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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2404.16139 |
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| _version_ | 1866917652147994624 |
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| author | Yazgan, Melih Graf, Thomas Liu, Min Fleck, Tobias Zoellner, J. Marius |
| author_facet | Yazgan, Melih Graf, Thomas Liu, Min Fleck, Tobias Zoellner, J. Marius |
| contents | This survey analyzes intermediate fusion methods in collaborative perception for autonomous driving, categorized by real-world challenges. We examine various methods, detailing their features and the evaluation metrics they employ. The focus is on addressing challenges like transmission efficiency, localization errors, communication disruptions, and heterogeneity. Moreover, we explore strategies to counter adversarial attacks and defenses, as well as approaches to adapt to domain shifts. The objective is to present an overview of how intermediate fusion methods effectively meet these diverse challenges, highlighting their role in advancing the field of collaborative perception in autonomous driving. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16139 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey on Intermediate Fusion Methods for Collaborative Perception Categorized by Real World Challenges Yazgan, Melih Graf, Thomas Liu, Min Fleck, Tobias Zoellner, J. Marius Computer Vision and Pattern Recognition Robotics This survey analyzes intermediate fusion methods in collaborative perception for autonomous driving, categorized by real-world challenges. We examine various methods, detailing their features and the evaluation metrics they employ. The focus is on addressing challenges like transmission efficiency, localization errors, communication disruptions, and heterogeneity. Moreover, we explore strategies to counter adversarial attacks and defenses, as well as approaches to adapt to domain shifts. The objective is to present an overview of how intermediate fusion methods effectively meet these diverse challenges, highlighting their role in advancing the field of collaborative perception in autonomous driving. |
| title | A Survey on Intermediate Fusion Methods for Collaborative Perception Categorized by Real World Challenges |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2404.16139 |