AI Vehicle Diagnostics Platform for Bidirectional State Sharing
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Solution Overview
Problem
Current vehicle diagnostic technologies require drivers to manually determine issues and schedule repairs, leading to potential over-repairing of simple problems and limited customer satisfaction due to inadequate information and vehicle state management.
Innovation Solution
An information sharing platform utilizing Deep Learning algorithms with big data from vibration and noise signals for bidirectional vehicle state diagnosis, enabling communication between drivers and vehicles, periodic learning of driving habits, and providing accurate, immediate information through a communication controller and graphic controller with AI modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional vehicle diagnostic methods are used where drivers manually determine problems and schedule repairs, then drivers have control over the diagnostic process, but this leads to over-repairing of simple problems and reduced customer satisfaction due to inadequate information
Solution Approach 1:
The vehicle performs self-diagnosis through embedded sensors and processors that automatically monitor component states, vibration patterns, and operational parameters. The system generates its own diagnostic data without requiring driver intervention, enabling precise detection of actual vehicle conditions while reducing unnecessary repairs.
Solution Approach 2:
Manual driver assessment of vehicle problems is replaced by electronic sensor-based monitoring systems. processors analyze vibration signals, noise patterns, and operational data to objectively determine vehicle state, substituting subjective human judgment with precise mechanical-electronic measurement systems.
2Device complexity
If traditional vehicle diagnostic methods are used with limited information collection, then system complexity is reduced, but management service cannot be provided due to insufficient vehicle state data
Solution Approach 1:
The diagnostic system serves multiple functions simultaneously: it monitors vehicle component states, analyzes vibration and noise patterns, stores historical operational data, generates predictive maintenance alerts, and provides management service information. This multi-functional approach enables comprehensive information collection without proportionally increasing system complexity.
Solution Approach 2:
The diagnostic system employs a hierarchical structure where sensors are nested within components, which are nested within subsystems, all communicating through integrated communication interfaces. This nested architecture allows comprehensive data collection at multiple levels while maintaining organized system complexity through modular design.
3Measurement precision
If Deep Learning algorithms with big data from vibration and noise signals are implemented for bidirectional vehicle state diagnosis, then diagnosis accuracy and customer satisfaction are improved, but system complexity and data processing requirements increase
Solution Approach 1:
An information sharing platform acts as an intermediary between the vehicle's diagnostic system and external servers. This platform collects, preprocesses, and manages vibration and noise data locally before transmitting to remote servers for Deep Learning analysis, distributing system complexity across multiple levels rather than concentrating all processing requirements in one location.
Solution Approach 2:
The system performs preliminary data collection, filtering, and organization through the information sharing platform before transmitting to external servers. This preliminary processing reduces the complexity burden on any single component by preparing data in advance, enabling more efficient Deep Learning analysis without requiring the entire system to handle all processing simultaneously.
4Speed
If bidirectional communication between driver and vehicle with AI modules is implemented, then immediate and accurate information is provided, but device complexity and computational requirements increase
Solution Approach 1:
The bidirectional communication system operates periodically, with the information sharing platform collecting and processing data at scheduled intervals rather than continuously. This periodic operation enables the system to provide timely diagnostic information while reducing computational burden and system complexity compared to continuous real-time processing.
Data Source
AI summary
An information sharing platform of providing bidirectional vehicle state information between a driver and a vehicle, the information sharing platform may include a communication controller which collects measured data and vehicle Controller Area Network (CAN) information by sensors installed to components capable of diagnosing a vehicle state; and a graphic controller which provides a driver with diagnosis result output information that is generated based on a predetermined selection criterion among the components through Deep Learning based diagnosis using big data having the collected data.