Autonomous Vehicle Damage Assessment via Overlay Comparison
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Solution Overview
Problem
Current methods for assessing vehicle damage after accidents require human intervention, which is time-consuming and costly, and still necessitate human analysis even with automated image capture systems.
Innovation Solution
A method that captures vehicle image data, compares it to manufacturer-specific overlay data to identify differences, and designates damaged areas based on predefined thresholds, using a processor to automate the damage assessment process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human review process is used to identify vehicle damage, then accuracy of damage identification is improved, but time consumption and costs increase
Solution Approach 1:
The patent replaces the mechanical human review process with an automated computer vision system that captures images of the vehicle and uses image processing algorithms to identify damage. The system compares captured images against a database of undamaged vehicle images to automatically detect and classify damage without human intervention, thereby reducing time consumption while maintaining identification accuracy.
Solution Approach 2:
The system enables the vehicle damage assessment process to be self-service by automatically capturing images, processing them through algorithms, comparing results, and generating damage reports without requiring human operators. The automated system performs the entire assessment workflow independently, eliminating manual labor while preserving diagnostic accuracy.
2Measurement precision
If human review process is used to identify vehicle damage, then expertise in damage assessment is utilized, but costs to damage management entities increase
Solution Approach 1:
The patent substitutes the expensive human expert review process with an automated image processing system that uses computer vision algorithms to perform damage assessment. The system captures vehicle images, processes them through trained algorithms, and generates assessment reports automatically, eliminating the need to pay human experts while maintaining or improving assessment quality through consistent algorithmic application.
Solution Approach 2:
The system creates and uses a digital copy of the undamaged vehicle state stored in a database, comparing it against captured images of the damaged vehicle. This copying approach allows automated comparison and damage identification without requiring physical inspection by experts, reducing costs while preserving assessment capability.
3Loss of time
If automated image capture system is used, then time consumption is reduced, but human analysis is still required at each step
Solution Approach 1:
The patent implements full automation where the system independently performs image capture, image processing, damage detection, comparison with reference data, and report generation. The automated system serves itself by completing the entire damage assessment workflow without requiring human intervention at any step, achieving complete automation while maintaining speed efficiency.
Solution Approach 2:
The patent replaces the semi-automated process (where humans analyzed automated images) with a fully automated computer vision system that performs all analysis functions algorithmically. The system uses image processing algorithms to automatically detect, classify, and report damage from captured images, eliminating the need for human analysts while preserving the time efficiency benefits of automation.
Data Source
AI summary
One example includes receiving captured data identifying a vehicle body including vehicle portions, identifying difference data between one or more distances provided by vehicle overlay data associated with known manufacturer data of the vehicle and the captured data of the vehicle body, determining whether the difference data identified is beyond one or more difference thresholds for one or more of the vehicle portions based on a difference of the one or more distances of the vehicle overlay data as compared to one or more distances of the captured data indicating the captured data includes one or more different lengths of distances measured across the one or more vehicle portions as compared to one or more distances measured across the one or more vehicle portions of the overlay data, and designating the one or more vehicle portions as damaged when the difference data identified is beyond the one or more difference thresholds.