A hierarchical prediction model for lane-changes based on combination of fuzzy C-means and adaptive neural network
•A hierarchical prediction model is proposed to predict steering angles.•The model combines fuzzy c-means and adaptive neural network.•A clustering learning method is adopted to optimize parameters of sub neural network.•Experiments are conducted in the driving simulator under different scenarios.•P...
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| Veröffentlicht in: | Expert systems with applications Jg. 130; S. 265 - 275 |
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15.09.2019
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | •A hierarchical prediction model is proposed to predict steering angles.•The model combines fuzzy c-means and adaptive neural network.•A clustering learning method is adopted to optimize parameters of sub neural network.•Experiments are conducted in the driving simulator under different scenarios.•Prediction results show the model can achieve high performance.
Lane changing behavior generally expresses uncertainty due to the impact of environmental factors, and unreasonable lane changes can cause serious collisions. High precision prediction of lane changing intent is helpful to enhance proactivity in driving safety protection. This study proposed a lane-changing prediction model based on Fuzzy C-means clustering algorithm and adaptive Neural Network (FCMNN), which introduced a new prediction process: (1) Unsupervised learning method: categorize original dataset into different clusters according to their distribution features; (2) Supervised learning method: optimize sub-Neural Network structures and weighting parameters for each cluster or pattern. Through comparing with several traditional methods under different simulation scenarios, the proposed model effectively improve the prediction performance and stability. The results obtained in this study will be helpful to deeply analyze the intent recognition of driving behavior, improve the safety of lane-changing behavior, and provide key technology in driving prediction of Advanced Driver Assistance System (ADAS). |
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| AbstractList | •A hierarchical prediction model is proposed to predict steering angles.•The model combines fuzzy c-means and adaptive neural network.•A clustering learning method is adopted to optimize parameters of sub neural network.•Experiments are conducted in the driving simulator under different scenarios.•Prediction results show the model can achieve high performance.
Lane changing behavior generally expresses uncertainty due to the impact of environmental factors, and unreasonable lane changes can cause serious collisions. High precision prediction of lane changing intent is helpful to enhance proactivity in driving safety protection. This study proposed a lane-changing prediction model based on Fuzzy C-means clustering algorithm and adaptive Neural Network (FCMNN), which introduced a new prediction process: (1) Unsupervised learning method: categorize original dataset into different clusters according to their distribution features; (2) Supervised learning method: optimize sub-Neural Network structures and weighting parameters for each cluster or pattern. Through comparing with several traditional methods under different simulation scenarios, the proposed model effectively improve the prediction performance and stability. The results obtained in this study will be helpful to deeply analyze the intent recognition of driving behavior, improve the safety of lane-changing behavior, and provide key technology in driving prediction of Advanced Driver Assistance System (ADAS). Lane changing behavior generally expresses uncertainty due to the impact of environmental factors, and unreasonable lane changes can cause serious collisions. High precision prediction of lane changing intent is helpful to enhance proactivity in driving safety protection. This study proposed a lane-changing prediction model based on Fuzzy C-means clustering algorithm and adaptive Neural Network (FCMNN), which introduced a new prediction process: (1) Unsupervised learning method: categorize original dataset into different clusters according to their distribution features; (2) Supervised learning method: optimize sub-Neural Network structures and weighting parameters for each cluster or pattern. Through comparing with several traditional methods under different simulation scenarios, the proposed model effectively improve the prediction performance and stability. The results obtained in this study will be helpful to deeply analyze the intent recognition of driving behavior, improve the safety of lane-changing behavior, and provide key technology in driving prediction of Advanced Driver Assistance System (ADAS). |
| Author | Tang, Jinjun Huang, Helai Yu, Shaowei Chen, Xinqiang Liu, Fang |
| Author_xml | – sequence: 1 givenname: Jinjun surname: Tang fullname: Tang, Jinjun email: jinjuntang@cus.edu.cn organization: School of Traffic and Transportation Engineering, Smart Transport Key Laboratory of Hunan Province, Central South University, Changsha, 410075, China – sequence: 2 givenname: Shaowei surname: Yu fullname: Yu, Shaowei email: swyu2016@chd.edu.cn organization: The Joint Laboratory for Internet of Vehicles, Ministry of Education-China Mobile Communications Corporation, Chang'an University, Xi'an, 710064, China – sequence: 3 givenname: Fang surname: Liu fullname: Liu, Fang email: rcliufang@163.com organization: School of Transportation Engineering, Changsha University of Science and Technology, Changsha, 410205, China – sequence: 4 givenname: Xinqiang surname: Chen fullname: Chen, Xinqiang email: chenxinqiang@stu.shmtu.edu.cn organization: Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, 2013066, China – sequence: 5 givenname: Helai surname: Huang fullname: Huang, Helai email: huanghelai@csu.edu.cn organization: School of Traffic and Transportation Engineering, Smart Transport Key Laboratory of Hunan Province, Central South University, Changsha, 410075, China |
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| Keywords | Driving simulation Lane changes Neural network Fuzzy C-means algorithm Driving prediction |
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| Snippet | •A hierarchical prediction model is proposed to predict steering angles.•The model combines fuzzy c-means and adaptive neural network.•A clustering learning... Lane changing behavior generally expresses uncertainty due to the impact of environmental factors, and unreasonable lane changes can cause serious collisions.... |
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| SubjectTerms | Adaptive algorithms Advanced driver assistance systems Artificial neural networks Clustering Computer simulation Driving prediction Driving simulation Environmental impact Fuzzy C-means algorithm Fuzzy logic Lane changes Lane changing Neural network Neural networks Supervised learning Teaching methods Vehicle safety |
| Title | A hierarchical prediction model for lane-changes based on combination of fuzzy C-means and adaptive neural network |
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