深度學(xué)習(xí)融合方法DeepLearningFusionMethod深度學(xué)習(xí)方法融合DeeplearningfusionmethodPART01多模態(tài)特征融合的核心難題MajorChallengestoMultimodalFeatureFusion多模態(tài)特征融合的核心難題MajorChallengestoMultimodalFeatureFusion相機(jī)輸出2D圖像，提供顏色、紋理等語(yǔ)義信息（比如識(shí)別交通信號(hào)燈、車(chē)道線）；激光雷達(dá)生成3D點(diǎn)云，給出高精度空間幾何輪廓。毫米波雷達(dá)則提供目標(biāo)距離、速度等動(dòng)態(tài)信息，且不受惡劣天氣影響。Thecameraoutputs2Dimagesandprovidessemanticinformationsuchascolorandtexture(forexample,byidentifyingsignallightsandlanelines).LiDARgenerates3Dpointcloudstoprovidehigh-precisionspatialgeometriccontours.Millimeter-waveradarprovidesdynamicinformationsuchasthetargetdistanceandspeed,andisnotaffectedbybadweather.圖像數(shù)據(jù)Imagedata激光數(shù)據(jù)Laserdata毫米波數(shù)據(jù)MillimeterwavedataPART02多模態(tài)融合方案MultimodalFusionSolution多模態(tài)融合方案MultimodalFusionSolution多模態(tài)融合方案，主張集成多種物理原理完全不同的傳感器（即異構(gòu)傳感器），并將它們的數(shù)據(jù)進(jìn)行深度融合，以構(gòu)建對(duì)環(huán)境的綜合感知。其典型的傳感器組合包括：車(chē)載相機(jī)、激光雷達(dá)（LiDAR）以及毫米波雷達(dá)。Themultimodalfusionsolutionadvocatestheintegrationofsensorswithcompletelydifferentphysicalprinciples(namely,heterogeneoussensors)anddeepfusionoftheirdatatobuildacomprehensiveperceptionoftheenvironment.Itstypicalsensorportfolioincludeson-boardcameras,LiDAR,andmillimeter-waveradar.-深度學(xué)習(xí)方法融合-Deeplearningfusionmethod多傳感器

Multi-sensor任何單一傳感器都存在其固有的物理局限性：相機(jī)受光照和天氣影響，激光雷達(dá)在極端天氣下性能衰減且成本高昂，毫米波雷達(dá)角分辨率低。通過(guò)將這些傳感器組合，可以實(shí)現(xiàn)優(yōu)勢(shì)互補(bǔ)。Anysinglesensorhasitsinherentphysicallimitations:camerasareaffectedbylightandweather,LiDARunderperformsinextremeweatherandiscostly,andmillimeter-waveradarhaslowangularresolution.Acombinationofthesesensorscancomplementtheirrespectiveadvantages.當(dāng)一種傳感器因特定場(chǎng)景失效或性能下降時(shí)，其他傳感器依然能夠提供有效信息，從而為整個(gè)系統(tǒng)提供關(guān)鍵的安全冗余。Whenoneofthesensorsfailsorunderperformsinaspecificscenario,othersensorscanstillprovidevalidinformation,providingcriticalsafetyredundancyfortheentiresystem.這是一種以硬件冗余為基礎(chǔ)，通過(guò)多源信息校驗(yàn)與融合來(lái)確保系統(tǒng)魯棒性的技術(shù)路徑。Thisisatechnicalpathbasedonhardwareredundancytoensuresystemrobustnessthroughmulti-sourceinformationverificationandfusion.PART03深度學(xué)習(xí)融合方法DeepLearningFusionMethod相機(jī)Cameras相機(jī)都是構(gòu)建環(huán)境感知能力的認(rèn)知基石。深度學(xué)習(xí)通過(guò)卷積神經(jīng)網(wǎng)絡(luò)(CNN)自動(dòng)提取影像特征,能夠高效完成地物分類(lèi)任務(wù)。Camerasarethecognitivecornerstonesforbuildingenvironmentalperceptioncapabilities.Deeplearningautomaticallyextractsimagefeaturesthroughtheconvolutionalneuralnetwork(CNN)andcanefficientlycompletethegroundobjectclassificationtask.-圖像深度學(xué)習(xí)-Imagedeeplearning毫米波雷達(dá)Millimeter-waveRadar毫米波雷達(dá)

Millimeter-waveradar神經(jīng)網(wǎng)絡(luò)

Neuralnetwork?其核心價(jià)值在于能夠直接、可靠地測(cè)量目標(biāo)的距離與相對(duì)速度，且此能力幾乎不受雨、雪、霧等惡劣天氣及光照條件的影響Itscorevalueliesinthecapabilitytodirectlyandreliablymeasurethedistanceandrelativespeedofatarget,andthiscapabilityisalmostunaffectedbylightingconditionsandadverseweathersuchasrain,snow,andfog.激光雷達(dá)LiDAR?激光雷達(dá)通過(guò)主動(dòng)測(cè)量生成確定性的三維點(diǎn)云，極大地降低了感知算法對(duì)環(huán)境進(jìn)行三維重構(gòu)的不確定性，從而顯著提升了整個(gè)感知系統(tǒng)的性能上限與可靠性LiDARgeneratesadeterministic3Dpointcloudthroughactivemeasurement,whichgreatlyreducestheuncertaintyintheperceptionalgorithm's3Dreconstructionoftheenvironment,therebysignificantlyimprovingtheupperperformancelimitandreliabilityoftheentireperceptionsystem.小結(jié)Summary?多模態(tài)深度學(xué)習(xí)融合方案該融合方法顯著提升了整個(gè)感知系統(tǒng)的性能上限與可靠性Multi