Vulnerable Road User Clustering for Collective Perception Messages: Efficient Representation Through Geometric Shapes
<|reference_start|>Vulnerable Road User Clustering for Collective Perception Messages: Efficient Representation Through Geometric Shapes: 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 (CA) and Comprehensive Area Density Information (CADI), to assess the precision and efficiency of each shape. Beyond comparing predefined shapes, we propose an adaptive algorithm that selects the preferred shape for cluster description, prioritizing accuracy while maintaining a high level of efficiency. The study culminates by demonstrating the benefits of clustering on data transmission rates. We simulate VRU movement using real-world data and the transmission of CPMs by a roadside unit. The results reveal that broadcasting cluster information, as opposed to individual object data, can reduce the data transmission volume by two-thirds on average. This finding underscores the potential of clustering in V2X communications to enhance VRU safety while optimizing network resources.<|reference_end|>
@article{xhoxhi2024vulnerable,
    title={Vulnerable Road User Clustering for Collective Perception Messages:
  Efficient Representation Through Geometric Shapes},
    author={Edmir Xhoxhi, Vincent Albert Wolff, Yao Li, Florian Alexander Schiegg},
    journal={arXiv preprint arXiv:2404.14925},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.14925},
    primaryClass={cs.NI}
}