自动驾驶论坛-【雷鸣丨英特尔】-《AI Sensor Fusion for Intelligent Transportation》.pdf

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1、Ming Lei(雷鸣),PhDSenior Platform AIntel CorporationAI Computing Conference(AICC),Beijing,ChinaNov.29,2023AI Sensor Fusion forIntelligent TransportationOutline Roadside Sensing Usage scenarios System architecture Traffic object attributes Pros&cons of traffic sensors Sensor Fusion(categorized by integ

2、ration level)Integrated&Distributed Sensor Fusion(categorized by involved sensor types)C+R:Camera+mmWave Radar C+L:Camera+LidarRoadside Sensing Usage Scenarios Roadside Sensing mainly relies on three types of sensors:cameras,mmWave radars,and lidars Edge Computing(e.g.,MEC deployed at roadside or mo

3、bile network edge)processes raw data from Roadside Sensing Deep learning(e.g.,object classification based on neural networks for video images or 3D point clouds)Traditional computer vision(e.g.,color space conversion for video images or clustering for 3D point clouds)Radar signal processing(e.g.,FFT

4、s for range,velocity,and AoA estimation)Roadside Sensing System ArchitectureRoadside Sensing Traffic Object AttributesRTSs(Road Traffic Signs)RTIs(Road Traffic Incidents)RTPs(Road Traffic Participants)RTS attributes defined in standards:Data Frame:DF_RTSData YD/T 3709-2020 T/CSAE 53-2020 Associated

5、RTS types GB 5768.2-2009RTI attributes defined in standards:Data Frame:DF_RTEData YD/T 3709-2020 T/CSAE 53-2020 Associated RTI types GB/T 29100-2012RTP attributes defined in standards:Data Frame:DF_ParticipantData YD/T 3709-2020 T/CSAE 53-2020Roadside Sensing Traffic Sensors Pros&ConsTraffic Sensor

6、Type(Strengths in detecting Traffic Object Attributes)AdvantagesDisadvantagesCamera(Object classification and estimation of pixel coordinates on a 2D plane)1)Rich in details,excellent discernibility;2)Can accurately capture the contour,texture,color distribution,and so on as well as generate semanti