Active Suspension Preview Control Using Binocular Road Perception
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Solution Overview
Problem
Traditional active suspension control technologies face a time lag issue due to the high-speed movement of vehicles, hindering further improvements in suspension performance and vehicle safety and comfort.
Innovation Solution
An active suspension control method utilizing a binocular camera and visual perception algorithms, such as YOLOV5, to collect road surface information in real time, combined with a preview H∞ controller, to reduce time lag and enhance suspension performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional signal sensors on the vehicle body are used to collect road surface information, then the suspension system can respond to road excitations, but there is a time lag between the controller and actuator due to high-speed vehicle movement
Solution Approach 1:
The patent uses a binocular camera to capture images of the road surface ahead of the vehicle, allowing the system to predict upcoming road excitations before they occur. This preliminary visual inspection enables the controller to prepare control commands in advance, eliminating the time lag that occurs with traditional sensors that only detect excitations after the vehicle encounters them.
Solution Approach 2:
The patent replaces traditional mechanical signal sensors with an optical vision-based measurement system. The binocular camera captures visual information about the road surface, and image processing algorithms extract road geometry data. This substitution allows for earlier detection of road conditions and provides the controller with advance information, improving responsiveness without time lag.
2Loss of time
If visual perception algorithms are used to collect road surface information in advance, then the time lag is reduced, but the system complexity increases
Solution Approach 1:
The binocular camera system serves multiple functions: it captures road surface geometry for prediction, provides visual feedback for control, and can potentially be used for other vehicle functions. By making the vision system multi-functional, the patent justifies the added complexity through broader utility, while the existing image processing infrastructure handles the computational load efficiently.
Solution Approach 2:
The patent introduces an image processing algorithm as an intermediary between the binocular camera and the suspension controller. This intermediary component extracts relevant road geometry information from raw images and transforms it into control-relevant data, simplifying the overall system architecture while enabling advance road surface information collection.
3Measurement precision
If the binocular camera and visual perception algorithm are used, then the road surface information collection accuracy is improved, but the computational load increases
Solution Approach 1:
The patent extracts only the essential road surface geometry information from the complete binocular camera images using targeted image processing algorithms. Instead of processing all image data, the system identifies and extracts only the relevant features needed for suspension control, such as road elevation and curvature, thereby reducing computational load while maintaining measurement precision.
Solution Approach 2:
The patent applies partial processing to the binocular images, focusing computational resources on the specific regions and features most relevant to suspension control. By processing only the necessary portions of the visual data rather than performing exhaustive analysis, the system achieves high measurement accuracy while minimizing energy consumption.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method improves the accuracy of road surface information collection and reduces time lag, resulting in enhanced vehicle safety and comfort by optimizing suspension control.
Implementation Method 1
calculating the actual distance S of the target with the binocular ranging algorithm; The parallax value xh−xt of the image is calculated according to the found feature point pairs, and the actual distance S can be obtained according to the triangle similarity principle
Implementation Method 2
using a YOLOV5 target detection algorithm to identify the instantaneous road excitation of the speed bump
Implementation Method 3
the edge of the speed bump is detected using the Canny edge detection algorithm, and the contour of the object is extracted using the findContours function
Implementation Method 4
The SIFT image processing algorithm is then used to detect feature points on the road surface image, and the corresponding feature point pairs in the image are obtained by the RANSAC matching algorithm
Data Source
AI summary
The present disclosure discloses an active suspension control method under vehicle-mounted visual perception preview. It uses a binocular camera combined with multiple visual perception algorithms, and monitors in real time the road surface conditions ahead of the vehicle. By accurately capturing and analyzing the road surface information, based on robust control theory and Lyapunov theory, it designs a matching preview H∞ controller. The vehicle can effectively reduce bumps and vibrations by timely adjusting the suspension system, providing passengers with a more stable and smooth driving experience. The present disclosure uses a machine vision method to sense in advance the road surface information ahead, improving the time lag problem in the traditional suspension control method, thereby significantly improving the vehicle safety and ride comfort.