Suspension Sensory Evaluation Using Time-Series Correlation
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Solution Overview
Problem
Existing sensory evaluation systems for automobiles, such as those described in Patent Literature 1, are inadequate in handling variations in driver operations, particularly in terms of ride quality and steering stability, as they do not effectively evaluate the correlation between multiple physical quantities affecting the driver's experience.
Innovation Solution
A sensory evaluation prediction system that includes an input unit for reading time series data from behavior sensors, a selection unit for choosing relevant physical quantities, a correlation creation unit for establishing time-series correlations, and an evaluation circuit to calculate sensory indices based on these correlations, which is integrated with a suspension control system to adjust damping forces accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple physical quantities are measured and their correlations are evaluated, then measurement precision and reliability of sensory evaluation are improved, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The system segments the complex sensory evaluation task into distinct functional modules: behavior sensors capture raw vehicle dynamics data, the selection unit identifies relevant physical quantities, the correlation creation unit establishes temporal relationships, and the evaluation circuit computes sensory indices. This modular segmentation reduces overall system complexity while maintaining high measurement precision through specialized processing at each stage.
Solution Approach 2:
The patent introduces intermediate processing units that mediate between raw sensor data and final sensory evaluation results. The selection unit acts as an intermediary that filters and identifies relevant physical quantities from multiple sensor inputs, while the correlation creation unit serves as another intermediary that establishes temporal relationships before final evaluation. These intermediaries simplify the overall system architecture by breaking down the complex measurement process into manageable stages.
2Reliability
If correlation analysis of multiple physical quantities is performed, then reliability of sensory evaluation is improved, but loss of time and computational load increase
Solution Approach 1:
The system performs preliminary actions by pre-identifying relevant physical quantities through the selection unit before conducting full correlation analysis. This preliminary filtering reduces the dimensionality of data that requires intensive correlation processing, thereby maintaining evaluation reliability while reducing computational time and resource requirements.
Solution Approach 2:
The patent applies partial action by selectively analyzing correlations only for the most relevant physical quantities identified by the selection unit, rather than computing all possible correlations among all measured parameters. This selective approach maintains sufficient evaluation reliability while significantly reducing computational burden and processing time.
Data Source
AI summary
A sensory evaluation prediction system includes an input unit that reads an output from a behavior sensor that measures two or more types of pieces of time series information regarding a moving body, a selection unit that selects two or more types of physical quantities from the output from the behavior sensor read by the input unit, a correlation creation unit that creates information showing a correlation in time series between the two or more types of the physical quantities selected by the selection unit, and an evaluation circuit that calculates an evaluation value of a sensory index based on the information showing the correlation in time series.