Vehicle Collision Characterization with Context-Aware Telematics
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Solution Overview
Problem
Conventional collision detection systems relying on 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 features, and do not effectively utilize additional telemetry data to improve detection accuracy.
Innovation Solution
A system that analyzes a combination of telematics data, including accelerometer data, GPS information, and other vehicle sensors over an extended 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 data is used for collision detection, then the system is simple and easy to implement, but the reliability is poor due to high false positive and false negative rates
Solution Approach 1:
The patent combines multiple data sources including accelerometer data, GPS information, vehicle sensor data, and telematics data into a unified collision detection system. By merging these diverse data types and analyzing them together through machine learning classifiers, the system achieves higher reliability in distinguishing actual collisions from non-collision events while maintaining manageable complexity through integrated processing.
Solution Approach 2:
The system employs a multi-functional approach where a single collision detection system performs multiple functions: detecting collisions, characterizing collision severity, determining collision context, and triggering appropriate responses. This universal system uses various data sources and processing methods to handle different collision scenarios, improving reliability without requiring separate specialized systems for each function.
2Measurement precision
If accelerometer data alone is used for collision detection, then the device complexity is low, but the measurement precision is insufficient to differentiate between actual collisions and non-collision events
Solution Approach 1:
The patent segments the collision detection process into distinct phases: data collection from multiple sources, preliminary filtering and characterization of collision indicators, machine learning classification, and final collision determination. This segmentation allows the system to process complex multi-source data systematically, improving measurement precision by analyzing collision indicators in context rather than relying on a single data type.
Solution Approach 2:
The system transitions from single-dimensional accelerometer data to multi-dimensional analysis by incorporating GPS spatial information, vehicle sensor data, and telematics data. This dimensional expansion provides additional context and features for machine learning classifiers to distinguish between actual collisions and non-collision events, significantly improving measurement precision through richer data representation.
3Reliability
If comprehensive telematics data is collected and analyzed, then the collision detection reliability improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and characterizing collision indicators from multiple data sources before final collision determination. Machine learning classifiers are trained in advance to recognize collision patterns, and the system pre-organizes data structures for efficient retrieval and analysis. This preliminary preparation enables rapid and accurate collision detection when events occur, improving reliability while managing processing time through optimized data preparation.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously learns from collision data and adjusts its detection algorithms. Machine learning models are refined based on actual collision outcomes, and the system uses feedback from collision characterization to improve future detections. This feedback loop enhances reliability over time while optimizing processing efficiency by learning from past performance and adapting to new collision scenarios.
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.