Comparison of Lightweight Methods for Vehicle Dynamics-Based Driver Drowsiness Detection

TL;DR

This paper compares vehicle dynamics-based driver drowsiness detection methods using a transparent framework and public dataset, revealing the effectiveness of a random forest approach with 88% accuracy.

Contribution

It introduces a fair, reproducible framework for evaluating lightweight vehicle dynamics-based DDD methods using open data and compares multiple approaches.

Findings

Random forest method achieved 88% accuracy.

Reproducibility issues exist in previous DDD studies.

Proper implementation can significantly improve DDD performance.

Abstract

Driver drowsiness detection (DDD) prevents road accidents caused by driver fatigue. Vehicle dynamics-based DDD has been proposed as a method that is both economical and high performance. However, there are concerns about the reliability of performance metrics and the reproducibility of many of the existing methods. For instance, some previous studies seem to have a data leakage issue among training and test datasets, and many do not openly provide the datasets they used. To this end, this paper aims to compare the performance of representative vehicle dynamics-based DDD methods under a transparent and fair framework that uses a public dataset. We first develop a framework for extracting features from an open dataset by Aygun et al. and performing DDD with lightweight ML models; the framework is carefully designed to support a variety of onfigurations. Second, we implement three existing representative methods and a concise random forest (RF)-based method in the framework. Finally, we report the results of experiments to verify the reproducibility and clarify the performance of DDD based on common metrics. Among the evaluated methods, the RF-based method achieved the highest accuracy of 88 %. Our findings imply the issues inherent in DDD methods developed in a non-standard manner, and demonstrate a high performance method implemented appropriately.