Mobile Sensor Accident Detection With Loop Buffer and ML
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Solution Overview
Problem
Existing accident detection methods using sensor data from smartphones and vehicles are inaccurate and unreliable, particularly in complex situations or for less severe accidents.
Innovation Solution
A method utilizing a mobile device with sensors to continuously acquire and store sensor data in a loop recording memory, setting time stamps when threshold values are crossed, and applying machine learning to evaluate characteristics of the data for accident detection and prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If limit values of sensor data are defined to detect accidents, then accident detection can be performed, but accuracy and reliability are insufficient for complex situations or less serious accidents
Solution Approach 1:
The patent segments the accident detection process into multiple stages: continuous sensor data acquisition, temporary storage in loop recording memory, threshold-based time stamping at different acceleration levels, and machine learning evaluation. This segmentation allows the system to capture both severe and minor accidents with different threshold sensitivities, improving overall detection reliability and precision
Solution Approach 2:
The system performs preliminary actions by continuously acquiring and temporarily storing sensor data in loop recording memory before an accident occurs. This pre-prepared data buffer enables immediate analysis when thresholds are exceeded, eliminating detection delays and improving reliability for both severe and minor accidents
2Measurement precision
If sensor data is continuously monitored with multiple threshold values, then detection accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
Sensor data is continuously acquired and temporarily stored in loop recording memory before any accident occurs. This preliminary data preparation ensures that when thresholds are exceeded, the system can immediately analyze pre-captured data without detection latency, maintaining both high precision and real-time response
Solution Approach 2:
The patent replaces traditional mechanical threshold-based detection systems with machine learning and evaluation processes. The machine learning model analyzes the characteristics of sensor data between time stamps, enabling more accurate detection of complex and minor accidents while maintaining efficient processing through automated pattern recognition
3Measurement precision
If machine learning and evaluation processes are applied to sensor data characteristics, then accident detection accuracy improves, but device complexity and computational requirements increase
Solution Approach 1:
The patent uses a mobile device with existing sensors (accelerometer, gyroscope, microphone) that serve multiple functions: normal device operation and accident detection. By utilizing already-present sensors and processing capabilities, the system achieves high detection precision without adding significant device complexity or requiring specialized hardware
Solution Approach 2:
The loop recording memory acts as an intermediary buffer between sensor data acquisition and machine learning analysis. This intermediary component temporarily stores sensor data in a structured format with time stamps, enabling efficient machine learning processing without requiring complex real-time data management systems
Data Source
AI summary
A method (200) for detecting and evaluating an accident of a vehicle, the method steps being carried out at least partially on a mobile device (400, 500, 600), the mobile device having at least one sensor, the mobile device being carried along with the vehicle, an accident monitoring system (307, 426, 612) being operated on the mobile device in such a manner that sensor data (101) of the sensor is continuously acquired by means of the mobile device and temporarily stored in a memory.