Optical Surface Classification for Adaptive Agricultural Vehicles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Agricultural utility vehicles face challenges in adapting their driving operations efficiently to varying underlying surfaces, leading to suboptimal fuel efficiency and increased wear, as existing methods lack accurate and automated surface classification capabilities.
Innovation Solution
An optical sensor system and data processing unit, utilizing neural networks, classify the underlying surface by processing optical data to determine applicable surface classes, allowing for automatic adaptation of technical features like tire pressure, transmission gear ratio, and differential lock states, thereby optimizing driving operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the driver manually adapts driving parameters to varying underlying surfaces, then the vehicle can respond to surface changes, but the driver's workload increases and adaptation efficiency decreases
Solution Approach 1:
The system enables automatic adaptation of driving parameters by having the vehicle itself perform the classification and adjustment tasks. The optical sensor system automatically detects surface characteristics, the data processing unit classifies the underlying surface, and the control unit automatically adjusts technical features without requiring driver intervention, thus reducing workload while maintaining adaptability
Solution Approach 2:
The patent replaces the manual mechanical adjustment process with an automated optical-electronic-control system. Instead of the driver visually assessing surfaces and manually adjusting parameters, an optical sensor system captures images, neural networks classify surfaces automatically, and control units adjust technical features electronically, substituting human cognitive and physical effort with automated systems
2Measurement precision
If complex classification algorithms are used to accurately classify underlying surfaces, then classification accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary classification of underlying surfaces using optical data before making driving parameter adjustments. By pre-processing and classifying surface characteristics in advance, the system prepares classification results that can be quickly referenced and acted upon, reducing the complexity of real-time decision-making while maintaining high accuracy
Solution Approach 2:
The patent introduces an intermediary data processing unit that acts as a mediator between the optical sensor system and the control unit. This intermediary processes optical data through neural networks to generate simplified classification results, which then guide the control unit's adjustments, reducing the overall system complexity while maintaining classification accuracy
3Loss of energy
If automatic adaptation of technical features is implemented, then fuel consumption decreases and wear is reduced, but system complexity and initial costs increase
Solution Approach 1:
The system automatically adjusts driving parameters such as engine speed, transmission gear ratio, and tire pressure based on classified underlying surface characteristics. By dynamically changing these parameters to match optimal values for each surface type, the system reduces fuel consumption and wear while the complexity is managed through automated classification and pre-defined adjustment rules
Data Source
AI summary
A method for classifying an underlying surface travelled by an agricultural utility vehicle includes acquiring a detail of a surface of the underlying surface in the form of optical data, classifying the optical data in a data processing unit with respect to different underlying surface classes, and determining an underlying surface class on the basis of the classifying step. Output data is output from the data processing unit representative of the determined underlying surface class as a classification result. A technical feature of the utility vehicle is adapted as a function of the classification result.