Multi-Computer Movement Data Fusion for Driver–Passenger Detection
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Solution Overview
Problem
Existing systems struggle to accurately determine whether a user captured movement data corresponds to being a driver or a non-driver passenger, which affects the accuracy of personalized services and outputs based on driving behaviors.
Innovation Solution
A system that evaluates movement data from mobile devices using GPS, accelerometer, and gyroscope data, combined with additional data like application usage and public transportation schedules, to differentiate between driver and non-driver passenger movements, and processes the data accordingly to generate relevant outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If movement data is collected from mobile devices to evaluate driving behaviors, then personalized services can be generated, but the accuracy of determining whether the user was a driver or passenger deteriorates
Solution Approach 1:
The patent combines multiple data sources including movement data from mobile devices, public transportation schedules, and application usage data to create a comprehensive evaluation system. This merging of data types enables accurate determination of whether a user was a driver or passenger by cross-referencing multiple data streams, thereby resolving the contradiction between generating personalized services and maintaining accurate user role determination.
Solution Approach 2:
The system uses a multi-functional data processing approach where movement data is evaluated alongside public transportation schedules and application usage patterns. This universal evaluation framework can accurately determine user roles across different scenarios (driving, public transportation, walking), enabling the system to maintain high measurement precision while processing diverse data types for personalized service generation.
2Measurement precision
If multiple data sources are integrated to improve determination accuracy, then user role identification improves, but system complexity increases
Solution Approach 1:
The patent segments the data processing system into distinct functional modules: movement data collection from mobile devices, public transportation schedule retrieval, application usage data gathering, and a centralized evaluation unit. This segmentation allows each component to process specific data types independently, reducing overall system complexity while maintaining high measurement precision through coordinated operation of modular components.
Solution Approach 2:
The system introduces an intermediary evaluation process that coordinates between different data sources (movement data, transportation schedules, application usage). This intermediary layer synthesizes information from multiple sources without requiring direct complex interactions between all components, thereby improving user role determination accuracy while managing system complexity through structured data flow management.
Data Source
AI summary
Methods, computer-readable media, systems, and/or apparatuses for evaluating movement data to identify a user as a driver or non-driver passenger are provided. In some examples, movement data may be received from a mobile device of a user. The movement data may include sensor data including location data, such as global positioning system (GPS) data, accelerometer and/or gyroscope data, and the like. Additional data may be retrieved from one or more other sources. For instance, additional data such as usage of applications on the mobile device, public transportation schedules and routes, image data, vehicle operation data, and the like, may be received and analyzed with the movement data to determine whether the user of the mobile device was a driver or non-driver passenger of the vehicle. Based on the determination, the data may be deleted in some examples or may be further processed to generate one or more outputs.