A Multiobjective Cooperative Driving Framework Based on Evolutionary Algorithm and Multitask Learning
The development of connected and automated vehicle (CAV) techniques brings an upcoming revolution to traffic management. The control of CAVs in potential conflict areas such as on-ramps and intersections will be complex to traffic management when considering their deployment. There is still a lack o...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/6653598 |
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| _version_ | 1849405241832767488 |
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| author | Xia Jiang Jian Zhang Qing-yang Li Tian-yi Chen |
| author_facet | Xia Jiang Jian Zhang Qing-yang Li Tian-yi Chen |
| author_sort | Xia Jiang |
| collection | DOAJ |
| description | The development of connected and automated vehicle (CAV) techniques brings an upcoming revolution to traffic management. The control of CAVs in potential conflict areas such as on-ramps and intersections will be complex to traffic management when considering their deployment. There is still a lack of a general framework for dispatching CAVs in these bottlenecks, which is expected to ensure safety, traffic efficiency, and energy consumption in real time. This study aimed to fill the technique gap, and a comprehensive cooperative intelligent driving framework is put forward to study the problem, which can be used in both on-ramp and intersection scenarios. Based on a multi-objective evolutionary algorithm, CAVs are denoted as a sequence to be searched in solution space, while a multitask learning neural network with adaptive loss function is implemented for optimization target feedback to surrogate the simulation test procedure. The simulation results show that the proposed framework can get satisfying performance with low time and energy consumption. It can reduce time consumption by up to 16.51% for the on-ramp scenario and 9.8% for the intersection scenario, while reducing energy consumption by up to 16.39% and 11.39% for the two scenarios. Meanwhile, an analysis of computation time is carried out, illuminating the flexibility and controllability of the new strategy. |
| format | Article |
| id | doaj-art-44a482e36d9c421caa07758e4f2b0576 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-44a482e36d9c421caa07758e4f2b05762025-08-20T03:36:43ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/6653598A Multiobjective Cooperative Driving Framework Based on Evolutionary Algorithm and Multitask LearningXia Jiang0Jian Zhang1Qing-yang Li2Tian-yi Chen3Jiangsu Key Laboratory of Urban ITSJiangsu Key Laboratory of Urban ITSJiangsu Key Laboratory of Urban ITSDepartment of Civil & Environmental EngineeringThe development of connected and automated vehicle (CAV) techniques brings an upcoming revolution to traffic management. The control of CAVs in potential conflict areas such as on-ramps and intersections will be complex to traffic management when considering their deployment. There is still a lack of a general framework for dispatching CAVs in these bottlenecks, which is expected to ensure safety, traffic efficiency, and energy consumption in real time. This study aimed to fill the technique gap, and a comprehensive cooperative intelligent driving framework is put forward to study the problem, which can be used in both on-ramp and intersection scenarios. Based on a multi-objective evolutionary algorithm, CAVs are denoted as a sequence to be searched in solution space, while a multitask learning neural network with adaptive loss function is implemented for optimization target feedback to surrogate the simulation test procedure. The simulation results show that the proposed framework can get satisfying performance with low time and energy consumption. It can reduce time consumption by up to 16.51% for the on-ramp scenario and 9.8% for the intersection scenario, while reducing energy consumption by up to 16.39% and 11.39% for the two scenarios. Meanwhile, an analysis of computation time is carried out, illuminating the flexibility and controllability of the new strategy.http://dx.doi.org/10.1155/2022/6653598 |
| spellingShingle | Xia Jiang Jian Zhang Qing-yang Li Tian-yi Chen A Multiobjective Cooperative Driving Framework Based on Evolutionary Algorithm and Multitask Learning Journal of Advanced Transportation |
| title | A Multiobjective Cooperative Driving Framework Based on Evolutionary Algorithm and Multitask Learning |
| title_full | A Multiobjective Cooperative Driving Framework Based on Evolutionary Algorithm and Multitask Learning |
| title_fullStr | A Multiobjective Cooperative Driving Framework Based on Evolutionary Algorithm and Multitask Learning |
| title_full_unstemmed | A Multiobjective Cooperative Driving Framework Based on Evolutionary Algorithm and Multitask Learning |
| title_short | A Multiobjective Cooperative Driving Framework Based on Evolutionary Algorithm and Multitask Learning |
| title_sort | multiobjective cooperative driving framework based on evolutionary algorithm and multitask learning |
| url | http://dx.doi.org/10.1155/2022/6653598 |
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