Details
| Original language | English |
|---|---|
| Title of host publication | 2024 IEEE Vehicular Networking Conference, VNC 2024 |
| Editors | Susumu Ishihara, Hiroshi Shigeno, Onur Altintas, Takeo Fujii, Raphael Frank, Florian Klingler, Tobias Hardes, Tobias Hardes |
| Publisher | IEEE Computer Society |
| Pages | 351-356 |
| Number of pages | 6 |
| ISBN (electronic) | 9798350362701 |
| Publication status | Published - 2024 |
| Event | 15th IEEE Vehicular Networking Conference, VNC 2024 - Kobe, Japan Duration: 29 May 2024 → 31 May 2024 |
Publication series
| Name | IEEE Vehicular Networking Conference, VNC |
|---|---|
| ISSN (Print) | 2157-9857 |
| ISSN (electronic) | 2157-9865 |
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 (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.
Keywords
- Clustering, CPM, V2X, Vulnerable Road User
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Engineering(all)
- Automotive Engineering
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Electrical and Electronic Engineering
- Social Sciences(all)
- Transportation
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2024 IEEE Vehicular Networking Conference, VNC 2024. ed. / Susumu Ishihara; Hiroshi Shigeno; Onur Altintas; Takeo Fujii; Raphael Frank; Florian Klingler; Tobias Hardes; Tobias Hardes. IEEE Computer Society, 2024. p. 351-356 (IEEE Vehicular Networking Conference, VNC).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Vulnerable Road User Clustering for Collective Perception Messages
T2 - 15th IEEE Vehicular Networking Conference, VNC 2024
AU - Xhoxhi, Edmir
AU - Wolff, Vincent Albert
AU - Li, Yao
AU - Schiegg, Florian Alexander
N1 - Publisher Copyright: © 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Clustering
KW - CPM
KW - V2X
KW - Vulnerable Road User
UR - http://www.scopus.com/inward/record.url?scp=85198340975&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2404.14925
DO - 10.48550/arXiv.2404.14925
M3 - Conference contribution
AN - SCOPUS:85198340975
T3 - IEEE Vehicular Networking Conference, VNC
SP - 351
EP - 356
BT - 2024 IEEE Vehicular Networking Conference, VNC 2024
A2 - Ishihara, Susumu
A2 - Shigeno, Hiroshi
A2 - Altintas, Onur
A2 - Fujii, Takeo
A2 - Frank, Raphael
A2 - Klingler, Florian
A2 - Hardes, Tobias
A2 - Hardes, Tobias
PB - IEEE Computer Society
Y2 - 29 May 2024 through 31 May 2024
ER -