Print Email Facebook Twitter Multi-class Road User Detection with 3+1D Radar in the View-of-Delft Dataset Title Multi-class Road User Detection with 3+1D Radar in the View-of-Delft Dataset Author Palffy, A. (TU Delft Intelligent Vehicles) Pool, E.A.I. (TU Delft Intelligent Vehicles) Baratam, Srimannarayana (Student TU Delft) Kooij, J.F.P. (TU Delft Intelligent Vehicles) Gavrila, D. (TU Delft Intelligent Vehicles) Date 2022 Abstract Next-generation automotive radars provide elevation data in addition to range-, azimuth- and Doppler velocity. In this experimental study, we apply a state-of-the-art object detector (PointPillars), previously used for LiDAR 3D data, to such 3+1D radar data (where 1D refers to Doppler). In ablation studies, we first explore the benefits of the additional elevation information, together with that of Doppler, radar cross section and temporal accumulation, in the context of multi-class road user detection. We subsequently compare object detection performance on the radar and LiDAR point clouds, object class-wise and as a function of distance. To facilitate our experimental study, we present the novel View-of-Delft (VoD) automotive dataset. It contains 8693 frames of synchronized and calibrated 64-layer LiDAR-, (stereo) camera-, and 3+1D radar-data acquired in complex, urban traffic. It consists of 123106 3D bounding box annotations of both moving and static objects, including 26587 pedestrian, 10800 cyclist and 26949 car labels. Our results show that object detection on 64-layer LiDAR data still outperforms that on 3+1D radar data, but the addition of elevation information and integration of successive radar scans helps close the gap. The VoD dataset is made freely available for scientific benchmarking. Subject AnnotationsAutomotive RadarsData Sets for Robotic VisionDoppler effectDoppler radarLaser radarObject DetectionRadarRadar detectionSegmentation and CategorizationThree-dimensional displays To reference this document use: http://resolver.tudelft.nl/uuid:663863c1-35b8-48a5-9bc7-e775df8d7fac DOI https://doi.org/10.1109/LRA.2022.3147324 Embargo date 2022-08-01 ISSN 2377-3766 Source IEEE Robotics and Automation Letters, 7 (2), 4961-4968 Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type journal article Rights © 2022 A. Palffy, E.A.I. Pool, Srimannarayana Baratam, J.F.P. Kooij, D. Gavrila Files PDF Multi_Class_Road_User_Det ... ataset.pdf 2.72 MB Close viewer /islandora/object/uuid:663863c1-35b8-48a5-9bc7-e775df8d7fac/datastream/OBJ/view