Evaluation of Traffic Sign Occlusion for Autonomous Vehicles Using Point Cloud Data

Abstract

This work aims to assess the occlusion of traffic signs for autonomous vehicles (AVs) using point cloud data, while addressing the limitations and recommendations of previous studies. Dense point cloud data are used to create a digital twin of existing roads and simulate a set of AV sensors within this environment. Convex polyhedrons or hulls with an octree data structure and semantic segmentation were used to assess traffic sign occlusion. Using the developed method, several case studies are presented to identify locations with occluded traffic signs for AVs. This work can help infrastructure operators and AV professionals make data-driven decisions about smart physical infrastructure investments for AVs.