Integrating Multi-Modal Sensors: A Review of Fusion Techniques for Intelligent Vehicles
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915754153082880 |
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| author | Wei, Chuheng Qin, Ziye Zhang, Ziyan Wu, Guoyuan Barth, Matthew J. |
| author_facet | Wei, Chuheng Qin, Ziye Zhang, Ziyan Wu, Guoyuan Barth, Matthew J. |
| contents | Multi-sensor fusion plays a critical role in enhancing perception for autonomous driving, overcoming individual sensor limitations, and enabling comprehensive environmental understanding. This paper first formalizes multi-sensor fusion strategies into data-level, feature-level, and decision-level categories and then provides a systematic review of deep learning-based methods corresponding to each strategy. We present key multi-modal datasets and discuss their applicability in addressing real-world challenges, particularly in adverse weather conditions and complex urban environments. Additionally, we explore emerging trends, including the integration of Vision-Language Models (VLMs), Large Language Models (LLMs), and the role of sensor fusion in end-to-end autonomous driving, highlighting its potential to enhance system adaptability and robustness. Our work offers valuable insights into current methods and future directions for multi-sensor fusion in autonomous driving. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21885 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Integrating Multi-Modal Sensors: A Review of Fusion Techniques for Intelligent Vehicles Wei, Chuheng Qin, Ziye Zhang, Ziyan Wu, Guoyuan Barth, Matthew J. Computer Vision and Pattern Recognition Multimedia Robotics Multi-sensor fusion plays a critical role in enhancing perception for autonomous driving, overcoming individual sensor limitations, and enabling comprehensive environmental understanding. This paper first formalizes multi-sensor fusion strategies into data-level, feature-level, and decision-level categories and then provides a systematic review of deep learning-based methods corresponding to each strategy. We present key multi-modal datasets and discuss their applicability in addressing real-world challenges, particularly in adverse weather conditions and complex urban environments. Additionally, we explore emerging trends, including the integration of Vision-Language Models (VLMs), Large Language Models (LLMs), and the role of sensor fusion in end-to-end autonomous driving, highlighting its potential to enhance system adaptability and robustness. Our work offers valuable insights into current methods and future directions for multi-sensor fusion in autonomous driving. |
| title | Integrating Multi-Modal Sensors: A Review of Fusion Techniques for Intelligent Vehicles |
| topic | Computer Vision and Pattern Recognition Multimedia Robotics |
| url | https://arxiv.org/abs/2506.21885 |