Mobile Trip Familiarity Detection Using GPS and Accelerometer Data
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Solution Overview
Problem
Current systems fail to accurately and reliably detect trip familiarity and route familiarity, which are crucial for road safety and traffic management, as they lack precise measurement and classification of route familiarity levels and struggle to differentiate between different levels of driver familiarity, leading to incomplete risk assessments and behavioral adaptations.
Innovation Solution
A smartphone-based system that uses GPS, accelerometer, and wireless connections to measure time series of sensory parameter values, normalizing geographical distances to calculate an overall familiarity parameter, enabling real-time trip familiarity detection and classification, independent of dedicated in-vehicle hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dedicated in-vehicle hardware is used for trip familiarity detection, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses a mobile device (smartphone or tablet) as an intermediary carrier that contains the detection system. The mobile device processes sensory data from its own sensors (GPS, accelerometer, gyroscope, magnetometer) to determine trip familiarity, eliminating the need for dedicated in-vehicle hardware while maintaining detection accuracy through sophisticated algorithms that analyze sensory patterns.
Solution Approach 2:
The system copies the functionality of dedicated trip familiarity detection hardware into a mobile device that users already possess. By replicating the detection capabilities using existing mobile device sensors and processing power, the system avoids the complexity and cost of installing specialized hardware in vehicles.
2Measurement precision
If multiple sensory parameters are measured and analyzed, then trip familiarity detection accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the trip familiarity detection process into distinct analytical components: sensory data collection from multiple sources, pattern recognition algorithms, trip chain identification, and familiarity level classification. Each segment processes specific aspects of the data independently, then integrates results to provide comprehensive trip familiarity assessment, making the complex processing manageable and efficient.
Solution Approach 2:
The system transforms raw sensory parameters (acceleration, GPS coordinates, orientation) into meaningful trip characteristics by applying threshold-based filtering and temporal pattern analysis. It changes the parameter representation from continuous sensor readings to discrete trip events with extracted features, reducing processing complexity while preserving detection accuracy.
3Reliability
If real-time trip familiarity detection is implemented, then road safety response time is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic sampling of sensory data rather than continuous monitoring, analyzing trip patterns at intervals sufficient to detect familiarity changes while allowing the mobile device to enter low-power states between measurements. This periodic approach maintains real-time detection capability while significantly reducing energy consumption compared to continuous processing.
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
The system provides accurate and reliable trip familiarity detection, enhancing road safety by assessing driver familiarity and optimizing traffic management through precise classification and risk analysis.
Implementation Method 1
the GPS sensor measures the mobile device's longitude, latitude and altitude positions as time series by measuring different speed of light delays in the signals receiving from two or more satellites
Implementation Method 2
the accelerometer measures acceleration forces being applied to the mobile device on all three physical axes
Data Source
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AI summary
Proposed is a method and system for electronic trip familiarity detection (114) based on sensory data (3) measured by a plurality of sensors (102) of a mobile telematics device (10) associated with a user (6) and/or a vehicle, the plurality of sensors (102) at least comprising a GPS sensor (1024) and/or an accelerometer (1025), the mobile device (10) comprising one or more wireless connections (105), wherein by at least one of the wireless connection (105) the mobile device (10) acts as a wireless node (221, …, 225) within a cellular data transmission network (2) by means of antenna connections of the mobile device (10) to the cellular data transmission network (2), and the plurality of sensors (102) being connected to a monitoring mobile node application (101) of the mobile device (10), wherein the monitoring mobile node application (101) captures usage-based (3) and/or user-based sensory data (3) of the plurality of sensors (102) of mobile device (10).