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Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization
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Published on: November 12, 2014

A Full-Chain Dataset for Intelligent New Energy Vehicles based on High-fidelity Digital Twin Platform.

Zhengxian Chen1,2, Yuyang Pan1,2, Tianyang Gong1,2

  • 1School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China.

Scientific Data
|June 20, 2026
PubMed
Summary
This summary is machine-generated.

A new dataset, 3ITVP (Intelligent Transportation, Intelligent Vehicle, Intelligent Power), offers comprehensive data for intelligent new energy vehicles. It supports system-level research by covering diverse vehicles, driving behaviors, and energy responses.

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Area of Science:

  • Intelligent Transportation Systems
  • Automotive Engineering
  • Energy Systems

Background:

  • Increasing demand for integrated datasets characterizing traffic environments, vehicle control, and energy consumption in intelligent new energy vehicles.
  • Existing datasets are insufficient for system-level closed-loop studies due to a focus on perception or isolated research areas.

Purpose of the Study:

  • To introduce the 3ITVP (Intelligent Transportation, Intelligent Vehicle, Intelligent Power) dataset, a novel full-chain dataset designed for comprehensive research.
  • To provide a high-fidelity dataset supporting system-level studies in intelligent transportation and new energy vehicles.

Main Methods:

  • Development of a high-fidelity digital twin platform to generate the dataset.
  • Collection of rigorously time-synchronized multimodal data from five vehicle types (cars, light trucks, semi-trailers, logistics vehicles, buses).
  • Inclusion of diverse driving conditions, behaviors, special event cases, and long-horizon continuous driving data.

Main Results:

  • The 3ITVP dataset systematically covers key aspects of intelligent transportation, autonomous driving, and new energy technologies.
  • The dataset captures driving conditions and behaviors across various vehicle types, including electric and fuel vehicles.
  • Comprehensive data supports research across perception, decision-making, planning, control, energy management, and Sim-to-Real transfer.

Conclusions:

  • The 3ITVP dataset addresses the limitations of existing datasets by providing a holistic view for intelligent vehicle research.
  • It serves as a valuable resource for advancing the development and deployment of intelligent new energy vehicles and systems.