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HUANG Wei, HUANG Qi-peng. Research review of control architecture and driving authority decision-making of driver-automation cooperative driving[J]. Journal of Traffic and Transportation Engineering, 2025, 25(1): 48-65. doi: 10.19818/j.cnki.1671-1637.2025.01.004
Citation: HUANG Wei, HUANG Qi-peng. Research review of control architecture and driving authority decision-making of driver-automation cooperative driving[J]. Journal of Traffic and Transportation Engineering, 2025, 25(1): 48-65. doi: 10.19818/j.cnki.1671-1637.2025.01.004
shu

Research review of control architecture and driving authority decision-making of driver-automation cooperative driving

doi: 10.19818/j.cnki.1671-1637.2025.01.004

  • School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, Fujian, China

Funds:

Natural Science Foundation of Fujian Province 2021J01559

National Natural Science Foundation of China 52272389

More Information
  • Corresponding author: HUANG Wei(1990-), male, associate professor, PhD, huangweiamoy@gmail.com
  • Received Date: 2023-12-23
  • Publish Date: 2025-02-25
    Abstract
  • In view of the control architecture and driving authority decision-making, the research status and development trend of driver-automation cooperative driving were expounded. In terms of control architecture, the characteristics and application range of switching control architecture and shared control architecture were analyzed, and the concept of hybrid control architecture was proposed. In terms of driving authority decision-making, the ways of using different sources and natures of information in different driving authority decision-making methods were discussed. The methods involved in the direct and indirect shared control methods when implementing the allocation of driving authority were summarized. The research perspectives and methods of decision-making at the strategy level and the executive level were sorted out. Research results show that for the safety problems of high-level automated driving on the road, the development of hybrid control architecture for describing the system dynamics under human safety intervention scenarios is conducive to avoiding model mismatch, which provides the foundation for control performance optimization and stability design. By integrating holographic situational awareness and data intelligence to collect and integrate data from multiple information sources, the dynamic changes of many factors in the driver-automation cooperative driving system can be more comprehensively understood, and the optimal driving authority decision can be made. Compared with direct shared control, indirect shared control can avoid direct confrontation between driver and automation control flows. However, at the executive level of dynamic driving authority allocation, it is necessary to consider the conflict feedback between driver and automation and ensure a reasonable interactive experience, so as to reflect the advantages of indirect shared control. The decision-making method based on the agent at the strategy level is independent of the accuracy of the mathematical model and can adapt to the dynamic change of the environment. The decision-making method based on game theory at the executive level can enhance the controllability and explainability of the driving authority decision-making system by modeling the driver-automation interaction process. In the future, the driver-automation cooperative driving system should be designed to further optimize the interactive experience. Meanwhile, the development of equal and inclusive driver-automation relationships is necessary. The robustness of the control system and the interpretation and adaptability of driving authority decision-making should be improved as well.

     

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