The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 255–263, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-255-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 255–263, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-255-2021

  28 Jun 2021

28 Jun 2021

COMAP: A SYNTHETIC DATASET FOR COLLECTIVE MULTI-AGENT PERCEPTION OF AUTONOMOUS DRIVING

Y. Yuan and M. Sester Y. Yuan and M. Sester
  • Institute of Cartography and Geoinformatics, Leibniz Universität Hannover, Germany

Keywords: Collective Perception, Point Cloud, Simulation, Data Fusion, 3D Object Detection, Semantic Segmentation, Uncertainty Estimation

Abstract. Collective perception of connected vehicles can sufficiently increase the safety and reliability of autonomous driving by sharing perception information. However, collecting real experimental data for such scenarios is extremely expensive. Therefore, we built a computational efficient co-simulation synthetic data generator through CARLA and SUMO simulators. The simulated data contain image and point cloud data as well as ground truth for object detection and semantic segmentation tasks. To verify the superior performance gain of collective perception over single-vehicle perception, we conducted experiments of vehicle detection, which is one of the most important perception tasks for autonomous driving, on this data set. A 3D object detector and a Bird’s Eye View (BEV) detector are trained and then test with different configurations of the number of cooperative vehicles and vehicle communication ranges. The experiment results showed that collective perception can not only dramatically increase the overall mean detection accuracy but also the localization accuracy of detected bounding boxes. Besides, a vehicle detection comparison experiment showed that the detection performance drop caused by sensor observation noise can be canceled out by redundant information collected by multiple vehicles.