In-Vehicle Driver Distraction Assessment Using Multi-Source Data
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Solution Overview
Problem
Current systems for monitoring driver distraction often fail to provide appropriate responses due to limited monitoring of driver and environmental data, leading to inadequate warnings that may not address the severity of distractions effectively, potentially increasing the risk of accidents.
Innovation Solution
An in-vehicle computing system that integrates data from wearable devices and vehicle systems to determine driver distraction severity by correlating driver state, object data, and vehicle state, allowing for tailored alerts or vehicle control actions based on the severity of distraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive data collection from multiple sources is implemented, then distraction detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments data collection by using multiple independent sensors (eye tracking camera, wearable device sensors, vehicle system sensors) that each capture specific types of data. This modular approach allows comprehensive monitoring while maintaining manageable system complexity through divided functionality.
Solution Approach 2:
The system merges data from diverse sources including eye gaze tracking, wearable device biometric data, vehicle telemetry, and environmental sensors into a unified distraction assessment model. This integration enables comprehensive distraction detection accuracy by correlating multiple data streams.
2Reliability
If real-time multi-parameter monitoring is implemented, then response appropriateness is improved, but computational load increases
Solution Approach 1:
The system performs preliminary data processing and feature extraction at the sensor level and edge devices before transmitting to central processing. Pre-computed features and filtered data reduce the computational burden on main processing units while maintaining real-time monitoring capabilities.
Solution Approach 2:
The system implements feedback mechanisms where processed distraction assessments trigger appropriate vehicle responses (alerts, warnings, control actions). This closed-loop feedback ensures response appropriateness by continuously monitoring and adjusting based on real-time driver state while optimizing computational efficiency through event-driven processing.
Data Source
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AI summary
Embodiments are described for determining and responding to driver distractions. An example in-vehicle computing system of a vehicle includes an external device interface communicatively connecting the in-vehicle computing system to a mobile device, an inter-vehicle system communication module communicatively connecting the in-vehicle computing system to one or more vehicle systems of the vehicle, a processor, and a storage device storing instructions executable by the processor to receive image data from the mobile device, and determine a driver state based on the received image data. The instructions are further executable to receive vehicle data from one or more of the vehicle systems, determine a vehicle state based on the vehicle data, determine a distraction severity level based on the driver state and the vehicle state, and control one or more devices of the vehicle to perform a selected action based on the distraction severity level.