Driver Fatigue Prediction Using Time-Segmented Data
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Solution Overview
Problem
Existing methods for predicting driver fatigue and inattentiveness require large storage capacities to manage extensive data over long driving periods, limiting their ability to include all relevant situations and data in fatigue forecasts.
Innovation Solution
A method that determines representative values of driving behavior and situational data across different time periods, using a vigilance index to calculate break recommendations, reducing memory requirements by focusing on immediate, short-term, medium-term, and long-term changes, allowing for a high-quality fatigue prognosis with minimal storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all measurement data over the entire travel time are stored for fatigue prediction, then prediction quality is improved, but memory requirements increase significantly
Solution Approach 1:
The patent divides the entire travel time into multiple time ranges (e.g., recent, medium-term, long-term periods) and processes measurement data separately for each time range. This segmentation allows the system to capture fatigue-related variations across different time scales without storing all raw data points, thereby reducing memory requirements while maintaining prediction quality.
Solution Approach 2:
The patent extracts representative values (such as mean values, minima, maxima, or standard deviations) from the measurement data in each time range. By storing only these extracted representative values rather than the complete raw measurement data set, the system achieves significant memory reduction while preserving the essential information needed for accurate fatigue prediction.
2Quantity of substance
If representative values are determined in multiple time ranges, then memory requirements are reduced, but processing complexity increases
Solution Approach 1:
By segmenting the time data into distinct ranges and assigning specific processing tasks to each range, the patent organizes the complexity in a structured manner. This makes the processing more manageable and systematic, even though multiple time ranges are involved, as each range can be handled independently with standardized operations.
Solution Approach 2:
The patent changes the parameter representation by transforming raw measurement data into representative values (mean, min, max, standard deviation) for each time range. This parameter transformation simplifies the data structure and reduces the computational burden compared to processing complete raw data sets, thereby managing processing complexity effectively.
Data Source
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AI summary
The method involves determining a driving characteristic of a driver corresponding to measurement data. Driving conditions of a vehicle are determined corresponding to the measurement data. The measurement data are observed in a time domain (14), and a preset representative value of the measurement data is determined in the time domain. A pause recommendation is calculated with a help of a preset vigilance index based on the representative value and output to the driver of the vehicle. Mean-, minimum- and maximum values and a standard deviation are determined as the representative value.