Identification of winter road friction coefficient based on multi-task distillation attention network-Reference-Cited by-同舟云学术

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Identification of winter road friction coefficient based on multi-task distillation attention network

Published:2022-02-15 Issue:2 Volume:25 Page:441-449
ISSN:1433-7541
Container-title:Pattern Analysis and Applications
language:en
Short-container-title:Pattern Anal Applic

Author:

Liu Feilin,Wu Yan,Yang Xinneng,Mo Yujian,Liao Yujun

Funder

national natural science foundation of china

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition

Link

https://link.springer.com/content/pdf/10.1007/s10044-022-01059-2.pdf

Reference40 articles.

1. Virgilio G VR, Sossa H, Zamora E (2020) Vision-based blind spot warning system by deep neural networks. In: Mexican conference on pattern recognition, Springer, pp 185–194

2. Qian Y, Almazan EJ, Elder JH (2016) Evaluating features and classifiers for road weather condition analysis. In: 2016 IEEE international conference on image processing (ICIP), IEEE, pp 4403–4407

3. Shibata Y, Sakuraba A, Arai Y, Saito Y, Hakura J (2021) Decision analysis of winter road conditions by crowd sensing platform. In: International conference on emerging internetworking. Springer, Data & Web Technologies, pp 276–287

4. Qi Z, Taheri S, Wang B, Yu H (2015) Estimation of the tyre-road maximum friction coefficient and slip slope based on a novel tyre model. Vehicle Syst Dyn 53(4):506–525

5. Wu Y, Liu F, Guan L, Yang X (2020) A survey of vision-based road parameter estimating methods. In: International conference on intelligent computing, Springer, pp 314–325

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