Multi-Class Road User Detection With 3+1D Radar in the View-of-Delft Dataset

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...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2022-04, Vol.7 (2), p.4961-4968
Main Authors: Palffy, Andras, Pool, Ewoud, Baratam, Srimannarayana, Kooij, Julian F. P., Gavrila, Dariu M.
Format: Article
Language:English
Subjects:
Ablation
Annotations
Automobiles
Automotive radar
automotive radars
Data acquisition
data sets for robotic vision
Datasets
Doppler effect
Doppler radar
Laser radar
Lidar
Object detection
Object recognition
Radar
Radar cross sections
Radar data
Radar detection
segmentation and catego- rization
Static objects
Three-dimensional displays
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Summary: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 at https://intelligent-vehicles.org/datasets/view-of-delft/ .
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3147324