[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126543-en":3,"doc-seo-126543-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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":27,"seo_description":14,"update_tm":28,"read_time":29},126543,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Driving-Style Assessment from a Motion Sickness Perspective Based on Machine Learning Techniques - read online free","Ride comfort improvement in driving scenarios is gaining traction as a research topic. This work presents a direct methodology that utilizes measured car signals and combines data processing techniques and machine learning algorithms to identify driver actions that negatively affect passenger motion sickness. Clustering models identify distinct driving patterns and associate them with passenger motion-sickness levels, enabling comfort-based driving recommendations that reduce discomfort. The designed and validated approach produces interpretable clusters from real data and highlights differences across driving patterns.","applied sciences  \nArticle  \nDriving-Style Assessment from a Motion Sickness Perspective Based on Machine Learning Techniques  \nJon Ander Ruiz Colmenares *, Estibaliz Asua Uriarte  and Inés del Campo   \nCitation: Ruiz Colmenares, J.A.; Asua Uriarte, E.; del Campo, I. Driving-Style Assessment from a Motion Sickness Perspective Based on Machine Learning Techniques. Appl. Sci. 2023, 13, 1510. [https://](https://)[ ](https://)[doi.org/10.3390/app13031510](doi.org/10.3390/app13031510)  \nAcademic Editor: José Salvador Sánchez Garreta  \nReceived: 30 December 2022  \nRevised: 17 January 2023  \nAccepted: 18 January 2023  \nPublished: 23 January 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nDepartment of Electricity and Electronics, Faculty of Science and Technology, University of the Basque Country UPV/EHU, 48940 Leioa, Spain  \n* [Correspondence: jonander.ruiz@ehu.eus](Correspondence: jonander.ruiz@ehu.eus)  \nAbstract: Ride comfort improvement in driving scenarios is gaining traction as a research topic. This work presents a direct methodology that utilizes measured car signals and combines data processing techniques and machine learning algorithms in order to identify driver actions that negatively affect passenger motion sickness. The obtained clustering models identify distinct driving patterns and associate them with the motion sickness levels suffered by the passenger, allowing a comfort-based driving recommendation system that reduces it. The designed and validated methodology shows satisfactory results, achieving (from a real datasheet) trained models that identify diverse interpretable clusters, while also shedding light on driving pattern differences. Therefore, a recommendation system to improve passenger motion sickness is proposed.  \nKeywords: motion sickness; ride comfort; driving style; ADAS; clustering  \n1. Introduction  \nResearch into advanced driver assistance system(s) (ADAS) has improved the activity of driving. Dynamic cruise control [1], automatic lane-keeping [2], risk prediction [3] among others, relieve the driver of certain duties, making driving a much more automatic and straightforward task, which translates into better driving conditions.  \nThis rapid development of ADAShasled to comfort being an objective tobe researched. Moreover, systems that combine all of these assignments are becoming the foundation of automated driving, and with high automation, drivers become passengers, which may negatively impact their riding comforts [4] . So, as the scope of automated driving widens, comfort should also be one of the considerations to be taken into account even in the early phases of development strategies or driving assistants [5] .  \nOverall, the subjective sensation of comfort makes its evaluation complicated with external factors becoming important. Regarding comfort, age is a notable condition [6] and personality or personal driving preferences make each driver have different assessments for the same situations [7] . In [8], other agents that affect motion sickness are summarized (e.g., smell, sound) . Recently, physiological data were 'tried' as predictors of levels of motion sickness. Forehead humidity [9] or complex wearable acquisition that combines brain activity and other physiological factors [10] shows promising results.  \nCurrent studies have attempted to accommodate comfort parameters by motion planning [11], speed, suspension control, uneven roads [12], or traffic sign information analysis [13] . Therefore, it is evident that the improvement of the ride comfort of passengers requires an exhaustive analysis of the origins of discomfort. Alternative approaches for motion sickness reducti","cbCainYAwVENiAK6","https://ap.wps.com/l/cbCainYAwVENiAK6","pdf",3589192,1,18,"English","en",105,"# Introduction\n## Context: ADAS and comfort objectives\n## Factors affecting comfort and motion sickness\n## Related approaches and driving-style evaluation\n## Signal-based and machine-learning approaches\n# Objective and sub-objectives","[{\"question\":\"What problem does the article address?\",\"answer\":\"The article addresses how driving style and measured car signals can be used to identify actions that worsen passenger motion sickness, with the goal of improving ride comfort.\"},{\"question\":\"How does the proposed method connect driving patterns to motion sickness?\",\"answer\":\"It uses data processing plus machine learning, particularly clustering models, to group distinct driving patterns and associate each cluster with motion-sickness levels experienced by passengers.\"},{\"question\":\"What outcome does the recommendation system aim to achieve?\",\"answer\":\"The recommendation system aims to provide comfort-based driving suggestions that reduce motion sickness by helping drivers apply driving actions that improve passenger comfort.\"}]","Driving-Style Assessment from a Motion Sickness Perspective Based on Machine Learning Techniques - 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