Details
| Original language | English |
|---|---|
| Title of host publication | 2024 IEEE Vehicular Networking Conference |
| Subtitle of host publication | VNC |
| Editors | Susumu Ishihara, Hiroshi Shigeno, Onur Altintas, Takeo Fujii, Raphael Frank, Florian Klingler, Tobias Hardes, Tobias Hardes |
| Publisher | IEEE Computer Society |
| Pages | 329-335 |
| Number of pages | 7 |
| ISBN (electronic) | 9798350362701 |
| ISBN (print) | 979-8-3503-6271-8 |
| Publication status | Published - 29 May 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
According to the World Health Organization, the involvement of Vulnerable Road Users (VRUs) in traffic accidents remains a significant concern, with VRUs accounting for over half of traffic fatalities. The increase of automation and connectivity levels of vehicles has still an uncertain impact on VRU safety. By deploying the Collective Perception Service (CPS), vehicles can include information about VRUs in Vehicle-To-Everything (V2X) messages, thus raising the general perception of the environment. Although an increased awareness is considered positive, one could argue that the awareness ratio, the metric used to measure perception, is only implicitly connected to the VRUs' safety. This paper introduces a tailored metric, the Risk Factor (RF), to measure the risk level for the interactions between Connected Automated Vehicles (CAVs) and VRUs. By evaluating the RF, we assess the impact of V2X communication on VRU collision risk mitigation. Our results show that high V2X penetration rates can reduce mean risk, quantified by our proposed metric, by up to 44 %. Although the median risk value shows a significant decrease, suggesting a reduction in overall risk, the distribution of risk values reveals that CPS's mitigation effectiveness is overestimated, which is indicated by the divergence between RF and awareness ratio. Additionally, by analyzing a real-world traffic dataset, we pinpoint high-risk locations within a scenario, identifying areas near intersections and behind parked cars as especially dangerous. Our methodology can be ported and applied to other scenarios in order to identify high-risk areas. We value the proposed RF as an insightful metric for quantifying VRU safety in a highly automated and connected environment.
Keywords
- collective perception, risk analysis, V2X, VRU awareness, VRU protection, VRU safety, vulnerable road users
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
Sustainable Development Goals
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2024 IEEE Vehicular Networking Conference: VNC . ed. / Susumu Ishihara; Hiroshi Shigeno; Onur Altintas; Takeo Fujii; Raphael Frank; Florian Klingler; Tobias Hardes; Tobias Hardes. IEEE Computer Society, 2024. p. 329-335 (IEEE Vehicular Networking Conference, VNC).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A Data-Driven Analysis of Vulnerable Road User Safety in Interaction with Connected Automated Vehicles
AU - Xhoxhi, Edmir
AU - Wolff, Vincent Albert
N1 - Publisher Copyright: © 2024 IEEE.
PY - 2024/5/29
Y1 - 2024/5/29
N2 - According to the World Health Organization, the involvement of Vulnerable Road Users (VRUs) in traffic accidents remains a significant concern, with VRUs accounting for over half of traffic fatalities. The increase of automation and connectivity levels of vehicles has still an uncertain impact on VRU safety. By deploying the Collective Perception Service (CPS), vehicles can include information about VRUs in Vehicle-To-Everything (V2X) messages, thus raising the general perception of the environment. Although an increased awareness is considered positive, one could argue that the awareness ratio, the metric used to measure perception, is only implicitly connected to the VRUs' safety. This paper introduces a tailored metric, the Risk Factor (RF), to measure the risk level for the interactions between Connected Automated Vehicles (CAVs) and VRUs. By evaluating the RF, we assess the impact of V2X communication on VRU collision risk mitigation. Our results show that high V2X penetration rates can reduce mean risk, quantified by our proposed metric, by up to 44 %. Although the median risk value shows a significant decrease, suggesting a reduction in overall risk, the distribution of risk values reveals that CPS's mitigation effectiveness is overestimated, which is indicated by the divergence between RF and awareness ratio. Additionally, by analyzing a real-world traffic dataset, we pinpoint high-risk locations within a scenario, identifying areas near intersections and behind parked cars as especially dangerous. Our methodology can be ported and applied to other scenarios in order to identify high-risk areas. We value the proposed RF as an insightful metric for quantifying VRU safety in a highly automated and connected environment.
AB - According to the World Health Organization, the involvement of Vulnerable Road Users (VRUs) in traffic accidents remains a significant concern, with VRUs accounting for over half of traffic fatalities. The increase of automation and connectivity levels of vehicles has still an uncertain impact on VRU safety. By deploying the Collective Perception Service (CPS), vehicles can include information about VRUs in Vehicle-To-Everything (V2X) messages, thus raising the general perception of the environment. Although an increased awareness is considered positive, one could argue that the awareness ratio, the metric used to measure perception, is only implicitly connected to the VRUs' safety. This paper introduces a tailored metric, the Risk Factor (RF), to measure the risk level for the interactions between Connected Automated Vehicles (CAVs) and VRUs. By evaluating the RF, we assess the impact of V2X communication on VRU collision risk mitigation. Our results show that high V2X penetration rates can reduce mean risk, quantified by our proposed metric, by up to 44 %. Although the median risk value shows a significant decrease, suggesting a reduction in overall risk, the distribution of risk values reveals that CPS's mitigation effectiveness is overestimated, which is indicated by the divergence between RF and awareness ratio. Additionally, by analyzing a real-world traffic dataset, we pinpoint high-risk locations within a scenario, identifying areas near intersections and behind parked cars as especially dangerous. Our methodology can be ported and applied to other scenarios in order to identify high-risk areas. We value the proposed RF as an insightful metric for quantifying VRU safety in a highly automated and connected environment.
KW - collective perception
KW - risk analysis
KW - V2X
KW - VRU awareness
KW - VRU protection
KW - VRU safety
KW - vulnerable road users
UR - http://www.scopus.com/inward/record.url?scp=85198330874&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2404.14935
DO - 10.48550/arXiv.2404.14935
M3 - Conference contribution
AN - SCOPUS:85198330874
SN - 979-8-3503-6271-8
T3 - IEEE Vehicular Networking Conference, VNC
SP - 329
EP - 335
BT - 2024 IEEE Vehicular Networking Conference
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
T2 - 15th IEEE Vehicular Networking Conference, VNC 2024
Y2 - 29 May 2024 through 31 May 2024
ER -