Anomaly Detection Model for Driver Concentration Decline
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Solution Overview
Problem
Detecting a decline in driver concentration due to tiredness or drowsiness is challenging in the automotive industry, as existing methods struggle to accurately differentiate between concentration decline and other anomalies, and require precise label information which is difficult to obtain.
Innovation Solution
A computer-implemented method using an anomaly detection model with optimized parameters to generate a score indicating concentration decline, trained with sensor data from driving activities, where the model calculates a weighted anomaly score to differentiate between longer and shorter cumulative driving times, effectively detecting concentration decline over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If anomaly detection model uses standard parameters, then detection speed is maintained, but detection precision for concentration decline is insufficient
Solution Approach 1:
The patent applies parameter changes by optimizing the anomaly detection model's parameters specifically for concentration decline detection. The model parameters are adjusted and trained using driving data to enhance sensitivity to concentration-related anomalies while maintaining computational efficiency. This resolves the contradiction by modifying model parameters to improve detection precision without proportionally increasing model complexity.
2Measurement precision
If model is trained with precise label information, then detection accuracy improves, but data preparation difficulty increases
Solution Approach 1:
The patent applies self-service by using the anomaly detection model to identify potential concentration decline events automatically from raw driving data, which then serve as training labels. This self-labeling approach eliminates the need for manual annotation of concentration states, significantly reducing data preparation effort while maintaining detection accuracy through iterative model training and validation.
3Measurement precision
If model differentiates between longer and shorter cumulative activity, then concentration decline detection accuracy improves, but model training complexity increases
Solution Approach 1:
The patent applies segmentation by dividing driving data into segments based on cumulative driving time (shorter vs. longer cumulative activity). The model is trained to recognize patterns specific to each segment, enabling it to differentiate concentration decline manifestations across different driving durations. This segmentation approach improves detection accuracy by accounting for time-dependent fatigue patterns while keeping training complexity manageable through focused segment-specific learning.
Data Source
AI summary
A computer-implemented method is disclosed. The method includes preparing a base of an anomaly detection model for generating a score that indicates an estimation of a concentration decline. The anomaly detection model has parameters affecting the score. The method also includes preparing a set of training data, each of which includes a sequence of sensor data relating to activity performed by an individual. The method also includes optimizing the parameters of the anomaly detection model using the set of the training data so as to make a score for longer cumulative activity high as compared to shorter cumulative activity. The method further includes outputting the parameters of the anomaly detection model, in which the anomaly detection model having the parameters is used for detecting a concentration decline of a target individual.