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Current multi-modal object detection approaches focus on the vehicle domainand are limited in the perception range and the processing capabilities.Roadside sensor units (RSUs) introduce a new domain for perception systems andleverage altitude to observe traffic. Cameras and LiDARs mounted on gantrybridges increase the perception range and produce a full digital twin of thetraffic. In this work, we introduce InfraDet3D, a multi-modal 3D objectdetector for roadside infrastructure sensors. We fuse two LiDARs using earlyfusion and further incorporate detections from monocular cameras to increasethe robustness and to detect small objects. Our monocular 3D detection moduleuses HD maps to ground object yaw hypotheses, improving the final perceptionresults. The perception framework is deployed on a real-world intersection thatis part of the A9 Test Stretch in Munich, Germany. We perform several ablationstudies and experiments and show that fusing two LiDARs with two cameras leadsto an improvement of +1.90 mAP compared to a camera-only solution. We evaluateour results on the A9 infrastructure dataset and achieve 68.48 mAP on the testset. The dataset and code will be available at https://a9-dataset.com to allowthe research community to further improve the perception results and makeautonomous driving safer.

InfraDet3D: Multi-Modal 3D Object Detection based on Roadside Infrastructure Camera and LiDAR Sensors