Vehicle Crash Impact Detection Using Multi-Phone Verification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle crash prediction systems face challenges in accuracy, particularly in verifying predictions before taking actions, as they often rely on data from a single mobile device, leading to potential false alarms and difficulties in independently confirming crash events.
Innovation Solution
A method and system that utilize data from two or more mobile devices to determine if multiple events correspond to the same crash event by aligning characteristics such as time, location, and point-of-impact, thereby reducing false predictions and enhancing verification.
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 and reliability of crash event verification deteriorates
Solution Approach 1:
The patent combines data from multiple mobile devices (first mobile device and second mobile device) to verify crash events. The system merges location data, time data, and crash detection data from different sources to improve verification accuracy and reduce false alarms, directly resolving the contradiction between reliability and system complexity.
Solution Approach 2:
The patent introduces a server as an intermediary that receives and processes crash detection data from multiple mobile devices. The server performs the verification by comparing location coordinates, time stamps, and crash event data, acting as a mediator that enhances reliability without significantly increasing the complexity of individual mobile devices.
2Measurement precision
If multiple mobile devices are used for crash prediction verification, then the accuracy of crash event detection is improved, but the quantity of data to be processed increases
Solution Approach 1:
The patent extracts only the essential verification data from multiple mobile devices, specifically location coordinates, time stamps, and crash detection flags. By taking out only these critical data elements rather than processing all available sensor data, the system improves detection accuracy while minimizing the quantity of data that needs to be transmitted and processed.
Solution Approach 2:
The patent implements a selective verification approach where only mobile devices that detect crash events are included in the verification process. Not all mobile devices in the system need to participate in every verification, allowing the system to achieve high accuracy through partial participation rather than requiring all devices to contribute data.
3Reliability
If crash events are verified using multiple data sources, then false alarms are reduced, but the time required for verification increases
Solution Approach 1:
The patent performs preliminary actions by continuously monitoring and pre-processing data from mobile devices before a crash event occurs. Location data and device status are maintained in ready-state, so when a crash event is detected, the verification can be performed rapidly by comparing pre-collected data rather than gathering information after the event.
Solution Approach 2:
The patent replaces complex mechanical verification processes with automated digital comparison algorithms. The server automatically compares location coordinates, time stamps, and crash detection data using computational methods, which is much faster than manual verification while maintaining high reliability in reducing false alarms.
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.