Academic Journal
A CNN–Mamba-Based Method for Visual Tire–Road Friction Potential Estimation Under Low-Excitation Variable Working Conditions.
| Τίτλος: | A CNN–Mamba-Based Method for Visual Tire–Road Friction Potential Estimation Under Low-Excitation Variable Working Conditions. |
|---|---|
| Συγγραφείς: | Wu, Ximeng, Han, Yaheng, Li, Zhi, Liang, Fang, Zhu, Jiandong |
| Πηγή: | Vehicles (2624-8921); Jul2026, Vol. 8 Issue 7, p169, 23p |
| Θεματικοί όροι: | Convolutional neural networks, Automobile safety, Static friction, Friction measurements, Prediction models, Image processing |
| Περίληψη: | The road surface adhesion coefficient is a key parameter affecting the dynamic control and driving safety of vehicles. Addressing the limitations of vehicle dynamics-based estimation methods under low-excitation conditions, this paper proposes a vision-perception-based CNN–Mamba hybrid regression network (CMHR-Net) for visual road friction potential estimation. By extracting road surface texture features from camera images, the proposed method predicts representative friction levels associated with different road conditions, providing prior information for vehicle active safety and control systems. First, ResNet18 is used to extract local texture features from road surface images. Second, a Mamba State Space Model is introduced to model long-distance dependencies between features, thereby enhancing global representation capabilities. Finally, the predicted adhesion coefficient value is output through a regression layer. Simultaneously, an adhesion coefficient mapping dataset based on road surface semantic attributes is constructed for model training and validation. Experimental results show that under various road surface conditions, including wet asphalt, waterlogged asphalt, waterlogged concrete, ice and snow, and joints, the proposed method significantly reduces the MAE (Mean Absolute Error) compared to the traditional CNN model. Specifically, under low adhesion conditions (μ ≈ 0.20), the error is reduced by approximately 56.9%. Furthermore, in complex variable conditions, the model significantly outperforms the traditional CNN model in both maximum error (Max Error) and mean absolute percentage error (MAPE). For example, under low adhesion conditions (μ ≈ 0.20), the MAPE decreases from 21.59% to 9.28%, and the maximum error decreases from 0.2025 to 0.0628, demonstrating superior stability and robustness. This method can achieve high-precision feedforward estimation of the adhesion coefficient without relying on vehicle dynamics excitation, providing effective support for feedforward control and active safety systems in vehicles. [ABSTRACT FROM AUTHOR] |
| Copyright of Vehicles (2624-8921) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 195806992 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.42175292969 |
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| Items | – Name: Title Label: Title Group: Ti Data: A CNN–Mamba-Based Method for Visual Tire–Road Friction Potential Estimation Under Low-Excitation Variable Working Conditions. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wu%2C+Ximeng%22">Wu, Ximeng</searchLink><br /><searchLink fieldCode="AR" term="%22Han%2C+Yaheng%22">Han, Yaheng</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Zhi%22">Li, Zhi</searchLink><br /><searchLink fieldCode="AR" term="%22Liang%2C+Fang%22">Liang, Fang</searchLink><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Jiandong%22">Zhu, Jiandong</searchLink> – Name: TitleSource Label: Source Group: Src Data: Vehicles (2624-8921); Jul2026, Vol. 8 Issue 7, p169, 23p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Automobile+safety%22">Automobile safety</searchLink><br /><searchLink fieldCode="DE" term="%22Static+friction%22">Static friction</searchLink><br /><searchLink fieldCode="DE" term="%22Friction+measurements%22">Friction measurements</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The road surface adhesion coefficient is a key parameter affecting the dynamic control and driving safety of vehicles. Addressing the limitations of vehicle dynamics-based estimation methods under low-excitation conditions, this paper proposes a vision-perception-based CNN–Mamba hybrid regression network (CMHR-Net) for visual road friction potential estimation. By extracting road surface texture features from camera images, the proposed method predicts representative friction levels associated with different road conditions, providing prior information for vehicle active safety and control systems. First, ResNet18 is used to extract local texture features from road surface images. Second, a Mamba State Space Model is introduced to model long-distance dependencies between features, thereby enhancing global representation capabilities. Finally, the predicted adhesion coefficient value is output through a regression layer. Simultaneously, an adhesion coefficient mapping dataset based on road surface semantic attributes is constructed for model training and validation. Experimental results show that under various road surface conditions, including wet asphalt, waterlogged asphalt, waterlogged concrete, ice and snow, and joints, the proposed method significantly reduces the MAE (Mean Absolute Error) compared to the traditional CNN model. Specifically, under low adhesion conditions (μ ≈ 0.20), the error is reduced by approximately 56.9%. Furthermore, in complex variable conditions, the model significantly outperforms the traditional CNN model in both maximum error (Max Error) and mean absolute percentage error (MAPE). For example, under low adhesion conditions (μ ≈ 0.20), the MAPE decreases from 21.59% to 9.28%, and the maximum error decreases from 0.2025 to 0.0628, demonstrating superior stability and robustness. This method can achieve high-precision feedforward estimation of the adhesion coefficient without relying on vehicle dynamics excitation, providing effective support for feedforward control and active safety systems in vehicles. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Vehicles (2624-8921) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/vehicles8070169 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 169 Subjects: – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Automobile safety Type: general – SubjectFull: Static friction Type: general – SubjectFull: Friction measurements Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Image processing Type: general Titles: – TitleFull: A CNN–Mamba-Based Method for Visual Tire–Road Friction Potential Estimation Under Low-Excitation Variable Working Conditions. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wu, Ximeng – PersonEntity: Name: NameFull: Han, Yaheng – PersonEntity: Name: NameFull: Li, Zhi – PersonEntity: Name: NameFull: Liang, Fang – PersonEntity: Name: NameFull: Zhu, Jiandong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 26248921 Numbering: – Type: volume Value: 8 – Type: issue Value: 7 Titles: – TitleFull: Vehicles (2624-8921) Type: main |
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