An interactive enhanced driving dataset for autonomous driving
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arXiv
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| Hauptverfasser: | , , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866915813159600128 |
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| author | Feng, Haojie Zhang, Peizhi Tian, Mengjie Zhang, Xinrui Li, Zhuoren Huang, Junpeng Wang, Xiurong Zhu, Junfan Wang, Jianzhou Yin, Dongxiao Xiong, Lu |
| author_facet | Feng, Haojie Zhang, Peizhi Tian, Mengjie Zhang, Xinrui Li, Zhuoren Huang, Junpeng Wang, Xiurong Zhu, Junfan Wang, Jianzhou Yin, Dongxiao Xiong, Lu |
| contents | The evolution of autonomous driving towards full automation demands robust interactive capabilities; however, the development of Vision-Language-Action (VLA) models is constrained by the sparsity of interactive scenarios and inadequate multimodal alignment in existing data. To this end, this paper proposes the Interactive Enhanced Driving Dataset (IEDD). We develop a scalable pipeline to mine million-level interactive segments from naturalistic driving data based on interactive trajectories, and design metrics to quantify the interaction processes. Furthermore, the IEDD-VQA dataset is constructed by generating synthetic Bird's Eye View (BEV) videos where semantic actions are strictly aligned with structured language. Benchmark results evaluating ten mainstream Vision Language Models (VLMs) are provided to demonstrate the dataset's reuse value in assessing and fine-tuning the reasoning capabilities of autonomous driving models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20575 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An interactive enhanced driving dataset for autonomous driving Feng, Haojie Zhang, Peizhi Tian, Mengjie Zhang, Xinrui Li, Zhuoren Huang, Junpeng Wang, Xiurong Zhu, Junfan Wang, Jianzhou Yin, Dongxiao Xiong, Lu Computer Vision and Pattern Recognition The evolution of autonomous driving towards full automation demands robust interactive capabilities; however, the development of Vision-Language-Action (VLA) models is constrained by the sparsity of interactive scenarios and inadequate multimodal alignment in existing data. To this end, this paper proposes the Interactive Enhanced Driving Dataset (IEDD). We develop a scalable pipeline to mine million-level interactive segments from naturalistic driving data based on interactive trajectories, and design metrics to quantify the interaction processes. Furthermore, the IEDD-VQA dataset is constructed by generating synthetic Bird's Eye View (BEV) videos where semantic actions are strictly aligned with structured language. Benchmark results evaluating ten mainstream Vision Language Models (VLMs) are provided to demonstrate the dataset's reuse value in assessing and fine-tuning the reasoning capabilities of autonomous driving models. |
| title | An interactive enhanced driving dataset for autonomous driving |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.20575 |