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Related Experiment Video

Updated: May 10, 2025

Driving Under the Influence: How Music Listening Affects Driving Behaviors

Driving Under the Influence: How Music Listening Affects Driving Behaviors

Published on: March 27, 2019

DUIncoder: Learning to Detect Driving Under the Influence Behaviors from Various Normal Driving Data.

Haoran Zhou¹, Alexander Carballo^2,3,4, Masaki Yamaoka⁵

¹Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan.

Sensors (Basel, Switzerland)

|April 28, 2025

Summary

This summary is machine-generated.

DUIncoder, an unsupervised framework, detects Driving Under the Influence (DUI) using only normal driving data. This approach overcomes challenges in acquiring risky DUI data, offering superior performance and real-world applicability.

Keywords:

driving behavior driving under influence increment learning unsupervised learning

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Published on: September 18, 2012

Area of Science:

Computer Science
Artificial Intelligence
Public Safety

Background:

Driving Under the Influence (DUI) presents a major public safety risk.
Acquiring sufficient DUI data for training detection models is challenging due to inherent risks.
Existing methods often struggle with data limitations for effective DUI detection.

Purpose of the Study:

To propose DUIncoder, an unsupervised framework for DUI behavior detection.
To address the data scarcity issue by utilizing readily available normal driving data.
To provide explanatory insights into detected DUI behaviors.

Main Methods:

Developed DUIncoder, an unsupervised learning framework.
Trained the model exclusively on diverse, normal driving scenarios.
Evaluated performance using simulator data.

Main Results:

DUIncoder demonstrated superior detection performance compared to supervised methods requiring DUI data.
The framework showed strong generalization capabilities across different driving scenarios.
Adaptability to incremental data was confirmed, enhancing real-world potential.

Conclusions:

Unsupervised learning with normal driving data is a viable strategy for DUI detection.
DUIncoder effectively mitigates the challenge of limited DUI data acquisition.
The framework offers a promising, adaptable solution for improving road safety.