Study on the Prediction of Lane Change Intention of Intelligent Vehicles in the Network Environment
The prediction of lane change intention of vehicles is an important part of the decision planning and control systems of intelligent vehicles. In the dynamic and complex traffic environment, the behaviors of traffic participants interact and influence each other. In lane change prediction, it is nec...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/12/1/27 |
| _version_ | 1797396554020028416 |
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| author | Shuaishuai Liu Di Tan Shilin Hong Hongxun Fu |
| author_facet | Shuaishuai Liu Di Tan Shilin Hong Hongxun Fu |
| author_sort | Shuaishuai Liu |
| collection | DOAJ |
| description | The prediction of lane change intention of vehicles is an important part of the decision planning and control systems of intelligent vehicles. In the dynamic and complex traffic environment, the behaviors of traffic participants interact and influence each other. In lane change prediction, it is necessary to study the predicted vehicle and surrounding vehicles as an interactive correlation system. Otherwise, great errors are made in the motion prediction. Based on this, the motion state of the predicted vehicle, the position relationship between the predicted vehicle and lane, as well as the motion state of vehicles around the predicted vehicle are considered systematically in this paper, and the prediction of lane change intention of vehicles is studied. The influence of the three above-mentioned factors on the prediction of lane change intention is analyzed in this paper. On the basis of screening the prediction features of lane change intention, the lane change intention of vehicles is predicted by a feed-forward neural network. The data collected by the virtual driving experiment platform are divided into a training set, a verification set, and a test set. The neural network parameters of vehicles’ lane change intentions are identified by a training set, and the effect of prediction is tested by a verification set and a test set. The results show that the accuracy of the prediction model is high. The model is compared with the model of common features at the present stage and the model based on a Support Vector Machine, and the results show that the accuracy of the prediction model proposed in this paper was improved by 6.4% and 2.8%, respectively, compared with the two models. Finally, the virtual driving experiment platform was used to predict the lane change intention of the front vehicle and the vehicle in the left adjacent lane. The results show that, based on the same model and input features, the lane change intention of the front vehicle and the vehicle in the left adjacent lane can be predicted by the model at 2.8 s and 3.4 s before the lane change, and the model is a certain generality for the prediction of lane change intention of adjacent vehicles. |
| first_indexed | 2024-03-09T00:53:04Z |
| format | Article |
| id | doaj.art-eab8dfc631c441a29014c2027d2f642c |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-09T00:53:04Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-eab8dfc631c441a29014c2027d2f642c2023-12-11T17:05:38ZengMDPI AGWorld Electric Vehicle Journal2032-66532021-02-011212710.3390/wevj12010027Study on the Prediction of Lane Change Intention of Intelligent Vehicles in the Network EnvironmentShuaishuai Liu0Di Tan1Shilin Hong2Hongxun Fu3School of Transportation and Vehicle Engineering, Shandong University of Technology, 12 Zhangzhou Road, Zhangdian, Zibo 255049, ChinaSchool of Transportation and Vehicle Engineering, Shandong University of Technology, 12 Zhangzhou Road, Zhangdian, Zibo 255049, ChinaSchool of Transportation and Vehicle Engineering, Shandong University of Technology, 12 Zhangzhou Road, Zhangdian, Zibo 255049, ChinaSchool of Transportation and Vehicle Engineering, Shandong University of Technology, 12 Zhangzhou Road, Zhangdian, Zibo 255049, ChinaThe prediction of lane change intention of vehicles is an important part of the decision planning and control systems of intelligent vehicles. In the dynamic and complex traffic environment, the behaviors of traffic participants interact and influence each other. In lane change prediction, it is necessary to study the predicted vehicle and surrounding vehicles as an interactive correlation system. Otherwise, great errors are made in the motion prediction. Based on this, the motion state of the predicted vehicle, the position relationship between the predicted vehicle and lane, as well as the motion state of vehicles around the predicted vehicle are considered systematically in this paper, and the prediction of lane change intention of vehicles is studied. The influence of the three above-mentioned factors on the prediction of lane change intention is analyzed in this paper. On the basis of screening the prediction features of lane change intention, the lane change intention of vehicles is predicted by a feed-forward neural network. The data collected by the virtual driving experiment platform are divided into a training set, a verification set, and a test set. The neural network parameters of vehicles’ lane change intentions are identified by a training set, and the effect of prediction is tested by a verification set and a test set. The results show that the accuracy of the prediction model is high. The model is compared with the model of common features at the present stage and the model based on a Support Vector Machine, and the results show that the accuracy of the prediction model proposed in this paper was improved by 6.4% and 2.8%, respectively, compared with the two models. Finally, the virtual driving experiment platform was used to predict the lane change intention of the front vehicle and the vehicle in the left adjacent lane. The results show that, based on the same model and input features, the lane change intention of the front vehicle and the vehicle in the left adjacent lane can be predicted by the model at 2.8 s and 3.4 s before the lane change, and the model is a certain generality for the prediction of lane change intention of adjacent vehicles.https://www.mdpi.com/2032-6653/12/1/27intelligent vehiclesprediction of lane change intentionnetwork environmentneural networkmotion state of surrounding vehicles |
| spellingShingle | Shuaishuai Liu Di Tan Shilin Hong Hongxun Fu Study on the Prediction of Lane Change Intention of Intelligent Vehicles in the Network Environment World Electric Vehicle Journal intelligent vehicles prediction of lane change intention network environment neural network motion state of surrounding vehicles |
| title | Study on the Prediction of Lane Change Intention of Intelligent Vehicles in the Network Environment |
| title_full | Study on the Prediction of Lane Change Intention of Intelligent Vehicles in the Network Environment |
| title_fullStr | Study on the Prediction of Lane Change Intention of Intelligent Vehicles in the Network Environment |
| title_full_unstemmed | Study on the Prediction of Lane Change Intention of Intelligent Vehicles in the Network Environment |
| title_short | Study on the Prediction of Lane Change Intention of Intelligent Vehicles in the Network Environment |
| title_sort | study on the prediction of lane change intention of intelligent vehicles in the network environment |
| topic | intelligent vehicles prediction of lane change intention network environment neural network motion state of surrounding vehicles |
| url | https://www.mdpi.com/2032-6653/12/1/27 |
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