Remote Vehicle Imaging for Automated Damage and Fraud Inspection
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Solution Overview
Problem
Current vehicle inspection methods are labor-intensive and costly, requiring manual processes that can lead to inaccurate damage or defect identification, resulting in incorrect repair estimates.
Innovation Solution
The use of remotely-controlled (RC) and/or autonomously operated inspection devices, such as RC cars or drones, to capture and analyze imaging data of vehicles, identifying damage or defects through computer-aided analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection processes are used, then inspection thoroughness may be maintained, but labor costs and inspection time increase significantly
Solution Approach 1:
The patent replaces manual mechanical inspection processes with an automated inspection system that uses imaging devices (cameras, sensors) to capture vehicle data and computer vision algorithms to analyze damage. This substitution eliminates the need for manual labor while maintaining inspection thoroughness, directly resolving the contradiction between inspection efficiency and system complexity.
Solution Approach 2:
The inspection system performs self-analysis through automated image processing and damage detection algorithms. The system independently captures images, processes them through computer vision models, and generates inspection reports without requiring continuous human intervention, thereby improving productivity while keeping the system manageable.
2Measurement precision
If manual inspection is performed, then detailed damage assessment is possible, but accuracy is reduced due to human error
Solution Approach 1:
The system replaces human inspectors with automated imaging and computer vision analysis. The imaging devices capture high-resolution images and the processing system automatically detects and assesses damage with consistent accuracy, eliminating human error while reducing inspection time through parallel processing of multiple images simultaneously.
Solution Approach 2:
The inspection system continuously processes images and data without interruption, maintaining constant analysis of vehicle damage. This continuous automated processing ensures no damage is missed (improving accuracy) while operating faster than sequential manual inspection methods (reducing time loss).
3Reliability
If complete vehicle inspection is conducted, then all damages are identified, but equipment and facility requirements increase
Solution Approach 1:
The inspection system uses multi-functional imaging devices and sensors that can capture various types of data (visual images, structural data, surface conditions) from a single platform. This universal approach enables complete vehicle inspection without requiring multiple specialized pieces of equipment, maintaining inspection completeness while reducing overall system complexity.
Solution Approach 2:
The system transitions from traditional ground-based inspection to aerial or multi-angle imaging perspectives. By capturing images from multiple dimensions and angles simultaneously, the system achieves complete inspection coverage without requiring physical access to all vehicle areas, thereby reducing equipment requirements while maintaining reliability.
4Productivity
If manual damage assessment is performed, then repair estimates can be generated, but cost and time increase
Solution Approach 1:
The system replaces manual damage assessment with automated image analysis and computer vision algorithms. The processing system extracts precise damage information from images, generates accurate repair estimates, and does so much faster than manual methods. This substitution maintains data accuracy through consistent algorithmic analysis while dramatically improving productivity in repair estimation.
Data Source
AI summary
A remotely-controlled (RC) and/or autonomously operated inspection device, such as a ground vehicle or drone, may capture one or more sets of imaging data indicative of at least a portion of an automotive vehicle, such as all or a portion of the undercarriage. The one or more sets of imaging data may be analyzed based upon data indicative of at least one of vehicle damage or a vehicle defect being shown in the one or more sets of imaging data. Based upon the analyzing of the one or more sets of imaging data, damage to the vehicle or a defect of the vehicle may be identified. The identified damage or defect may be compared to a claimed damage or defect to determine whether the claimed damage or defect occurred.