Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network
Driving intention prediction is one of the key technologies for the development of advanced assisted driving systems (ADAS), which could greatly reduce traffic accidents caused by lane change and ensure driving safety. In this paper, an advanced predictive method based on Multi-LSTM (Long Short-Term...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9146622/ |
| _version_ | 1829471964085878784 |
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| author | Liang Tang Hengyang Wang Wenhao Zhang Zhongyi Mei Liang Li |
| author_facet | Liang Tang Hengyang Wang Wenhao Zhang Zhongyi Mei Liang Li |
| author_sort | Liang Tang |
| collection | DOAJ |
| description | Driving intention prediction is one of the key technologies for the development of advanced assisted driving systems (ADAS), which could greatly reduce traffic accidents caused by lane change and ensure driving safety. In this paper, an advanced predictive method based on Multi-LSTM (Long Short-Term Memory) is proposed to predict lane change intention effectively. First, the training data set and test set based on real road information data set NGSIM (Next Generation SIMulation) are built considering ego vehicle driving state and the influence of surrounding vehicles. Second, the Multi-LSTM-based prediction controller is constructed to learn vehicle behavior characteristics and time series relation of various states in the process of lane change. Then, the influences of prediction model structure change and data structure change on test results are verified. Finally, the verification tests based on HIL (Hardware-in-the-Loop) simulation are constructed. The results show that the proposed prediction model can accurately predict the vehicle lane change intention in highway scenarios and the maximum prediction accuracy can reach 83.75%, which is higher than that of common method SVM (Support Vector Machine). |
| first_indexed | 2024-12-14T02:05:19Z |
| format | Article |
| id | doaj.art-993b45367eef46faaab0d9aacd435cb6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T02:05:19Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-993b45367eef46faaab0d9aacd435cb62022-12-21T23:20:55ZengIEEEIEEE Access2169-35362020-01-01813689813690510.1109/ACCESS.2020.30115509146622Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory NetworkLiang Tang0https://orcid.org/0000-0002-2012-5367Hengyang Wang1https://orcid.org/0000-0002-6054-243XWenhao Zhang2https://orcid.org/0000-0002-1035-0608Zhongyi Mei3https://orcid.org/0000-0003-2844-0908Liang Li4https://orcid.org/0000-0002-1577-408XSchool of Technology, Beijing Forestry University, Beijing, ChinaSchool of Electrical Engineering, Beijing Jiaotong University, Beijing, ChinaState Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, ChinaSchool of Technology, Beijing Forestry University, Beijing, ChinaState Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, ChinaDriving intention prediction is one of the key technologies for the development of advanced assisted driving systems (ADAS), which could greatly reduce traffic accidents caused by lane change and ensure driving safety. In this paper, an advanced predictive method based on Multi-LSTM (Long Short-Term Memory) is proposed to predict lane change intention effectively. First, the training data set and test set based on real road information data set NGSIM (Next Generation SIMulation) are built considering ego vehicle driving state and the influence of surrounding vehicles. Second, the Multi-LSTM-based prediction controller is constructed to learn vehicle behavior characteristics and time series relation of various states in the process of lane change. Then, the influences of prediction model structure change and data structure change on test results are verified. Finally, the verification tests based on HIL (Hardware-in-the-Loop) simulation are constructed. The results show that the proposed prediction model can accurately predict the vehicle lane change intention in highway scenarios and the maximum prediction accuracy can reach 83.75%, which is higher than that of common method SVM (Support Vector Machine).https://ieeexplore.ieee.org/document/9146622/Intelligent vehiclelane changedriving intention predictionadvanced assisted driving systemsmulti-LSTM |
| spellingShingle | Liang Tang Hengyang Wang Wenhao Zhang Zhongyi Mei Liang Li Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network IEEE Access Intelligent vehicle lane change driving intention prediction advanced assisted driving systems multi-LSTM |
| title | Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network |
| title_full | Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network |
| title_fullStr | Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network |
| title_full_unstemmed | Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network |
| title_short | Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network |
| title_sort | driver lane change intention recognition of intelligent vehicle based on long short term memory network |
| topic | Intelligent vehicle lane change driving intention prediction advanced assisted driving systems multi-LSTM |
| url | https://ieeexplore.ieee.org/document/9146622/ |
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