Personalized Fatigue Routing With Real-Time Driver State Feedback
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Solution Overview
Problem
Current navigation systems and driver assistance technologies do not adequately address driver fatigue, which remains a significant safety concern on roads, as they lack personalized and real-time fatigue monitoring and adaptive routing solutions.
Innovation Solution
A computer-implemented method and device that generate a personalized fatigue map by combining historical and current fatigue data with online map data, using machine learning algorithms to predict driver fatigue and provide real-time alerts and route adjustments to mitigate fatigue-related risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional navigation systems provide static routing based on traffic and accident reports, then route planning efficiency is improved, but driver fatigue safety cannot be addressed
Solution Approach 1:
The navigation system transitions from static routing to dynamic routing that adapts in real-time based on detected driver fatigue levels. The system continuously monitors driver state through sensors and automatically adjusts route recommendations, selecting alternative routes or suggesting rest stops when fatigue is detected, thereby addressing driver fatigue safety while maintaining routing efficiency.
Solution Approach 2:
The system implements a feedback loop where driver fatigue is continuously monitored through sensors (cameras, microphones, sensors), analyzed by machine learning models, and used to trigger route adjustments. This closed-loop feedback mechanism enables the system to respond to driver state changes in real-time, improving both safety and routing effectiveness.
2Measurement precision
If driver fatigue monitoring is implemented using sensors and machine learning, then driver fatigue detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system employs multi-functional sensors (cameras, microphones, various detectors) that serve multiple purposes: monitoring driver fatigue, detecting surrounding vehicle conditions, and providing input for machine learning models. This multi-functionality reduces the need for dedicated specialized components, thereby managing system complexity while maintaining high detection accuracy.
Solution Approach 2:
The machine learning models are trained using historical fatigue data and continuously improve their accuracy through self-learning from new data. The system automatically updates its detection capabilities without requiring manual reconfiguration or complex external intervention, enabling high measurement precision while keeping the system architecture relatively simple.
3Adaptability or versatility
If personalized fatigue maps are generated using historical and current fatigue data, then routing personalization is improved, but data processing time increases
Solution Approach 1:
The system pre-generates personalized fatigue maps by combining historical fatigue data with current conditions before the driver actually needs routing recommendations. Machine learning models process and analyze data in advance, creating pre-computed personalized routes that can be quickly presented to the driver without causing noticeable delays during actual navigation decisions.
Data Source
AI summary
The disclosure relates to technology for a navigation system that enhances the safety of drivers using fatigue detection mapping. The navigation system accesses data sources storing map data a route for drivers of one or more first vehicles. Based on the map data, a personalized fatigue map for a driver of a second vehicle is generated based on the map data. The personalized fatigue map displays predicted driver fatigue of the driver of the second vehicle on the route. Drivers in the first and second vehicles are monitored to detect driver fatigue and a level of the driver fatigue is measured according to a calculated fatigue score. When driver fatigue is detected, a recommendation is output to the driver of the second vehicle that is based on the level of the driver fatigue. The personalized fatigue map is updated to reflect the recommendation.