Fatigue Detection ECU with Preferred Stop Navigation
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Solution Overview
Problem
Drivers often become tired while driving, leading to unsafe conditions due to unawareness of their fatigue levels, and existing systems lack effective methods to determine and address driver tiredness in real-time, especially for autonomous vehicles.
Innovation Solution
A system comprising sensors for detecting vehicle performance and driver condition data, a memory for preferred stops, and an ECU that determines a tired value and provides navigation instructions to a safe destination before the driver reaches a tired threshold, using GPS and output devices to guide the driver to a suitable stop.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If drivers continue driving despite feeling tired, then driving duration is extended, but driver safety deteriorates due to increased fatigue and reduced response times
Solution Approach 1:
The system performs preliminary detection of driver fatigue indicators (eye closure, head position, steering patterns) and predicts future tiredness levels before the driver actually becomes too tired to drive safely. This allows the system to issue advance warnings and suggest rest stops proactively, preventing the safety deterioration that would occur if drivers waited until they felt extremely tired.
Solution Approach 2:
The system continuously monitors driver condition through sensors (cameras, steering wheel sensors, seat sensors) and provides real-time feedback about fatigue levels. The ECU compares current sensor data against historical patterns and threshold values, then feeds this information back to the driver through visual, auditory, or haptic warnings, creating a closed-loop system that helps drivers maintain awareness of their fatigue state.
2Duration of action of moving object
If drivers are unaware of their fatigue levels, then they can continue driving longer, but the risk of accidents increases due to undetected tiredness
Solution Approach 1:
The system enables drivers to self-monitor their fatigue state through continuous automated detection without requiring them to subjectively assess their own condition. Sensors automatically track eye closure duration, head nodding frequency, and steering corrections, providing an objective measure of fatigue that the driver might not perceive themselves, thereby extending safe driving duration through self-awareness.
Solution Approach 2:
The patent replaces the driver's subjective mechanical self-assessment of fatigue with an automated electronic detection system. Instead of relying on the driver's internal sense of tiredness, the system uses cameras to detect eye closure, sensors to monitor steering patterns, and algorithms to calculate fatigue scores, substituting human self-perception with objective electronic measurement.
3Reliability
If the system provides real-time fatigue detection and navigation suggestions, then driver safety is improved, but device complexity increases due to multiple sensors and processing requirements
Solution Approach 1:
The system uses multi-functional sensors that serve multiple purposes: cameras detect both road conditions for autonomous driving and driver eye closure for fatigue detection; steering wheel sensors monitor both vehicle control inputs and driver steering patterns for fatigue assessment; seat sensors detect both occupancy and driver posture. This multi-functionality reduces the need for dedicated fatigue sensors, thereby limiting the increase in device complexity.
Solution Approach 2:
The patent merges the fatigue detection system with the existing autonomous vehicle control system. The ECU that processes autonomous driving data also processes fatigue indicators, and the same camera system used for road monitoring is also used for driver monitoring. By combining these functions into unified processing architectures, the system achieves comprehensive safety monitoring without proportionally increasing hardware complexity.
4Reliability
If the system automatically determines destinations and provides navigation instructions, then driver awareness of fatigue is improved, but ease of operation deteriorates due to reduced driver control over navigation decisions
Solution Approach 1:
Instead of the driver initiating navigation requests and the system responding, the system inverts the control flow by proactively initiating destination suggestions based on detected fatigue levels. When the ECU determines the driver is approaching dangerous fatigue thresholds, it automatically searches for nearby suitable destinations (rest stops, hotels, gas stations) and presents them to the driver, reversing the traditional driver-initiated navigation paradigm.
Solution Approach 2:
The system provides partial automation in navigation by offering pre-selected destination suggestions rather than fully autonomous navigation control. The driver retains the ability to accept, reject, or modify the suggested destinations, maintaining partial control while benefiting from the system's automated fatigue-based decision-making. This partial action approach balances automation benefits with driver agency.
Data Source
AI summary
A system includes a first sensor for detecting vehicle data and a second sensor for detecting driver data. The system also includes a memory for storing preferred stops and a GPS unit for detecting a location. The system also includes an electrical control unit (ECU) that can determine a tired value of the driver based on the vehicle performance data and the driver condition data. The ECU can also determine that the driver is getting tired when the tired value is equal to or greater than a first tired threshold. The ECU can also determine a desired destination of the driver based on the preferred stops that the vehicle can reach before the tired value reaches or exceeds a second tired threshold. The system also includes an output device for outputting navigation instructions providing directions to the desired destination from the current location of the vehicle.