Autonomous Vehicle Undercarriage Imaging for Claim Damage Verification
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Solution Overview
Problem
Conventional vehicle inspection methods are labor-intensive and costly, requiring manual processes that can lead to inaccurate damage or defect identification, thereby affecting insurance claims and 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, allowing for the identification of damages and defects without the need for physical repositioning or specialized machinery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection processes are used, then detailed examination of vehicle damages and defects can be performed, but high equipment and labor costs are incurred
Solution Approach 1:
The patent replaces manual mechanical inspection processes with an automated imaging system. A camera mounted on a vehicle captures images of the inspected vehicle, and a processor automatically analyzes these images to identify damages and defects. This substitution eliminates the need for manual inspection by mechanics and complex inspection equipment like vehicle lifts, while maintaining accurate damage identification through computer vision algorithms.
2Measurement precision
If manual inspection processes are used, then comprehensive vehicle examination can be conducted, but significant time is required for moving and inspecting the vehicle
Solution Approach 1:
The system performs preliminary actions by capturing comprehensive images of the entire vehicle before analysis begins. The camera mounted on the inspecting vehicle takes multiple images covering different angles and areas of the inspected vehicle. This preliminary image capture allows the processor to subsequently analyze all captured areas simultaneously, eliminating the time required for sequential manual inspection and vehicle repositioning.
3Ease of operation
If manual inspection is performed, then repair cost estimation can be provided, but inaccurate damage identification may occur
Solution Approach 1:
The system implements feedback through automated image analysis where the processor evaluates captured images against known damage patterns and provides objective identification of damages and defects. This feedback mechanism eliminates human error and subjectivity in damage assessment, ensuring consistent and accurate identification. The system can also provide feedback on repair cost estimates based on the identified damages, creating a closed-loop inspection process that improves both accuracy and operational simplicity.
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.