Implementation of the Human-Like Lane Changing Driver Model Based on Bi-LSTM
If the driving behavior of an autonomous vehicle is similar to that of a skilled driver, the human driver can extricate himself from fatigue operation and the comfort of passengers can also be guaranteed. Therefore, this paper studies the human-like lane-changing model of an autonomous vehicle. The...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/9934292 |
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| _version_ | 1832562174129602560 |
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| author | Junyu Cai Haobin Jiang Junyan Wang |
| author_facet | Junyu Cai Haobin Jiang Junyan Wang |
| author_sort | Junyu Cai |
| collection | DOAJ |
| description | If the driving behavior of an autonomous vehicle is similar to that of a skilled driver, the human driver can extricate himself from fatigue operation and the comfort of passengers can also be guaranteed. Therefore, this paper studies the human-like lane-changing model of an autonomous vehicle. The lane-changing characteristic data of skilled drivers are collected and analyzed through a real vehicle test. Then, comparing the MPC-based driver model with the steering wheel angle of human drivers, we found that the MPC-based model could hardly reflect the maneuvering characteristics of human drivers, so we proposed a driver model with steering wheel angle continuity for human drivers. This paper uses four neural network models to compare the prediction on the test set, then uses different input types to compare the prediction accuracy of the model, and finally verifies the generalization ability of the model on the verification set. These three test results show that the prediction results of the human-like lane-changing driving model based on Bi-LSTM are closest to the real steering wheel angle sequence of skilled drivers. The test results demonstrate that the Bi-LSTM-based human-like lane-changing driving model achieves 9.8% RMSE and 6.8% MAE, which improves 10.8% RMSE and 10.3% MAE over LSTM. The model can generate the steering wheel angle sequence in the process of lane changing like a human, so as to realize the human simulation control of an autonomous vehicle for lane-changing conditions. |
| format | Article |
| id | doaj-art-329942692b3e4e02b2ba5e34bb7f452c |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-329942692b3e4e02b2ba5e34bb7f452c2025-02-03T01:23:09ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/9934292Implementation of the Human-Like Lane Changing Driver Model Based on Bi-LSTMJunyu Cai0Haobin Jiang1Junyan Wang2School of Automobile and Traffic EngineeringSchool of Automobile and Traffic EngineeringSchool of Automotive EngineeringIf the driving behavior of an autonomous vehicle is similar to that of a skilled driver, the human driver can extricate himself from fatigue operation and the comfort of passengers can also be guaranteed. Therefore, this paper studies the human-like lane-changing model of an autonomous vehicle. The lane-changing characteristic data of skilled drivers are collected and analyzed through a real vehicle test. Then, comparing the MPC-based driver model with the steering wheel angle of human drivers, we found that the MPC-based model could hardly reflect the maneuvering characteristics of human drivers, so we proposed a driver model with steering wheel angle continuity for human drivers. This paper uses four neural network models to compare the prediction on the test set, then uses different input types to compare the prediction accuracy of the model, and finally verifies the generalization ability of the model on the verification set. These three test results show that the prediction results of the human-like lane-changing driving model based on Bi-LSTM are closest to the real steering wheel angle sequence of skilled drivers. The test results demonstrate that the Bi-LSTM-based human-like lane-changing driving model achieves 9.8% RMSE and 6.8% MAE, which improves 10.8% RMSE and 10.3% MAE over LSTM. The model can generate the steering wheel angle sequence in the process of lane changing like a human, so as to realize the human simulation control of an autonomous vehicle for lane-changing conditions.http://dx.doi.org/10.1155/2022/9934292 |
| spellingShingle | Junyu Cai Haobin Jiang Junyan Wang Implementation of the Human-Like Lane Changing Driver Model Based on Bi-LSTM Discrete Dynamics in Nature and Society |
| title | Implementation of the Human-Like Lane Changing Driver Model Based on Bi-LSTM |
| title_full | Implementation of the Human-Like Lane Changing Driver Model Based on Bi-LSTM |
| title_fullStr | Implementation of the Human-Like Lane Changing Driver Model Based on Bi-LSTM |
| title_full_unstemmed | Implementation of the Human-Like Lane Changing Driver Model Based on Bi-LSTM |
| title_short | Implementation of the Human-Like Lane Changing Driver Model Based on Bi-LSTM |
| title_sort | implementation of the human like lane changing driver model based on bi lstm |
| url | http://dx.doi.org/10.1155/2022/9934292 |
| work_keys_str_mv | AT junyucai implementationofthehumanlikelanechangingdrivermodelbasedonbilstm AT haobinjiang implementationofthehumanlikelanechangingdrivermodelbasedonbilstm AT junyanwang implementationofthehumanlikelanechangingdrivermodelbasedonbilstm |