The driving interaction-a critical yet complex aspect of daily driving-lies at the core of autonomous driving research. However, real-world driving scenarios sparsely capture rich interaction events, limiting the availability of comprehensive trajectory datasets for this purpose. To address this challenge, we present InterHub, a dense interaction dataset derived by mining interaction events from extensive naturalistic driving records. We employ formal methods to describe and extract multi-agent interaction events, exposing the limitations of existing autonomous driving solutions. Additionally, we introduce a user-friendly toolkit enabling the expansion of InterHub with both public and private data. By unifying, categorizing, and analyzing diverse interaction events, InterHub facilitates cross-comparative studies and large-scale research, thereby advancing the evaluation and development of autonomous driving technologies.
@article{arxiv.2411.18302,
title = {InterHub: A Naturalistic Trajectory Dataset with Dense Interaction for Autonomous Driving},
author = {Xiyan Jiang and Xiaocong Zhao and Yiru Liu and Zirui Li and Peng Hang and Lu Xiong and Jian Sun},
journal= {arXiv preprint arXiv:2411.18302},
year = {2024}
}