Driving Intention Identification Based on Long Short-Term Memory and A Case Study in Shifting Strategy Optimization
Identification of driving intentions has increasingly attracted wide attention since it can be a valuable reference input of vehicle intelligent control systems. In this study, the long short-term memory (LSTM) is employed to identify the longitudinal intention online with high precision. To this en...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8827465/ |
| _version_ | 1818855567817441280 |
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| author | Yonggang Liu Pan Zhao Datong Qin Guang Li Zheng Chen Yi Zhang |
| author_facet | Yonggang Liu Pan Zhao Datong Qin Guang Li Zheng Chen Yi Zhang |
| author_sort | Yonggang Liu |
| collection | DOAJ |
| description | Identification of driving intentions has increasingly attracted wide attention since it can be a valuable reference input of vehicle intelligent control systems. In this study, the long short-term memory (LSTM) is employed to identify the longitudinal intention online with high precision. To this end, the driving intentions when the vehicle runs on a straight and flat road are divided into five categories. The vehicle driving states such as the vehicle speed and acceleration are pre-processed to label the road test data. Subsequently, a LSTM classification model is established to identify the driving intention with inputs of opening degree of the accelerator pedal, vehicle speed and brake pedal force. Identification results reveal that the highest accuracy of the proposed algorithm attains 95.36%, which is around 20% higher than that of the traditional back propagation neural network. Finally, a driving intention-perceptive gear shifting strategy is developed with the help of the built recognition algorithm, and simulation results highlight that the strategy can effectively reduce the number of shifts and achieve better fuel economy. |
| first_indexed | 2024-12-19T08:10:40Z |
| format | Article |
| id | doaj.art-2e2a1ee04a814992a2c39f5dd4a881b5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T08:10:40Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2e2a1ee04a814992a2c39f5dd4a881b52022-12-21T20:29:39ZengIEEEIEEE Access2169-35362019-01-01712859312860510.1109/ACCESS.2019.29401148827465Driving Intention Identification Based on Long Short-Term Memory and A Case Study in Shifting Strategy OptimizationYonggang Liu0https://orcid.org/0000-0001-5814-104XPan Zhao1Datong Qin2Guang Li3Zheng Chen4https://orcid.org/0000-0002-1634-7231Yi Zhang5State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, ChinaState Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, ChinaState Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, ChinaSchool of Engineering and Materials Science, Queen Mary University of London, London, U.K.Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, ChinaDepartment of Mechanical Engineering, University of Michigan–Dearborn, Dearborn, MI, USAIdentification of driving intentions has increasingly attracted wide attention since it can be a valuable reference input of vehicle intelligent control systems. In this study, the long short-term memory (LSTM) is employed to identify the longitudinal intention online with high precision. To this end, the driving intentions when the vehicle runs on a straight and flat road are divided into five categories. The vehicle driving states such as the vehicle speed and acceleration are pre-processed to label the road test data. Subsequently, a LSTM classification model is established to identify the driving intention with inputs of opening degree of the accelerator pedal, vehicle speed and brake pedal force. Identification results reveal that the highest accuracy of the proposed algorithm attains 95.36%, which is around 20% higher than that of the traditional back propagation neural network. Finally, a driving intention-perceptive gear shifting strategy is developed with the help of the built recognition algorithm, and simulation results highlight that the strategy can effectively reduce the number of shifts and achieve better fuel economy.https://ieeexplore.ieee.org/document/8827465/Driving intentionlong short-term memory (LSTM)back propagation neural network (BPNN)dual clutch transmission (DCT)shift strategyfuel economy |
| spellingShingle | Yonggang Liu Pan Zhao Datong Qin Guang Li Zheng Chen Yi Zhang Driving Intention Identification Based on Long Short-Term Memory and A Case Study in Shifting Strategy Optimization IEEE Access Driving intention long short-term memory (LSTM) back propagation neural network (BPNN) dual clutch transmission (DCT) shift strategy fuel economy |
| title | Driving Intention Identification Based on Long Short-Term Memory and A Case Study in Shifting Strategy Optimization |
| title_full | Driving Intention Identification Based on Long Short-Term Memory and A Case Study in Shifting Strategy Optimization |
| title_fullStr | Driving Intention Identification Based on Long Short-Term Memory and A Case Study in Shifting Strategy Optimization |
| title_full_unstemmed | Driving Intention Identification Based on Long Short-Term Memory and A Case Study in Shifting Strategy Optimization |
| title_short | Driving Intention Identification Based on Long Short-Term Memory and A Case Study in Shifting Strategy Optimization |
| title_sort | driving intention identification based on long short term memory and a case study in shifting strategy optimization |
| topic | Driving intention long short-term memory (LSTM) back propagation neural network (BPNN) dual clutch transmission (DCT) shift strategy fuel economy |
| url | https://ieeexplore.ieee.org/document/8827465/ |
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