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Related Experiment Video

Updated: Sep 3, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

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Published on: December 18, 2020

Data-Driven Estimation of a Driving Safety Tolerance Zone Using Imbalanced Machine Learning.

Thodoris Garefalakis¹, Christos Katrakazas¹, George Yannis¹

¹Department of Transportation Planning and Engineering, National Technical University of Athens, 5 Iroon Polytechniou Str., 157 73 Athens, Greece.

Sensors (Basel, Switzerland)

|July 27, 2022

Summary

This summary is machine-generated.

This study introduces a framework to identify risky driving behavior and its duration using machine learning. Random Forest and MLP models achieved high accuracy, with vehicle speed being a key predictor.

Keywords:

driving behavior analysis driving behavior classification imbalanced machine learning

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

Area of Science:

Road safety research
Machine learning applications in transportation

Background:

Real-time driving behavior prediction for interventions remains a challenge.
The i-DREAMS project aims to establish a Safety Tolerance Zone (STZ) to mitigate risky driving.

Purpose of the Study:

To propose a framework for identifying risky driving behavior levels and durations.
To address data class imbalance in STZ level conceptualization.

Main Methods:

Utilized Support Vector Machines (SVM), Random Forest (RF), AdaBoost, and Multilayer Perceptron (MLP) for classification.
Employed the Adaptive Synthetic (ADASYN) technique to handle imbalanced datasets.
Applied Ridge, Lasso, and Elastic Net regression for predicting time duration in risk levels.

Main Results:

Random Forest (RF) achieved 84% accuracy, and Multilayer Perceptron (MLP) achieved 82% accuracy.
Maximum vehicle speed within a 30-second interval was identified as the most significant predictor.
The framework effectively identified driving risk levels and durations.

Conclusions:

The proposed framework successfully identifies driving risk levels and durations.
Machine learning models, particularly RF and MLP, are effective for real-time driving risk assessment.
Vehicle speed is a critical factor in determining driving safety levels.