Vulnerable Road User Clustering for Collective Perception Messages: Efficient Representation Through Geometric Shapes
Edmir Xhoxhi, Vincent Albert Wolff, Yao Li, Florian Alexander Schiegg

TL;DR
This paper evaluates geometric shapes for representing VRU clusters in V2X messages, introducing adaptive shape selection to improve transmission efficiency and safety in vehicular networks.
Contribution
It proposes an adaptive algorithm for selecting geometric shapes to describe VRU clusters, optimizing data transmission and accuracy in V2X communications.
Findings
Clustering reduces data transmission volume by two-thirds.
Adaptive shape selection improves cluster representation accuracy.
Clustering enhances VRU safety and network efficiency.
Abstract
Ensuring the safety of Vulnerable Road Users (VRUs) is a critical concern in transportation, demanding significant attention from researchers and engineers. Recent advancements in Vehicle-to-Everything (V2X) technology offer promising solutions to enhance VRU safety. Notably, VRUs often travel in groups, exhibiting similar movement patterns that facilitate the formation of clusters. The standardized Collective Perception Message (CPM) and VRU Awareness Message in ETSI's Release 2 consider this clustering behavior, allowing for the description of VRU clusters. Given the constraints of narrow channel bandwidth, the selection of an appropriate geometric shape for representing a VRU cluster becomes crucial for efficient data transmission. In our study we conduct a comprehensive evaluation of different geometric shapes used to describe VRU clusters. We introduce two metrics: Cluster Accuracy…
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Taxonomy
TopicsUser Authentication and Security Systems · Video Surveillance and Tracking Methods · Data Management and Algorithms
