Vehicle Turn Detection with GPS and Heading Sensor Fusion
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Solution Overview
Problem
Current navigation systems fail to detect vehicle turns in real-time and do not generate correlations between vehicle movements and events like accidents or traffic violations, limiting predictive capabilities.
Innovation Solution
A system that utilizes GPS, cellular, and wireless networks, combined with vehicle sensors, to detect turns and record vehicle characteristics, enabling real-time correlation with other events for risk assessment and database creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current navigation systems use position-based turn detection, then the system complexity is low, but the measurement precision of turn detection is insufficient and real-time detection capability is lacking
Solution Approach 1:
The patent combines multiple detection methods (position-based detection from GPS and heading-based detection from sensors) into a unified turn detection system. The system merges data from GPS position information with sensor data (accelerometers, gyroscopes, magnetometers) to achieve both high precision and real-time detection capabilities while managing system complexity through integrated processing.
Solution Approach 2:
The navigation system is enhanced to perform multiple functions: it continues to provide basic position-based navigation while simultaneously enabling precise turn detection, real-time monitoring, and predictive analysis. The system uses the same hardware infrastructure (GPS receiver, sensors, processor) to serve multiple purposes including turn detection, route guidance, and risk assessment.
2Reliability
If the system collects and analyzes vehicle movement data in real-time, then the predictive capability improves, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing vehicle movement data in the background before predictive analysis is needed. Historical movement patterns, turn characteristics, and risk factors are pre-analyzed and stored, enabling rapid retrieval and comparison when real-time predictions are required, thus reducing actual processing time while maintaining high reliability.
Solution Approach 2:
The system implements feedback mechanisms where detected turns and movement patterns are immediately fed back into the predictive model. This continuous feedback loop allows the system to update predictions in real-time based on current vehicle state and historical data, improving reliability without significant time loss through efficient iterative processing.
3Loss of information
If the system correlates vehicle movements with other events for risk assessment, then the information value increases, but the device complexity increases
Solution Approach 1:
The correlation system is segmented into modular components: data collection module (gathering movement data and event data), data processing module (analyzing correlations), and output module (generating risk assessments). Each module handles specific tasks independently, reducing overall system complexity while maximizing information value through specialized processing at each stage.
Data Source
AI summary
A turn detection system is configured to determine headings or a course of a vehicle over a period of time and evaluate whether the vehicle has registered a turn based on these headings/course. In some arrangements, upon detecting a turn, sensor data may be collected to determine one or more characteristics or attributes of the turn. Such data may indicate a loss event associated with the turn and be used to calculate a probability or risk of loss given the various characteristics of the turn. These probabilities may further be applied to determine various costs and premiums.