Position Estimation and Prediction of Surrounding Traffic Participants Observed by Vehicle with Self-localization Uncertainty for Cooperative Safety
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- Publication code
- 20264529
- Paper/Info type
- International Journal of Automotive Engineering
Vol.17 No.3
- Pages
- 136-149(Total 14 p)
- Date of publication
- Jul 2026
- Publisher
- JSAE
- Language
- English
Detailed Information
| Category(E) | Research paper |
|---|---|
| Author(E) | 1) Kota Watanabe, 2) Takuma Ito |
| Affiliation(E) | 1) The University of Tokyo, 2) The University of Tokyo |
| Abstract(E) | In Japan, the prevention of traffic accidents on community roads, which frequently have poor visibility at intersections, is an ongoing issue. As a solution to this issue, we focus on Vehicle-to-Network-to-Vehicle (V2N2V) safety systems, which use cellular networks and allow vehicles to share information over a wider area. In V2N2V safety systems, vehicles provide their self-localization data and local observations of surrounding traffic participants to a central server, and then the system predicts potential collisions for other vehicles based on the provided information. However, in the coordinate transformation of information from vehicles, the system must consider two sources of uncertainty: (1) self-localization uncertainty of the vehicle and (2) local observation uncertainty of the surrounding participants. Based on this motivation, we propose a framework that integrates these two uncertainties in coordinate transformation and then estimates the global state of the traffic participant observed by the vehicle. We validate this framework through both simulation and real-world experiments under conditions that assume on-board sensors of widespread commercial vehicles. In the simulation, we compare the proposed framework with a baseline that does not consider self-localization uncertainty. The results demonstrate that incorporating self-localization uncertainty is crucial for appropriately estimating the overall uncertainty after the coordinate transformation under the assumed on-board sensor setup. In addition, results of real-world experiments demonstrate that the framework can integrate states with appropriate uncertainty. The proposed framework will enable a broader range of vehicles to provide information in V2N2V-based safety systems and contribute to more comprehensive road safety. |
