Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement Learning
The time that a vehicle merges in a lane reduction can significantly affect passengers’ safety, comfort, and energy consumption, which can, in turn, affect the global adoption of autonomous electric vehicles. In this regard, this paper analyzes how connected and automated vehicles should cooperative...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/2/990 |
| _version_ | 1797437137568661504 |
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| author | Ali Irshayyid Jun Chen |
| author_facet | Ali Irshayyid Jun Chen |
| author_sort | Ali Irshayyid |
| collection | DOAJ |
| description | The time that a vehicle merges in a lane reduction can significantly affect passengers’ safety, comfort, and energy consumption, which can, in turn, affect the global adoption of autonomous electric vehicles. In this regard, this paper analyzes how connected and automated vehicles should cooperatively drive to reduce energy consumption and improve traffic flow. Specifically, a model-free deep reinforcement learning approach is used to find the optimal driving behavior in the scenario in which two platoons are merging into one. Several metrics are analyzed, including the time of the merge, energy consumption, and jerk, etc. Numerical simulation results show that the proposed framework can reduce the energy consumed by up to 76.7%, and the average jerk can be decreased by up to 50%, all by only changing the cooperative merge behavior. The present findings are essential since reducing the jerk can decrease the longitudinal acceleration oscillations, enhance comfort and drivability, and improve the general acceptance of autonomous vehicle platooning as a new technology. |
| first_indexed | 2024-03-09T11:15:31Z |
| format | Article |
| id | doaj.art-0597d8b01ae14f2ca26eb461a37b1cbb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:15:31Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0597d8b01ae14f2ca26eb461a37b1cbb2023-12-01T00:30:56ZengMDPI AGSensors1424-82202023-01-0123299010.3390/s23020990Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement LearningAli Irshayyid0Jun Chen1Department of Electrical and Computer Engineering, Oakland University, Rochester, MI 48309, USADepartment of Electrical and Computer Engineering, Oakland University, Rochester, MI 48309, USAThe time that a vehicle merges in a lane reduction can significantly affect passengers’ safety, comfort, and energy consumption, which can, in turn, affect the global adoption of autonomous electric vehicles. In this regard, this paper analyzes how connected and automated vehicles should cooperatively drive to reduce energy consumption and improve traffic flow. Specifically, a model-free deep reinforcement learning approach is used to find the optimal driving behavior in the scenario in which two platoons are merging into one. Several metrics are analyzed, including the time of the merge, energy consumption, and jerk, etc. Numerical simulation results show that the proposed framework can reduce the energy consumed by up to 76.7%, and the average jerk can be decreased by up to 50%, all by only changing the cooperative merge behavior. The present findings are essential since reducing the jerk can decrease the longitudinal acceleration oscillations, enhance comfort and drivability, and improve the general acceptance of autonomous vehicle platooning as a new technology.https://www.mdpi.com/1424-8220/23/2/990vehicle platoonmergingdeep reinforcement learningproximal policy optimizationfuel consumption |
| spellingShingle | Ali Irshayyid Jun Chen Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement Learning Sensors vehicle platoon merging deep reinforcement learning proximal policy optimization fuel consumption |
| title | Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement Learning |
| title_full | Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement Learning |
| title_fullStr | Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement Learning |
| title_full_unstemmed | Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement Learning |
| title_short | Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement Learning |
| title_sort | comparative study of cooperative platoon merging control based on reinforcement learning |
| topic | vehicle platoon merging deep reinforcement learning proximal policy optimization fuel consumption |
| url | https://www.mdpi.com/1424-8220/23/2/990 |
| work_keys_str_mv | AT aliirshayyid comparativestudyofcooperativeplatoonmergingcontrolbasedonreinforcementlearning AT junchen comparativestudyofcooperativeplatoonmergingcontrolbasedonreinforcementlearning |