AI Driver Inattention Detection Using Seat Vibration and Camera
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Solution Overview
Problem
Current driver monitoring systems face challenges in accurately determining driver inattention, leading to potential false alarms and decreased driver satisfaction, as they often fail to account for individual driving habits and provide inadequate feedback for improving inattention detection.
Innovation Solution
An AI apparatus and method that generates movement information of a driver's seat, receives vehicle status information, and uses image data to determine driver inattention, applying personalized criteria and updating models based on driver feedback to improve accuracy and relevance of inattention alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional driver monitoring systems use fixed criteria for determining inattention, then the system structure is simple, but the measurement precision of driver inattention determination is low leading to false alarms
Solution Approach 1:
The system dynamically adjusts monitoring criteria based on individual driver characteristics. It collects baseline data during a learning period to establish personalized reference values for each driver's normal behavior patterns, then uses these adaptive thresholds to determine inattention status, replacing fixed universal criteria with dynamic personalized ones.
Solution Approach 2:
The system performs preliminary data collection and analysis during a learning period before actual monitoring begins. It gathers baseline information about each driver's normal driving behavior, establishes reference values, and trains recognition models in advance, so that accurate personalized monitoring can be conducted without false alarms during normal operation.
2Reliability
If the system provides frequent inattention alarms to ensure safety, then the reliability of safety monitoring is improved, but driver satisfaction decreases due to false alarms
Solution Approach 1:
The system incorporates driver feedback mechanisms where drivers can confirm or deny inattention alarms. This feedback is used to continuously refine and update the personalized monitoring criteria and recognition models, improving accuracy over time while reducing false alarms that would otherwise decrease driver satisfaction.
Solution Approach 2:
By establishing personalized baseline behavior patterns during a preliminary learning period, the system proactively configures accurate monitoring thresholds before false alarms occur. This preliminary customization ensures reliable safety monitoring from the start while minimizing false alarms that would upset drivers.
3Adaptability or versatility
If the system uses generic inattention criteria for all drivers, then the ease of operation is maintained, but the adaptability to individual driving habits is poor
Solution Approach 1:
The system automatically collects driver behavior data, analyzes patterns, and generates personalized monitoring criteria without requiring manual configuration. It self-adapts to each driver's habits through automated machine learning and pattern recognition, providing high adaptability while keeping the user interface simple and maintenance-free.
Solution Approach 2:
The system performs preliminary automated analysis of driver behavior during a learning period to establish personalized reference values and training data. This upfront automated personalization enables the system to adapt to individual driving habits without adding operational complexity for the driver during normal use.
Data Source
AI summary
Disclosed herein an artificial intelligence apparatus for determining inattention of a driver including a vibration sensor or a gyro sensor configured to sense movement of a driver's seat of a vehicle, a camera configured to receive image data including a face of a driver, a communication modem configured to receive vehicle status information from an ECU (Electronic Control Unit) of the vehicle, and a processor configured to generate movement information of the driver's seat using vibration sensor information received from the vibration sensor or gyro sensor information received from the gyro sensor, generate driver status information corresponding to the driver from the received image data, determine whether the driver is in an inattention status based on the movement information of the driver's seat, the driver status information and the vehicle status information, and output an inattention alarm if the driver is in the inattention status.