A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Octree‐based point cloud simulation to assess the readiness of highway infrastructure for autonomous vehicles
Autonomous vehicles (AVs) are anticipated to supersede human drivers with an expectation of improved safety and operation. Since current infrastructure is designed based on the constraints caused by human drivers, it must be reassessed for autonomous driving compatibility. Recently, representatives from the infrastructure owners/operators (IOOs), automotive industry, and academia have advocated for new approaches to prepare roadways for the deployment of AVs over the next decade. Following these recommendations, this paper proposes a novel, simulation‐based approach for the assessment of highways "readiness" for AVs using 3D point cloud data. The proposed method uses octrees to perform volumetric queries for potential obstructions within an AV sensory field. The proposed approach is compared to a state‐of‐the‐art raycasting approach. Consequently, available sight distances and maximum safe speed limits based on road and AV characteristics are proposed. Finally, a discussion of the potential mitigation measures at the locations with limited sight distances is presented.
Octree‐based point cloud simulation to assess the readiness of highway infrastructure for autonomous vehicles
Autonomous vehicles (AVs) are anticipated to supersede human drivers with an expectation of improved safety and operation. Since current infrastructure is designed based on the constraints caused by human drivers, it must be reassessed for autonomous driving compatibility. Recently, representatives from the infrastructure owners/operators (IOOs), automotive industry, and academia have advocated for new approaches to prepare roadways for the deployment of AVs over the next decade. Following these recommendations, this paper proposes a novel, simulation‐based approach for the assessment of highways "readiness" for AVs using 3D point cloud data. The proposed method uses octrees to perform volumetric queries for potential obstructions within an AV sensory field. The proposed approach is compared to a state‐of‐the‐art raycasting approach. Consequently, available sight distances and maximum safe speed limits based on road and AV characteristics are proposed. Finally, a discussion of the potential mitigation measures at the locations with limited sight distances is presented.
Octree‐based point cloud simulation to assess the readiness of highway infrastructure for autonomous vehicles
Gouda, Maged (author) / Mirza, Jehanzeb (author) / Weiß, Jonas (author) / Ribeiro Castro, Augusto (author) / El‐Basyouny, Karim (author)
Computer‐Aided Civil and Infrastructure Engineering ; 36 ; 922-940
2021-07-01
19 pages
Article (Journal)
Electronic Resource
English
Octree-based region growing for point cloud segmentation
| Online Contents | 2015
Octree-based segmentation for terrestrial LiDAR point cloud data in industrial applications
| Online Contents | 2016
A Fast Reconstruction of Dense Unorganized Point Cloud Based on 3-Color Octree
| British Library Online Contents | 2009