Mobile Sensor Crash Prediction and Reconstruction for Vehicles
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Solution Overview
Problem
Current systems lack effective methods for predicting, detecting, and reconstructing vehicle accidents using mobile devices, despite advancements in collecting driver data.
Innovation Solution
A mobile device-based system that collects and analyzes vehicle movement data to generate an accident likelihood metric, detect accidents by identifying threshold-exceeding measurements, and reconstruct accidents by analyzing sensor data before, during, and after the incident.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If mobile devices are used to collect driver data, then data collection capability is improved, but accident prediction and detection capability remains insufficient
Solution Approach 1:
The system segments accident detection into multiple stages: prediction phase (analyzing driving behavior patterns), detection phase (real-time sensor monitoring for crash events), and reconstruction phase (post-accident analysis). This segmentation allows each phase to be optimized independently, resolving the contradiction between data collection and reliable accident detection.
Solution Approach 2:
The system performs preliminary actions by continuously collecting and analyzing driving behavior data before accidents occur. This enables the creation of baseline profiles and risk assessments that improve prediction capability, while also preparing the system to quickly detect and reconstruct accidents when they happen.
2Measurement precision
If sensor-based detection methods are implemented, then accident detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system uses universal sensor components (accelerometers, gyroscopes, magnetometers) that serve multiple functions:日常的 driving behavior monitoring, accident prediction, real-time accident detection, and post-accident reconstruction. This multi-functionality improves detection accuracy without proportionally increasing system complexity.
Solution Approach 2:
The mobile device's existing sensors serve the system's needs without requiring additional dedicated accident detection hardware. The system leverages sensors already present in smartphones for navigation and other functions, making them work self-service style for accident detection and reconstruction.
3Loss of information
If comprehensive sensor data collection is performed, then accident reconstruction capability is improved, but data processing requirements increase
Solution Approach 1:
The system performs preliminary data processing by continuously organizing and categorizing sensor data during normal operation. This pre-processing creates structured datasets that are ready for rapid analysis during accident reconstruction, reducing the processing burden when accidents occur.
Solution Approach 2:
The system applies different processing qualities to different data segments: continuous monitoring data receives standard processing, while accident-related data segments receive enhanced processing with higher sampling rates and more detailed analysis. This local quality approach preserves critical reconstruction information without uniformly increasing processing requirements.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time prediction and detection of accidents, providing users with risk alerts and facilitating post-accident analysis for reconstruction purposes, enhancing safety and accident investigation processes.
Implementation Method 1
an accelerometer of the mobile device, a gyroscope of the mobile device, and a magnetometer of the mobile device
Implementation Method 2
an accelerometer of the mobile device, a gyroscope of the mobile device, and a magnetometer of the mobile device
Implementation Method 3
an accelerometer of the mobile device, a gyroscope of the mobile device, and a magnetometer of the mobile device
Data Source
AI summary
Embodiments relate to transportation systems. More particularly, embodiments relate to methods and systems of vehicle data collection by a user having a mobile device. In a particular embodiment, vehicle data (also termed herein “driving data” or “data”) is collected, analyzed and transformed, and combinations of collected data and transformed data are used in different ways, including, but not limited to, predicting, detecting, and reconstructing vehicle accidents.