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5G communication delay dataset for cloud-based vehicle planning and control.

Xinrui Zhang1, Lu Xiong1, Peizhi Zhang2

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A new 5G dataset, CICV5G, captures communication delays in cloud-based intelligent connected vehicles (CICVs). This resource aids in developing better delay models and control strategies for safer autonomous driving.

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Area of Science:

  • Intelligent Transportation Systems
  • Wireless Communication Networks
  • Autonomous Driving

Background:

  • Cloud-based intelligent connected vehicles (CICVs) depend on vehicle-to-network-to-vehicle (V2N2V) communication for cooperative planning and control (PnC).
  • Communication delay in V2N2V significantly impacts the control performance and safety of CICVs.
  • Existing research requires comprehensive, real-world data on 5G communication delays in CICV scenarios.

Purpose of the Study:

  • To introduce CICV5G, a novel, publicly available dataset of 5G communication delays specifically for cloud-based CICV applications.
  • To provide a foundational resource for researchers investigating communication delay modeling and developing delay-aware control strategies for CICVs.
  • To facilitate advancements in the safety and efficiency of autonomous driving through better understanding of network performance.

Main Methods:

  • Collected over 150,000 synchronized records from field experiments using a real 5G V2N2V testbed.
  • Captured data across diverse driving environments, network modes (public and private), and vehicle velocities.
  • Recorded millisecond-level timestamps for network indicators (RSRP, SINR, Cell ID) and vehicle motion data (position, heading, velocity).

Main Results:

  • The CICV5G dataset offers a comprehensive collection of synchronized V2N2V communication delay data.
  • Statistical analysis confirmed the validity of delay distributions and network consistency within the dataset.
  • The dataset covers a wide range of operational conditions relevant to CICV applications.

Conclusions:

  • CICV5G provides a reproducible foundation for developing advanced communication delay models and prediction methods.
  • The dataset will enable the creation of more robust cloud-based planning and control strategies for CICVs.
  • This resource is crucial for accelerating research and development in the field of connected and autonomous vehicles.