Survey on Blind Spot Detection Systems: Deep Learning and Ultra-Wideband Approaches
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| Format: | Recurso digital |
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2026
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| _version_ | 1866901825149468672 |
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| author | Martins, Polyanna Torres Santos, Larissa Dos Santos Dias Moura Matos, Joao Gabriel dos santos Manoel da silva, Jaime Oliveira, Matheus Menenguci, Roger Souza, Vitor Amadeu |
| author_facet | Martins, Polyanna Torres Santos, Larissa Dos Santos Dias Moura Matos, Joao Gabriel dos santos Manoel da silva, Jaime Oliveira, Matheus Menenguci, Roger Souza, Vitor Amadeu |
| contents | <p>Blind Spot Detection (BSD) is an essential component of Advanced Driver Assistance Systems (ADAS), contributing directly to the reduction of lateral collisions in urban and road environments. This paper explores the study by Muzammel et al. (2022), which proposes a blind spot collision detection system using multiple convolutional neural networks integrated with object detection architectures, and the work by Sarı et al. (2023), which introduces a system based on Ultra-Wideband (UWB) technology for detecting vulnerable road users. The analysis shows that deep learning approaches present high semantic interpretation capability, while UWB-based systems offer superior robustness in occlusion scenarios. The paper discusses theoretical foundations, architectures, performance, challenges, and future trends, highlighting the integration of multiple technologies as a promising solution for next-generation BSD systems.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19741801 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Survey on Blind Spot Detection Systems: Deep Learning and Ultra-Wideband Approaches Martins, Polyanna Torres Santos, Larissa Dos Santos Dias Moura Matos, Joao Gabriel dos santos Manoel da silva, Jaime Oliveira, Matheus Menenguci, Roger Souza, Vitor Amadeu Blind Spot Detection Deep Learning UWB Computer Vision ADAS <p>Blind Spot Detection (BSD) is an essential component of Advanced Driver Assistance Systems (ADAS), contributing directly to the reduction of lateral collisions in urban and road environments. This paper explores the study by Muzammel et al. (2022), which proposes a blind spot collision detection system using multiple convolutional neural networks integrated with object detection architectures, and the work by Sarı et al. (2023), which introduces a system based on Ultra-Wideband (UWB) technology for detecting vulnerable road users. The analysis shows that deep learning approaches present high semantic interpretation capability, while UWB-based systems offer superior robustness in occlusion scenarios. The paper discusses theoretical foundations, architectures, performance, challenges, and future trends, highlighting the integration of multiple technologies as a promising solution for next-generation BSD systems.</p> |
| title | Survey on Blind Spot Detection Systems: Deep Learning and Ultra-Wideband Approaches |
| topic | Blind Spot Detection Deep Learning UWB Computer Vision ADAS |
| url | https://doi.org/10.5281/zenodo.19741801 |