纵目--Radar Perception and Fusion with Deep Learning.pdf

1、Radar Perception and Fusion with Deep Learning2023.11.14Yu Su 苏煜Senior Perception Algorithm ExpertZongMu Technology2About MevWork in AcademyPh.D.at Harbin Institute of Technology/ICT,Chinese Academy of Sciences,19992009Research Engineer at University of Caen/CNRS,France,20092012Research on Computer

2、Vision,Pattern Recognition and Machine LearningFocus on Face Recognition,Large-Scale Image Classification/Retrieval30+Publications in IJCV,TIP,ICCV,CVPR,ECCV,etc.with 2500+citations.vWork in IndustrySenior Algorithm Engineer at APTIV(previously DELPHI),2013202311 Years Experience in Autonomous Drivi

3、ng R&DVision-based ADAS:Object/Lane DetectionRadar/LiDAR Perception with Deep Learning20+Patents in Related Fields3Contentv Traditional Radar Signal Processingv Radar Perception with Deep Learningv Deep Fusion of Radar and Camera4Traditional Radar Signal ProcessingDopplerAngleRAD DataCube(Dense)Rang

4、eDopplerPointAngleRangeADC Data Point Cloud(Sparse)Red:Radar points,Grey:LiDAR pointsSample from NuScenes DatasetFFTsCFARv LimitationsRadar point cloud is very sparseAngular resolution is low and no height informationDoppler spectrum information is not fully exploited(Range,Angle,Doppler,RCS)5Radar

5、Perception with Sparse Point Cloudv Pipeline1.Detection:find target proposals by clustering,e.g.DBSCAN2.Tracking:temporally track target by filtering,e.g.Kalman filter3.Classification:predict semantic labels by classifier,e.g.SVMv LimitationsHard to classify stationary targetsLow performance on VRU(

6、Vulnerable Road User)Need handcraft design and tuningDetection by ClusteringTracking by Kalman FilterClassification by SVM6Impact on Autonomous Driving ApplicationsStationary target is filtered outVRU(Pedestrian/Bicyclist)detection is unreliable No grid-level information to support high-level AD app