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- Title
SaBi3d—A LiDAR Point Cloud Data Set of Car-to-Bicycle Overtaking Maneuvers.
- Authors
Odenwald, Christian; Beeking, Moritz
- 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. Dataset: https://osf.io/k7cg9 (accessed on 18 July 2024). Dataset License: CC-By Attribution 4.0 International.
- Subjects
OBJECT recognition (Computer vision); CYCLING; CYCLING safety; CITY traffic; POINT cloud; TRAFFIC safety
- Publication
Data (2306-5729), 2024, Vol 9, Issue 8, p90
- ISSN
2306-5729
- Publication type
Article
- DOI
10.3390/data9080090