Driver Distraction Detection via Context-Aware Event Filtering
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Solution Overview
Problem
Current systems fail to effectively identify and mitigate driver distraction caused by portable electronic devices during vehicle operation, as they lack comprehensive data analysis and real-time monitoring capabilities.
Innovation Solution
A system that generates and utilizes a database of driving trip data and event data to identify when a driver is distracted by filtering usage events based on driving context, scoring risk factors, and taking appropriate actions, including alerts and remediation measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive data analysis and real-time monitoring capabilities are implemented, then driver distraction identification improves, but system complexity increases
Solution Approach 1:
The system segments the monitoring process into distinct modules: data collection module that gathers usage events from portable devices, driving context determination module that establishes vehicle operation status, filtering module that identifies distraction events, and scoring module that evaluates risk levels. This segmentation allows comprehensive monitoring while managing system complexity through modular design.
Solution Approach 2:
The patent introduces an intermediary processing system that acts as a mediator between raw usage event data and driver distraction identification. The system uses driving context data and filtering rules as intermediaries to transform comprehensive usage data into actionable distraction insights, reducing the complexity burden on the core monitoring function.
2Measurement precision
If usage event data is filtered based on driving context, then relevant distraction events are identified, but data processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing driving context parameters and filtering criteria before actual usage event analysis. Driving context data such as vehicle speed, location, and operational status are determined in advance, allowing rapid filtering of usage events against pre-defined distraction scenarios without real-time computational delays.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting filtering thresholds and scoring weights based on driving context. For example, the system modifies what constitutes a distraction event depending on whether the vehicle is moving or stationary, and adjusts risk scores based on contextual factors like time of day, location, and driving conditions, enabling efficient processing with context-aware precision.
3Adaptability or versatility
If multiple data sources are integrated for comprehensive analysis, then monitoring capability improves, but system complexity increases
Solution Approach 1:
The patent implements a universal data processing framework that handles multiple data sources through a common architecture. The system uses standardized interfaces to collect usage events from various portable devices (smartphones, tablets, wearables) and integrates them with vehicle telematics data, GPS information, and contextual data from external sources. This multi-functional approach allows comprehensive monitoring while managing integration complexity through unified data handling protocols.
Data Source
AI summary
A system and program of instructions for generating and utilizing a database of driving trip data and event data to identify drivers by mobile devices during a driving trip, comprising identifying a driver, determining a driving trip based on driving trip data, identifying device usage events associated with the driver, determining a driving context for the driver based on the driving trip, the usage events and context data, filtering the usage event data associated with the portable electronic device based on the driving context to create a set of risk events that occurred during the trip, and taking some action based on the set of risk events. The usage event data includes phone calls, text and other forms of messages, emails, applications, and other usage information, including usage associated with third party sources, each of which occur during the operation of a motor vehicle during a trip.