Research on Reinforcement-Learning-Based Truck Platooning Control Strategies in Highway On-Ramp Regions
With the development of autonomous driving technology, truck platooning control has become a reality. Truck platooning can improve road capacity by maintaining a minor headway. Platooning systems can significantly reduce fuel consumption and emissions, especially for trucks. In this study, we design...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/14/10/273 |
| _version_ | 1827719622572376064 |
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| author | Jiajia Chen Zheng Zhou Yue Duan Biao Yu |
| author_facet | Jiajia Chen Zheng Zhou Yue Duan Biao Yu |
| author_sort | Jiajia Chen |
| collection | DOAJ |
| description | With the development of autonomous driving technology, truck platooning control has become a reality. Truck platooning can improve road capacity by maintaining a minor headway. Platooning systems can significantly reduce fuel consumption and emissions, especially for trucks. In this study, we designed a Platoon-MAPPO algorithm to implement truck platooning control based on multi-agent reinforcement learning for a platooning facing an on-ramp scenario on highway. A centralized training, decentralized execution algorithm was used in this paper. Each truck only computes its actions, avoiding the data computation delay problem caused by centralized computation. Each truck considers the truck status in front of and behind itself, maximizing the overall gain of the platooning and improving the global operational efficiency. In terms of performance evaluation, we used the traditional rule-based platooning following model as a benchmark. To ensure fairness, the model used the same network structure and traffic scenario as our proposed model. The simulation results show that the algorithm proposed in this paper has good performance and improves the overall efficiency of the platoon while guaranteeing traffic safety. The average energy consumption decreased by 14.8%, and the road occupancy rate decreased by 43.3%. |
| first_indexed | 2024-03-10T20:48:39Z |
| format | Article |
| id | doaj.art-3878d3f5d12641c6993c7cfb78e26caa |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-10T20:48:39Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-3878d3f5d12641c6993c7cfb78e26caa2023-11-19T18:31:51ZengMDPI AGWorld Electric Vehicle Journal2032-66532023-10-01141027310.3390/wevj14100273Research on Reinforcement-Learning-Based Truck Platooning Control Strategies in Highway On-Ramp RegionsJiajia Chen0Zheng Zhou1Yue Duan2Biao Yu3School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, ChinaSchool of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, ChinaSchool of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, ChinaHefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaWith the development of autonomous driving technology, truck platooning control has become a reality. Truck platooning can improve road capacity by maintaining a minor headway. Platooning systems can significantly reduce fuel consumption and emissions, especially for trucks. In this study, we designed a Platoon-MAPPO algorithm to implement truck platooning control based on multi-agent reinforcement learning for a platooning facing an on-ramp scenario on highway. A centralized training, decentralized execution algorithm was used in this paper. Each truck only computes its actions, avoiding the data computation delay problem caused by centralized computation. Each truck considers the truck status in front of and behind itself, maximizing the overall gain of the platooning and improving the global operational efficiency. In terms of performance evaluation, we used the traditional rule-based platooning following model as a benchmark. To ensure fairness, the model used the same network structure and traffic scenario as our proposed model. The simulation results show that the algorithm proposed in this paper has good performance and improves the overall efficiency of the platoon while guaranteeing traffic safety. The average energy consumption decreased by 14.8%, and the road occupancy rate decreased by 43.3%.https://www.mdpi.com/2032-6653/14/10/273truck platoonreinforcement learningPlatoon-MAPPO algorithmon-ramp region |
| spellingShingle | Jiajia Chen Zheng Zhou Yue Duan Biao Yu Research on Reinforcement-Learning-Based Truck Platooning Control Strategies in Highway On-Ramp Regions World Electric Vehicle Journal truck platoon reinforcement learning Platoon-MAPPO algorithm on-ramp region |
| title | Research on Reinforcement-Learning-Based Truck Platooning Control Strategies in Highway On-Ramp Regions |
| title_full | Research on Reinforcement-Learning-Based Truck Platooning Control Strategies in Highway On-Ramp Regions |
| title_fullStr | Research on Reinforcement-Learning-Based Truck Platooning Control Strategies in Highway On-Ramp Regions |
| title_full_unstemmed | Research on Reinforcement-Learning-Based Truck Platooning Control Strategies in Highway On-Ramp Regions |
| title_short | Research on Reinforcement-Learning-Based Truck Platooning Control Strategies in Highway On-Ramp Regions |
| title_sort | research on reinforcement learning based truck platooning control strategies in highway on ramp regions |
| topic | truck platoon reinforcement learning Platoon-MAPPO algorithm on-ramp region |
| url | https://www.mdpi.com/2032-6653/14/10/273 |
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