Predictive Suspension Control Using Stereo Camera Lean Angle Adaptation

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Solution Overview

Problem

Predictive suspension control systems for single-track vehicles like motorcycles and scooters face challenges due to restricted processing capabilities and dynamic driving characteristics, limiting their efficiency and adaptability compared to two-track vehicles.

Innovation Solution

A predictive suspension control system utilizing a stereo camera sensor to generate image data, extract relevant image portions based on future vehicle path data, calculate road unevenness, and adapt suspension parameters in real-time, specifically designed to handle the dynamic movements of single-track vehicles by adjusting the search direction and correlation area of the stereo algorithm based on the vehicle's lean angle.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If predictive suspension control is implemented for single-track vehicles, then driving comfort and handling are improved, but processing capabilities and computational resources are severely restricted

Engineering Contradiction:
Improvedriving comfortVSAvoidprocessing capabilities
Core Design Contradiction:
Ease of operationVSDevice complexity

Solution Approach 1:

The patent segments the image processing task by extracting only the relevant image portion corresponding to the future vehicle path using predicted path data, rather than processing the entire image. This segmentation reduces computational load while maintaining predictive suspension control functionality for single-track vehicles

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The system applies local quality by adapting the search direction and correlation area of the stereo algorithm specifically to the lean angle of the vehicle. This localized adaptation optimizes processing for the dynamic characteristics of single-track vehicles without requiring full-system complexity

Inventive Principle:
Principle #3Local quality

2Measurement precision

If the entire image data is processed to detect road irregularities, then measurement precision is improved, but computation time increases

Engineering Contradiction:
Improveroad irregularity detection precisionVSAvoidcomputation time
Core Design Contradiction:
Measurement precisionVSLoss of time

Solution Approach 1:

The patent extracts only the relevant image portion that corresponds to the future vehicle path based on predicted path data, removing unnecessary image data from processing. This extraction maintains detection precision for road irregularities while significantly reducing computation time

Inventive Principle:
Principle #2Taking out (Extraction)

Solution Approach 2:

The system performs partial action by processing only the necessary portion of the image data rather than the entire image. This partial processing approach achieves sufficient measurement precision for predictive suspension control without the excessive computation time required for full-image processing

Inventive Principle:
Principle #16Partial or excessive action

3Measurement precision

If the stereo algorithm processes all image areas, then road surface scanning accuracy is improved, but processing efficiency decreases

Engineering Contradiction:
Improveroad surface scanning accuracyVSAvoidprocessing efficiency
Core Design Contradiction:
Measurement precisionVSProductivity

Solution Approach 1:

The patent segments the stereo algorithm processing to focus only on the relevant image portion corresponding to the future vehicle path, improving processing efficiency while maintaining scanning accuracy through targeted analysis of critical areas

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The system changes parameters by adapting the search direction and correlation area of the stereo algorithm based on the vehicle's lean angle. This parameter adaptation optimizes both scanning accuracy and processing efficiency for the dynamic conditions of single-track vehicles

Inventive Principle:
Principle #35Parameter changes

4Adaptability or versatility

If predictive suspension control is adapted for dynamic vehicle movements, then adaptability is improved, but device complexity increases

Engineering Contradiction:
Improveadaptability to dynamic movementsVSAvoidsystem complexity
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamics by adapting the search direction and correlation area of the stereo algorithm based on the lean angle of the vehicle. This dynamic adaptation improves versatility for handling single-track vehicle movements without requiring complex system architecture

Inventive Principle:
Principle #15Dynamics

Solution Approach 2:

The system achieves adaptability through parameter changes in the stereo algorithm based on lean angle data, allowing the predictive suspension control to adapt to dynamic vehicle movements without increasing overall device complexity

Inventive Principle:
Principle #35Parameter changes

Data Source

PatentEP3176013B1Predictive suspension control for a vehicle using a stereo camera sensor
Publication Date: 2019.07.17 HONDA RES INST EUROPE
  • EP3176013B1 patent drawingFigure 1
  • EP3176013B1 patent drawingFigure 2
  • EP3176013B1 patent drawingFigure 3~4

AI summary

The invention addresses the area of predictive suspension control system for a vehicle, particularly a two-wheel vehicle such as a motor cycle or a scooter. The system for adapting a suspension includes a stereo sensor unit which for generating image data, a computing unit which extracts a relevant image portion from the image data based on future vehicle path data, and calculates road unevenness on a future vehicle path of the vehicle based on the generated image data. A suspension control unit generates an adaptation signal for adapting the suspension based on the calculated road unevenness. The computing unit adapts a search direction of a stereo algorithm or a correlation area of the stereo algorithm based on a lean angle of the vehicle to generate the three-dimensional partial image data from the relevant image portion, and fits a road model to the three-dimensional partial image data to calculate the road unevenness.