Driver Drowsiness Detection Using In-Cabin Camera and Neural Network
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Solution Overview
Problem
Existing vehicle maintenance schedules are often inconveniently timed and fail to predict component failures proactively, posing safety hazards due to breakdowns or malfunctions during operation.
Innovation Solution
Implementing a system with sensors and an artificial neural network (ANN) to collect and analyze vehicle data, allowing for predictive maintenance by identifying deviations from normal operating patterns and suggesting maintenance services based on sensor data, including unsupervised learning to adapt to individual vehicle environments and driving habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fixed maintenance schedules are used, then maintenance timing is simple to determine, but component failures cannot be predicted proactively and safety hazards occur due to breakdowns during operation
Solution Approach 1:
The patent replaces traditional mechanical/time-based maintenance scheduling with an intelligence-based system using sensors, neural networks, and data processing to predict component failures. The system substitutes fixed calendar-based schedules with dynamic, data-driven predictions that analyze actual vehicle operating conditions and component degradation patterns.
Solution Approach 2:
The maintenance scheduling system performs self-diagnosis and self-prediction by continuously monitoring vehicle data through sensors and using neural networks to identify degradation patterns. The system autonomously determines when maintenance is needed without requiring external assessment, enabling proactive scheduling before failures occur.
2Reliability
If sensors and neural networks are implemented for predictive maintenance, then component failures can be predicted proactively, but system complexity increases
Solution Approach 1:
The predictive maintenance system is segmented into distinct functional modules: sensor units for data collection, neural network processing units for pattern recognition, and maintenance scheduling units for action planning. This segmentation allows the complex system to be managed through specialized subsystems, each handling specific tasks independently.
Solution Approach 2:
The neural network-based prediction system serves multiple functions: it monitors component degradation, predicts failure timing, identifies abnormal operating patterns, and generates maintenance recommendations. This multi-functional approach consolidates what could be separate systems into a unified predictive maintenance platform.
3Measurement precision
If unsupervised learning is used to adapt to individual vehicle environments, then maintenance predictions become more accurate for specific vehicles, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary unsupervised learning during vehicle operation to establish baseline degradation patterns specific to each vehicle's environment and usage. By pre-adapting to individual vehicle characteristics through continuous learning, the system reduces the need for extensive processing during critical prediction phases, balancing accuracy with timely recommendations.
Data Source
AI summary
Systems, methods and apparatus of drowsiness detection for vehicle control. For example, a vehicle includes: a camera configured to face a driver of the vehicle and generate a sequence of images of the driver driving the vehicle; an artificial neural network configured to analyze the sequence of images and classify, based on the sequence of images, whether the driver is in a drowsy state; and an infotainment system configured to provide instructions to the driver in response to a classification by the artificial neural network that the driver is in the drowsy state.