Fuzzy rule-based man-machine co-driving control right flexible transfer method and system

Abstract

The invention provides a fuzzy rule-based man-machine co-driving control right flexible transfer method and system, and the method comprises the steps: building a steering controller based on a kinematic model of a vehicle to control the steering, and obtaining a target steering angle of a steering wheel; constructing a random function to simulate steering wheel rotation angle input of the driver in a fatigue and distraction state, enabling the vehicle to have lane deviation in a preset time period, and simulating vehicle driving conditions of the driver in the distraction and fatigue state; judging the driving state of a driver according to steering wheel rotation angle input and information of the distance between the vehicle mass center and the lane center line, and obtaining a corresponding man-machine co-driving weight coefficient based on a fuzzy logic rule; establishing a man-machine co-driving right sharing model, and obtaining steering wheel rotation angle input for finally controlling vehicle steering motion; controlling steering motion of the vehicle according to the calculated steering wheel angle input, and correcting the current lane departure condition of the vehicle.

Description

technical field [0001] The present invention relates to the field of intelligent vehicle decision-making and control, in particular to a method and system for the flexible transfer of control rights for man-machine co-driving based on fuzzy rules. Background technique [0002] In recent years, with the gradual improvement of my country's comprehensive national strength and people's living standards, people's demand for cars is increasing, and cars have become one of the necessities of people in today's society. With the new generation of information technology, the advancement of artificial intelligence theory and the rapid development of artificial intelligence and other technologies, the global automobile industry is also undergoing a period of deep transformation. Electrification, intelligence and networking have become the new strategic commanding heights of the automobile industry. It is expected that By 2025, the potential economic impact of global smart cars will be 0...

AI Technical Summary

Problems solved by technology

[0009] At present, there has been some research on the allocation and transfer of driving control rights for human-machine co-driving smart cars, such as a human-machine co-driving system based on a layered architecture based on driver status disclosed in patent document CN201910954724.2 A method of allocating driving control rights by comparing the driver's input rotation angle with the dynamic threshold calculated by the auxiliary driving system and combining the driver's driving state, but the algorithm of this invention is too complicated to be popularized; the patent document CN201610839897.6 is disclosed A human-machine co-driving model electric power steering system and mode switching method are proposed. A rear torque / angle sensor is installed on the existing electric power steering system to judge the steering mode of the vehicle and realize the human-driving mode and the machine mode. Driving mode switching, but this invention does not take into account the impact of the human driver’s state on mode switching, and the judgment factors are not comprehensive; patent document CN201810846175.2 discloses a human-machine co-driving lateral driving right considering the driver’s driving skills The allocation method proposes a method for allocating lateral driving rights by evaluating the driver's driving skills and considering the difference between the driver's expected corner angle and the expected corner angle of the lane departure controller, but this invention is only for the driver in the active driving state. Failure to consider the possible fatigue and distraction of the driver during driving may easily lead to accidents during the allocation of driving rights

[0010]Although there has been some research on the allocation and transfer of driving control rights for human-machine co-driving smart cars, in summary, in the existing research results , the consideration of the influence of the driver's own state on driving behavior is not complete, it is easy to cause rigid switching of driving control between man and machine, and then human-machine conflicts occur, and some algorithms are too complicated, which is not conducive to widespread promotion

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