《智能传感器装调与测试（双语）》课件-28.深度学习融合方法

深度学习融合方法DeepLearningFusionMethod深度学习方法融合DeeplearningfusionmethodPART01多模态特征融合的核心难题MajorChallengestoMultimodalFeatureFusion多模态特征融合的核心难题MajorChallengestoMultimodalFeatureFusion相机输出2D图像，提供颜色、纹理等语义信息（比如识别交通信号灯、车道线）；激光雷达生成3D点云，给出高精度空间几何轮廓。毫米波雷达则提供目标距离、速度等动态信息，且不受恶劣天气影响。Thecameraoutputs2Dimagesandprovidessemanticinformationsuchascolorandtexture(forexample,byidentifyingsignallightsandlanelines).LiDARgenerates3Dpointcloudstoprovidehigh-precisionspatialgeometriccontours.Millimeter-waveradarprovidesdynamicinformationsuchasthetargetdistanceandspeed,andisnotaffectedbybadweather.图像数据Imagedata激光数据Laserdata毫米波数据MillimeterwavedataPART02多模态融合方案MultimodalFusionSolution多模态融合方案MultimodalFusionSolution多模态融合方案，主张集成多种物理原理完全不同的传感器（即异构传感器），并将它们的数据进行深度融合，以构建对环境的综合感知。其典型的传感器组合包括：车载相机、激光雷达（LiDAR）以及毫米波雷达。Themultimodalfusionsolutionadvocatestheintegrationofsensorswithcompletelydifferentphysicalprinciples(namely,heterogeneoussensors)anddeepfusionoftheirdatatobuildacomprehensiveperceptionoftheenvironment.Itstypicalsensorportfolioincludeson-boardcameras,LiDAR,andmillimeter-waveradar.-深度学习方法融合-Deeplearningfusionmethod多传感器

Multi-sensor任何单一传感器都存在其固有的物理局限性：相机受光照和天气影响，激光雷达在极端天气下性能衰减且成本高昂，毫米波雷达角分辨率低。通过将这些传感器组合，可以实现优势互补。Anysinglesensorhasitsinherentphysicallimitations:camerasareaffectedbylightandweather,LiDARunderperformsinextremeweatherandiscostly,andmillimeter-waveradarhaslowangularresolution.Acombinationofthesesensorscancomplementtheirrespectiveadvantages.当一种传感器因特定场景失效或性能下降时，其他传感器依然能够提供有效信息，从而为整个系统提供关键的安全冗余。Whenoneofthesensorsfailsorunderperformsinaspecificscenario,othersensorscanstillprovidevalidinformation,providingcriticalsafetyredundancyfortheentiresystem.这是一种以硬件冗余为基础，通过多源信息校验与融合来确保系统鲁棒性的技术路径。Thisisatechnicalpathbasedonhardwareredundancytoensuresystemrobustnessthroughmulti-sourceinformationverificationandfusion.PART03深度学习融合方法DeepLearningFusionMethod相机Cameras相机都是构建环境感知能力的认知基石。深度学习通过卷积神经网络(CNN)自动提取影像特征,能够高效完成地物分类任务。Camerasarethecognitivecornerstonesforbuildingenvironmentalperceptioncapabilities.Deeplearningautomaticallyextractsimagefeaturesthroughtheconvolutionalneuralnetwork(CNN)andcanefficientlycompletethegroundobjectclassificationtask.-图像深度学习-Imagedeeplearning毫米波雷达Millimeter-waveRadar毫米波雷达

Millimeter-waveradar神经网络

Neuralnetwork▶其核心价值在于能够直接、可靠地测量目标的距离与相对速度，且此能力几乎不受雨、雪、雾等恶劣天气及光照条件的影响Itscorevalueliesinthecapabilitytodirectlyandreliablymeasurethedistanceandrelativespeedofatarget,andthiscapabilityisalmostunaffectedbylightingconditionsandadverseweathersuchasrain,snow,andfog.激光雷达LiDAR▶激光雷达通过主动测量生成确定性的三维点云，极大地降低了感知算法对环境进行三维重构的不确定性，从而显著提升了整个感知系统的性能上限与可靠性LiDARgeneratesadeterministic3Dpointcloudthroughactivemeasurement,whichgreatlyreducestheuncertaintyintheperceptionalgorithm's3Dreconstructionoftheenvironment,therebysignificantlyimprovingtheupperperformancelimitandreliabilityoftheentireperceptionsystem.小结Summary▶多模态深度学习融合方案该融合方法显著提升了整个感知系统的性能上限与可靠性Multi