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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2407.11551 |
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| _version_ | 1866916885520449536 |
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| author | Wang, Haoran Lian, Zhexi Li, Zhenning Wang, Jiawei Eichberger, Arno Hu, Jia Chen, Yongyu Gao, Yongji |
| author_facet | Wang, Haoran Lian, Zhexi Li, Zhenning Wang, Jiawei Eichberger, Arno Hu, Jia Chen, Yongyu Gao, Yongji |
| contents | Cooperative Adaptive Cruise Control (CACC) often requires human takeover for tasks such as exiting a freeway. Direct human takeover can pose significant risks, especially given the close-following strategy employed by CACC, which might cause drivers to feel unsafe and execute hard braking, potentially leading to collisions. This research aims to develop a CACC takeover controller that ensures a smooth transition from automated to human control. The proposed CACC takeover maneuver employs an indirect human-machine shared control approach, modeled as a Stackelberg competition where the machine acts as the leader and the human as the follower. The machine guides the human to respond in a manner that aligns with the machine's expectations, aiding in maintaining following stability. Additionally, the human reaction function is integrated into the machine's predictive control system, moving beyond a simple "prediction-planning" pipeline to enhance planning optimality. The controller has been verified to i) enable a smooth takeover maneuver of CACC; ii) ensure string stability in the condition that the platoon has less than 6 CAVs and human control authority is less than 40%; iii) enhance both perceived and actual safety through machine interventions; and iv) reduce the impact on upstream traffic by up to 60%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11551 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Human-Machine Shared Control Approach for the Takeover of CACC Wang, Haoran Lian, Zhexi Li, Zhenning Wang, Jiawei Eichberger, Arno Hu, Jia Chen, Yongyu Gao, Yongji Robotics Cooperative Adaptive Cruise Control (CACC) often requires human takeover for tasks such as exiting a freeway. Direct human takeover can pose significant risks, especially given the close-following strategy employed by CACC, which might cause drivers to feel unsafe and execute hard braking, potentially leading to collisions. This research aims to develop a CACC takeover controller that ensures a smooth transition from automated to human control. The proposed CACC takeover maneuver employs an indirect human-machine shared control approach, modeled as a Stackelberg competition where the machine acts as the leader and the human as the follower. The machine guides the human to respond in a manner that aligns with the machine's expectations, aiding in maintaining following stability. Additionally, the human reaction function is integrated into the machine's predictive control system, moving beyond a simple "prediction-planning" pipeline to enhance planning optimality. The controller has been verified to i) enable a smooth takeover maneuver of CACC; ii) ensure string stability in the condition that the platoon has less than 6 CAVs and human control authority is less than 40%; iii) enhance both perceived and actual safety through machine interventions; and iv) reduce the impact on upstream traffic by up to 60%. |
| title | Human-Machine Shared Control Approach for the Takeover of CACC |
| topic | Robotics |
| url | https://arxiv.org/abs/2407.11551 |