶Automaticparkingandlow-speeddriving短距激光雷达用于车辆周围环境探测，精准测量车位尺寸、周围障碍物距离，实现全自动泊车（ValetParking），避免低速行驶时的碰撞风险。Short-rangeLiDARisusedtodetectthesurroundingenvironmentofvehicles,accuratelymeasurethesizeofparkingspacesandthedistancefromsurroundingobstacles,realizefullyautomaticparking(ValetParking),andavoidcollisionrisksduringlow-speeddriving.ADAS升级与特殊场景适配ADASUpgradeandAdaptationtoSpecialScenarios特殊场景适配Specialscenarioadaptation在暴雨、大雾、夜间等恶劣环境下，激光雷达不受光照和能见度影响，能弥补视觉传感器的不足，确保自动驾驶系统持续稳定工作。Inharshenvironmentssuchasheavyrain,fogandnight,LiDARisnotaffectedbylightandvisibility.Itcanmakeupfortheshortcomingsofvisionsensorsandensurethecontinuousandstableoperationoftheautonomousdrivingsystem.车路协同应用Vehicle-infrastructurecooperation除了车载激光雷达，路侧激光雷达也广泛应用于车路协同系统，通过路侧部署的激光雷达监测道路全局交通状况，为过往车辆提供实时路况信息，提升交通安全性。Inadditiontovehicle-borneLiDAR,roadsideLiDARisalsowidelyusedinvehicle-infrastructurecooperationsystem.TheLiDARdeployedontheroadsidemonitorstheglobaltrafficconditionsoftheroad,providesreal-timeroadconditioninformationforpassingvehicles,andimprovestrafficsafety.三维之眼未来融合路3DEyesforFutureIntegration分类速记Classification机械、固态、混合固态三大扫描方式，按距离与波长再细分，形成多维选型矩阵。Thethreemajorscanningmethods,namelymechanical,solid-stateandhybridsoli