Vehicle Collision Characterization With Multi-Source Telematics
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Solution Overview
Problem
Conventional collision detection methods using accelerometer data are unreliable due to high false positive and false negative rates, as they fail to differentiate between actual collisions and non-collision events such as bumps, door slams, or road surface features, and do not effectively utilize additional telemetry data to improve detection accuracy.
Innovation Solution
Analyze a combination of telematics data, including acceleration, GPS, engine diagnostics, and image/video data, over a time period before and after a potential collision to determine a likelihood of a non-collision event, using criteria and machine learning classifiers to reduce false positives and negatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional accelerometer-based collision detection is used, then collision detection capability is provided, but false positive and false negative rates are high
Solution Approach 1:
The patent combines multiple data sources including accelerometer data, GPS data, engine diagnostic data, and image/video data into a unified analysis system. This merging of diverse data types enables more reliable collision detection by cross-validating signals across multiple sensors, thereby reducing false positives and false negatives while improving measurement precision in differentiating actual collisions from non-collision events.
2Measurement precision
If only accelerometer data is analyzed, then detection simplicity is maintained, but detection accuracy is insufficient
Solution Approach 1:
The system employs a multi-functional analysis platform that processes various data types (accelerometer, GPS, engine diagnostics, image/video) through a unified machine learning framework. This universal system handles multiple detection tasks and data formats simultaneously, improving detection accuracy while managing complexity through integrated processing rather than separate specialized systems.
Solution Approach 2:
The patent introduces machine learning classifiers as intermediary components that bridge raw multi-source data and collision detection decisions. These classifiers process and interpret data from multiple sensors, transforming complex raw data into reliable collision determinations, thereby improving accuracy while abstracting the complexity of multi-source integration.
3Reliability
If comprehensive telematics data is analyzed, then false positives are reduced, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary filtering and preprocessing of telematics data before full analysis. By pre-processing data streams from multiple sources and preparing features in advance, the system reduces the computational burden during actual collision detection events, thereby maintaining high reliability in reducing false positives while minimizing processing time delays.
Data Source
AI summary
Described herein are examples of a computerized method that comprises: in response to obtaining information regarding a potential collision between a vehicle and an object, obtaining data describing the vehicle for a time period extending before and after a time of the potential collision. The method may determine a likelihood that the potential collision is a non-collision event based on the data describing the vehicle by performing one or more assessments. The assessments may include telematics monitor assessment, driver behavior assessment, road surface feature assessment, trip correlation assessment, and/or context assessment. In response to determining that the likelihood indicates that the potential collision is not a non-collision event, the method may trigger one or more actions responding to the potential collision.