Vehicle Damage Identification Using Sensor-Triggered Camera Capture
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Solution Overview
Problem
Vehicles often suffer from unnoticed minor or major damage due to collisions or other incidents, which can go unreported, leading to delayed detection and inefficient claim processing.
Innovation Solution
Implementing a system that uses sensors and machine learning models to detect and classify vehicle damage in real-time, activating cameras to capture evidence, and utilizing blockchain for instant reporting and insurance claim automation, applicable to both connected and non-connected vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual damage detection methods are used, then users can report damage, but detection is delayed and efficiency is low
Solution Approach 1:
The system performs preliminary damage detection by continuously monitoring vehicles with sensors before manual reporting can occur. The machine learning model is pre-trained to automatically identify damage patterns, enabling detection to happen in advance of traditional reporting methods, thus reducing both detection time and improving overall productivity.
Solution Approach 2:
The patent replaces manual mechanical inspection methods with automated sensor-based detection systems. Accelerometers, gyroscopes, and cameras substitute for human observers, while machine learning algorithms replace manual assessment processes. This substitution dramatically improves detection efficiency and eliminates the time loss associated with manual reporting.
2Measurement precision
If multiple sensors and cameras are deployed for real-time detection, then damage identification accuracy improves, but device complexity increases
Solution Approach 1:
The system segments the damage detection function across multiple specialized sensors (accelerometers for impact detection, gyroscopes for orientation changes, cameras for visual documentation). Each sensor handles a specific aspect of detection, improving overall accuracy while managing complexity through functional segmentation rather than requiring a single complex system.
Solution Approach 2:
The machine learning model serves as a universal processing unit that handles data from multiple different sensor types. Rather than requiring separate processing systems for each sensor, the single ML model can analyze accelerometer data, gyroscope data, and camera images, reducing system complexity while maintaining high detection accuracy through multi-functional processing.
3Productivity
If instant reporting and claim automation are implemented, then claim processing speed improves, but system complexity increases
Solution Approach 1:
The system performs preliminary data collection and damage assessment automatically at the moment damage occurs, before any manual intervention is needed. Sensors continuously monitor and the ML model pre-processes information, so when damage is detected, the reporting system already has prepared data ready for instant transmission, improving claim processing speed without proportionally increasing complexity.
Solution Approach 2:
The system enables self-service reporting where the vehicle itself automatically generates and transmits damage reports without requiring user intervention. The embedded sensors and ML model autonomously detect damage, document it with camera images, and initiate the claims process, improving processing speed while managing complexity through automation rather than complex human-in-the-loop systems.
Data Source
AI summary
Vehicle damage identification and incident management systems and methods are provided herein. An example method can include determining occurrence of a damage event for a first vehicle based on a vehicle sensor signal, determining a location on the first vehicle where damage has occurred using the vehicle sensor signal, activating a camera on a side of the first vehicle corresponding with, or adjacent to, the location on the first vehicle where damage has occurred, determining, from camera images, identifying information related to an object captured in the camera images, and transmitting a message to a recipient that includes the identifying information.