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Driving Under the Influence: How Music Listening Affects Driving Behaviors

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Drunk driving detection based on classification of multivariate time series.

Zhenlong Li¹, Xue Jin¹, Xiaohua Zhao¹

¹College of Metropolitan Transportation, Beijing University of Technology, Beijing, China.

Journal of Safety Research

|September 26, 2015

Summary

Detecting drunk driving is feasible using multivariate time series analysis. This method analyzes driving performance data to classify drivers as normal or drunk, achieving 80% accuracy.

Keywords:

Bottom-up segmentation Drunk driving detection Multivariate time series Support vector machine

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Area of Science:

Traffic Safety Research
Machine Learning Applications

Background:

Drunk driving remains a significant public safety concern.
Accurate detection methods are crucial for preventing alcohol-impaired driving incidents.

Purpose of the Study:

To develop and evaluate a novel approach for drunk driving detection.
To classify driver states (normal or drunk) using multivariate time series analysis.

Main Methods:

Collected driving performance data (lateral position, steering angle) in a driving simulator.
Applied piecewise linear representation and a bottom-up algorithm for multivariate time series feature extraction.
Utilized a support vector machine classifier for state classification.

Main Results:

The proposed method achieved an overall accuracy of 80.0% in classifying driver states.
Extracted features, including slope and time interval from segmented time series, proved effective for classification.

Conclusions:

Drunk driving detection using multivariate time series analysis is a feasible and effective strategy.
This approach holds practical implications for developing advanced drunk driving detection systems.