Driver Alertness Monitoring Using Passenger Sleep Risk Factors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing driver monitoring systems lack predictive capabilities and rely solely on driver gaze information for determining alertness, failing to account for passenger influence and environmental factors.
Innovation Solution
A driver monitoring system that incorporates multiple alertness detectors for the driver and passengers, using a controller to predictively determine a sleep risk factor based on passenger alertness, seating position, time, and road conditions, and adjusts vehicle features to mitigate driver sleepiness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If driver monitoring systems use only driver gaze information to determine alertness, then the system is simple to implement, but the accuracy of determining driver sleepiness is insufficient
Solution Approach 1:
The patent combines multiple monitoring approaches including driver gaze detection, passenger alertness detection, and environmental factor analysis into a unified driver alertness monitoring system. This integration allows the system to cross-validate information from multiple sources, thereby improving the accuracy of sleepiness determination while managing complexity through systematic integration.
Solution Approach 2:
The monitoring system performs multiple functions: it detects driver gaze, monitors passenger alertness, evaluates environmental conditions, and predicts sleep risk. This multi-functional approach enables comprehensive driver alertness assessment without requiring separate dedicated systems for each function, optimizing the balance between accuracy and complexity.
2Reliability
If driver monitoring systems lack predictive capability, then the system is simpler to operate, but the ability to prevent driver sleepiness is reduced
Solution Approach 1:
The system performs preliminary assessment by continuously monitoring driver and passenger alertness levels, evaluating environmental factors, and predicting sleep risk before actual sleepiness occurs. This predictive capability enables the system to issue early warnings and suggest preventive measures, thereby improving reliability in preventing driver sleepiness.
Solution Approach 2:
The system implements feedback mechanisms where monitoring data from drivers and passengers, along with environmental conditions, is continuously analyzed to update sleep risk predictions. This feedback loop enables dynamic adjustment of alertness assessments and predictive warnings, enhancing the system's ability to prevent sleepiness while maintaining manageable complexity through iterative refinement.
Data Source
Figure 1
Figure 2
AI summary
Alertness monitoring for vehicle driver which takes into account also the sleepiness of passengers in the vehicle, their number and their location in the vehicle (front or rear seat) to calculate the likelihood that the driver is becoming drowsy. An alertness detector is configured to detect an alertness condition of a driver of a vehicle and an alertness condition of a passenger in the vehicle. A controller is configured to determine a sleep risk factor based on the alertness condition of the passenger. The controller is also configured to determine a likelihood that the driver is sleepy based on the alertness condition of the driver and the sleep risk factor. The controller is configured to control a feature of the vehicle to assist the driver when the determined likelihood satisfies a predetermined criterion.