Chassis prediction control system and method based on binocular vision and multi-domain cooperation

By using a chassis predictive control system with binocular vision and multi-domain collaboration, and utilizing ADAS cameras and extended Kalman filters, the vertical motion state of the four corners of the suspension is calculated. This solves the problems of high installation difficulty, high cost, and insufficient accuracy in traditional suspension control, and improves the real-time performance and accuracy of suspension control.

CN121105649APending Publication Date: 2025-12-12辰致科技有限公司
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Patent Information

Application Number
CN202511371788.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-24
Publication Date
2025-12-12

AI Technical Summary

Technical Problem

In existing technologies for vehicle suspension control, the installation of dedicated IMUs or acceleration sensors is difficult, costly, and lacks sufficient measurement accuracy, leading to inaccurate suspension control and increasing driving safety hazards.

Method used

A chassis predictive control system based on binocular vision and multi-domain collaboration is adopted. IMU data and visual observation data are acquired through ADAS binocular cameras. Extended Kalman filter fusion module and rigid body kinematics are used to calculate the vertical velocity and acceleration of the four corners of the suspension and perform time delay compensation, which is then provided to the electronic suspension controller.

Benefits of technology

It simplifies sensor installation, reduces costs, improves the real-time performance and accuracy of suspension control, and reduces the occurrence of driving safety accidents.

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Abstract

The invention relates to the technical field of vehicle electric control suspensions, in particular to a chassis prediction control system and method based on binocular vision and multi-domain cooperation, and constructs the chassis prediction control system based on binocular vision and multi-domain cooperation based on an ADAS binocular camera. IMU data of a binocular camera is obtained by using a data acquisition module of the chassis prediction control system, visual observation data of a vehicle body is obtained by using a binocular visual processing module, and then the IMU data and the visual observation data are fused and converted into a three-dimensional image by using modules such as an extended Kalman filtering fusion module and a motion conversion module. The sprung mass vertical acceleration and sprung mass vertical speed of the vehicle four-corner suspension provide accurate suspension four-corner vertical motion state input for an electric control suspension controller, and the accuracy and real-time performance of the electric control suspension for vehicle body posture control are guaranteed. Meanwhile, the problems that a special IMU is difficult to arrange, high in cost and insufficient in precision and IMU resources of an ADAS system are wasted are solved.
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