SaBi3d—A LiDAR Point Cloud Data Set of Car-to-Bicycle Overtaking Maneuvers

Christian Odenwald and Moritz Beeking ()
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Christian Odenwald: School of Informatics, University of Edinburgh, 10 Crichton St., Edinburgh EH8 9AB, UK
Moritz Beeking: Salzburg Research, Jakob Haringer Straße 5/3, A-5020 Salzburg, Austria

Data, 2024, vol. 9, issue 8, 1-12

Abstract: While cycling presents environmental benefits and promotes a healthy lifestyle, the risks associated with overtaking maneuvers by motorized vehicles represent a significant barrier for many potential cyclists. A large-scale analysis of overtaking maneuvers could inform traffic researchers and city planners how to reduce these risks by better understanding these maneuvers. Drawing from the fields of sensor-based cycling research and from LiDAR-based traffic data sets, this paper provides a step towards addressing these safety concerns by introducing the Salzburg Bicycle 3d (SaBi3d) data set, which consists of LiDAR point clouds capturing car-to-bicycle overtaking maneuvers. The data set, collected using a LiDAR-equipped bicycle, facilitates the detailed analysis of a large quantity of overtaking maneuvers without the need for manual annotation through enabling automatic labeling by a neural network. Additionally, a benchmark result for 3D object detection using a competitive neural network is provided as a baseline for future research. The SaBi3d data set is structured identically to the nuScenes data set, and therefore offers compatibility with numerous existing object detection systems. This work provides valuable resources for future researchers to better understand cycling infrastructure and mitigate risks, thus promoting cycling as a viable mode of transportation.

Keywords: LiDAR; 3D object detection; bicycle safety (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
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