AI Vehicle Dashboard Analysis Using ML Component Recognition
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Solution Overview
Problem
Current methods for automated vehicle dashboard data extraction are inefficient and prone to human error, as they rely on manual input or proprietary telematics devices, and standard computer vision algorithms like OCR struggle to read analog and digital gauges, warning lights, and other non-textual dashboard components.
Innovation Solution
A system using machine learning and feature extraction to identify and read components on a vehicle dashboard, including digital and analog gauges, and warning lights, through a portable device equipped with a camera, which captures images and processes them using trained models to determine make and model, and extract readings, optionally using OCR for mechanical odometers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data collection methods are used, then data can be obtained from any vehicle, but the process is labor intensive and prone to human error
Solution Approach 1:
The patent replaces manual mechanical data collection with an automated computer vision system using machine learning models. The system captures dashboard images and uses trained models to automatically identify and extract data from various dashboard components, eliminating manual intervention while maintaining high accuracy across different vehicle types.
Solution Approach 2:
The machine learning models are trained on diverse dashboard images to enable self-service data extraction. The system automatically adapts to different dashboard layouts and component types without requiring manual reconfiguration, performing self-learning through the trained models to extract data from odometers, gauges, warning lights, and other components.
2Adaptability or versatility
If OCR algorithms are used to read dashboard components, then text-based data can be extracted, but the system cannot read analog gauges, digital gauges, or warning lights
Solution Approach 1:
The patent implements a universal machine learning-based system that can handle multiple dashboard component types through a single integrated approach. The trained models are designed to recognize and extract data from various component types including mechanical odometers, digital odometers, analog gauges, digital gauges, and warning lights, providing multi-functional capability that OCR alone cannot achieve.
Solution Approach 2:
The system changes the approach from text-based OCR recognition to image-based machine learning recognition. By transforming the data extraction method from reading text to analyzing visual patterns and features, the system can accurately interpret analog gauge positions, digital display values, and warning light states that are not readable by traditional OCR.
3Reliability
If customized algorithms are developed for each vehicle make and model, then accurate data extraction is achieved, but the system becomes extremely complex and cost prohibitive
Solution Approach 1:
The patent creates a universal machine learning system that handles multiple vehicle makes and models through a single platform. The trained models are designed to adapt to different dashboard layouts and component configurations without requiring separate customized algorithms for each vehicle type, significantly reducing system complexity while maintaining accuracy.
Solution Approach 2:
The system performs preliminary training of machine learning models on diverse dashboard images from various vehicle makes and models before deployment. This pre-training enables the system to recognize and extract data from different dashboard configurations without requiring customization at runtime, reducing complexity while preserving extraction accuracy.
Data Source
AI summary
A portable computing device equipped with an image capture device captures an image of a vehicle dashboard of a vehicle. Then, the portable computing device identifies the location of one or more components of the vehicle dashboard in the captured image. Based on the location of the one or more components, the portable computing device segments the captured image to obtain an image of each of the one or more components. Further, the portable computing device processes the images of the one or more components using one or more machine learning models to determine a reading associated with each of the one or more components. An accuracy of the readings is verified and responsively, the portable computing device inputs the readings in respective data fields of an electronic form. The readings associated with the one or more components of the vehicle dashboard represent data associated with the vehicle.