Evolution towards optimal driving strategies for large‐scale autonomous vehicles
Abstract With rapidly developing autonomous vehicle (AV) technologies, the optimal driving strategy should consider multi‐objective optimization problems of large‐scale transportation systems, including safety and efficiency. Different driving strategies have different performance, and there is an i...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-08-01
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| Series: | IET Intelligent Transport Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/itr2.12076 |
| _version_ | 1797994929195057152 |
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| author | Runsong Jiang Zhangjie Liu Huiyun Li |
| author_facet | Runsong Jiang Zhangjie Liu Huiyun Li |
| author_sort | Runsong Jiang |
| collection | DOAJ |
| description | Abstract With rapidly developing autonomous vehicle (AV) technologies, the optimal driving strategy should consider multi‐objective optimization problems of large‐scale transportation systems, including safety and efficiency. Different driving strategies have different performance, and there is an interaction between vehicles with different strategies. Since the Nash equilibrium is hard to find for an n‐player game, it is difficult to get an analytical solution to this multi‐objective optimization problem. Therefor a coevolutionary algorithm is proposed to explore the interactions between populations with different strategies and investigate the cooperation and competition among vehicles. Combining the multi‐objective optimization algorithm and the incentive mechanism of survival of the fittest reproduction law, the system structure reaches a stable equilibrium state and the optimal group driving strategy evolves. Simulation results, with 40,000 vehicles driving in Luxembourg SUMO Traffic Scenario, demonstrate that the rational–rational strategy performs best among six typical strategies. Meanwhile, the accident rate drops by 56%, while the overall average speed increases by 30%. The results of multi‐vehicle and multi‐objective coevolution are enlightening in designing optimal driving strategy with AVs. |
| first_indexed | 2024-04-11T09:53:32Z |
| format | Article |
| id | doaj.art-f898ba5e4f8d4e0da0bb72f1acb243c8 |
| institution | Directory Open Access Journal |
| issn | 1751-956X 1751-9578 |
| language | English |
| last_indexed | 2024-04-11T09:53:32Z |
| publishDate | 2021-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj.art-f898ba5e4f8d4e0da0bb72f1acb243c82022-12-22T04:30:43ZengWileyIET Intelligent Transport Systems1751-956X1751-95782021-08-011581018102710.1049/itr2.12076Evolution towards optimal driving strategies for large‐scale autonomous vehiclesRunsong Jiang0Zhangjie Liu1Huiyun Li2Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen ChinaUniversity of Chinese Academy of Sciences Beijing ChinaShenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen ChinaAbstract With rapidly developing autonomous vehicle (AV) technologies, the optimal driving strategy should consider multi‐objective optimization problems of large‐scale transportation systems, including safety and efficiency. Different driving strategies have different performance, and there is an interaction between vehicles with different strategies. Since the Nash equilibrium is hard to find for an n‐player game, it is difficult to get an analytical solution to this multi‐objective optimization problem. Therefor a coevolutionary algorithm is proposed to explore the interactions between populations with different strategies and investigate the cooperation and competition among vehicles. Combining the multi‐objective optimization algorithm and the incentive mechanism of survival of the fittest reproduction law, the system structure reaches a stable equilibrium state and the optimal group driving strategy evolves. Simulation results, with 40,000 vehicles driving in Luxembourg SUMO Traffic Scenario, demonstrate that the rational–rational strategy performs best among six typical strategies. Meanwhile, the accident rate drops by 56%, while the overall average speed increases by 30%. The results of multi‐vehicle and multi‐objective coevolution are enlightening in designing optimal driving strategy with AVs.https://doi.org/10.1049/itr2.12076Optimisation techniquesRoad‐traffic system controlMobile robotsGame theory |
| spellingShingle | Runsong Jiang Zhangjie Liu Huiyun Li Evolution towards optimal driving strategies for large‐scale autonomous vehicles IET Intelligent Transport Systems Optimisation techniques Road‐traffic system control Mobile robots Game theory |
| title | Evolution towards optimal driving strategies for large‐scale autonomous vehicles |
| title_full | Evolution towards optimal driving strategies for large‐scale autonomous vehicles |
| title_fullStr | Evolution towards optimal driving strategies for large‐scale autonomous vehicles |
| title_full_unstemmed | Evolution towards optimal driving strategies for large‐scale autonomous vehicles |
| title_short | Evolution towards optimal driving strategies for large‐scale autonomous vehicles |
| title_sort | evolution towards optimal driving strategies for large scale autonomous vehicles |
| topic | Optimisation techniques Road‐traffic system control Mobile robots Game theory |
| url | https://doi.org/10.1049/itr2.12076 |
| work_keys_str_mv | AT runsongjiang evolutiontowardsoptimaldrivingstrategiesforlargescaleautonomousvehicles AT zhangjieliu evolutiontowardsoptimaldrivingstrategiesforlargescaleautonomousvehicles AT huiyunli evolutiontowardsoptimaldrivingstrategiesforlargescaleautonomousvehicles |