[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117023-en":3,"doc-seo-117023-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},117023,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning Approaches for Detecting Driver Drowsiness - A Critical Review","Driver drowsiness is a major threat to road safety, increasing the risk of accidents and injuries through delayed reaction and reduced focus. This study conducts a systematic critical review of machine learning methods for detecting driver drowsiness, including recent machine learning and deep learning models. It compares approaches that draw on driver behaviour, physiological signals, and vehicle behaviour data, assessing accuracy and reliability. The review concludes that these methods can improve detection, while data requirements, feature extraction, and model design remain key limitations.","Machine Learning Approaches for Detecting Driver Drowsiness: A Critical Review  \nKhubab Ahmad1 , Poh Ping Em2* , Nor Azlina Ab. Aziz3  \n1,2,3 Faculty of Engineering and Technology, Multimedia University, Malaysia; [E-mail: ](E-mail: ppem@mmu.edu.my)[ppem@mmu.edu.my](E-mail: ppem@mmu.edu.my)  \nAbstracts: Driver drowsiness is a serious issue that poses a significant threat to road safety, as it can lead to accidentsand injuries. In response to this problem, a thorough review of machine learning techniques for detecting driver drowsiness was conducted. The review examined a range of techniques , including more recent approaches that use machine learning and deep learning algorithms as well as different types of data sources driver behaviours, physiological signals, and vehicle behaviours. The primary objective of this paper was to critically analyse and provide a comprehensive overview of the current state-of-the-art in detecting driver drowsiness, evaluate the effectiveness of each technique in terms of accuracy and reliability, and identify potential areas for future research and improvement. In order to achieve this, a systematic review of relevant research studies was undertaken. The review determined that machine learning-based techniques can improve the accuracy and reliability of driver drowsiness detection systems. However, certain limitations, such as the need for large amounts of data, feature extraction, and model structure, must be addressed. By overcoming these limitations, machine learning-based systems have the potential to enhance road safety and prevent accidents. In conclusion, this paper provides a thorough review of machine learning techniques for driver drowsiness detection, evaluates their effectiveness, identifies potential research directions, and highlights their significance and contribution to road safety. The insights gained from this study can be used to guide the development of  \nmore effective driver drowsiness detection systems and improve road safety for the community.  \nKeywords: Driver Drowsiness, Machine Learning Techniques, Road Safety, Detection Systems and Critical Review.  \n1. INTRODUCTION  \nRoad traffic accidents are a significant cause of mortality and morbidity worldwide. According to Ministry of Transport Malaysia, each year approximately 1.35 million people die in road crashes, and an average of 3,700 people lose their lives every day on the roads. Not only do these accidents cause devastating personallosses, but they also result in considerable economic losses for individuals, their families, and entire nations. In particular, the value of a human life lost in a car accident can have significant financial implications forgovernments. Based on the value of statistical life (VSOL) calculation used by the Malaysian Institute of Road Safety Research (MIROS) in 2018 , the Malaysian government loses at least 3.12 million for each life lost in a car accident (\"Ministry of Transport Malaysia Official Portal,\") . The statistics of road accidents and road fatalities of Malaysia road are shown in Fig. 1.  \nFig. 1: Malaysia Road Accidents and Fatalities 2010 – 2021(\"Ministry of Transport Malaysia Official Portal,\")  \nThe significant number of deaths suggests that sleepy driving is a serious issue that requires attention in order to lessen its effects. Drowsiness is the term for drowsiness, frequently in unsuitable contexts.(\"Drowsiness: MedlinePlus Medical Encyclopedia,\") Driving lengthy distances without getting adequate rest or  \ndoing so when the driver should be sleeping might make one drowsy. (\"Fatigued Driving,\") . In thesesituations, the primary issue is the driver's loss of focus, which causes a delayed response to any on-the- road occurrence.(\"Drowsy Driving,\") .  \nDespite being distinct concepts, several studies equated sleepiness with tiredness because of their comparable effects. An accurate measuring scale for sleepiness levels is required in order to analyse stages of tiredness syst","cbCaielEJecu8SjX","https://ap.wps.com/l/cbCaielEJecu8SjX","pdf",444663,1,18,"English","en",105,"# Introduction\n## Problem background and road safety impact\n## Drowsiness vs. fatigue and need for measurement\n## Early warning behaviours\n## Overview of machine learning and deep learning","[{\"question\":\"Why is driver drowsiness considered a serious road-safety problem?\",\"answer\":\"Driver drowsiness leads to loss of focus and delayed responses, which increases the likelihood of accidents and injuries. The document also notes substantial mortality and economic losses associated with road crashes.\"},{\"question\":\"What kinds of data sources are reviewed for drowsiness detection?\",\"answer\":\"The review covers data based on driver behaviours, physiological signals, and vehicle behaviours. These inputs support different machine learning and deep learning detection approaches.\"},{\"question\":\"What limitations must be addressed for machine learning-based detection systems?\",\"answer\":\"Key limitations include the need for large amounts of data, challenges in feature extraction, and issues related to model structure. Improving these areas is necessary to enhance accuracy and reliability.\"}]",1785673122,45,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"machine-learning-approaches-for-detecting-driver-drowsiness-a-critical-review","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/machine-learning-approaches-for-detecting-driver-drowsiness-a-critical-review/117023/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is driver drowsiness considered a serious road-safety problem?","Question",{"text":74,"@type":75},"Driver drowsiness leads to loss of focus and delayed responses, which increases the likelihood of accidents and injuries. The document also notes substantial mortality and economic losses associated with road crashes.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What kinds of data sources are reviewed for drowsiness detection?",{"text":79,"@type":75},"The review covers data based on driver behaviours, physiological signals, and vehicle behaviours. These inputs support different machine learning and deep learning detection approaches.",{"name":81,"@type":72,"acceptedAnswer":82},"What limitations must be addressed for machine learning-based detection systems?",{"text":83,"@type":75},"Key limitations include the need for large amounts of data, challenges in feature extraction, and issues related to model structure. 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