{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T07:16:37Z","timestamp":1776064597618,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,25]],"date-time":"2021-09-25T00:00:00Z","timestamp":1632528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hong Kong Metropolitan University","award":["2019\/1.7."],"award-info":[{"award-number":["2019\/1.7."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Road traffic accidents have been listed in the top 10 global causes of death for many decades. Traditional measures such as education and legislation have contributed to limited improvements in terms of reducing accidents due to people driving in undesirable statuses, such as when suffering from stress or drowsiness. Attention is drawn to predicting drivers\u2019 future status so that precautions can be taken in advance as effective preventative measures. Common prediction algorithms include recurrent neural networks (RNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks. To benefit from the advantages of each algorithm, nondominated sorting genetic algorithm-III (NSGA-III) can be applied to merge the three algorithms. This is named NSGA-III-optimized RNN-GRU-LSTM. An analysis can be made to compare the proposed prediction algorithm with the individual RNN, GRU, and LSTM algorithms. Our proposed model improves the overall accuracy by 11.2\u201313.6% and 10.2\u201312.2% in driver stress prediction and driver drowsiness prediction, respectively. Likewise, it improves the overall accuracy by 6.9\u201312.7% and 6.9\u20138.9%, respectively, compared with boosting learning with multiple RNNs, multiple GRUs, and multiple LSTMs algorithms. Compared with existing works, this proposal offers to enhance performance by taking some key factors into account\u2014namely, using a real-world driving dataset, a greater sample size, hybrid algorithms, and cross-validation. Future research directions have been suggested for further exploration and performance enhancement.<\/jats:p>","DOI":"10.3390\/s21196412","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T22:16:38Z","timestamp":1632780998000},"page":"6412","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness"],"prefix":"10.3390","volume":"21","author":[{"given":"Kwok Tai","family":"Chui","sequence":"first","affiliation":[{"name":"Department of Technology, School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brij B.","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, National Institute of Technology Kurukshetra, Kurukshetra 136119, India"},{"name":"Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan"},{"name":"Macquarie University, Sydney, NSW 2109, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1591-5583","authenticated-orcid":false,"given":"Ryan Wen","family":"Liu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Navigation College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0755-1046","authenticated-orcid":false,"given":"Pandian","family":"Vasant","sequence":"additional","affiliation":[{"name":"Modeling Evolutionary Algorithms Simulation & Artificial Intelligence (MERLIN), Faculty of Electrical & Electronic Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9992-6094","authenticated-orcid":false,"given":"J. Joshua","family":"Thomas","sequence":"additional","affiliation":[{"name":"Department of Computing, UOW Malaysia, KDU Penang University College, George Town 10400, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,25]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2018). Global Status Report on Road Safety 2018, World Health Organization."},{"key":"ref_2","unstructured":"United Nations (2015). Transforming Our World: The 2030 Agenda for Sustainable Development, United Nations."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.aap.2018.02.025","article-title":"What are the factors that contribute to road accidents? An assessment of law enforcement views, ordinary drivers\u2019 opinions, and road accident records","volume":"115","author":"Rolison","year":"2018","journal-title":"Accid. Anal. 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