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Matching and Fusing Multi-Source Vehicle Information in Highway Gantry Scenarios Using Convolutional Neural Networks Southeast University, School of Transportation
- Format:
- Book
- Conference/Event
- Author/Creator:
- Wang, Junjun, author.
- Conference Name:
- 2024 International Conference on Smart Transportation Interdisciplinary Studies (2024-12-13 : Nanjing, China)
- Language:
- English
- Physical Description:
- 1 online resource cm
- Place of Publication:
- Warrendale, PA SAE International 2025
- Summary:
- The performance differences of multiple sensors lead to inconsistencies, incompleteness, and distortion in the perception data of multi-source vehicle information in highway scenarios. Optimizing data fusion methods is important for intelligent toll collection systems on highways. First, this paper constructs a dataset for matching and fusing multi-source vehicle information in highway gantry scenarios. Second, it develops convolutional neural network models, Match-Pyramid-MVIMF-EGS and CDSSM-MVIMF-EGS, for this purpose. Finally, comparative experiments are conducted based on the constructed dataset to assess the performance of the Match-Pyramid-MVIMF-EGS and CDSSM-MVIMF-EGS models. The experimental results indicate that the Match-Pyramid-MVIMF-EGS model performs better than the CDSSM-MVIMF-EGS model, achieving matching and fusion accuracy of 93.07%, precision of 95.71%, recall of 89.17%, F1 scores of 92.32%, and 186 of training throughput respectively
- Notes:
- Vendor supplied data
- Publisher Number:
- 2025-01-7136
- Access Restriction:
- Restricted for use by site license
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