Vehicle Behavior Model Matching for Abnormality Identification
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Solution Overview
Problem
Identifying vehicle abnormalities, such as malfunctions, often requires significant human effort and can be challenging due to the difficulty in locating and estimating the severity of the abnormalities, leading to increased work burdens.
Innovation Solution
A data processing apparatus and method that utilizes multiple vehicle models with different parameter settings to simulate vehicle behavior based on driving operation data, evaluating the similarity of the simulated behavior with actual measured data to identify the appropriate model, thereby reducing the need for manual inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual inspection methods are used to identify abnormal items in vehicles, then the inspection can be performed with simple equipment, but the work burden increases and identification accuracy decreases
Solution Approach 1:
The patent creates a digital twin (virtual model) of the vehicle that replicates the physical vehicle's behavior and characteristics. This virtual copy allows for simulation and analysis without physical inspection, reducing work burden while maintaining or improving identification accuracy through computational analysis of the digital replica's responses under various conditions.
Solution Approach 2:
The patent replaces manual mechanical inspection processes with computational simulation and data analysis. Instead of physically examining vehicle components, the system uses virtual models and algorithms to detect abnormalities, thereby reducing human work burden while enhancing identification precision through systematic computational methods.
2Loss of information
If comprehensive inspection including degree of occurrence estimation is performed, then the diagnostic information becomes more complete, but the work burden increases significantly
Solution Approach 1:
The patent performs preliminary simulations and analyses on the digital twin vehicle to pre-establish baseline behaviors and potential failure modes. By preparing the virtual model in advance with pre-computed response characteristics, the system can quickly assess diagnostic information during actual inspections without performing time-consuming comprehensive examinations.
Solution Approach 2:
The system uses feedback from the digital twin simulations to guide the inspection process. The virtual model provides predictive information about potential abnormalities and their expected manifestations, allowing the inspection system to focus on specific areas of concern rather than performing exhaustive comprehensive checks, thereby reducing inspection time while maintaining information completeness.
3Measurement precision
If multiple vehicle models with different parameter settings are used for simulation, then the identification accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent segments the vehicle modeling into multiple discrete models, each representing different operational conditions or component states. By dividing the complex vehicle system into separate manageable models with specific parameter settings, the system can accurately simulate various scenarios while organizing the computational complexity into structured, manageable segments rather than one monolithic complex model.
Data Source
AI summary
A data processing apparatus performs a simulation process based on driving operation data of a vehicle. In the simulation process, the apparatus simulates a vehicle behavior using multiple vehicle models having different parameter settings. The data processing apparatus also performs a model identifying process. In the model identifying process, the apparatus evaluates a similarity between a vehicle behavior obtained by the simulation using each vehicle model and an actual measured vehicle behavior of the vehicle. The apparatus identifies one or more vehicle models that satisfy a predetermined similarity condition based on the evaluated similarity.