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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/2405.05579 |
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| _version_ | 1866911871406178304 |
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| author | Yang, Junyi Xu, Zefei Lai, Huayi Chen, Hongjian Kong, Sifan Wu, Yutong Yang, Huan |
| author_facet | Yang, Junyi Xu, Zefei Lai, Huayi Chen, Hongjian Kong, Sifan Wu, Yutong Yang, Huan |
| contents | Sudden glare from trailing vehicles significantly increases driving safety risks. Existing anti-glare technologies such as electronic, manually-adjusted, and electrochromic rearview mirrors, are expensive and lack effective adaptability in different lighting conditions. To address these issues, our research introduces an intelligent rearview mirror system utilizing novel all-liquid electrochromic technology. This system integrates IoT with ensemble and federated learning within a cloud edge collaboration framework, dynamically controlling voltage to effectively eliminate glare and maintain clear visibility. Utilizing an ensemble learning model, it automatically adjusts mirror transmittance based on light intensity, achieving a low RMSE of 0.109 on the test set. Furthermore, the system leverages federated learning for distributed data training across devices, which enhances privacy and updates the cloud model continuously. Distinct from conventional methods, our experiment utilizes the Schmidt-Clausen and Bindels de Boer 9-point scale with TOPSIS for comprehensive evaluation of rearview mirror glare. Designed to be convenient and costeffective, this system demonstrates how IoT and AI can significantly enhance rearview mirror anti-glare performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05579 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Intelligent EC Rearview Mirror: Enhancing Driver Safety with Dynamic Glare Mitigation via Cloud Edge Collaboration Yang, Junyi Xu, Zefei Lai, Huayi Chen, Hongjian Kong, Sifan Wu, Yutong Yang, Huan Human-Computer Interaction Systems and Control Sudden glare from trailing vehicles significantly increases driving safety risks. Existing anti-glare technologies such as electronic, manually-adjusted, and electrochromic rearview mirrors, are expensive and lack effective adaptability in different lighting conditions. To address these issues, our research introduces an intelligent rearview mirror system utilizing novel all-liquid electrochromic technology. This system integrates IoT with ensemble and federated learning within a cloud edge collaboration framework, dynamically controlling voltage to effectively eliminate glare and maintain clear visibility. Utilizing an ensemble learning model, it automatically adjusts mirror transmittance based on light intensity, achieving a low RMSE of 0.109 on the test set. Furthermore, the system leverages federated learning for distributed data training across devices, which enhances privacy and updates the cloud model continuously. Distinct from conventional methods, our experiment utilizes the Schmidt-Clausen and Bindels de Boer 9-point scale with TOPSIS for comprehensive evaluation of rearview mirror glare. Designed to be convenient and costeffective, this system demonstrates how IoT and AI can significantly enhance rearview mirror anti-glare performance. |
| title | Intelligent EC Rearview Mirror: Enhancing Driver Safety with Dynamic Glare Mitigation via Cloud Edge Collaboration |
| topic | Human-Computer Interaction Systems and Control |
| url | https://arxiv.org/abs/2405.05579 |