An Adaptive Weight Collaborative Driving Strategy Based on Stackelberg Game Theory
In response to the problem of cooperative steering control between drivers and intelligent driving systems, a master–slave Game-Based human–machine cooperative steering control framework with adaptive weight fuzzy decision-making is constructed. Firstly, within this framework, a dynamic weight appro...
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MDPI AG
2025-07-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/7/386 |
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| author | Zhongjin Zhou Jingbo Zhao Jianfeng Zheng Haimei Liu |
| author_facet | Zhongjin Zhou Jingbo Zhao Jianfeng Zheng Haimei Liu |
| author_sort | Zhongjin Zhou |
| collection | DOAJ |
| description | In response to the problem of cooperative steering control between drivers and intelligent driving systems, a master–slave Game-Based human–machine cooperative steering control framework with adaptive weight fuzzy decision-making is constructed. Firstly, within this framework, a dynamic weight approach is established. This approach takes into account the driver’s state, traffic environment risks, and the vehicle’s global control deviation to adjust the driving weights between humans and machines. Secondly, the human–machine cooperative relationship with unconscious competition is characterized as a master–slave game interaction. The cooperative steering control under the master–slave game scenario is then transformed into an optimization problem of model predictive control. Through theoretical derivation, the optimal control strategies for both parties at equilibrium in the human–machine master–slave game are obtained. Coordination of the manipulation actions of the driver and the intelligent driving system is achieved by balancing the master–slave game. Finally, different types of drivers are simulated by varying the parameters of the driver models. The effectiveness of the proposed driving weight allocation scheme was validated on the constructed simulation test platform. The results indicate that the human–machine collaborative control strategy can effectively mitigate conflicts between humans and machines. By giving due consideration to the driver’s operational intentions, this strategy reduces the driver’s workload. Under high-risk scenarios, while ensuring driving safety and providing the driver with a satisfactory experience, this strategy significantly enhances the stability of vehicle motion. |
| format | Article |
| id | doaj-art-d945d6be395c443ea80c984dc46e563c |
| institution | Kabale University |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-d945d6be395c443ea80c984dc46e563c2025-08-20T03:32:28ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-07-0116738610.3390/wevj16070386An Adaptive Weight Collaborative Driving Strategy Based on Stackelberg Game TheoryZhongjin Zhou0Jingbo Zhao1Jianfeng Zheng2Haimei Liu3School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, ChinaSchool of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, ChinaSchool of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, ChinaSchool of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai 201209, ChinaIn response to the problem of cooperative steering control between drivers and intelligent driving systems, a master–slave Game-Based human–machine cooperative steering control framework with adaptive weight fuzzy decision-making is constructed. Firstly, within this framework, a dynamic weight approach is established. This approach takes into account the driver’s state, traffic environment risks, and the vehicle’s global control deviation to adjust the driving weights between humans and machines. Secondly, the human–machine cooperative relationship with unconscious competition is characterized as a master–slave game interaction. The cooperative steering control under the master–slave game scenario is then transformed into an optimization problem of model predictive control. Through theoretical derivation, the optimal control strategies for both parties at equilibrium in the human–machine master–slave game are obtained. Coordination of the manipulation actions of the driver and the intelligent driving system is achieved by balancing the master–slave game. Finally, different types of drivers are simulated by varying the parameters of the driver models. The effectiveness of the proposed driving weight allocation scheme was validated on the constructed simulation test platform. The results indicate that the human–machine collaborative control strategy can effectively mitigate conflicts between humans and machines. By giving due consideration to the driver’s operational intentions, this strategy reduces the driver’s workload. Under high-risk scenarios, while ensuring driving safety and providing the driver with a satisfactory experience, this strategy significantly enhances the stability of vehicle motion.https://www.mdpi.com/2032-6653/16/7/386human–machine collaborative drivingmodel predictive controlgame theorydriving weight |
| spellingShingle | Zhongjin Zhou Jingbo Zhao Jianfeng Zheng Haimei Liu An Adaptive Weight Collaborative Driving Strategy Based on Stackelberg Game Theory World Electric Vehicle Journal human–machine collaborative driving model predictive control game theory driving weight |
| title | An Adaptive Weight Collaborative Driving Strategy Based on Stackelberg Game Theory |
| title_full | An Adaptive Weight Collaborative Driving Strategy Based on Stackelberg Game Theory |
| title_fullStr | An Adaptive Weight Collaborative Driving Strategy Based on Stackelberg Game Theory |
| title_full_unstemmed | An Adaptive Weight Collaborative Driving Strategy Based on Stackelberg Game Theory |
| title_short | An Adaptive Weight Collaborative Driving Strategy Based on Stackelberg Game Theory |
| title_sort | adaptive weight collaborative driving strategy based on stackelberg game theory |
| topic | human–machine collaborative driving model predictive control game theory driving weight |
| url | https://www.mdpi.com/2032-6653/16/7/386 |
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