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A naturalistic trajectory dataset with dense interaction for autonomous driving.

Xiyan Jiang1, Xiaocong Zhao2, Yiru Liu1

  • 1Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai, 201804, China.

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InterHub is a new dataset for analyzing multi-agent driving interactions, crucial for autonomous driving systems. It includes tools to expand the dataset, improving algorithm evaluation and research reproducibility.

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Area of Science:

  • Autonomous Driving Systems
  • Artificial Intelligence
  • Robotics

Background:

  • Driving interaction is vital for autonomous driving but underrepresented in current trajectory datasets.
  • Existing datasets and methods have limitations in capturing and analyzing complex multi-agent interactions.

Purpose of the Study:

  • Introduce InterHub, a novel dataset of dense multi-agent interaction events.
  • Provide tools for extracting and expanding interaction data to support autonomous driving research.
  • Establish a unified framework for interaction analysis and benchmarking.

Main Methods:

  • Utilized formal methods for describing and extracting multi-agent interaction events.
  • Curated a dataset from large-scale naturalistic driving recordings.
  • Developed an open-source toolkit for dataset expansion and analysis.

Main Results:

  • Created InterHub, a comprehensive dataset of multi-agent driving interactions.
  • Demonstrated limitations in current autonomous driving solutions through interaction analysis.
  • Developed an extensible toolkit for mining and annotating interaction events.

Conclusions:

  • InterHub addresses the need for rich, curated interaction data in autonomous driving.
  • The accompanying toolkit promotes scalability, reproducibility, and cross-dataset comparisons.
  • Facilitates advancements in interaction behavior modeling and algorithm benchmarking for self-driving technologies.