Researchers have introduced MINT-V2X, a new dataset designed to bridge the gap in vehicle-to-everything (V2X) communication research by integrating both mobility and wireless network parameters. This comprehensive dataset was generated by combining traffic simulations from SUMO with network simulations from OMNeT++/Simu5G, adhering to 3GPP Release 14 and ETSI standards. MINT-V2X contains over 9.87 million synchronized data points from 1,386 vehicles, offering a realistic urban traffic scenario. The dataset's utility is demonstrated through a case study on roadside unit load prediction, showing improved performance when trajectory data is incorporated. AI
IMPACT Provides integrated mobility and network data crucial for developing advanced AI-driven predictive resource management in V2X systems.
RANK_REASON Publication of a new research dataset and paper on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
- Abdolazim Rezaei
- CQI-SINR
- European Telecommunications Standards Institute
- GitHub
- MINT-V2X
- OMNeT++
- Release 14
- Shannon
- Simu5G
- SINR-PDR
- SUMO
- vehicle-to-everything
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
