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Multimodal biosignal acquisition enables real-time monitoring of heart rate and blood pressure, while domain controllers and edge computing reduce latency and improve processing. Machine learning supports tailored health baselines and high-precision risk prediction models (e.g., CNN, LSTM) that enable multi-level interventions, including environment adjustments, health recommendations, and ADAS-assisted emergency parking with telemedicine. Key challenges include sensor accuracy, model interpretability, data privacy security, and legal liability attribution; future work targets cognitive digital twins, L4/L5 autonomy, novel biomedical sensors, and smart-city medical ecosystems.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/ai-driven-smart-cockpit-monitoring-of-sudden-illnesses-health-risk-intervention-and-future-prospects/463007/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/ai-driven-smart-cockpit-monitoring-of-sudden-illnesses-health-risk-intervention-and-future-prospects/463007.png","ImageObject",300,407,{"name":92,"@type":93},"Fans","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does the proposed smart cockpit monitor sudden illnesses in real time?","Question",{"text":112,"@type":113},"It uses multimodal biosignal acquisition (flexible sensors and non-contact sensing) to continuously capture physiological indicators such as heart rate and blood pressure, enabling real-time monitoring and assessment.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What role do edge computing and domain controllers play in the system?",{"text":117,"@type":113},"They reduce data latency and enhance real-time processing capabilities, while also supporting integration of internal and external cabin data through machine learning.",{"name":119,"@type":110,"acceptedAnswer":120},"What future directions are highlighted for further development?",{"text":121,"@type":113},"Future work focuses on building cognitive digital twins, integrating L4/L5 autonomous driving, deploying new biomedical sensors, and developing smart-city medical ecosystems.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},463007,1790816381,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},5909892330395,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Review  \nAI-Driven Smart Cockpit: Monitoring of Sudden Illnesses, Health Risk Intervention, and Future Prospects  \nDonghai Ye 1,2, Kehan Liu 1, Chenfei Luo 3 and Ning Hu 2, *  \nAcademic Editor: Hiroshi Aoki  \nReceived: 15 November 2025  \nRevised: 19 December 2025  \nAccepted: 21 December 2025  \nPublished: 25 December 2025  \nCopyright: © 2025 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.  \n1 School of Arts, Chongqing University, Chongqing 400044, China; [yedonghai@cqu.edu.cn](yedonghai@cqu.edu.cn) (D.Y.)  \n2 School of Bioengineering, Chongqing University, Chongqing 400044, China  \n3 School of Automation, Chongqing University, Chongqing 400044, China  \n* [Correspondence: huning@cqu.edu.cn](Correspondence: huning@cqu.edu.cn)  \nAbstract  \nIntelligent driving cabins operated by artificial intelligence technology are evolving into the third living space. They aim to integrate perception, analysis, decision making, and intervention. By using multimodal biosignal acquisition technologies (flexible sensors and non-contact sensing), it is possible to monitor the physiological indicators of heart rate and blood pressure in real time. Leveraging the benefits of domain controllers in the vehicle and edge computing helps the AI platform reduce data latency and enhance real-time processing capabilities, as well as integrate the cabin’s internal and external data through machine learning. Its aim is to build tailored health baselines and highprecision risk prediction models (e.g., CNN, LSTM) . This system can initiate multi-level interventions such as adjustments to the environment, health recommendations, and ADAS-assisted emergency parking with telemedicine help. Current issues consist of sensor precision, AI model interpretation, security of data privacy, and whom to attribute legal liability to. Future development will mainly focus on cognitive digital twin construction, L4/L5 autonomous driving integration, new biomedical sensor applications, and smart city medical ecosystems.  \nKeywords: disease surveillance; intelligent vehicle cockpit; risk intervention  \n1. Introduction  \nWith the rapid development of automobile-related technologies, the smart cockpit, which is an essential part of intelligent and connected vehicles, has gradually transformed from a simple means of transportation into a “third living space”. The comfort level, intelligence level, and human–machine interaction experience of smart cockpits have increasingly attracted attention [1–4] . Smart cockpit health monitoring and risk intervention can essentially improve driving safety, enhance passenger comfort, and meet the demand for in-vehicle health management [5] .  \nEarly cockpit designs focused on driving assistance functions and mass flow meters, but not overall driving capability. For instance, the idea of an AI-supported cockpit proposed in 1985 examined the possibility of using artificial intelligence on all sorts of assistive systems in commercial transport [6] . Today, the smart cockpit is no longer an infotainment system, but a complex platform with advanced driver assistance systems (ADASs) and human–machine interaction (HMI) thanks to the rapid development of Internet of Things (IoT) and vehicle computing technologies [7,8] . As one of the most important future platforms for human–computer interaction (HCI) and human–machine interfaces (HMIs),  \nthe smart cockpit has also become a major application scenario for large language models (LLMs) [9] . As illustrated in Figure 1, during 2010–2018, cockpit design centered around the central control screen, which provided basic functions such as navigation and entertainment. Between 2018 and 2024, through the deep integration of multi-screen systems and ADASs, driver monitoring systems (DMSs) were introduced to track driver states—though their intervention capability remained limited.","cbCaisFhK8jVH84S","https://ap.wps.com/l/cbCaisFhK8jVH84S","pdf",2246160,23,"English","# Introduction\n## Integration Trends of Artificial Intelligence and Smart Cockpits","[{\"question\":\"How does the proposed smart cockpit monitor sudden illnesses in real time?\",\"answer\":\"It uses multimodal biosignal acquisition (flexible sensors and non-contact sensing) to continuously capture physiological indicators such as heart rate and blood pressure, enabling real-time monitoring and assessment.\"},{\"question\":\"What role do edge computing and domain controllers play in the system?\",\"answer\":\"They reduce data latency and enhance real-time processing capabilities, while also supporting integration of internal and external cabin data through machine learning.\"},{\"question\":\"What future directions are highlighted for further development?\",\"answer\":\"Future work focuses on building cognitive digital twins, integrating L4/L5 autonomous driving, deploying new biomedical sensors, and developing smart-city medical ecosystems.\"}]","AI-Driven Smart Cockpit: Monitoring of Sudden Illnesses, Health Risk Intervention, and Future Prospects | PDF",1790765927,58]