InterHub: A Naturalistic Trajectory Dataset with Dense Interaction for Autonomous Driving
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916500528431104 |
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| author | Jiang, Xiyan Zhao, Xiaocong Liu, Yiru Li, Zirui Hang, Peng Xiong, Lu Sun, Jian |
| author_facet | Jiang, Xiyan Zhao, Xiaocong Liu, Yiru Li, Zirui Hang, Peng Xiong, Lu Sun, Jian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_18302 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | InterHub: A Naturalistic Trajectory Dataset with Dense Interaction for Autonomous Driving Jiang, Xiyan Zhao, Xiaocong Liu, Yiru Li, Zirui Hang, Peng Xiong, Lu Sun, Jian Robotics 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. |
| title | InterHub: A Naturalistic Trajectory Dataset with Dense Interaction for Autonomous Driving |
| topic | Robotics |
| url | https://arxiv.org/abs/2411.18302 |