一种模糊规则自学习的人机协同控制方法
By employing a human-machine collaborative control method based on fuzzy rule self-learning, the problem of multi-source risk fusion and dynamic allocation of control rights in autonomous driving systems under complex driving scenarios was solved, realizing collaborative steering control between the driver and the controller, and improving vehicle safety and stability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHANGCHUN UNIV OF TECH
- Filing Date
- 2026-05-11
- Publication Date
- 2026-07-17
AI Technical Summary
Existing autonomous driving systems struggle to achieve multi-source risk fusion identification, online updating of fuzzy rules, dynamic allocation of human-machine control, and game-theoretic collaborative control in complex driving scenarios, resulting in instability and insufficient safety in the human-machine collaborative control process.
A human-machine collaborative control method based on fuzzy rule self-learning is adopted. By applying reinforcement learning to the risk cognition and fuzzy rule layers, a multi-source driving risk quantification model is constructed. Combined with the expected path module, collision risk identification module, driving state identification module, driving risk fuzzy identification module, reinforcement learning module and non-cooperative game module, collaborative steering control between driver and controller is achieved.
It improves the adaptability and accuracy of human-machine collaborative control, coordinates conflicts between human and machine control intentions, and enhances the vehicle's path tracking performance, lateral control stability, and driving safety in complex driving scenarios.
Smart Images
Figure CN122166150B_ABST