A path tracking control method for a man-machine co-driving type intelligent vehicle

By employing a hierarchical control architecture and non-singular fast terminal sliding mode control, combined with radial basis function neural networks and finite state machines, the problems of high-precision path tracking and driving permission switching in human-machine co-driving systems are solved, achieving a high-precision, stable, and comfortable driving experience.

CN122354579APending Publication Date: 2026-07-10NANJING FORESTRY UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
NANJING FORESTRY UNIV
Filing Date
2026-05-19
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing human-machine co-driving systems have shortcomings in high-precision path tracking and driving permission switching, including the decrease in accuracy of linear control methods under nonlinear conditions, vibration caused by sliding mode control, vehicle shaking caused by driving permission switching, and lack of coordination in human-machine interaction.

Method used

A hierarchical control architecture is adopted, which combines radial basis function neural network and non-singular fast terminal sliding mode control. The front wheel steering angle is generated by the upper controller, and driving permission switching is performed by finite state machine and generalized logistic function to establish a path tracking control method for human-machine co-driving intelligent vehicle.

Benefits of technology

It improves path tracking accuracy, reduces vibration, enables flexible switching of driving permissions and human-machine collaboration, and enhances vehicle stability and ride comfort.

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Abstract

The application discloses a path tracking control method for a man-machine co-driving type intelligent vehicle, and steps are as follows: a vehicle path tracking error model is established, and a hierarchical control architecture is constructed, the hierarchical control architecture comprising an upper controller and a lower controller; the upper controller is configured to: in a human driving mode, generating a first front wheel steering angle through a two-point preview driver model, and in an automatic driving mode, generating a second front wheel steering angle through an automatic driving controller fusing a radial basis function neural network, a heuristic reinforcement adjustment mechanism and a non-singular fast terminal sliding mode control; the lower controller is configured to: driving a finite state machine to switch the driving mode based on a driving risk assessment index, and performing weighted fusion on the first front wheel steering angle and the second front wheel steering angle in a driving authority transition stage to output a final front wheel steering angle control instruction; the application solves the contradiction between path tracking precision and chattering, switching safety and comfort, and is suitable for man-machine collaborative control of an intelligent vehicle under multiple working conditions.
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