Lane-Changing Behavior Prediction Based on Game Theory and Deep Learning
Lane changing is an important scenario in traffic environments, and accurate prediction of lane-changing behavior is essential to ensure traffic and driver safety. To achieve this goal, a vehicle lane-changing prediction model based on game theory and deep learning is developed. In the game theory c...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/6634960 |
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| _version_ | 1832565248355205120 |
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| author | Shuo Jia Fei Hui Cheng Wei Xiangmo Zhao Jianbei Liu |
| author_facet | Shuo Jia Fei Hui Cheng Wei Xiangmo Zhao Jianbei Liu |
| author_sort | Shuo Jia |
| collection | DOAJ |
| description | Lane changing is an important scenario in traffic environments, and accurate prediction of lane-changing behavior is essential to ensure traffic and driver safety. To achieve this goal, a vehicle lane-changing prediction model based on game theory and deep learning is developed. In the game theory component, the interaction between vehicles during lane changing is analyzed according to the running state of the vehicle, with the probability of lane changing as its output. For the deep-learning component, long short-term memory and a convolutional neural network are used to extract and learn data features during the lane-changing process as well as combine the output of the game theory component to obtain the prediction result of whether the vehicle will change lanes. By using an open-source traffic dataset to train and verify the proposed model, the verification results show that the prediction accuracy can reach 94.56% within 0.4 s of lane-changing operation and that the model can achieve timely and accurate prediction of the lane-changing behavior of vehicles. |
| format | Article |
| id | doaj-art-90401bdb545742e09024d44104a1bbf4 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-90401bdb545742e09024d44104a1bbf42025-02-03T01:08:52ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/66349606634960Lane-Changing Behavior Prediction Based on Game Theory and Deep LearningShuo Jia0Fei Hui1Cheng Wei2Xiangmo Zhao3Jianbei Liu4School of Information Engineering, Chang’an University, Xi’an 710000, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710000, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710000, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710000, ChinaResearch and Development Center on Emergency Support Technologies for Transport, CCCC First Highway Consultants Co., Ltd., Xi’an 710000, ChinaLane changing is an important scenario in traffic environments, and accurate prediction of lane-changing behavior is essential to ensure traffic and driver safety. To achieve this goal, a vehicle lane-changing prediction model based on game theory and deep learning is developed. In the game theory component, the interaction between vehicles during lane changing is analyzed according to the running state of the vehicle, with the probability of lane changing as its output. For the deep-learning component, long short-term memory and a convolutional neural network are used to extract and learn data features during the lane-changing process as well as combine the output of the game theory component to obtain the prediction result of whether the vehicle will change lanes. By using an open-source traffic dataset to train and verify the proposed model, the verification results show that the prediction accuracy can reach 94.56% within 0.4 s of lane-changing operation and that the model can achieve timely and accurate prediction of the lane-changing behavior of vehicles.http://dx.doi.org/10.1155/2021/6634960 |
| spellingShingle | Shuo Jia Fei Hui Cheng Wei Xiangmo Zhao Jianbei Liu Lane-Changing Behavior Prediction Based on Game Theory and Deep Learning Journal of Advanced Transportation |
| title | Lane-Changing Behavior Prediction Based on Game Theory and Deep Learning |
| title_full | Lane-Changing Behavior Prediction Based on Game Theory and Deep Learning |
| title_fullStr | Lane-Changing Behavior Prediction Based on Game Theory and Deep Learning |
| title_full_unstemmed | Lane-Changing Behavior Prediction Based on Game Theory and Deep Learning |
| title_short | Lane-Changing Behavior Prediction Based on Game Theory and Deep Learning |
| title_sort | lane changing behavior prediction based on game theory and deep learning |
| url | http://dx.doi.org/10.1155/2021/6634960 |
| work_keys_str_mv | AT shuojia lanechangingbehaviorpredictionbasedongametheoryanddeeplearning AT feihui lanechangingbehaviorpredictionbasedongametheoryanddeeplearning AT chengwei lanechangingbehaviorpredictionbasedongametheoryanddeeplearning AT xiangmozhao lanechangingbehaviorpredictionbasedongametheoryanddeeplearning AT jianbeiliu lanechangingbehaviorpredictionbasedongametheoryanddeeplearning |