Road Adhesion Coefficient Estimation Based on Vehicle-Road Coordination and Deep Learning
Accurate estimation of the road adhesion coefficient can help drivers and vehicles perceive changes in road state effectively, reducing the occurrence of traffic crashes accordingly. Therefore, this paper proposes a road adhesion coefficient estimation method based on vehicle-road coordination and d...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hindawi-Wiley
2023-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/3633058 |
| _version_ | 1797828234205724672 |
|---|---|
| author | Chunjie Li Pan Liu Zhenlong Xie Zhibin Li Huan Huan |
| author_facet | Chunjie Li Pan Liu Zhenlong Xie Zhibin Li Huan Huan |
| author_sort | Chunjie Li |
| collection | DOAJ |
| description | Accurate estimation of the road adhesion coefficient can help drivers and vehicles perceive changes in road state effectively, reducing the occurrence of traffic crashes accordingly. Therefore, this paper proposes a road adhesion coefficient estimation method based on vehicle-road coordination and deep learning. Firstly, a vehicle-based data feedback system combined with a vehicle-road network cloud is introduced, and CarSim simulation is used to expand the data set and train the model effectively. Then, the dynamic analysis of the whole vehicle is carried out, and the vehicle operation data related to the adhesion coefficient are obtained as the input of the estimation model. Then a combined model of road adhesion coefficient estimation based on self-attention (SA), convolutional neural network (CNN), and long short-term memory (LSTM) is established, to reduce the instability of the prediction, Q-learning is used to optimize the weight of the model. Finally, the model is verified by the simulation data and the actual vehicle-based data. The results show that the vehicle-based data feedback system combined with the vehicle-road network Ccloud is effective, and compared with other commonly used model, the estimation model proposed in this paper can effectively predict the road adhesion coefficient. |
| first_indexed | 2024-04-09T13:01:20Z |
| format | Article |
| id | doaj.art-10d6db4ee9e349418c9156a41140e52f |
| institution | Directory Open Access Journal |
| issn | 2042-3195 |
| language | English |
| last_indexed | 2024-04-09T13:01:20Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi-Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj.art-10d6db4ee9e349418c9156a41140e52f2023-05-13T00:00:10ZengHindawi-WileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/3633058Road Adhesion Coefficient Estimation Based on Vehicle-Road Coordination and Deep LearningChunjie Li0Pan Liu1Zhenlong Xie2Zhibin Li3Huan Huan4School of TransportationSchool of TransportationHebei Provincial Communications Planning and Design InstituteSchool of TransportationVehicle-Road-Cloud-Network (Hebei) Industrial Research CenterAccurate estimation of the road adhesion coefficient can help drivers and vehicles perceive changes in road state effectively, reducing the occurrence of traffic crashes accordingly. Therefore, this paper proposes a road adhesion coefficient estimation method based on vehicle-road coordination and deep learning. Firstly, a vehicle-based data feedback system combined with a vehicle-road network cloud is introduced, and CarSim simulation is used to expand the data set and train the model effectively. Then, the dynamic analysis of the whole vehicle is carried out, and the vehicle operation data related to the adhesion coefficient are obtained as the input of the estimation model. Then a combined model of road adhesion coefficient estimation based on self-attention (SA), convolutional neural network (CNN), and long short-term memory (LSTM) is established, to reduce the instability of the prediction, Q-learning is used to optimize the weight of the model. Finally, the model is verified by the simulation data and the actual vehicle-based data. The results show that the vehicle-based data feedback system combined with the vehicle-road network Ccloud is effective, and compared with other commonly used model, the estimation model proposed in this paper can effectively predict the road adhesion coefficient.http://dx.doi.org/10.1155/2023/3633058 |
| spellingShingle | Chunjie Li Pan Liu Zhenlong Xie Zhibin Li Huan Huan Road Adhesion Coefficient Estimation Based on Vehicle-Road Coordination and Deep Learning Journal of Advanced Transportation |
| title | Road Adhesion Coefficient Estimation Based on Vehicle-Road Coordination and Deep Learning |
| title_full | Road Adhesion Coefficient Estimation Based on Vehicle-Road Coordination and Deep Learning |
| title_fullStr | Road Adhesion Coefficient Estimation Based on Vehicle-Road Coordination and Deep Learning |
| title_full_unstemmed | Road Adhesion Coefficient Estimation Based on Vehicle-Road Coordination and Deep Learning |
| title_short | Road Adhesion Coefficient Estimation Based on Vehicle-Road Coordination and Deep Learning |
| title_sort | road adhesion coefficient estimation based on vehicle road coordination and deep learning |
| url | http://dx.doi.org/10.1155/2023/3633058 |
| work_keys_str_mv | AT chunjieli roadadhesioncoefficientestimationbasedonvehicleroadcoordinationanddeeplearning AT panliu roadadhesioncoefficientestimationbasedonvehicleroadcoordinationanddeeplearning AT zhenlongxie roadadhesioncoefficientestimationbasedonvehicleroadcoordinationanddeeplearning AT zhibinli roadadhesioncoefficientestimationbasedonvehicleroadcoordinationanddeeplearning AT huanhuan roadadhesioncoefficientestimationbasedonvehicleroadcoordinationanddeeplearning |