Vehicle Dashboard Symbol Recognition Using Mobile Camera AI
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Solution Overview
Problem
Drivers face difficulties in interpreting and understanding the symbols and shapes on their vehicle's dashboard, which can represent features or warnings, often requiring time-consuming searches through user manuals or online resources.
Innovation Solution
A mobile device application uses camera-based image recognition and machine learning models to scan the vehicle's interior, identify symbols or shapes, and provide real-time data on their meanings, including scheduling maintenance appointments if errors are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If drivers manually search through user manuals or online resources to understand dashboard symbols, then they can obtain information about vehicle features and warnings, but it consumes significant time and effort
Solution Approach 1:
The patent replaces the mechanical manual search process with an automated optical recognition system. The mobile device camera captures images of dashboard symbols, and machine learning models automatically identify and interpret them, substituting the manual mechanical action of searching through manuals with an automated visual recognition system.
Solution Approach 2:
The system enables the vehicle dashboard information to serve itself by being automatically recognized and interpreted. The symbols and shapes on the dashboard are self-identified through image recognition without requiring external manual intervention, allowing the information to be extracted and presented automatically.
2Ease of operation
If drivers spend time researching symbol meanings online, then they can understand vehicle warnings and features, but it interrupts driving focus and safety
Solution Approach 1:
The patent replaces the manual information-seeking behavior with an automated visual recognition system that provides instant feedback. The mobile device automatically captures, identifies, and presents symbol meanings without requiring the driver to divert attention to external resources, maintaining driving focus while providing necessary information.
Solution Approach 2:
The mobile device application serves as an intermediary between the dashboard symbols and the driver. Instead of the driver directly searching for information, the application mediates by automatically capturing the symbol image, processing it through machine learning models, and presenting the interpreted meaning, thereby bridging the information gap without requiring driver distraction.
3Measurement precision
If the system scans the entire vehicle interior continuously, then it can detect all symbols and errors, but it consumes excessive battery power and processing resources
Solution Approach 1:
The system employs periodic scanning instead of continuous scanning. The mobile device captures images of the dashboard at specific intervals or when triggered by user interaction, rather than continuously scanning the entire vehicle interior. This periodic approach maintains symbol detection capability while significantly reducing energy consumption and processing resource usage.
Solution Approach 2:
The system extracts only the relevant information from the dashboard area rather than processing the entire vehicle interior. The image recognition focuses specifically on identifying symbols and shapes within the dashboard region, extracting only the necessary data while ignoring other areas, thereby reducing computational load and energy consumption.
4Adaptability or versatility
If the machine learning model processes all detected shapes, then it can identify all vehicle features, but it increases processing time and computational complexity
Solution Approach 1:
The patent segments the dashboard area into distinct regions and identifies symbols within each region separately. The machine learning model processes individual symbols rather than analyzing the entire dashboard as a single complex image, breaking down the recognition task into manageable segments that reduce computational complexity while maintaining comprehensive feature recognition.
Solution Approach 2:
The system performs partial processing by focusing only on the most relevant symbols and shapes detected in the dashboard area. Rather than exhaustively analyzing every detected feature, the machine learning model prioritizes processing symbols that are more likely to represent important vehicle information, reducing processing time while maintaining adequate recognition capability for critical features.
Data Source
AI summary
Systems and methods are provided for determining features in a vehicle. The interior of the vehicle can be scanned with a camera of a mobile device to locate one or more symbols or shapes within the interior. The one or more symbols or shapes can be processed using one or more machine learning models to obtain data associated with the one or more symbols or shapes. The data associated with the one or more symbols or shapes can be displayed to a driver of the vehicle.