Vehicle Lateral Velocity Estimation Based on Long Short-Term Memory Network
Lateral velocity is an important parameter to characterize vehicle stability. The acquisition of lateral velocity is of great significance to vehicle stability control and the trajectory following control of autonomous vehicles. Aiming to resolve the problems of poor estimation accuracy caused by th...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/13/1/1 |
| _version_ | 1818057955892789248 |
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| author | Debao Kong Wenhao Wen Rui Zhao Zheng Lv Kewang Liu Yujie Liu Zhenhai Gao |
| author_facet | Debao Kong Wenhao Wen Rui Zhao Zheng Lv Kewang Liu Yujie Liu Zhenhai Gao |
| author_sort | Debao Kong |
| collection | DOAJ |
| description | Lateral velocity is an important parameter to characterize vehicle stability. The acquisition of lateral velocity is of great significance to vehicle stability control and the trajectory following control of autonomous vehicles. Aiming to resolve the problems of poor estimation accuracy caused by the insufficient modeling of traditional model-based methods and significant decline in performance in the case of a change in road friction coefficient, a deep learning method for lateral velocity estimation using an LSTM, long-term and short-term memory network, is designed. LSTM can well reflect the inertial characteristics of vehicles. The training data set contains sensor data under various working conditions and roads. The simulation results show that the prediction model has high accuracy in general and robustness to the change of road friction coefficient. |
| first_indexed | 2024-12-10T12:52:58Z |
| format | Article |
| id | doaj.art-dcc0421c113841649eff8ef86ff6eb59 |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-12-10T12:52:58Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-dcc0421c113841649eff8ef86ff6eb592022-12-22T01:48:12ZengMDPI AGWorld Electric Vehicle Journal2032-66532021-12-01131110.3390/wevj13010001Vehicle Lateral Velocity Estimation Based on Long Short-Term Memory NetworkDebao Kong0Wenhao Wen1Rui Zhao2Zheng Lv3Kewang Liu4Yujie Liu5Zhenhai Gao6State Key Laboratory of Comprehensive Technology on Automobile Vibration and Noise & Safety Control, Changchun 130013, ChinaState Key Laboratory of Automotive Simulation and Control, Changchun 130025, ChinaState Key Laboratory of Automotive Simulation and Control, Changchun 130025, ChinaState Key Laboratory of Comprehensive Technology on Automobile Vibration and Noise & Safety Control, Changchun 130013, ChinaState Key Laboratory of Comprehensive Technology on Automobile Vibration and Noise & Safety Control, Changchun 130013, ChinaState Key Laboratory of Comprehensive Technology on Automobile Vibration and Noise & Safety Control, Changchun 130013, ChinaState Key Laboratory of Automotive Simulation and Control, Changchun 130025, ChinaLateral velocity is an important parameter to characterize vehicle stability. The acquisition of lateral velocity is of great significance to vehicle stability control and the trajectory following control of autonomous vehicles. Aiming to resolve the problems of poor estimation accuracy caused by the insufficient modeling of traditional model-based methods and significant decline in performance in the case of a change in road friction coefficient, a deep learning method for lateral velocity estimation using an LSTM, long-term and short-term memory network, is designed. LSTM can well reflect the inertial characteristics of vehicles. The training data set contains sensor data under various working conditions and roads. The simulation results show that the prediction model has high accuracy in general and robustness to the change of road friction coefficient.https://www.mdpi.com/2032-6653/13/1/1LSTMlateral velocitydeep learningstate estimationvehicle stability |
| spellingShingle | Debao Kong Wenhao Wen Rui Zhao Zheng Lv Kewang Liu Yujie Liu Zhenhai Gao Vehicle Lateral Velocity Estimation Based on Long Short-Term Memory Network World Electric Vehicle Journal LSTM lateral velocity deep learning state estimation vehicle stability |
| title | Vehicle Lateral Velocity Estimation Based on Long Short-Term Memory Network |
| title_full | Vehicle Lateral Velocity Estimation Based on Long Short-Term Memory Network |
| title_fullStr | Vehicle Lateral Velocity Estimation Based on Long Short-Term Memory Network |
| title_full_unstemmed | Vehicle Lateral Velocity Estimation Based on Long Short-Term Memory Network |
| title_short | Vehicle Lateral Velocity Estimation Based on Long Short-Term Memory Network |
| title_sort | vehicle lateral velocity estimation based on long short term memory network |
| topic | LSTM lateral velocity deep learning state estimation vehicle stability |
| url | https://www.mdpi.com/2032-6653/13/1/1 |
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