Abnormality Detection for Vehicle Space Recognition
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Solution Overview
Problem
Existing abnormality detection methods for outside space recognition devices in vehicles fail to accurately distinguish between recognition failures caused by device malfunctions and environmental factors, leading to increased man-hours in investigating causes and incorrect identification of device abnormalities.
Innovation Solution
An abnormality detection device connected to vehicles via a communication network, which acquires space recognition information and environment information, uses a space recognition success determination unit, environment dependence recognition failure classification unit, and abnormality detection unit to exclude environment-dependent recognition failures, thereby accurately detecting device abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If space recognition information is collected without filtering environmental factors, then the quantity of detection data increases, but the accuracy of abnormality detection deteriorates due to false positives from environmental conditions
Solution Approach 1:
The system performs preliminary classification of space recognition failures by determining whether environmental factors are present before conducting full abnormality analysis. This preliminary action filters out environmentally-caused failures early in the process, preventing them from consuming additional investigation resources and improving overall detection accuracy.
Solution Approach 2:
The abnormality detection process is segmented into distinct stages: environmental factor determination, failure classification, and abnormality detection. This segmentation allows the system to handle different types of failures through appropriate pathways, separating environmental failures from device malfunctions to improve detection precision.
2Reliability
If all space recognition failures are investigated as potential device abnormalities, then no abnormality cases are missed, but the time required for investigation increases due to including environmental failures
Solution Approach 1:
The system performs preliminary classification to identify environmentally-caused failures before full investigation. By determining environmental factors in advance, the system can immediately exclude these cases from detailed abnormality investigation, significantly reducing investigation time while maintaining complete coverage of actual device abnormalities.
Solution Approach 2:
The investigation process dynamically adapts based on environmental determination results. When environmental factors are detected, the system automatically adjusts the investigation pathway to exclude environmental failures, creating a flexible process that optimizes time allocation based on the specific failure cause.
3Device complexity
If environmental factors are not considered in abnormality determination, then the detection process is simpler, but false abnormality detections increase reducing system reliability
Solution Approach 1:
The detection process is segmented into environmental assessment and abnormality determination stages. This segmentation adds a structured approach that systematically evaluates environmental factors without creating excessive complexity, as the environmental determination follows clear criteria and decision pathways.
Solution Approach 2:
Environmental factor determination acts as an intermediary step between raw space recognition data and final abnormality conclusions. This intermediary layer filters and contextualizes data before abnormality assessment, improving reliability by preventing false detections while maintaining manageable process complexity through standardized evaluation criteria.
Data Source
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AI summary
An abnormality detection device 1 includes a space recognition success determination unit 12 which determines whether an outside space recognition device 51 is successful in space recognition from information which contains space recognition information and environment information, an environment dependence recognition failure classifying unit which determines and classifies whether a failure of the space recognition corresponds to any one of a failure type previously stored in an environment dependence recognition failure type storage unit 24 with respect to the space recognition information determined as failing in the space recognition, and an abnormality detection unit 14 which uses the space recognition information determined as not corresponding to any failure type by the environment dependence recognition failure classifying unit to detect an abnormality of the outside space recognition device 51 in the space recognition information determined as failing in the space recognition.