Mobile Crash Impact Detection With Multi-Device Event Verification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle crash prediction systems face challenges in improving accuracy, particularly in reducing false predictions and verifying crash events independently, which can lead to unnecessary emergency reporting and potential fraud in insurance claims.
Innovation Solution
A method and system that utilize data from two or more mobile devices associated with different vehicles to determine if multiple predicted crash events correspond to the same crash event, by aligning various characteristics such as time, location, and point-of-impact, thereby reducing the likelihood of false predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle crash prediction is performed based on data from a single mobile device, then the system complexity is low, but the accuracy of prediction is reduced due to inability to independently verify the prediction
Solution Approach 1:
The patent combines data from multiple mobile devices (first mobile device and second mobile device) to verify crash predictions. The server receives crash data from both devices and determines whether they correspond to the same crash event by comparing characteristics such as time, location, and point of impact. This merging of data sources improves prediction accuracy through independent verification while managing system complexity through centralized processing.
2Reliability
If multiple mobile devices are used to verify crash events, then the accuracy of prediction is improved, but the device complexity increases
Solution Approach 1:
The patent introduces a server as an intermediary that receives crash data from multiple mobile devices, processes the data, and determines whether the events correspond to the same crash. The server compares characteristics (time, location, point of impact) from different devices and makes the final determination. This intermediary approach allows multiple devices to contribute data without each device needing complex verification capabilities, thus improving accuracy while managing overall system complexity.
3Reliability
If crash events are verified using multiple data sources, then false predictions are reduced, but the loss of time increases due to additional verification steps
Solution Approach 1:
The patent performs preliminary actions by having mobile devices continuously collect and prepare crash data (motion data, location data, audio data, image/video data) before a crash event occurs. The devices are ready to immediately detect and report crash characteristics when an event happens. This preliminary preparation reduces the verification time needed after a crash, as the data is already collected and structured for comparison, thus reducing false predictions without excessive time loss.
4Object-affected harmful factors
If independent verification of crash events is performed, then fraudulent insurance claims are reduced, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent replaces manual verification processes with automated data analysis. The server automatically compares crash characteristics (time, location, point of impact) from multiple mobile devices using computational algorithms. This substitution of automated mechanical/computational systems for manual verification reduces fraudulent claims effectively while managing data processing complexity through algorithmic approaches rather than manual review processes.
Data Source
AI summary
Techniques are disclosed for detecting a point-of-impact of a vehicle collision. A mobile device receives a set of sensor measurements from sensors of the mobile device. The mobile device generates a crash feature vector from the sensor measurements and one or more point-of-impact features. The mobile device executes a set of classifiers using the crash feature vector and the one or more point-of-impact features. Each classifier of the set of classifiers is configured to generate an output that is partially indicative of a point-of-impact of the collision. The mobile device generates, from the output of each classifier of the set of classifiers, a prediction of a point of impact of the vehicle collision. The mobile device then transmits an indication of the point of impact to a remote device.