对比轻量级方法，发现随机森林模型在疲劳驾驶检测中准确率达88%。
Comparison of Lightweight Methods for Vehicle Dynamics-Based Driver Drowsiness Detection
- 基于公开数据集构建可复现的轻量级检测框架
- 随机森林方法达88%准确率，优于现有多数方法
- 强调方法透明性，适合关注可复现性的研究者
驾驶疲劳检测（DDD）可预防因疲劳导致的道路事故。基于车辆动力学的DDD方法具有成本低、性能高的优势，但现有研究普遍存在性能指标可靠性差、结果不可复现的问题，如训练与测试数据存在泄露，且多数未公开使用数据集。为此，本文在透明、公平的框架下，利用公开数据集对代表性车辆动力学驱动的DDD方法进行对比。首先，我们构建一个从Aygun等人公开数据集中提取特征并使用轻量机器学习模型进行检测的框架，该框架支持多种配置；其次，在此框架中实现三种现有代表性方法及一种简化的随机森林（RF）方法；最后，通过实验验证方法的可复现性，并评估其在通用指标下的表现。结果显示，所提出的RF方法达到最高准确率88%。研究揭示了非标准方法中存在的问题，同时展示了一种恰当实现的高性能检测方案。
原文摘要 · Abstract (English)
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.
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