MIPD: A Multi-sensory Interactive Perception Dataset for Embodied Intelligent Driving
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913575214252032 |
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| author | Li, Zhiwei Zhang, Tingzhen Zhou, Meihua Tang, Dandan Zhang, Pengwei Liu, Wenzhuo Yang, Qiaoning Shen, Tianyu Wang, Kunfeng Liu, Huaping |
| author_facet | Li, Zhiwei Zhang, Tingzhen Zhou, Meihua Tang, Dandan Zhang, Pengwei Liu, Wenzhuo Yang, Qiaoning Shen, Tianyu Wang, Kunfeng Liu, Huaping |
| contents | During the process of driving, humans usually rely on multiple senses to gather information and make decisions. Analogously, in order to achieve embodied intelligence in autonomous driving, it is essential to integrate multidimensional sensory information in order to facilitate interaction with the environment. However, the current multi-modal fusion sensing schemes often neglect these additional sensory inputs, hindering the realization of fully autonomous driving. This paper considers multi-sensory information and proposes a multi-modal interactive perception dataset named MIPD, enabling expanding the current autonomous driving algorithm framework, for supporting the research on embodied intelligent driving. In addition to the conventional camera, lidar, and 4D radar data, our dataset incorporates multiple sensor inputs including sound, light intensity, vibration intensity and vehicle speed to enrich the dataset comprehensiveness. Comprising 126 consecutive sequences, many exceeding twenty seconds, MIPD features over 8,500 meticulously synchronized and annotated frames. Moreover, it encompasses many challenging scenarios, covering various road and lighting conditions. The dataset has undergone thorough experimental validation, producing valuable insights for the exploration of next-generation autonomous driving frameworks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05881 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MIPD: A Multi-sensory Interactive Perception Dataset for Embodied Intelligent Driving Li, Zhiwei Zhang, Tingzhen Zhou, Meihua Tang, Dandan Zhang, Pengwei Liu, Wenzhuo Yang, Qiaoning Shen, Tianyu Wang, Kunfeng Liu, Huaping Robotics During the process of driving, humans usually rely on multiple senses to gather information and make decisions. Analogously, in order to achieve embodied intelligence in autonomous driving, it is essential to integrate multidimensional sensory information in order to facilitate interaction with the environment. However, the current multi-modal fusion sensing schemes often neglect these additional sensory inputs, hindering the realization of fully autonomous driving. This paper considers multi-sensory information and proposes a multi-modal interactive perception dataset named MIPD, enabling expanding the current autonomous driving algorithm framework, for supporting the research on embodied intelligent driving. In addition to the conventional camera, lidar, and 4D radar data, our dataset incorporates multiple sensor inputs including sound, light intensity, vibration intensity and vehicle speed to enrich the dataset comprehensiveness. Comprising 126 consecutive sequences, many exceeding twenty seconds, MIPD features over 8,500 meticulously synchronized and annotated frames. Moreover, it encompasses many challenging scenarios, covering various road and lighting conditions. The dataset has undergone thorough experimental validation, producing valuable insights for the exploration of next-generation autonomous driving frameworks. |
| title | MIPD: A Multi-sensory Interactive Perception Dataset for Embodied Intelligent Driving |
| topic | Robotics |
| url | https://arxiv.org/abs/2411.05881 |